diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..29cbe28
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,9 @@
+# .gitignore
+#
+# SPDX-FileCopyrightText: Copyright (C) 2022 Frank C Langbein <frank@langbein.org>, Cardiff University
+# SPDX-License-Identifier: AGPL-3.0-or-later
+*.pyc
+__pycache__
+.ipynb_checkpoints
+/cfg.json
+/public/
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
new file mode 100644
index 0000000..2ed853b
--- /dev/null
+++ b/.gitlab-ci.yml
@@ -0,0 +1,19 @@
+# .gitignore
+#                                                                                     
+# SPDX-FileCopyrightText: Copyright (C) 2022-2023 Frank C Langbein <frank@langbein.org>, Cardiff University
+# SPDX-License-Identifier: AGPL-3.0-or-later
+image: python:3.11
+
+before_script:
+  - pip3 install pdoc
+  - pip3 install -r requirements.txt
+
+pages:
+  script:
+    - export PDOC_ALLOW_EXEC=1
+    - pdoc bca -o ./public
+  artifacts:
+    paths:
+      - public
+  only:
+    - main
diff --git a/LATUP-Net/Att_Module.py b/LATUP-Net/Att_Module.py
new file mode 100644
index 0000000..3c46e5d
--- /dev/null
+++ b/LATUP-Net/Att_Module.py
@@ -0,0 +1,118 @@
+import tensorflow as tf
+from keras.models import Model
+from keras.layers import Input, Reshape, Dense,Add, Conv3D, BatchNormalization, UpSampling3D,Concatenate,Activation,Multiply, AveragePooling3D, MaxPooling3D, concatenate, GlobalAveragePooling3D, GlobalMaxPooling3D, Conv3DTranspose, BatchNormalization, Dropout, Lambda
+
+# Inception Module
+def inception_module_3d(x, base_channels=32):
+    # 1x1x1 convolution
+    a = Conv3D(base_channels*2, (1, 1, 1), activation='relu')(x)
+
+    # 1x1x1 followed by 3x3x3 convolution
+    b_1 = Conv3D(base_channels*4, (1, 1, 1), activation='relu')(x)
+    b_2 = Conv3D(base_channels*4, (3, 3, 3), padding='same', activation='relu')(b_1)
+
+    # 1x1x1 followed by 5x5x5 convolution
+    c_1 = Conv3D(base_channels, (1, 1, 1), activation='relu')(x)
+    c_2 = Conv3D(base_channels, (5, 5, 5), padding='same', activation='relu')(c_1)
+
+    # 3x3x3 max-pooling followed by 1x1x1 convolution
+    d_1 = MaxPooling3D((3, 3, 3), strides=(1, 1, 1), padding='same')(x)
+    d_2 = Conv3D(base_channels, (1, 1, 1), activation='relu')(d_1)
+
+    return Concatenate(axis=-1)([a, b_2, c_2, d_2])
+
+# Squeeze And Excitation attention Module 
+
+def SqueezeAndExcitation(inputs, ratio=8 ,name="attention"):
+    b,_, _, _,c= inputs.shape
+    x = GlobalAveragePooling3D()(inputs)
+    x = Dense(c//ratio, activation="relu", use_bias=False)(x)
+    x = Dense(c, activation="sigmoid", use_bias=False)(x)
+    x = inputs * x
+    return x
+
+# ECA attention Module 
+def ECALayer(inputs):
+    b,_, _, _,c= inputs.shape
+    x = GlobalAveragePooling3D()(inputs)
+    x = Dense(c, activation="softmax", use_bias=False)(x)
+    x = tf.expand_dims(tf.expand_dims(tf.expand_dims(x, 1), 1), 1)
+    x = inputs * x
+    return x
+
+# Squeeze And Excitation with 3d Conv instead of dense layer Module
+def SqueezeAndExcitation3dConv(inputs, ratio=8):
+    b,_, _, _,c= inputs.shape
+    x = GlobalAveragePooling3D()(inputs)
+    x = Reshape((1, 1, 1, c))(x)
+    x = Conv3D(c//ratio, kernel_size=(1, 1, 1), strides=(1, 1, 1), activation="relu", use_bias=False)(x)
+    x = Conv3D(c, kernel_size=(1, 1, 1), strides=(1, 1, 1), activation="sigmoid", use_bias=False)(x)
+    x = Multiply()([inputs, x])
+    return x
+
+# CBAM Module
+def channel_attention(inputs, ratio=8):
+    b,_, _, _,c= inputs.shape
+    l1 = Dense(c, activation="sigmoid", use_bias=False)
+    l2 = Dense(c, use_bias=False)
+
+    #average pooling 
+    x1 = GlobalAveragePooling3D()(inputs)
+    x1= l1(x1)
+    x1= l2(x1)
+
+    #max pooling 
+    x2 = GlobalMaxPooling3D()(inputs)
+    x2= l1(x2)
+    x2= l2(x2)
+
+    #add both and apply sigmoid
+
+    feats = x1 + x2 
+    feats = Activation("sigmoid")(feats)
+    feats = Multiply()([inputs, feats]) 
+    return feats 
+
+
+def spatial_attention(inputs):
+
+    b,_, _, _,c= inputs.shape
+    #average pooling 
+    x1 = tf.reduce_mean(inputs, axis=-1)
+    x1 = tf.expand_dims(x1, axis=-1)
+
+    #max pooling 
+    x2 = tf.reduce_max(inputs, axis=-1)
+    x2 = tf.expand_dims(x2, axis=-1)
+
+    #contatenate 
+
+    feats = Concatenate()([x1 , x2])
+
+    #conv layer  
+    feats = Conv3D(c, kernel_size=7 ,padding='same', activation="sigmoid")(feats)
+    feats = Multiply()([inputs, feats]) 
+    return feats 
+
+
+def CBAM(x):
+        x = channel_attention(x)
+        x = spatial_attention(x)
+        return x
+
+# Proposed MultiModal Attention
+
+def MultiModalAttention(inputs, ratio=16):
+    # compute attention maps for each channel
+    _, h, w, d, c = inputs.shape
+    x = Reshape((h*w*d, c))(inputs)
+    x = Dense(c//ratio, activation=relu, use_bias=False)(x)
+    x = Dense(c, activation=relu, use_bias=False)(x)
+    x = Reshape((h, w, d, c))(x)
+    # sum the attention maps across channels
+    x = Lambda(lambda x: tf.reduce_sum(x, axis=-1, keepdims=True))(x)
+    x = Softmax()(x)
+    # multiply attention maps with original feature maps
+    x = Multiply()([inputs, x])
+    return x
+
diff --git a/LATUP-Net/IM_Loader.py b/LATUP-Net/IM_Loader.py
new file mode 100644
index 0000000..49ca83d
--- /dev/null
+++ b/LATUP-Net/IM_Loader.py
@@ -0,0 +1,53 @@
+import glob
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.utils import to_categorical
+import os
+
+# Set the base directory dynamically
+base_directory = os.getcwd()
+
+# Define the dataset path dynamically
+DATA_PATH = os.path.join(base_directory, 'Results', 'data2020')  # Modify 'data2020' as needed
+
+def load_img_mask(img_mask_list):
+    images = []
+    masks = []
+
+    for i, image_name in enumerate(img_mask_list):
+        image_path = os.path.join(DATA_PATH, image_name, 'image_*.npy')
+        mask_path = os.path.join(DATA_PATH, image_name, 'mask_*.npy')
+
+        try:
+            image = np.load(glob.glob(image_path)[0])
+            mask = np.load(glob.glob(mask_path)[0])
+
+            mask = to_categorical(mask, num_classes=4)
+            mask = mask.astype(np.float64)
+
+            masks.append(mask)
+            images.append(image)
+
+        except Exception as e:
+            print('Error: ', e)
+            print('List: ', image_name, os.listdir(os.path.join(DATA_PATH, image_name)))
+            print('Shapes: ', mask.shape, image.shape)
+
+    images = tf.convert_to_tensor(np.array(images))
+    masks = tf.convert_to_tensor(np.array(masks))
+
+    return images, masks
+
+def imageLoader(images_names, batch_size):
+    L = len(images_names)
+    while True:
+        batch_start = 0
+        batch_end = batch_size
+
+        while batch_start < L:
+            limit = min(batch_end, L)
+            X, Y = load_img_mask(images_names[batch_start:limit])
+            yield (X, Y)  # a tuple with two numpy arrays with batch_size samples     
+
+            batch_start += batch_size   
+            batch_end += batch_size
\ No newline at end of file
diff --git a/LATUP-Net/Interpret.py b/LATUP-Net/Interpret.py
new file mode 100644
index 0000000..a94e825
--- /dev/null
+++ b/LATUP-Net/Interpret.py
@@ -0,0 +1,230 @@
+
+import numpy as np
+import pickle
+from matplotlib import pyplot as plt
+from matplotlib.ticker import MaxNLocator
+import copy
+import cv2
+import numpy as np
+from IM_Loader import *
+from LATUP_Net import *
+
+# GradCAM 
+def make_gradcam_heatmap(img_array, model, layer_name, class_idx):
+    grad_model = tf.keras.models.Model(
+        [model.inputs], [model.get_layer(layer_name).output, model.output]
+    )
+
+    with tf.GradientTape() as tape:
+        conv_outputs, predictions = grad_model(img_array)
+        loss = predictions[0, :, :, :, class_idx]
+
+    grads = tape.gradient(loss, conv_outputs)
+    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
+
+    conv_outputs = conv_outputs[0]
+    heatmap = conv_outputs @ pooled_grads[..., tf.newaxis]
+    heatmap = tf.squeeze(heatmap)
+
+    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
+    return heatmap.numpy()
+def make_gradcam_heatmapmine(img_array, model, layer_name, class_idx):
+    grad_model = tf.keras.models.Model(
+        [model.inputs], [model.get_layer(layer_name).output, model.output]
+    )
+
+    with tf.GradientTape() as tape:
+        conv_outputs, predictions = grad_model(img_array)
+        loss = predictions[0, :, :, :, class_idx]
+
+    output = conv_outputs[0]
+    grads = tape.gradient(loss, conv_outputs)[0]
+    gate_f = tf.cast(output > 0, 'float32')
+    gate_r = tf.cast(grads > 0, 'float32')
+    guided_grads = tf.cast(output > 0, 'float32') * tf.cast(grads > 0, 'float32') * grads
+
+    weights = tf.reduce_mean(guided_grads, axis=(0, 1,2))
+   
+    cam = np.ones(output.shape[0: 3], dtype = np.float32)
+
+    for i, w in enumerate(weights):
+        
+        cam += w * output[:, :, :, i]
+    heatmap=cam
+    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)
+    return heatmap
+
+def visualise_attention_per_model(image,mask,prediction,attention_heatmaps,layer_index):
+    fig, axs = plt.subplots(3, len(attention_heatmaps[0])+3, figsize=(14,10))
+    model_names=['Attention', 'No Attention']
+    class_names=['Background','(NCR/NET)','(ED)','(ET)']
+    for k in range(0,2):
+      heatmap_3clsses=np.zeros(attention_heatmaps[k][0][layer_index].shape[0:2])
+      for class_idx in range(len(attention_heatmaps[0])):
+         # selected_slice = int(attention_heatmaps[k][class_idx][layer_index].shape[2]/2)
+          selected_slice = 80
+          # First display the image slice
+          axs[k,class_idx].imshow(image[0, :, :, selected_slice, 0], cmap='gray')
+          # Then overlay the heatmap, using a suitable alpha
+          axs[k,class_idx].imshow(attention_heatmaps[k][class_idx][layer_index][:, :, selected_slice], cmap='hot', alpha=0.5)
+          axs[k,class_idx].set_title(f'{class_names[class_idx]}')
+          heatmap_3clsses=heatmap_3clsses+attention_heatmaps[k][class_idx][layer_index][:, :, selected_slice]
+      axs[k,class_idx+1].imshow(mask[:, :, selected_slice])
+      axs[k,class_idx+1].set_title('Ground Truth')
+      axs[k,class_idx+2].imshow(prediction[k][:, :, selected_slice])
+      axs[k,class_idx+2].set_title('Prediction')
+      axs[k,class_idx+3].imshow(heatmap_3clsses)
+      axs[k,class_idx+3].set_title(model_names[k])
+    plt.show()
+
+
+def get_heatmap_layers(image,my_model,list_layers,class_idx):
+  features_layer=[]
+  for i, layer_name in enumerate(list_layers):
+      heatmap = make_gradcam_heatmapmine(image, my_model, layer_name, class_idx)
+      features_layer.append(heatmap)
+  return features_layer
+from matplotlib import colors
+import numpy as np
+import matplotlib.pyplot as plt
+
+def visualise_attention_per_model(image, mask, prediction, attention_heatmaps, layer_index):
+    # Change here: 2 rows instead of 3
+    fig, axs = plt.subplots(2, len(attention_heatmaps[0])+3, figsize=(14, 10))
+
+    model_names = ['Attention', 'No Attention']
+    class_names = ['Background', '(NCR/NET)', '(ED)', '(ET)']
+
+    # Custom colormap
+    cmap = colors.ListedColormap(['blue', 'green', 'red'])
+    norm = colors.BoundaryNorm([0.5, 1.5, 2.5, 4.5], cmap.N)
+
+    for k in range(2):
+        heatmap_3classes = np.zeros(attention_heatmaps[k][0][layer_index].shape[:2])
+        for class_idx in range(len(attention_heatmaps[0])):
+            selected_slice = 80
+            # First display the image slice
+            axs[k, class_idx].imshow(image[0, :, :, selected_slice, 0], cmap='gray')
+            # Then overlay the heatmap
+            axs[k, class_idx].imshow(attention_heatmaps[k][class_idx][layer_index][:, :, selected_slice], cmap='hot', alpha=0.5)
+            axs[k, class_idx].set_title(f'{class_names[class_idx]}')
+
+            heatmap_3classes += attention_heatmaps[k][class_idx][layer_index][:, :, selected_slice]
+
+        # Adjustments for the indexing of Ground Truth and Prediction plots
+        axs[k, len(attention_heatmaps[0])].imshow(image[0, :, :, selected_slice, 0], cmap='gray')
+        masked_mask = np.ma.masked_where(mask[:, :, selected_slice] == 0, mask[:, :, selected_slice])
+        axs[k, len(attention_heatmaps[0])].imshow(masked_mask, cmap='jet', alpha=0.5)
+        axs[k, len(attention_heatmaps[0])].set_title('Ground Truth')
+
+        axs[k, len(attention_heatmaps[0])+1].imshow(image[0, :, :, selected_slice, 0], cmap='gray')
+        masked_prediction = np.ma.masked_where(prediction[k][:, :, selected_slice] == 0, prediction[k][:, :, selected_slice])
+        axs[k, len(attention_heatmaps[0])+1].imshow(masked_prediction, cmap='jet', alpha=0.5)
+        axs[k, len(attention_heatmaps[0])+1].set_title('Prediction')
+
+        # Plotting the combined heatmap
+        axs[k, len(attention_heatmaps[0])+2].imshow(heatmap_3classes, cmap='hot', alpha=0.5)
+        axs[k, len(attention_heatmaps[0])+2].set_title(model_names[k])
+
+    plt.tight_layout()
+    plt.show()
+
+
+#How to use and plot GradCam heatmaps 
+# load my_model and my_modelno  
+# my_model=CNN_Model(.....)
+# load my_mdoel weights
+# my_modelno=CNN_Model_no_attention(....) 
+#load my_mdoelno weights
+# images_path = "path/to/image/directory"    
+#visualise_heatmaps(image_names, index_of_image_in_image_names, images_path, my_model, my_modelno)
+
+
+
+ # Confusion Matrics
+import numpy as np
+import os
+import glob
+from sklearn.metrics import confusion_matrix
+import matplotlib.pyplot as plt
+import seaborn as sns
+from keras.models import load_model
+
+def plot_confusion_matrix(y_true, y_pred):
+    y_true = np.reshape(y_true, -1)
+    y_pred = np.reshape(y_pred, -1)
+    cm = confusion_matrix(y_true, y_pred)
+    return cm
+    
+def get_con_matrix_all_sklrn(best_model, images_dir_path, class_names=['Background', '(NCR/NET)', '(ED)', '(ET)']):
+    et_class_index = class_names.index('(ET)')
+    aggregated_cm = np.zeros([len(class_names), len(class_names)])
+    conf_matrices_all = []
+    image_names_list = os.listdir(images_dir_path)
+
+    for dir_name in image_names_list:
+        image_files = glob.glob(os.path.join(images_dir_path, dir_name, 'image_*.npy'))
+        mask_files = glob.glob(os.path.join(images_dir_path, dir_name, 'mask_*.npy'))
+
+        if not image_files or not mask_files:
+            continue
+
+        image = np.load(image_files[0])
+        mask = np.load(mask_files[0])
+        image = np.expand_dims(image, 0)
+        prediction = best_model.predict(image)
+        prediction_mask = np.argmax(prediction, axis=-1)[0, :, :, :]
+
+        cm = plot_confusion_matrix(mask, prediction_mask)
+
+        # Include this matrix only if 'ET' class is present in ground truth or predictions
+        if et_class_index in np.unique(mask):
+            conf_matrices_all.append(cm)
+            aggregated_cm += cm
+
+    return aggregated_cm, conf_matrices_all
+
+def calculate_mean_std(matrices):
+    normalized_matrices = []
+
+    for cm in matrices:
+        row_sums = cm.sum(axis=1)[:, np.newaxis]
+        normalized_cm = cm.astype('float') / (row_sums + 1e-10)
+        normalized_matrices.append(normalized_cm)
+
+    normalized_matrices_array = np.array(normalized_matrices)
+    mean_matrix = np.mean(normalized_matrices_array, axis=0)
+    std_matrix = np.std(normalized_matrices_array, axis=0)
+
+    return mean_matrix, std_matrix
+
+
+# How to use and plot  confusion Matrics 
+# my_model = ... # Initialize your model here
+# images_path = "path/to/image/directory"
+# cf_matrix_sklrn, all_conf_mat = get_con_matrix_all_sklrn(my_model, images_path)
+
+#How to Plot
+# Compute the mean and std from individual normalized matrices
+mn, std = calculate_mean_std(all_conf_mat)
+
+# Normalize the mean and std matrices
+row_sums_mean = mn.sum(axis=1)[:, np.newaxis]
+mn_normalized = mn.astype('float') / (row_sums_mean + 1e-10)
+
+row_sums_std = std.sum(axis=1)[:, np.newaxis]
+std_normalized = std.astype('float') / (row_sums_std + 1e-10)
+
+# Plotting
+class_names = ['Background', '(NCR/NET)', '(ED)', '(ET)']
+fig, ax = plt.subplots(figsize=(len(class_names) * 1.3, len(class_names) * 1.3))
+sns.heatmap(mn_normalized, annot=True, fmt=".2f", cmap="RdBu_r", xticklabels=class_names, yticklabels=class_names)
+plt.ylabel('Actual')
+plt.xlabel('Predicted')
+plt.show()
+
+fig, ax = plt.subplots(figsize=(len(class_names) * 1.3, len(class_names) * 1.3))
+sns.heatmap(std_normalized, annot=True, fmt=".2f", cmap="RdBu_r", xticklabels=class_names, yticklabels=class_names)
+plt.ylabel('Actual')
+plt.xlabel('Predicted')
+plt.show()
\ No newline at end of file
diff --git a/LATUP-Net/LATUP_Net.py b/LATUP-Net/LATUP_Net.py
new file mode 100644
index 0000000..a310879
--- /dev/null
+++ b/LATUP-Net/LATUP_Net.py
@@ -0,0 +1,170 @@
+#Build the model
+import tensorflow as tf
+from keras.models import Model
+from keras.layers import Input, Reshape, Dense, Conv3D, BatchNormalization, UpSampling3D,Concatenate,Activation,Multiply, AveragePooling3D, MaxPooling3D, concatenate, GlobalAveragePooling3D, GlobalMaxPooling3D, Conv3DTranspose, BatchNormalization, Dropout, Lambda
+from keras import regularizers
+from tensorflow.keras.optimizers import Adam
+from keras.metrics import MeanIoU
+Lrelu = tf.keras.layers.LeakyReLU(alpha=0.1)
+from tensorflow_addons.layers import InstanceNormalization
+from tensorflow.python.keras.layers import Dropout, SpatialDropout3D
+from Att_Module import SqueezeAndExcitation  
+# Attention Model
+
+
+def CNN_Model(IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS, num_classes):
+    kernel_initializer = 'he_uniform'
+    inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS), name='input_layer')
+    s = inputs
+
+    # Initial layers
+    conv = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_1')(s)
+    conv1 = Conv3D(32, (1, 1, 1), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_2')(conv)
+    pool1 = MaxPooling3D((2, 2, 2), name='layer_3_maxpool')(conv1)
+    conv2 = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_4')(conv)
+    pool2 = MaxPooling3D((2, 2, 2), name='layer_5_maxpool')(conv2)
+    conv3 = Conv3D(32, (5, 5, 5), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_6')(conv)
+    pool3 = MaxPooling3D((2, 2, 2), name='layer_7_maxpool')(conv3)
+    layer_out = concatenate([pool1, pool2, pool3], axis=-1, name='layer_8_concatenate')
+    attention_layer1 = SqueezeAndExcitation(layer_out, name='attention_layer1')
+
+    conv4 = Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_9')(attention_layer1)
+    B4 = InstanceNormalization(axis=-1, name='layer_10_instance_norm')(conv4)
+    conv4 = Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_11')(B4)
+    drop2 = Dropout(0.2, name='layer_12_dropout')(conv4)
+    pool4 = MaxPooling3D((2, 2, 2), name='layer_13_maxpool')(drop2)
+
+    # Following layers
+    conv5 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_14_conv')(pool4)
+    B5 = InstanceNormalization(axis=-1, name='layer_15_instance_norm')(conv5)
+    conv5 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_16_conv')(B5)
+    drop5 = Dropout(0.2, name='layer_17_dropout')(conv5)
+    pool5 = MaxPooling3D((2, 2, 2), name='layer_18_maxpool')(drop5)
+    attention_layer2 = SqueezeAndExcitation(pool5, name='attention_layer2')
+
+    # Upsampling layers
+    u9 = Conv3D(128, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='upsample_layer_1_conv')(UpSampling3D(size=(2,2,2))(attention_layer2))
+    u9 = InstanceNormalization(axis=-1, name='upsample_layer_1_instance_norm')(u9)
+    u9 = concatenate([u9, conv5], name='upsample_layer_1_concatenate')
+    c9 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,
+                kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_1_conv1')(u9)
+    c9 = SqueezeAndExcitation(c9, name='attention_layer3')
+    c9 = Dropout(0.2, name='upsample_layer_1_dropout')(c9)
+    c9 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,
+               kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_1_conv2')(c9)
+
+
+    u10= Conv3D(64, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='upsample_layer_2_conv')(UpSampling3D(size =(2,2,2))(c9))
+    u10 = InstanceNormalization(axis=-1, name='upsample_layer_2_instance_norm')(u10)
+    u10 = concatenate([u10, conv4], name='upsample_layer_2_concatenate')
+    c10= Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,
+              kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_2_conv1')(u10)
+    c10 = SqueezeAndExcitation(c10, name='attention_layer4')
+    c10 = Dropout(0.2, name='upsample_layer_2_dropout')(c10)
+    c10= Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,
+                 kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_2_conv2')(c10)
+
+    u11= Conv3D(32, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='upsample_layer_3_conv')(UpSampling3D(size =(2,2,2))(c10))
+    u11 = InstanceNormalization(axis=-1, name='upsample_layer_3_instance_norm')(u11)
+    u11 = concatenate([u11,  conv], name='upsample_layer_3_concatenate')
+    c11= Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,
+             kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_3_conv1')(u11)
+    c11 = SqueezeAndExcitation(c11, name='attention_layer5')
+    c11 = Dropout(0.2, name='upsample_layer_3_dropout')(c11)
+    c11= Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,
+                 kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_3_conv2')(c11)
+
+    outputs = Conv3D(num_classes, (1, 1, 1), kernel_regularizer=regularizers.l2(0.02), activation='softmax', name='final_output')(c11)
+
+    model = Model(inputs=[inputs], outputs=[outputs])
+
+    return model
+
+#Test if everything is working ok.
+model = CNN_Model(128, 128, 128, 3,4)
+
+model.summary()
+print(model.input_shape)
+print(model.output_shape)
+
+
+
+# No Attention Model
+Lrelu = tf.keras.layers.LeakyReLU(alpha=0.1)
+from tensorflow_addons.layers import InstanceNormalization
+from tensorflow.python.keras.layers import Dropout, SpatialDropout3D
+
+
+def CNN_ModelNo(IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS, num_classes):
+    kernel_initializer = 'he_uniform'
+    inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS), name='input_layer')
+    s = inputs
+
+    # Initial layers
+    conv = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_1')(s)
+    conv1 = Conv3D(32, (1, 1, 1), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_2')(conv)
+    pool1 = MaxPooling3D((2, 2, 2), name='layer_3_maxpool')(conv1)
+    conv2 = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_4')(conv)
+    pool2 = MaxPooling3D((2, 2, 2), name='layer_5_maxpool')(conv2)
+    conv3 = Conv3D(32, (5, 5, 5), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_6')(conv)
+    pool3 = MaxPooling3D((2, 2, 2), name='layer_7_maxpool')(conv3)
+    layer_out = concatenate([pool1, pool2, pool3], axis=-1, name='layer_8_concatenate')
+
+    conv4 = Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_9')(layer_out)
+    B4 = InstanceNormalization(axis=-1, name='layer_10_instance_norm')(conv4)
+    conv4 = Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_11')(B4)
+    drop2 = Dropout(0.2, name='layer_12_dropout')(conv4)
+    pool4 = MaxPooling3D((2, 2, 2), name='layer_13_maxpool')(drop2)
+
+    # Following layers
+    conv5 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_14_conv')(pool4)
+    B5 = InstanceNormalization(axis=-1, name='layer_15_instance_norm')(conv5)
+    conv5 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_16_conv')(B5)
+    drop5 = Dropout(0.2, name='layer_17_dropout')(conv5)
+    pool5 = MaxPooling3D((2, 2, 2), name='layer_18_maxpool')(drop5)
+
+    # Upsampling layers
+    u9 = Conv3D(128, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='upsample_layer_1_conv')(UpSampling3D(size=(2,2,2))(pool5))
+    u9 = InstanceNormalization(axis=-1, name='upsample_layer_1_instance_norm')(u9)
+    u9 = concatenate([u9, conv5], name='upsample_layer_1_concatenate')
+    c9 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,
+                kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_1_conv1')(u9)
+    c9 = Dropout(0.2, name='upsample_layer_1_dropout')(c9)
+    c9 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,
+               kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_1_conv2')(c9)
+
+
+    u10= Conv3D(64, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='upsample_layer_2_conv')(UpSampling3D(size =(2,2,2))(c9))
+    u10 = InstanceNormalization(axis=-1, name='upsample_layer_2_instance_norm')(u10)
+    u10 = concatenate([u10, conv4], name='upsample_layer_2_concatenate')
+    c10= Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,
+              kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_2_conv1')(u10)
+    c10 = Dropout(0.2, name='upsample_layer_2_dropout')(c10)
+    c10= Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,
+                 kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_2_conv2')(c10)
+
+    u11= Conv3D(32, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='upsample_layer_3_conv')(UpSampling3D(size =(2,2,2))(c10))
+    u11 = InstanceNormalization(axis=-1, name='upsample_layer_3_instance_norm')(u11)
+    u11 = concatenate([u11,  conv], name='upsample_layer_3_concatenate')
+    c11= Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,
+             kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_3_conv1')(u11)
+    c11 = Dropout(0.2, name='upsample_layer_3_dropout')(c11)
+    c11= Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,
+                 kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_3_conv2')(c11)
+
+    outputs = Conv3D(num_classes, (1, 1, 1), kernel_regularizer=regularizers.l2(0.02), activation='softmax', name='final_output')(c11)
+
+    model = Model(inputs=[inputs], outputs=[outputs])
+
+    return model
+
+#Test if everything is working ok.
+modelno = CNN_ModelNo(128, 128, 128, 3,4)
+
+
+modelno.summary()
+print(modelno.input_shape)
+print(modelno.output_shape)
+layer_namesno = [layer.name for layer in model.layers]
+
+print(layer_namesno)
\ No newline at end of file
diff --git a/LATUP-Net/Loss.py b/LATUP-Net/Loss.py
new file mode 100644
index 0000000..01a8034
--- /dev/null
+++ b/LATUP-Net/Loss.py
@@ -0,0 +1,74 @@
+import os
+import numpy as np
+import glob
+import keras
+import tensorflow as tf
+import keras.backend as K
+
+# compute the class weights according to the ENet paper:
+print("computing class weights")
+
+base_directory = os.getcwd()
+images_path = os.path.join(base_directory, 'Results', 'data2020')
+image_names = os.listdir(images_path)
+
+for i, dir_name in enumerate(image_names):
+    print('*********', i, dir_name)
+
+def get_class_weights(images_dir_path, class_names=['backg', '1', '2', '3']):
+    num_classes = len(class_names)
+    trainId_to_count = [0] * num_classes
+    image_names_list = os.listdir(images_dir_path)
+    for i, dir_name in enumerate(image_names_list):
+        print('*********', i, dir_name)
+        mask_path = glob.glob(os.path.join(images_dir_path, dir_name, 'mask_*.npy'))[0]
+        label_img = np.load(mask_path)
+        for trainId in range(num_classes):
+            trainId_mask = np.equal(label_img, trainId)
+            trainId_to_count[trainId] += np.sum(trainId_mask)
+        if i % 10 == 0:
+            print(i + 1, '- patients so far', trainId_to_count)
+# compute the class weights according to the ENet paper:
+    trainId_to_count[1]=trainId_to_count0[1]+trainId_to_count0[2]+trainId_to_count0[3]
+    trainId_to_count[2]=trainId_to_count0[1]+trainId_to_count0[3]
+    trainId_to_count[3]=trainId_to_count0[3]
+    class_weights = []
+    total_count = sum(trainId_to_count[1:])
+    for trainId, count in enumerate(trainId_to_count[1:]):
+        trainId_prob = float(count)/float(total_count)
+        trainId_weight = 1/np.log(1.22 + trainId_prob)
+        class_weights.append(trainId_weight)
+    s=sum(class_weights)
+    for idx, w in enumerate(class_weights):
+        class_weights[idx]=class_weights[idx]/s
+    data_info=dict(class_seg=['WT','CT','ET'],pixel_count=trainId_to_count[1:],class_weights=class_weights)
+    return data_info
+images_dir_path = os.path.join(base_directory,'Results', 'data2020')
+data_pixels_weights_info = get_class_weights(images_dir_path, class_names=['backg', '1', '2', '3'])
+print(data_pixels_weights_info)
+
+#Define the weighted dice loss 
+
+def dice_coef_class(y_true, y_pred,i, epsilon=0.00001):
+    """
+    Dice = (2*|X & Y|)/ (|X|+ |Y|)
+         =  2*sum(|A*B|)/(sum(A^2)+sum(B^2))
+    ref: https://arxiv.org/pdf/1606.04797v1.pdf
+
+    """
+    axis = (0,1,2,3)
+    dice_numerator = 2. * K.sum(y_true[:,:,:,:,i:i+1] * y_pred[:,:,:,:,i:i+1], axis=axis) + epsilon
+    dice_denominator = K.sum(y_true[:,:,:,:,i:i+1]*y_true[:,:,:,:,i:i+1], axis=axis) + K.sum(y_pred[:,:,:,:,i:i+1]*y_pred[:,:,:,:,i:i+1], axis=axis) + epsilon
+    return K.mean((dice_numerator)/(dice_denominator))
+
+
+def dice_coef_loss_3classes(y_true, y_pred):
+    core=2-dice_coef_class(y_true, y_pred,1) -dice_coef_class(y_true, y_pred,3)
+    whole=3-dice_coef_class(y_true, y_pred,1)-dice_coef_class(y_true, y_pred,2) -dice_coef_class(y_true, y_pred,3)
+    enhance=1-dice_coef_class(y_true, y_pred,3)
+    return 0.6*core + 0.5*whole +0.7*enhance
+
+
+
+
+
diff --git a/LATUP-Net/Train_Predict.py b/LATUP-Net/Train_Predict.py
new file mode 100644
index 0000000..5bda789
--- /dev/null
+++ b/LATUP-Net/Train_Predict.py
@@ -0,0 +1,96 @@
+#training
+import os
+import keras.backend as K
+import tensorflow as tf
+import glob
+import pickle
+import pandas as pd 
+from keras.callbacks import EarlyStopping, ReduceLROnPlateau
+from sklearn.model_selection import KFold # import KFold
+import time
+from IM_Loader import *
+from Loss import dice_coef_loss_3classes
+from LATUP_Net import CNN_Model
+
+# import evaluation matrics from segmentation_models_3D and set the optimizer, and the learning rate for training 
+
+from segmentation_models_3D.metrics import IOUScore, FScore
+#from segmentation_models_3D.losses import DiceLoss
+#dice_loss = DiceLoss()
+FScores = FScore()
+IOUScores = IOUScore(threshold=0.5)
+LR=0.0001
+metrics = ['accuracy',FScores,IOUScores]
+optim = tf.keras.optimizers.Adam(LR)
+
+# Set the base directory dynamically
+base_directory = os.getcwd()
+
+# Define the dataset path dynamically
+DATA_PATH = os.path.join(base_directory, 'Results', 'data2020')  # Modify  as needed
+
+# Define the checkpoint directory
+CHECKPOINT_DIR = os.path.join(base_directory, 'Results','2020')  # Modify as needed
+
+# Load histories
+with open('folds_dic.pkl', 'rb') as m:
+      folds_dict = pickle.load(m)
+
+kf = KFold(n_splits=5, random_state=1, shuffle=True)
+train_img_list = os.listdir(DATA_PATH)
+Kfolds = kf.split(train_img_list, train_img_list)
+trainlist = []
+validationlist = []
+Histories = []
+batch_size = 1
+
+for nb_fold in range(0, 5):
+    print('Training for the fold ' + str(nb_fold) + ' started ...')
+
+    train_img_fold = folds_dict['train'][nb_fold]
+    valid_img_fold = folds_dict['validation'][nb_fold]
+    steps_per_epoch = len(train_img_fold) // batch_size
+    val_steps_per_epoch = len(valid_img_fold) // batch_size
+    train_img_datagen1 = imageLoader(train_img_fold, batch_size)
+    val_img_datagen1 = imageLoader(valid_img_fold, batch_size)
+    LR = 0.0001
+    metrics = ['accuracy', FScores, IOUScores]
+
+    optim = tf.keras.optimizers.Adam(LR)
+    model = CNN_Model(IMG_HEIGHT=128, IMG_WIDTH=128, IMG_DEPTH=128, IMG_CHANNELS=3, num_classes=4)
+
+    print(model.summary())
+    checkpoint_filepath = os.path.join(CHECKPOINT_DIR, '2020fold_' + str(nb_fold) + '-{epoch:03d}-{val_f1-score:.04f}.hdf5')
+
+    model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_filepath,
+                                                                   monitor='val_f1-score', verbose=1,
+                                                                   save_best_only=True, mode='max')
+
+    model.compile(optimizer=optim, loss=dice_coef_loss_3classes, metrics=metrics)
+
+    start_time = time.time()
+    model_history = model.fit(train_img_datagen1,
+                              steps_per_epoch=steps_per_epoch,
+                              epochs=200,
+                              verbose=1,
+                              validation_data=val_img_datagen1,
+                              validation_steps=val_steps_per_epoch,
+                              callbacks=[model_checkpoint_callback])
+
+    end_time = time.time()
+    training_time = end_time - start_time
+    print("Training time: ", training_time, "seconds")
+    model.save(os.path.join(DATA_PATH, 'last_model_fold' + str(nb_fold) + '.hdf5'))
+
+    history_df = pd.DataFrame(model_history.history)
+    with open(os.path.join(DATA_PATH, 'fold_' + str(nb_fold) + '_history.csv'), mode='w') as f:
+        history_df.to_csv(f)
+
+    with open(os.path.join(DATA_PATH, 'model_history_fold_' + str(nb_fold) + '.pkl'), 'wb') as f:
+        pickle.dump(model_history.history, f)
+
+    Histories.append(model_history.history)
+
+# Save all histories
+with open('all_models_history.pkl', 'wb') as f:
+    pickle.dump(Histories, f)
\ No newline at end of file
diff --git a/LATUP-Net/evaluation.py b/LATUP-Net/evaluation.py
new file mode 100644
index 0000000..a8c60b2
--- /dev/null
+++ b/LATUP-Net/evaluation.py
@@ -0,0 +1,219 @@
+import numpy
+from scipy.ndimage import _ni_support
+from scipy.ndimage.morphology import distance_transform_edt, binary_erosion,\
+    generate_binary_structure
+from scipy.ndimage.measurements import label, find_objects
+from scipy.stats import pearsonr
+import difflib
+import scipy.spatial
+import numpy as np
+import os
+import glob
+import SimpleITK as sitk
+import medpy
+import scipy.spatial
+import medpy.metric.binary as medpyMetrics
+
+# Define the evalation matrics 
+def getHausdorff(testImage, resultImage):
+    """Compute the Hausdorff distance."""
+
+    # Hausdorff distance is only defined when something is detected
+    resultStatistics = sitk.StatisticsImageFilter()
+    resultStatistics.Execute(resultImage)
+    if resultStatistics.GetSum() == 0:
+        return float('nan')
+
+    # Edge detection is done by ORIGINAL - ERODED, keeping the outer boundaries of lesions. Erosion is performed in 2D
+    eTestImage   = sitk.BinaryErode(testImage, (1,1,0) )
+    eResultImage = sitk.BinaryErode(resultImage, (1,1,0) )
+
+    hTestImage   = sitk.Subtract(testImage, eTestImage)
+    hResultImage = sitk.Subtract(resultImage, eResultImage)
+
+    hTestArray   = sitk.GetArrayFromImage(hTestImage)
+    hResultArray = sitk.GetArrayFromImage(hResultImage)
+
+    # Convert voxel location to world coordinates. Use the coordinate system of the test image
+
+    testCoordinates   = [testImage.TransformIndexToPhysicalPoint(x.tolist()) for x in np.transpose( np.flipud( np.nonzero(hTestArray) ))]
+    resultCoordinates = [testImage.TransformIndexToPhysicalPoint(x.tolist()) for x in np.transpose( np.flipud( np.nonzero(hResultArray) ))]
+
+
+    # Use a kd-tree for fast spatial search
+    def getDistancesFromAtoB(a, b):
+        kdTree = scipy.spatial.KDTree(a, leafsize=100)
+        return kdTree.query(b, k=1, eps=0, p=2)[0]
+
+    # Compute distances from test to result; and result to test
+    dTestToResult = getDistancesFromAtoB(testCoordinates, resultCoordinates)
+    dResultToTest = getDistancesFromAtoB(resultCoordinates, testCoordinates)
+
+    return max(np.percentile(dTestToResult, 95), np.percentile(dResultToTest, 95))
+def hd95(result, reference, voxelspacing=None, connectivity=1):
+    try:
+        hd1 = medpyMetrics.__surface_distances(result, reference, voxelspacing, connectivity)
+        hd2 = medpyMetrics.__surface_distances(reference, result, voxelspacing, connectivity)
+        hd95 = numpy.percentile(numpy.hstack((hd1, hd2)), 95)
+    except:
+        hd95 = 95
+    return hd95
+def getDSC(testImage, resultImage):
+    """Compute the Dice Similarity Coefficient."""
+    testArray   = sitk.GetArrayFromImage(testImage).flatten()
+    resultArray = sitk.GetArrayFromImage(resultImage).flatten()
+
+    # similarity = 1.0 - dissimilarity
+    return 1.0 - scipy.spatial.distance.dice(testArray, resultArray)
+
+def recall(result, reference):
+    result = np.atleast_1d(result.astype(np.bool))
+    reference = np.atleast_1d(reference.astype(np.bool))
+
+    tp = np.count_nonzero(result & reference)
+    fn = np.count_nonzero(~result & reference)
+
+    try:
+        recall = tp / float(tp + fn)
+    except ZeroDivisionError:
+        recall = 0.0
+
+    return recall
+
+
+def sensitivity(result, reference):
+
+    return recall(result, reference)
+def specificity(result, reference):
+
+    result = np.atleast_1d(result.astype(np.bool))
+    reference = np.atleast_1d(reference.astype(np.bool))
+
+    tn = np.count_nonzero(~result & ~reference)
+    fp = np.count_nonzero(result & ~reference)
+
+    try:
+        specificity = tn / float(tn + fp)
+    except ZeroDivisionError:
+        specificity = 0.0
+
+    return specificity
+Dice_all=[]
+H95_all=[]
+
+
+# get the evaluation matrics results per_sample (per_patient)
+import math
+def get_score_per_sampe(my_model,image, maks):
+           Disces=[list() for i in range(0,3)]
+           Haus95s=[list() for i in range(0,3)]
+           Specs=[list() for i in range(0,3)]
+           Senss=[list() for i in range(0,3)]
+           domains=['whole','Core','Enhance'] #[0,1,2]
+           predcition=my_model.predict(image)
+           prediction=np.argmax(predcition, axis=4)[0,:,:,:]
+
+           for k, dom in enumerate(domains):
+                if k==0:
+                    test_prediction = (prediction>0.4).astype(int)
+                    msk=(mask >0.4).astype(int)
+                elif k==1:
+                    test_prediction1=(prediction ==1).astype(int)
+                    test_prediction2=(prediction ==3).astype(int)
+                    test_prediction1=(test_prediction1 >0.4).astype(float)
+                    test_prediction2=(test_prediction2 >0.4).astype(float)
+                    test_prediction=test_prediction1 + test_prediction2
+                    test_prediction=(test_prediction >0.4).astype(int)
+
+                    msk1=(mask ==1).astype(int)
+                    msk2=(mask ==3).astype(int)
+                    msk1=(msk1 >0.4).astype(float)
+                    msk2=(msk2 >0.4).astype(float)
+                    msk=msk1+msk2
+                    msk=(msk > 0.3).astype(int)
+                   # print(np.unique(msk),np.unique(test_prediction))
+                else:
+                    test_prediction =(prediction ==3).astype(int)
+                    test_prediction =(test_prediction >0.4).astype(int)
+                    msk=(mask ==3).astype(int)
+
+                Spec=specificity(test_prediction , msk)
+                Sens=sensitivity(test_prediction , msk)
+                image_sitk = sitk.GetImageFromArray(msk)
+                image_sitk1 = sitk.GetImageFromArray(test_prediction)
+                Dice=getDSC(image_sitk, image_sitk1)
+                if math.isnan(Dice):
+                    Dice=1
+                print('dice ---- ',Dice)
+
+                Haus=hd95(test_prediction , msk, voxelspacing=None, connectivity=1)
+                Senss[k].append(100*Sens)
+                Specs[k].append(100*Spec)
+                Disces[k].append(100*Dice)
+                Haus95s[k].append(Haus)
+           return  Senss,Specs,Disces,Haus95s
+
+# To Evaluate 
+from keras.models import load_model
+from tensorflow_addons.layers import InstanceNormalization
+import pickle
+import numpy as np
+import glob
+import os
+
+# Set the base directory dynamically
+base_directory = os.getcwd()
+
+# Define the dataset path dynamically
+images_path = os.path.join(base_directory, 'Results','data2020')  # Adjust as needed
+models_dir = os.path.join(base_directory, 'Results','2020')  # Adjust as needed
+
+# Load train and validation list
+folds_file_path = os.path.join(base_directory, 'Results','folds_dic.pkl')
+with open(folds_file_path, "rb") as open_file:
+    trainvallist = pickle.load(open_file)
+
+# Initialize lists for metrics
+Dices_all = []
+H95s_all = []
+Senss_all = []
+Specs_all = []
+
+for k in range(0, 5):
+    model_for_this_fold = [m for m in os.listdir(models_dir) if '2020fold_' + str(k) in m]
+    model_path = os.path.join(models_dir, model_for_this_fold[0])
+    model = load_model(model_path, compile=False)
+
+    val_list = trainvallist['validation'][k][:69]
+    print('******', len(val_list))
+    Dice_fold, H95_fold, Sens_fold, Spec_fold = [], [], [], []
+
+    for i, dir_name in enumerate(val_list):
+        print('*********', i, dir_name)
+        image_path = glob.glob(os.path.join(images_path, dir_name, 'image_*.npy'))[0]
+        image = np.load(image_path)
+        image = np.expand_dims(image, 0)
+
+        mask_path = glob.glob(os.path.join(images_path, dir_name, 'mask_*.npy'))[0]
+        mask = np.load(mask_path)
+
+        Senss, Specs, Disces, Haus95s = get_score_per_sample(model, image, mask)  
+        Dice_fold.append(Disces)
+        H95_fold.append(Haus95s)
+        Spec_fold.append(Specs)
+        Sens_fold.append(Senss)
+
+    Dices_all.append(Dice_fold)
+    H95s_all.append(H95_fold)
+    Specs_all.append(Spec_fold)
+    Senss_all.append(Sens_fold)
+
+# Save the metrics
+for metric_name, metric_data in zip(['dscs2020_paper1', 'hds2020_paper1', 'specs2020_paper1', 'sensis2020_paper1'], 
+                                    [Dices_all, H95s_all, Specs_all, Senss_all]):
+    with open(os.path.join(base_directory, metric_name + '.pkl'), "wb") as open_file:
+        pickle.dump(metric_data, open_file)
+
+# How to use it and plot the results:
+# plot_scores([dsc_scores, hd95_scores], ['DSC', 'HD95'])
+# plot_scores_per_region(1,metric='HD95')
diff --git a/LATUP-Net/five_fold_BraTs2020_2021.ipynb b/LATUP-Net/five_fold_BraTs2020_2021.ipynb
new file mode 100644
index 0000000..3641464
--- /dev/null
+++ b/LATUP-Net/five_fold_BraTs2020_2021.ipynb
@@ -0,0 +1,15738 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7oz9tZ3HodGe"
+      },
+      "outputs": [],
+      "source": [
+        "pip install segmentation_models_3D\n",
+        "pip install tensorflow-addons\n",
+        "pip install SimpleITK\n",
+        "pip install medpy"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hggLxBS9kOIV",
+        "outputId": "37b9a1a9-c4da-456b-db64-6c93ab4cf609"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Num GPUs Available:  1\n"
+          ]
+        }
+      ],
+      "source": [
+        "import tensorflow as tf\n",
+        "print(\"Num GPUs Available: \" , len(tf.config.experimental.list_physical_devices('GPU')))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "nlZE1Bw7wFB3"
+      },
+      "outputs": [],
+      "source": [
+        "#Code can be divided into a few parts....\n",
+        "#1-Combine\n",
+        "#2-Changing mask pixel values (labels) from 4 to 3 (as the original labels are 0, 1, 2, 4)\n",
+        "#3-Visualize"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "I5UQIdb-kOL0"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import nibabel as nib\n",
+        "import os\n",
+        "import glob\n",
+        "#from tensorflow.keras.utils import to_categorical\n",
+        "import matplotlib.pyplot as plt\n",
+        "from tifffile import imsave\n",
+        "from sklearn.preprocessing import MinMaxScaler"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vP9o4E66kOO6",
+        "outputId": "6eab628e-1491-46a3-9002-8b689736482b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "uV3T1UmIkOYy",
+        "outputId": "2ed071a0-3d77-4b15-8b11-ebf98c493f36"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  Grade BraTS_2017_subject_ID BraTS_2018_subject_ID TCGA_TCIA_subject_ID  \\\n",
+              "0   HGG   Brats17_CBICA_AAB_1   Brats18_CBICA_AAB_1                  NaN   \n",
+              "1   HGG   Brats17_CBICA_AAG_1   Brats18_CBICA_AAG_1                  NaN   \n",
+              "2   HGG   Brats17_CBICA_AAL_1   Brats18_CBICA_AAL_1                  NaN   \n",
+              "3   HGG   Brats17_CBICA_AAP_1   Brats18_CBICA_AAP_1                  NaN   \n",
+              "4   HGG   Brats17_CBICA_ABB_1   Brats18_CBICA_ABB_1                  NaN   \n",
+              "\n",
+              "  BraTS_2019_subject_ID BraTS_2020_subject_ID  \n",
+              "0   BraTS19_CBICA_AAB_1  BraTS20_Training_001  \n",
+              "1   BraTS19_CBICA_AAG_1  BraTS20_Training_002  \n",
+              "2   BraTS19_CBICA_AAL_1  BraTS20_Training_003  \n",
+              "3   BraTS19_CBICA_AAP_1  BraTS20_Training_004  \n",
+              "4   BraTS19_CBICA_ABB_1  BraTS20_Training_005  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-cc05e35a-1e7a-490a-8236-76a3b14eeac9\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Grade</th>\n",
+              "      <th>BraTS_2017_subject_ID</th>\n",
+              "      <th>BraTS_2018_subject_ID</th>\n",
+              "      <th>TCGA_TCIA_subject_ID</th>\n",
+              "      <th>BraTS_2019_subject_ID</th>\n",
+              "      <th>BraTS_2020_subject_ID</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>HGG</td>\n",
+              "      <td>Brats17_CBICA_AAB_1</td>\n",
+              "      <td>Brats18_CBICA_AAB_1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>BraTS19_CBICA_AAB_1</td>\n",
+              "      <td>BraTS20_Training_001</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>HGG</td>\n",
+              "      <td>Brats17_CBICA_AAG_1</td>\n",
+              "      <td>Brats18_CBICA_AAG_1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>BraTS19_CBICA_AAG_1</td>\n",
+              "      <td>BraTS20_Training_002</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>HGG</td>\n",
+              "      <td>Brats17_CBICA_AAL_1</td>\n",
+              "      <td>Brats18_CBICA_AAL_1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>BraTS19_CBICA_AAL_1</td>\n",
+              "      <td>BraTS20_Training_003</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>HGG</td>\n",
+              "      <td>Brats17_CBICA_AAP_1</td>\n",
+              "      <td>Brats18_CBICA_AAP_1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>BraTS19_CBICA_AAP_1</td>\n",
+              "      <td>BraTS20_Training_004</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>HGG</td>\n",
+              "      <td>Brats17_CBICA_ABB_1</td>\n",
+              "      <td>Brats18_CBICA_ABB_1</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>BraTS19_CBICA_ABB_1</td>\n",
+              "      <td>BraTS20_Training_005</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cc05e35a-1e7a-490a-8236-76a3b14eeac9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-cc05e35a-1e7a-490a-8236-76a3b14eeac9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-cc05e35a-1e7a-490a-8236-76a3b14eeac9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-17fa7cf3-fdc4-4512-a0e6-7a2ebbd0678b\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-17fa7cf3-fdc4-4512-a0e6-7a2ebbd0678b')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-17fa7cf3-fdc4-4512-a0e6-7a2ebbd0678b button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "root = '/content/drive/MyDrive/Bratsdataset/BraTs2020_TrainingData/MICCAI_BraTS2020_TrainingData'\n",
+        "data = pd.read_csv('/content/drive/MyDrive/Bratsdataset/name_mapping.csv')\n",
+        "data.head(5)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6v_hK6C7kOdA"
+      },
+      "outputs": [],
+      "source": [
+        "scaler = MinMaxScaler()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TGo9XioVkOho",
+        "outputId": "3fbe347c-8560-417f-d496-b1e52dcc75db"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1854.603271484375\n",
+            "1.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "TRAIN_DATASET_PATH = '/content/drive/MyDrive/Bratsdataset/BraTs2020_TrainingData/MICCAI_BraTS2020_TrainingData/'\n",
+        "\n",
+        "test_image_flair=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_flair.nii').get_fdata()\n",
+        "print(test_image_flair.max())\n",
+        "#Scalers are applied to 1D so let us reshape and then reshape back to original shape.\n",
+        "test_image_flair=scaler.fit_transform(test_image_flair.reshape(-1, test_image_flair.shape[-1])).reshape(test_image_flair.shape)\n",
+        "\n",
+        "print(test_image_flair.max())\n",
+        "test_image_t1=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_t1.nii').get_fdata()\n",
+        "test_image_t1=scaler.fit_transform(test_image_t1.reshape(-1, test_image_t1.shape[-1])).reshape(test_image_t1.shape)\n",
+        "\n",
+        "test_image_t1ce=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_t1ce.nii').get_fdata()\n",
+        "test_image_t1ce=scaler.fit_transform(test_image_t1ce.reshape(-1, test_image_t1ce.shape[-1])).reshape(test_image_t1ce.shape)\n",
+        "\n",
+        "test_image_t2=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_t2.nii').get_fdata()\n",
+        "test_image_t2=scaler.fit_transform(test_image_t2.reshape(-1, test_image_t2.shape[-1])).reshape(test_image_t2.shape)\n",
+        "\n",
+        "test_mask=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_seg.nii').get_fdata()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 692
+        },
+        "id": "FCB4rCE3kOl-",
+        "outputId": "47f70adf-0aff-407f-9c54-0de3ce879067"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x800 with 5 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "\n",
+        "import random\n",
+        "n_slice=random.randint(0, test_mask.shape[2])\n",
+        "\n",
+        "plt.figure(figsize=(12, 8))\n",
+        "\n",
+        "plt.subplot(231)\n",
+        "plt.imshow(test_image_flair[:,:,n_slice], cmap='gray')\n",
+        "plt.title('Image flair')\n",
+        "plt.subplot(232)\n",
+        "plt.imshow(test_image_t1[:,:,n_slice], cmap='gray')\n",
+        "plt.title('Image t1')\n",
+        "plt.subplot(233)\n",
+        "plt.imshow(test_image_t1ce[:,:,n_slice], cmap='gray')\n",
+        "plt.title('Image t1ce')\n",
+        "plt.subplot(234)\n",
+        "plt.imshow(test_image_t2[:,:,n_slice], cmap='gray')\n",
+        "plt.title('Image t2')\n",
+        "plt.subplot(235)\n",
+        "plt.imshow(test_mask[:,:,n_slice])\n",
+        "plt.title('Mask')\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WA8LvdgnkOpW"
+      },
+      "outputs": [],
+      "source": [
+        "#Flair, T1CE, annd T2 have the most information\n",
+        "#Combine t1ce, t2, and flair into single multichannel image\n",
+        "combined_x = np.stack([test_image_flair, test_image_t1ce,test_image_t2,test_image_t1], axis=3)\n",
+        "#cropping x, y, and z\n",
+        "combined_x=combined_x[56:184, 56:184, 13:141] #Crop to 128x128x128x4\n",
+        "#Do the same for mask\n",
+        "test_mask = test_mask[56:184, 56:184, 13:141]\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 692
+        },
+        "id": "TLghfVwdlF7I",
+        "outputId": "bd17d36b-4aa5-4b20-e208-36136d0680e3"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x800 with 5 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "n_slice=random.randint(0, test_mask.shape[2])\n",
+        "plt.figure(figsize=(12, 8))\n",
+        "\n",
+        "plt.subplot(231)\n",
+        "plt.imshow(combined_x[:,:,n_slice, 0], cmap='gray')\n",
+        "plt.title('Image flair')\n",
+        "plt.subplot(232)\n",
+        "plt.imshow(combined_x[:,:,n_slice, 1], cmap='gray')\n",
+        "plt.title('Image t1ce')\n",
+        "plt.subplot(233)\n",
+        "plt.imshow(combined_x[:,:,n_slice, 2], cmap='gray')\n",
+        "plt.title('Image t2')\n",
+        "plt.subplot(234)\n",
+        "plt.imshow(combined_x[:,:,n_slice, 3], cmap='gray')\n",
+        "plt.title('Image t1')\n",
+        "plt.subplot(235)\n",
+        "plt.imshow(test_mask[:,:,n_slice])\n",
+        "plt.title('Mask')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MemLKcbclF98"
+      },
+      "outputs": [],
+      "source": [
+        "# # # images lists harley\n",
+        "#t1_list = sorted(glob.glob('/content/drive/MyDrive/Bratsdataset/BraTs2020_TrainingData/MICCAI_BraTS2020_TrainingData/*/*t1.nii'))\n",
+        "t2_list = sorted(glob.glob('/content/drive/MyDrive/Bratsdataset/BraTs2020_TrainingData/MICCAI_BraTS2020_TrainingData/*/*t2.nii'))\n",
+        "t1ce_list = sorted(glob.glob('/content/drive/MyDrive/Bratsdataset/BraTs2020_TrainingData/MICCAI_BraTS2020_TrainingData/*/*t1ce.nii'))\n",
+        "flair_list = sorted(glob.glob('/content/drive/MyDrive/Bratsdataset/BraTs2020_TrainingData/MICCAI_BraTS2020_TrainingData/*/*flair.nii'))\n",
+        "mask_list = sorted(glob.glob('/content/drive/MyDrive/Bratsdataset/BraTs2020_TrainingData/MICCAI_BraTS2020_TrainingData/*/*seg.nii'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1epOsGZElGA8"
+      },
+      "outputs": [],
+      "source": [
+        "for img in range(len(t2_list)):   #Using t2_list as all lists are of same size\n",
+        "  print(\"Now preparing image and masks number: \", img)\n",
+        "\n",
+        "    #temp_image_t1=nib.load(t1_list[img]).get_fdata()\n",
+        "    #temp_image_t1=scaler.fit_transform(temp_image_t1.reshape(-1, temp_image_t1.shape[-1])).reshape(temp_image_t1.shape)\n",
+        "\n",
+        "  temp_image_t2=nib.load(t2_list[img]).get_fdata()\n",
+        "  temp_image_t2=scaler.fit_transform(temp_image_t2.reshape(-1, temp_image_t2.shape[-1])).reshape(temp_image_t2.shape)\n",
+        "\n",
+        "  temp_image_t1ce=nib.load(t1ce_list[img]).get_fdata()\n",
+        "  temp_image_t1ce=scaler.fit_transform(temp_image_t1ce.reshape(-1, temp_image_t1ce.shape[-1])).reshape(temp_image_t1ce.shape)\n",
+        "\n",
+        "  temp_image_flair=nib.load(flair_list[img]).get_fdata()\n",
+        "  temp_image_flair=scaler.fit_transform(temp_image_flair.reshape(-1, temp_image_flair.shape[-1])).reshape(temp_image_flair.shape)\n",
+        "\n",
+        "  temp_mask=nib.load(mask_list[img]).get_fdata()\n",
+        "\n",
+        "\n",
+        "  temp_mask[temp_mask==4] = 3  #Reassign mask values 4 to3\n",
+        "\n",
+        "\n",
+        "  temp_combined_images = np.stack([temp_image_flair, temp_image_t1ce, temp_image_t2], axis=3)\n",
+        "       #cropping x, y, and z\n",
+        "  temp_combined_images=temp_combined_images[56:184, 56:184, 13:141]\n",
+        "  temp_mask = temp_mask[56:184, 56:184, 13:141]\n",
+        "\n",
+        "  val, counts = np.unique(temp_mask, return_counts=True)\n",
+        "\n",
+        "  if (1 - (counts[0]/counts.sum())) > 0.01:  #At least 1% useful volume with labels that are not 0 because 0 is unlabel class which is=background\n",
+        "    print(\"Save Me\")\n",
+        "\n",
+        "    os.mkdir('/content/drive/MyDrive/Bratsdataset/BraTs2020/train'+str(img))\n",
+        "    np.save('/content/drive/MyDrive/Bratsdataset/BraTs2020/train'+str(img)+'/'+'image_'+str(img)+'.npy', temp_combined_images)\n",
+        "    np.save('/content/drive/MyDrive/Bratsdataset/BraTs2020/train'+str(img)+'/'+'mask_'+str(img)+'.npy', temp_mask)\n",
+        "\n",
+        "  else:\n",
+        "      print(\"I am useless\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NB8GIonklGJb"
+      },
+      "outputs": [],
+      "source": [
+        "import glob\n",
+        "import numpy as np\n",
+        "import tensorflow as tf\n",
+        "#from skimage.transform import resize\n",
+        "from keras.utils import to_categorical\n",
+        "\n",
+        "dir_name='/content/drive/MyDrive/Bratsdataset/BraTs2020'\n",
+        "def load_img_mask(img_mask_list):\n",
+        "\n",
+        "    images=[]\n",
+        "\n",
+        "    masks=[]\n",
+        "    for i, image_name in enumerate(img_mask_list):\n",
+        "\n",
+        "       try:\n",
+        "         image = np.load(glob.glob(dir_name+'/'+image_name +'/image_*.npy')[0])\n",
+        "\n",
+        "         mask  =  np.load(glob.glob(dir_name+'/'+image_name+'/mask_*.npy')[0])\n",
+        "\n",
+        "         mask=to_categorical(mask, num_classes=4)\n",
+        "         mask=mask.astype(np.float64)\n",
+        "\n",
+        "\n",
+        "         masks.append(mask)\n",
+        "         images.append(image)\n",
+        "       except Exception  as e:\n",
+        "         print('here is your error : ',e)\n",
+        "         print('my lists  : ', image_name, os.listdir(dir_name+'/'+image_name +'/') )\n",
+        "         print('shapes : ', mask.shape,image.shape)\n",
+        "\n",
+        "\n",
+        "    images = tf.convert_to_tensor(np.array(images))\n",
+        "    masks = tf.convert_to_tensor(np.array(masks))\n",
+        "\n",
+        "    return(images,masks)\n",
+        "\n",
+        "def imageLoader(images_names, batch_size):\n",
+        "\n",
+        "\n",
+        "    L = len(images_names)\n",
+        "\n",
+        "    #keras needs the generator infinite, so we will use while true\n",
+        "    while True:\n",
+        "\n",
+        "        batch_start = 0\n",
+        "        batch_end = batch_size\n",
+        "\n",
+        "        while batch_start < L:\n",
+        "            limit = min(batch_end, L)\n",
+        "\n",
+        "            X,Y = load_img_mask(images_names[batch_start:limit])\n",
+        "\n",
+        "            yield (X,Y) #a tuple with two numpy arrays with batch_size samples\n",
+        "\n",
+        "            batch_start += batch_size\n",
+        "            batch_end += batch_size\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 499
+        },
+        "id": "wS_-5eov7jRj",
+        "outputId": "ca7f0a85-7ce7-4b7d-c6cb-42db0ce72a7c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0 1 2 3]\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(1, 128, 128, 128, 4)\n",
+            "[0. 1.]\n",
+            "(1, 128, 128, 128, 3)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# To Test Output of the Generator\n",
+        "train_path='/content/drive/MyDrive/Bratsdataset/BraTs2020'\n",
+        "val_path='/content/drive/MyDrive/Bratsdataset/BraTs2020'\n",
+        "train_img_list= os.listdir(train_path)\n",
+        "val_img_list= os.listdir(val_path)\n",
+        "batch_size=1\n",
+        "train_img_datagen1=imageLoader(train_img_list,batch_size)\n",
+        "val_img_datagen1=imageLoader(val_img_list,batch_size)\n",
+        "\n",
+        "# #To verify generator\n",
+        "imgt, mskt = train_img_datagen1.__next__()\n",
+        "imgv, mskv = val_img_datagen1.__next__()\n",
+        "n_slice=58\n",
+        "test_mask=np.argmax(mskt, axis=4)\n",
+        "print(np.unique(test_mask))\n",
+        "plt.subplot(231)\n",
+        "plt.imshow(imgt[0,:,:,n_slice, 0], cmap='gray')\n",
+        "plt.title('Image flair')\n",
+        "plt.subplot(232)\n",
+        "plt.imshow(imgt[0,:,:,n_slice, 1], cmap='gray')\n",
+        "plt.title('Image t1ce')\n",
+        "plt.subplot(233)\n",
+        "plt.imshow(imgt[0,:,:,n_slice, 2], cmap='gray')\n",
+        "plt.title('Image t2')\n",
+        "\n",
+        "# plot the mask on top of the image\n",
+        "masked_image = np.ma.masked_where(test_mask[0,:,:,n_slice] == 0, test_mask[0,:,:,n_slice])\n",
+        "plt.subplot(234)\n",
+        "plt.imshow(imgt[0,:,:,n_slice, 0], cmap='gray')\n",
+        "plt.imshow(masked_image, cmap='jet', alpha=0.5)\n",
+        "plt.title('Segmentation mask')\n",
+        "\n",
+        "plt.show()\n",
+        "\n",
+        "plt.show()\n",
+        "\n",
+        "\n",
+        "\n",
+        "print(mskt.shape)\n",
+        "print(np.unique(mskt))\n",
+        "\n",
+        "print(imgt.shape)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ss-ile5AlGPZ"
+      },
+      "outputs": [],
+      "source": [
+        "import glob\n",
+        "import os"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xNXKns6P2FlR",
+        "outputId": "b4e72599-f9f8-41b4-ea53-64db9af955d9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " Training for the fold0 started ...\n",
+            "275 69\n",
+            " Training for the fold1 started ...\n",
+            "275 69\n",
+            " Training for the fold2 started ...\n",
+            "275 69\n",
+            " Training for the fold3 started ...\n",
+            "275 69\n",
+            " Training for the fold4 started ...\n",
+            "276 68\n"
+          ]
+        }
+      ],
+      "source": [
+        "import pickle\n",
+        "import keras\n",
+        "import keras.backend as K\n",
+        "import pandas as pd\n",
+        "from keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
+        "from sklearn.model_selection import KFold # import KFold\n",
+        "import time\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "kf = KFold(n_splits=5, random_state=1, shuffle=True)\n",
+        "#kfold = kf.split(train_img_list)\n",
+        "train_img_list=  os.listdir('/content/drive/MyDrive/Bratsdataset/BraTs2020/')\n",
+        "Kfolds=kf.split(train_img_list,train_img_list)\n",
+        "trainlist=[]\n",
+        "validationlist=[]\n",
+        "Histories=[]\n",
+        "nb_fold=0\n",
+        "train_5fold=[]\n",
+        "valid_5fold=[]\n",
+        "for train_idx, val_idx in Kfolds:\n",
+        "\n",
+        "    print(' Training for the fold' +str(nb_fold)+' started ...')\n",
+        "    train_img_fold=[train_img_list[k] for k in list(train_idx)]\n",
+        "    valid_img_fold=[train_img_list[k] for k in list(val_idx)]\n",
+        "    list_valid = open('valid_list' + str(nb_fold) + '.pkl', \"wb\")\n",
+        "    pickle.dump(valid_img_fold, list_valid)\n",
+        "    list_valid.close()\n",
+        "\n",
+        "    list_train = open('train_list' + str(nb_fold) + '.pkl', \"wb\")\n",
+        "    pickle.dump(train_img_fold, list_train)\n",
+        "    list_train.close()\n",
+        "    print(len(train_img_fold),len(valid_img_fold))\n",
+        "    train_5fold.append(train_img_fold)\n",
+        "    valid_5fold.append(valid_img_fold)\n",
+        "    nb_fold=nb_fold+1\n",
+        "\n",
+        "five_fold_dic=dict(train=train_5fold,validation=valid_5fold)\n",
+        "list_train = open('folds_dic.pkl', \"wb\")\n",
+        "pickle.dump(five_fold_dic, list_train)\n",
+        "list_train.close()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RbEhtlAvrnai",
+        "outputId": "af639da3-57e3-43db-d9f2-b88f917a31bb"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['train5',\n",
+              " 'train7',\n",
+              " 'train12',\n",
+              " 'train13',\n",
+              " 'train19',\n",
+              " 'train29',\n",
+              " 'train31',\n",
+              " 'train64',\n",
+              " 'train65',\n",
+              " 'train71',\n",
+              " 'train73',\n",
+              " 'train80',\n",
+              " 'train89',\n",
+              " 'train94',\n",
+              " 'train99',\n",
+              " 'train100',\n",
+              " 'train101',\n",
+              " 'train102',\n",
+              " 'train103',\n",
+              " 'train105',\n",
+              " 'train114',\n",
+              " 'train118',\n",
+              " 'train119',\n",
+              " 'train124',\n",
+              " 'train130',\n",
+              " 'train133',\n",
+              " 'train135',\n",
+              " 'train136',\n",
+              " 'train139',\n",
+              " 'train142',\n",
+              " 'train146',\n",
+              " 'train147',\n",
+              " 'train153',\n",
+              " 'train161',\n",
+              " 'train169',\n",
+              " 'train174',\n",
+              " 'train181',\n",
+              " 'train185',\n",
+              " 'train190',\n",
+              " 'train206',\n",
+              " 'train214',\n",
+              " 'train226',\n",
+              " 'train229',\n",
+              " 'train230',\n",
+              " 'train236',\n",
+              " 'train241',\n",
+              " 'train248',\n",
+              " 'train255',\n",
+              " 'train266',\n",
+              " 'train271',\n",
+              " 'train277',\n",
+              " 'train284',\n",
+              " 'train288',\n",
+              " 'train294',\n",
+              " 'train308',\n",
+              " 'train310',\n",
+              " 'train312',\n",
+              " 'train314',\n",
+              " 'train317',\n",
+              " 'train325',\n",
+              " 'train330',\n",
+              " 'train339',\n",
+              " 'train348',\n",
+              " 'train354',\n",
+              " 'train356',\n",
+              " 'train357',\n",
+              " 'train358',\n",
+              " 'train366',\n",
+              " 'train368']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ],
+      "source": [
+        "'load histories'\n",
+        "\n",
+        "with open('folds_dic.pkl', 'rb') as m:\n",
+        "      folds_dict = pickle.load(m)\n",
+        "valid_img_fold =  folds_dict['train'][0]\n",
+        "train_img_fold =  folds_dict['validation'][0]\n",
+        "valid_img_fold\n",
+        "train_img_fold\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zAbmE1_klGb1",
+        "outputId": "9adad777-1ef7-4eeb-e19a-00ed72d19b3c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
+            "\n",
+            "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
+            "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
+            "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
+            "\n",
+            "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
+            "\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"model\"\n",
+            "__________________________________________________________________________________________________\n",
+            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
+            "==================================================================================================\n",
+            " input_layer (InputLayer)    [(None, 128, 128, 128, 3)]   0         []                            \n",
+            "                                                                                                  \n",
+            " layer_1 (Conv3D)            (None, 128, 128, 128, 32)    2624      ['input_layer[0][0]']         \n",
+            "                                                                                                  \n",
+            " layer_2 (Conv3D)            (None, 128, 128, 128, 32)    1056      ['layer_1[0][0]']             \n",
+            "                                                                                                  \n",
+            " layer_4 (Conv3D)            (None, 128, 128, 128, 32)    27680     ['layer_1[0][0]']             \n",
+            "                                                                                                  \n",
+            " layer_6 (Conv3D)            (None, 128, 128, 128, 32)    128032    ['layer_1[0][0]']             \n",
+            "                                                                                                  \n",
+            " layer_3_maxpool (MaxPoolin  (None, 64, 64, 64, 32)       0         ['layer_2[0][0]']             \n",
+            " g3D)                                                                                             \n",
+            "                                                                                                  \n",
+            " layer_5_maxpool (MaxPoolin  (None, 64, 64, 64, 32)       0         ['layer_4[0][0]']             \n",
+            " g3D)                                                                                             \n",
+            "                                                                                                  \n",
+            " layer_7_maxpool (MaxPoolin  (None, 64, 64, 64, 32)       0         ['layer_6[0][0]']             \n",
+            " g3D)                                                                                             \n",
+            "                                                                                                  \n",
+            " layer_8_concatenate (Conca  (None, 64, 64, 64, 96)       0         ['layer_3_maxpool[0][0]',     \n",
+            " tenate)                                                             'layer_5_maxpool[0][0]',     \n",
+            "                                                                     'layer_7_maxpool[0][0]']     \n",
+            "                                                                                                  \n",
+            " global_average_pooling3d (  (None, 96)                   0         ['layer_8_concatenate[0][0]'] \n",
+            " GlobalAveragePooling3D)                                                                          \n",
+            "                                                                                                  \n",
+            " dense (Dense)               (None, 12)                   1152      ['global_average_pooling3d[0][\n",
+            "                                                                    0]']                          \n",
+            "                                                                                                  \n",
+            " dense_1 (Dense)             (None, 96)                   1152      ['dense[0][0]']               \n",
+            "                                                                                                  \n",
+            " tf.math.multiply (TFOpLamb  (None, 64, 64, 64, 96)       0         ['layer_8_concatenate[0][0]', \n",
+            " da)                                                                 'dense_1[0][0]']             \n",
+            "                                                                                                  \n",
+            " layer_9 (Conv3D)            (None, 64, 64, 64, 64)       165952    ['tf.math.multiply[0][0]']    \n",
+            "                                                                                                  \n",
+            " layer_10_instance_norm (In  (None, 64, 64, 64, 64)       128       ['layer_9[0][0]']             \n",
+            " stanceNormalization)                                                                             \n",
+            "                                                                                                  \n",
+            " layer_11 (Conv3D)           (None, 64, 64, 64, 64)       110656    ['layer_10_instance_norm[0][0]\n",
+            "                                                                    ']                            \n",
+            "                                                                                                  \n",
+            " tf.identity (TFOpLambda)    (None, 64, 64, 64, 64)       0         ['layer_11[0][0]']            \n",
+            "                                                                                                  \n",
+            " layer_13_maxpool (MaxPooli  (None, 32, 32, 32, 64)       0         ['tf.identity[0][0]']         \n",
+            " ng3D)                                                                                            \n",
+            "                                                                                                  \n",
+            " layer_14_conv (Conv3D)      (None, 32, 32, 32, 128)      221312    ['layer_13_maxpool[0][0]']    \n",
+            "                                                                                                  \n",
+            " layer_15_instance_norm (In  (None, 32, 32, 32, 128)      256       ['layer_14_conv[0][0]']       \n",
+            " stanceNormalization)                                                                             \n",
+            "                                                                                                  \n",
+            " layer_16_conv (Conv3D)      (None, 32, 32, 32, 128)      442496    ['layer_15_instance_norm[0][0]\n",
+            "                                                                    ']                            \n",
+            "                                                                                                  \n",
+            " tf.identity_1 (TFOpLambda)  (None, 32, 32, 32, 128)      0         ['layer_16_conv[0][0]']       \n",
+            "                                                                                                  \n",
+            " layer_18_maxpool (MaxPooli  (None, 16, 16, 16, 128)      0         ['tf.identity_1[0][0]']       \n",
+            " ng3D)                                                                                            \n",
+            "                                                                                                  \n",
+            " global_average_pooling3d_1  (None, 128)                  0         ['layer_18_maxpool[0][0]']    \n",
+            "  (GlobalAveragePooling3D)                                                                        \n",
+            "                                                                                                  \n",
+            " dense_2 (Dense)             (None, 16)                   2048      ['global_average_pooling3d_1[0\n",
+            "                                                                    ][0]']                        \n",
+            "                                                                                                  \n",
+            " dense_3 (Dense)             (None, 128)                  2048      ['dense_2[0][0]']             \n",
+            "                                                                                                  \n",
+            " tf.math.multiply_1 (TFOpLa  (None, 16, 16, 16, 128)      0         ['layer_18_maxpool[0][0]',    \n",
+            " mbda)                                                               'dense_3[0][0]']             \n",
+            "                                                                                                  \n",
+            " up_sampling3d (UpSampling3  (None, 32, 32, 32, 128)      0         ['tf.math.multiply_1[0][0]']  \n",
+            " D)                                                                                               \n",
+            "                                                                                                  \n",
+            " upsample_layer_1_conv (Con  (None, 32, 32, 32, 128)      131200    ['up_sampling3d[0][0]']       \n",
+            " v3D)                                                                                             \n",
+            "                                                                                                  \n",
+            " upsample_layer_1_instance_  (None, 32, 32, 32, 128)      256       ['upsample_layer_1_conv[0][0]'\n",
+            " norm (InstanceNormalizatio                                         ]                             \n",
+            " n)                                                                                               \n",
+            "                                                                                                  \n",
+            " upsample_layer_1_concatena  (None, 32, 32, 32, 256)      0         ['upsample_layer_1_instance_no\n",
+            " te (Concatenate)                                                   rm[0][0]',                    \n",
+            "                                                                     'layer_16_conv[0][0]']       \n",
+            "                                                                                                  \n",
+            " upsample_layer_1_conv1 (Co  (None, 32, 32, 32, 128)      884864    ['upsample_layer_1_concatenate\n",
+            " nv3D)                                                              [0][0]']                      \n",
+            "                                                                                                  \n",
+            " global_average_pooling3d_2  (None, 128)                  0         ['upsample_layer_1_conv1[0][0]\n",
+            "  (GlobalAveragePooling3D)                                          ']                            \n",
+            "                                                                                                  \n",
+            " dense_4 (Dense)             (None, 16)                   2048      ['global_average_pooling3d_2[0\n",
+            "                                                                    ][0]']                        \n",
+            "                                                                                                  \n",
+            " dense_5 (Dense)             (None, 128)                  2048      ['dense_4[0][0]']             \n",
+            "                                                                                                  \n",
+            " tf.math.multiply_2 (TFOpLa  (None, 32, 32, 32, 128)      0         ['upsample_layer_1_conv1[0][0]\n",
+            " mbda)                                                              ',                            \n",
+            "                                                                     'dense_5[0][0]']             \n",
+            "                                                                                                  \n",
+            " tf.identity_2 (TFOpLambda)  (None, 32, 32, 32, 128)      0         ['tf.math.multiply_2[0][0]']  \n",
+            "                                                                                                  \n",
+            " upsample_layer_1_conv2 (Co  (None, 32, 32, 32, 128)      442496    ['tf.identity_2[0][0]']       \n",
+            " nv3D)                                                                                            \n",
+            "                                                                                                  \n",
+            " up_sampling3d_1 (UpSamplin  (None, 64, 64, 64, 128)      0         ['upsample_layer_1_conv2[0][0]\n",
+            " g3D)                                                               ']                            \n",
+            "                                                                                                  \n",
+            " upsample_layer_2_conv (Con  (None, 64, 64, 64, 64)       65600     ['up_sampling3d_1[0][0]']     \n",
+            " v3D)                                                                                             \n",
+            "                                                                                                  \n",
+            " upsample_layer_2_instance_  (None, 64, 64, 64, 64)       128       ['upsample_layer_2_conv[0][0]'\n",
+            " norm (InstanceNormalizatio                                         ]                             \n",
+            " n)                                                                                               \n",
+            "                                                                                                  \n",
+            " upsample_layer_2_concatena  (None, 64, 64, 64, 128)      0         ['upsample_layer_2_instance_no\n",
+            " te (Concatenate)                                                   rm[0][0]',                    \n",
+            "                                                                     'layer_11[0][0]']            \n",
+            "                                                                                                  \n",
+            " upsample_layer_2_conv1 (Co  (None, 64, 64, 64, 64)       221248    ['upsample_layer_2_concatenate\n",
+            " nv3D)                                                              [0][0]']                      \n",
+            "                                                                                                  \n",
+            " global_average_pooling3d_3  (None, 64)                   0         ['upsample_layer_2_conv1[0][0]\n",
+            "  (GlobalAveragePooling3D)                                          ']                            \n",
+            "                                                                                                  \n",
+            " dense_6 (Dense)             (None, 8)                    512       ['global_average_pooling3d_3[0\n",
+            "                                                                    ][0]']                        \n",
+            "                                                                                                  \n",
+            " dense_7 (Dense)             (None, 64)                   512       ['dense_6[0][0]']             \n",
+            "                                                                                                  \n",
+            " tf.math.multiply_3 (TFOpLa  (None, 64, 64, 64, 64)       0         ['upsample_layer_2_conv1[0][0]\n",
+            " mbda)                                                              ',                            \n",
+            "                                                                     'dense_7[0][0]']             \n",
+            "                                                                                                  \n",
+            " tf.identity_3 (TFOpLambda)  (None, 64, 64, 64, 64)       0         ['tf.math.multiply_3[0][0]']  \n",
+            "                                                                                                  \n",
+            " upsample_layer_2_conv2 (Co  (None, 64, 64, 64, 64)       110656    ['tf.identity_3[0][0]']       \n",
+            " nv3D)                                                                                            \n",
+            "                                                                                                  \n",
+            " up_sampling3d_2 (UpSamplin  (None, 128, 128, 128, 64)    0         ['upsample_layer_2_conv2[0][0]\n",
+            " g3D)                                                               ']                            \n",
+            "                                                                                                  \n",
+            " upsample_layer_3_conv (Con  (None, 128, 128, 128, 32)    16416     ['up_sampling3d_2[0][0]']     \n",
+            " v3D)                                                                                             \n",
+            "                                                                                                  \n",
+            " upsample_layer_3_instance_  (None, 128, 128, 128, 32)    64        ['upsample_layer_3_conv[0][0]'\n",
+            " norm (InstanceNormalizatio                                         ]                             \n",
+            " n)                                                                                               \n",
+            "                                                                                                  \n",
+            " upsample_layer_3_concatena  (None, 128, 128, 128, 64)    0         ['upsample_layer_3_instance_no\n",
+            " te (Concatenate)                                                   rm[0][0]',                    \n",
+            "                                                                     'layer_1[0][0]']             \n",
+            "                                                                                                  \n",
+            " upsample_layer_3_conv1 (Co  (None, 128, 128, 128, 32)    55328     ['upsample_layer_3_concatenate\n",
+            " nv3D)                                                              [0][0]']                      \n",
+            "                                                                                                  \n",
+            " global_average_pooling3d_4  (None, 32)                   0         ['upsample_layer_3_conv1[0][0]\n",
+            "  (GlobalAveragePooling3D)                                          ']                            \n",
+            "                                                                                                  \n",
+            " dense_8 (Dense)             (None, 4)                    128       ['global_average_pooling3d_4[0\n",
+            "                                                                    ][0]']                        \n",
+            "                                                                                                  \n",
+            " dense_9 (Dense)             (None, 32)                   128       ['dense_8[0][0]']             \n",
+            "                                                                                                  \n",
+            " tf.math.multiply_4 (TFOpLa  (None, 128, 128, 128, 32)    0         ['upsample_layer_3_conv1[0][0]\n",
+            " mbda)                                                              ',                            \n",
+            "                                                                     'dense_9[0][0]']             \n",
+            "                                                                                                  \n",
+            " tf.identity_4 (TFOpLambda)  (None, 128, 128, 128, 32)    0         ['tf.math.multiply_4[0][0]']  \n",
+            "                                                                                                  \n",
+            " upsample_layer_3_conv2 (Co  (None, 128, 128, 128, 32)    27680     ['tf.identity_4[0][0]']       \n",
+            " nv3D)                                                                                            \n",
+            "                                                                                                  \n",
+            " final_output (Conv3D)       (None, 128, 128, 128, 4)     132       ['upsample_layer_3_conv2[0][0]\n",
+            "                                                                    ']                            \n",
+            "                                                                                                  \n",
+            "==================================================================================================\n",
+            "Total params: 3068036 (11.70 MB)\n",
+            "Trainable params: 3068036 (11.70 MB)\n",
+            "Non-trainable params: 0 (0.00 Byte)\n",
+            "__________________________________________________________________________________________________\n",
+            "(None, 128, 128, 128, 3)\n",
+            "(None, 128, 128, 128, 4)\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Build the model\n",
+        "# Attention Model\n",
+        "import tensorflow as tf\n",
+        "from keras.models import Model\n",
+        "from keras.layers import Input, Reshape, Dense, Conv3D, BatchNormalization, UpSampling3D,Concatenate,Activation,Multiply, AveragePooling3D, MaxPooling3D, concatenate, GlobalAveragePooling3D, GlobalMaxPooling3D, Conv3DTranspose, BatchNormalization, Dropout, Lambda\n",
+        "from keras import regularizers\n",
+        "from tensorflow.keras.optimizers import Adam\n",
+        "from keras.metrics import MeanIoU\n",
+        "Lrelu = tf.keras.layers.LeakyReLU(alpha=0.1)\n",
+        "from tensorflow_addons.layers import InstanceNormalization\n",
+        "from tensorflow.python.keras.layers import Dropout, SpatialDropout3D\n",
+        "\n",
+        "def SqueezeAndExcitation(inputs, ratio=8 ,name=\"attention\"):\n",
+        "    b,_, _, _,c= inputs.shape\n",
+        "    x = GlobalAveragePooling3D()(inputs)\n",
+        "    x = Dense(c//ratio, activation=\"relu\", use_bias=False)(x)\n",
+        "    x = Dense(c, activation=\"sigmoid\", use_bias=False)(x)\n",
+        "    x = inputs * x\n",
+        "    return x\n",
+        "\n",
+        "def CNN_Model(IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS, num_classes):\n",
+        "    kernel_initializer = 'he_uniform'\n",
+        "    inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS), name='input_layer')\n",
+        "    s = inputs\n",
+        "\n",
+        "    # Initial layers\n",
+        "    conv = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_1')(s)\n",
+        "    conv1 = Conv3D(32, (1, 1, 1), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_2')(conv)\n",
+        "    pool1 = MaxPooling3D((2, 2, 2), name='layer_3_maxpool')(conv1)\n",
+        "    conv2 = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_4')(conv)\n",
+        "    pool2 = MaxPooling3D((2, 2, 2), name='layer_5_maxpool')(conv2)\n",
+        "    conv3 = Conv3D(32, (5, 5, 5), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_6')(conv)\n",
+        "    pool3 = MaxPooling3D((2, 2, 2), name='layer_7_maxpool')(conv3)\n",
+        "    layer_out = concatenate([pool1, pool2, pool3], axis=-1, name='layer_8_concatenate')\n",
+        "    attention_layer1 = SqueezeAndExcitation(layer_out, name='attention_layer1')\n",
+        "    # Following layers\n",
+        "    conv4 = Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_9')(attention_layer1)\n",
+        "    B4 = InstanceNormalization(axis=-1, name='layer_10_instance_norm')(conv4)\n",
+        "    conv4 = Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_11')(B4)\n",
+        "    drop2 = Dropout(0.2, name='layer_12_dropout')(conv4)\n",
+        "    pool4 = MaxPooling3D((2, 2, 2), name='layer_13_maxpool')(drop2)\n",
+        "\n",
+        "    # Following layers\n",
+        "    conv5 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_14_conv')(pool4)\n",
+        "    B5 = InstanceNormalization(axis=-1, name='layer_15_instance_norm')(conv5)\n",
+        "    conv5 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_16_conv')(B5)\n",
+        "    drop5 = Dropout(0.2, name='layer_17_dropout')(conv5)\n",
+        "    pool5 = MaxPooling3D((2, 2, 2), name='layer_18_maxpool')(drop5)\n",
+        "    attention_layer2 = SqueezeAndExcitation(pool5, name='attention_layer2')\n",
+        "\n",
+        "    # Upsampling layers\n",
+        "    u9 = Conv3D(128, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='upsample_layer_1_conv')(UpSampling3D(size=(2,2,2))(attention_layer2))\n",
+        "    u9 = InstanceNormalization(axis=-1, name='upsample_layer_1_instance_norm')(u9)\n",
+        "    u9 = concatenate([u9, conv5], name='upsample_layer_1_concatenate')\n",
+        "    c9 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
+        "                kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_1_conv1')(u9)\n",
+        "    c9 = SqueezeAndExcitation(c9, name='attention_layer3')\n",
+        "    c9 = Dropout(0.2, name='upsample_layer_1_dropout')(c9)\n",
+        "    c9 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
+        "               kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_1_conv2')(c9)\n",
+        "\n",
+        "\n",
+        "    u10= Conv3D(64, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='upsample_layer_2_conv')(UpSampling3D(size =(2,2,2))(c9))\n",
+        "    u10 = InstanceNormalization(axis=-1, name='upsample_layer_2_instance_norm')(u10)\n",
+        "    u10 = concatenate([u10, conv4], name='upsample_layer_2_concatenate')\n",
+        "    c10= Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
+        "              kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_2_conv1')(u10)\n",
+        "    c10 = SqueezeAndExcitation(c10, name='attention_layer4')\n",
+        "    c10 = Dropout(0.2, name='upsample_layer_2_dropout')(c10)\n",
+        "    c10= Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
+        "                 kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_2_conv2')(c10)\n",
+        "\n",
+        "    u11= Conv3D(32, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='upsample_layer_3_conv')(UpSampling3D(size =(2,2,2))(c10))\n",
+        "    u11 = InstanceNormalization(axis=-1, name='upsample_layer_3_instance_norm')(u11)\n",
+        "    u11 = concatenate([u11,  conv], name='upsample_layer_3_concatenate')\n",
+        "    c11= Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
+        "             kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_3_conv1')(u11)\n",
+        "    c11 = SqueezeAndExcitation(c11, name='attention_layer5')\n",
+        "    c11 = Dropout(0.2, name='upsample_layer_3_dropout')(c11)\n",
+        "    c11= Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
+        "                 kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_3_conv2')(c11)\n",
+        "\n",
+        "    outputs = Conv3D(num_classes, (1, 1, 1), kernel_regularizer=regularizers.l2(0.02), activation='softmax', name='final_output')(c11)\n",
+        "\n",
+        "    model = Model(inputs=[inputs], outputs=[outputs])\n",
+        "\n",
+        "    return model\n",
+        "\n",
+        "#Test if everything is working ok.\n",
+        "model = CNN_Model(128, 128, 128, 3,4)\n",
+        "\n",
+        "\n",
+        "model.summary()\n",
+        "print(model.input_shape)\n",
+        "print(model.output_shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# No Attention Model\n",
+        "Lrelu = tf.keras.layers.LeakyReLU(alpha=0.1)\n",
+        "from tensorflow_addons.layers import InstanceNormalization\n",
+        "from tensorflow.python.keras.layers import Dropout, SpatialDropout3D\n",
+        "\n",
+        "\n",
+        "def CNN_ModelNo(IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS, num_classes):\n",
+        "    kernel_initializer = 'he_uniform'\n",
+        "    inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS), name='input_layer')\n",
+        "    s = inputs\n",
+        "\n",
+        "    # Initial layers\n",
+        "    conv = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_1')(s)\n",
+        "    conv1 = Conv3D(32, (1, 1, 1), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_2')(conv)\n",
+        "    pool1 = MaxPooling3D((2, 2, 2), name='layer_3_maxpool')(conv1)\n",
+        "    conv2 = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_4')(conv)\n",
+        "    pool2 = MaxPooling3D((2, 2, 2), name='layer_5_maxpool')(conv2)\n",
+        "    conv3 = Conv3D(32, (5, 5, 5), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='layer_6')(conv)\n",
+        "    pool3 = MaxPooling3D((2, 2, 2), name='layer_7_maxpool')(conv3)\n",
+        "    layer_out = concatenate([pool1, pool2, pool3], axis=-1, name='layer_8_concatenate')\n",
+        "\n",
+        "    conv4 = Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_9')(layer_out)\n",
+        "    B4 = InstanceNormalization(axis=-1, name='layer_10_instance_norm')(conv4)\n",
+        "    conv4 = Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_11')(B4)\n",
+        "    drop2 = Dropout(0.2, name='layer_12_dropout')(conv4)\n",
+        "    pool4 = MaxPooling3D((2, 2, 2), name='layer_13_maxpool')(drop2)\n",
+        "\n",
+        "    # Following layers\n",
+        "    conv5 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_14_conv')(pool4)\n",
+        "    B5 = InstanceNormalization(axis=-1, name='layer_15_instance_norm')(conv5)\n",
+        "    conv5 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, kernel_regularizer=regularizers.l2(0.02), padding='same', name='layer_16_conv')(B5)\n",
+        "    drop5 = Dropout(0.2, name='layer_17_dropout')(conv5)\n",
+        "    pool5 = MaxPooling3D((2, 2, 2), name='layer_18_maxpool')(drop5)\n",
+        "\n",
+        "    # Upsampling layers\n",
+        "    u9 = Conv3D(128, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='upsample_layer_1_conv')(UpSampling3D(size=(2,2,2))(pool5))\n",
+        "    u9 = InstanceNormalization(axis=-1, name='upsample_layer_1_instance_norm')(u9)\n",
+        "    u9 = concatenate([u9, conv5], name='upsample_layer_1_concatenate')\n",
+        "    c9 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
+        "                kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_1_conv1')(u9)\n",
+        "    c9 = Dropout(0.2, name='upsample_layer_1_dropout')(c9)\n",
+        "    c9 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
+        "               kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_1_conv2')(c9)\n",
+        "\n",
+        "\n",
+        "    u10= Conv3D(64, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='upsample_layer_2_conv')(UpSampling3D(size =(2,2,2))(c9))\n",
+        "    u10 = InstanceNormalization(axis=-1, name='upsample_layer_2_instance_norm')(u10)\n",
+        "    u10 = concatenate([u10, conv4], name='upsample_layer_2_concatenate')\n",
+        "    c10= Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
+        "              kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_2_conv1')(u10)\n",
+        "    c10 = Dropout(0.2, name='upsample_layer_2_dropout')(c10)\n",
+        "    c10= Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
+        "                 kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_2_conv2')(c10)\n",
+        "\n",
+        "    u11= Conv3D(32, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same', name='upsample_layer_3_conv')(UpSampling3D(size =(2,2,2))(c10))\n",
+        "    u11 = InstanceNormalization(axis=-1, name='upsample_layer_3_instance_norm')(u11)\n",
+        "    u11 = concatenate([u11,  conv], name='upsample_layer_3_concatenate')\n",
+        "    c11= Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
+        "             kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_3_conv1')(u11)\n",
+        "    c11 = Dropout(0.2, name='upsample_layer_3_dropout')(c11)\n",
+        "    c11= Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
+        "                 kernel_regularizer=regularizers.l2(0.02), padding='same', name='upsample_layer_3_conv2')(c11)\n",
+        "\n",
+        "    outputs = Conv3D(num_classes, (1, 1, 1), kernel_regularizer=regularizers.l2(0.02), activation='softmax', name='final_output')(c11)\n",
+        "\n",
+        "    model = Model(inputs=[inputs], outputs=[outputs])\n",
+        "\n",
+        "    return model\n",
+        "\n",
+        "#Test if everything is working ok.\n",
+        "modelno = CNN_ModelNo(128, 128, 128, 3,4)\n",
+        "\n",
+        "\n",
+        "modelno.summary()\n",
+        "print(modelno.input_shape)\n",
+        "print(modelno.output_shape)\n",
+        "layer_namesno = [layer.name for layer in model.layers]\n",
+        "\n",
+        "print(layer_namesno)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UUWpEWWn0Asz",
+        "outputId": "f8db422b-45cc-4a62-efda-20adc4625fa0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"model_1\"\n",
+            "__________________________________________________________________________________________________\n",
+            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
+            "==================================================================================================\n",
+            " input_layer (InputLayer)    [(None, 128, 128, 128, 3)]   0         []                            \n",
+            "                                                                                                  \n",
+            " layer_1 (Conv3D)            (None, 128, 128, 128, 32)    2624      ['input_layer[0][0]']         \n",
+            "                                                                                                  \n",
+            " layer_2 (Conv3D)            (None, 128, 128, 128, 32)    1056      ['layer_1[0][0]']             \n",
+            "                                                                                                  \n",
+            " layer_4 (Conv3D)            (None, 128, 128, 128, 32)    27680     ['layer_1[0][0]']             \n",
+            "                                                                                                  \n",
+            " layer_6 (Conv3D)            (None, 128, 128, 128, 32)    128032    ['layer_1[0][0]']             \n",
+            "                                                                                                  \n",
+            " layer_3_maxpool (MaxPoolin  (None, 64, 64, 64, 32)       0         ['layer_2[0][0]']             \n",
+            " g3D)                                                                                             \n",
+            "                                                                                                  \n",
+            " layer_5_maxpool (MaxPoolin  (None, 64, 64, 64, 32)       0         ['layer_4[0][0]']             \n",
+            " g3D)                                                                                             \n",
+            "                                                                                                  \n",
+            " layer_7_maxpool (MaxPoolin  (None, 64, 64, 64, 32)       0         ['layer_6[0][0]']             \n",
+            " g3D)                                                                                             \n",
+            "                                                                                                  \n",
+            " layer_8_concatenate (Conca  (None, 64, 64, 64, 96)       0         ['layer_3_maxpool[0][0]',     \n",
+            " tenate)                                                             'layer_5_maxpool[0][0]',     \n",
+            "                                                                     'layer_7_maxpool[0][0]']     \n",
+            "                                                                                                  \n",
+            " layer_9 (Conv3D)            (None, 64, 64, 64, 64)       165952    ['layer_8_concatenate[0][0]'] \n",
+            "                                                                                                  \n",
+            " layer_10_instance_norm (In  (None, 64, 64, 64, 64)       128       ['layer_9[0][0]']             \n",
+            " stanceNormalization)                                                                             \n",
+            "                                                                                                  \n",
+            " layer_11 (Conv3D)           (None, 64, 64, 64, 64)       110656    ['layer_10_instance_norm[0][0]\n",
+            "                                                                    ']                            \n",
+            "                                                                                                  \n",
+            " tf.identity_5 (TFOpLambda)  (None, 64, 64, 64, 64)       0         ['layer_11[0][0]']            \n",
+            "                                                                                                  \n",
+            " layer_13_maxpool (MaxPooli  (None, 32, 32, 32, 64)       0         ['tf.identity_5[0][0]']       \n",
+            " ng3D)                                                                                            \n",
+            "                                                                                                  \n",
+            " layer_14_conv (Conv3D)      (None, 32, 32, 32, 128)      221312    ['layer_13_maxpool[0][0]']    \n",
+            "                                                                                                  \n",
+            " layer_15_instance_norm (In  (None, 32, 32, 32, 128)      256       ['layer_14_conv[0][0]']       \n",
+            " stanceNormalization)                                                                             \n",
+            "                                                                                                  \n",
+            " layer_16_conv (Conv3D)      (None, 32, 32, 32, 128)      442496    ['layer_15_instance_norm[0][0]\n",
+            "                                                                    ']                            \n",
+            "                                                                                                  \n",
+            " tf.identity_6 (TFOpLambda)  (None, 32, 32, 32, 128)      0         ['layer_16_conv[0][0]']       \n",
+            "                                                                                                  \n",
+            " layer_18_maxpool (MaxPooli  (None, 16, 16, 16, 128)      0         ['tf.identity_6[0][0]']       \n",
+            " ng3D)                                                                                            \n",
+            "                                                                                                  \n",
+            " up_sampling3d_3 (UpSamplin  (None, 32, 32, 32, 128)      0         ['layer_18_maxpool[0][0]']    \n",
+            " g3D)                                                                                             \n",
+            "                                                                                                  \n",
+            " upsample_layer_1_conv (Con  (None, 32, 32, 32, 128)      131200    ['up_sampling3d_3[0][0]']     \n",
+            " v3D)                                                                                             \n",
+            "                                                                                                  \n",
+            " upsample_layer_1_instance_  (None, 32, 32, 32, 128)      256       ['upsample_layer_1_conv[0][0]'\n",
+            " norm (InstanceNormalizatio                                         ]                             \n",
+            " n)                                                                                               \n",
+            "                                                                                                  \n",
+            " upsample_layer_1_concatena  (None, 32, 32, 32, 256)      0         ['upsample_layer_1_instance_no\n",
+            " te (Concatenate)                                                   rm[0][0]',                    \n",
+            "                                                                     'layer_16_conv[0][0]']       \n",
+            "                                                                                                  \n",
+            " upsample_layer_1_conv1 (Co  (None, 32, 32, 32, 128)      884864    ['upsample_layer_1_concatenate\n",
+            " nv3D)                                                              [0][0]']                      \n",
+            "                                                                                                  \n",
+            " tf.identity_7 (TFOpLambda)  (None, 32, 32, 32, 128)      0         ['upsample_layer_1_conv1[0][0]\n",
+            "                                                                    ']                            \n",
+            "                                                                                                  \n",
+            " upsample_layer_1_conv2 (Co  (None, 32, 32, 32, 128)      442496    ['tf.identity_7[0][0]']       \n",
+            " nv3D)                                                                                            \n",
+            "                                                                                                  \n",
+            " up_sampling3d_4 (UpSamplin  (None, 64, 64, 64, 128)      0         ['upsample_layer_1_conv2[0][0]\n",
+            " g3D)                                                               ']                            \n",
+            "                                                                                                  \n",
+            " upsample_layer_2_conv (Con  (None, 64, 64, 64, 64)       65600     ['up_sampling3d_4[0][0]']     \n",
+            " v3D)                                                                                             \n",
+            "                                                                                                  \n",
+            " upsample_layer_2_instance_  (None, 64, 64, 64, 64)       128       ['upsample_layer_2_conv[0][0]'\n",
+            " norm (InstanceNormalizatio                                         ]                             \n",
+            " n)                                                                                               \n",
+            "                                                                                                  \n",
+            " upsample_layer_2_concatena  (None, 64, 64, 64, 128)      0         ['upsample_layer_2_instance_no\n",
+            " te (Concatenate)                                                   rm[0][0]',                    \n",
+            "                                                                     'layer_11[0][0]']            \n",
+            "                                                                                                  \n",
+            " upsample_layer_2_conv1 (Co  (None, 64, 64, 64, 64)       221248    ['upsample_layer_2_concatenate\n",
+            " nv3D)                                                              [0][0]']                      \n",
+            "                                                                                                  \n",
+            " tf.identity_8 (TFOpLambda)  (None, 64, 64, 64, 64)       0         ['upsample_layer_2_conv1[0][0]\n",
+            "                                                                    ']                            \n",
+            "                                                                                                  \n",
+            " upsample_layer_2_conv2 (Co  (None, 64, 64, 64, 64)       110656    ['tf.identity_8[0][0]']       \n",
+            " nv3D)                                                                                            \n",
+            "                                                                                                  \n",
+            " up_sampling3d_5 (UpSamplin  (None, 128, 128, 128, 64)    0         ['upsample_layer_2_conv2[0][0]\n",
+            " g3D)                                                               ']                            \n",
+            "                                                                                                  \n",
+            " upsample_layer_3_conv (Con  (None, 128, 128, 128, 32)    16416     ['up_sampling3d_5[0][0]']     \n",
+            " v3D)                                                                                             \n",
+            "                                                                                                  \n",
+            " upsample_layer_3_instance_  (None, 128, 128, 128, 32)    64        ['upsample_layer_3_conv[0][0]'\n",
+            " norm (InstanceNormalizatio                                         ]                             \n",
+            " n)                                                                                               \n",
+            "                                                                                                  \n",
+            " upsample_layer_3_concatena  (None, 128, 128, 128, 64)    0         ['upsample_layer_3_instance_no\n",
+            " te (Concatenate)                                                   rm[0][0]',                    \n",
+            "                                                                     'layer_1[0][0]']             \n",
+            "                                                                                                  \n",
+            " upsample_layer_3_conv1 (Co  (None, 128, 128, 128, 32)    55328     ['upsample_layer_3_concatenate\n",
+            " nv3D)                                                              [0][0]']                      \n",
+            "                                                                                                  \n",
+            " tf.identity_9 (TFOpLambda)  (None, 128, 128, 128, 32)    0         ['upsample_layer_3_conv1[0][0]\n",
+            "                                                                    ']                            \n",
+            "                                                                                                  \n",
+            " upsample_layer_3_conv2 (Co  (None, 128, 128, 128, 32)    27680     ['tf.identity_9[0][0]']       \n",
+            " nv3D)                                                                                            \n",
+            "                                                                                                  \n",
+            " final_output (Conv3D)       (None, 128, 128, 128, 4)     132       ['upsample_layer_3_conv2[0][0]\n",
+            "                                                                    ']                            \n",
+            "                                                                                                  \n",
+            "==================================================================================================\n",
+            "Total params: 3056260 (11.66 MB)\n",
+            "Trainable params: 3056260 (11.66 MB)\n",
+            "Non-trainable params: 0 (0.00 Byte)\n",
+            "__________________________________________________________________________________________________\n",
+            "(None, 128, 128, 128, 3)\n",
+            "(None, 128, 128, 128, 4)\n",
+            "['input_layer', 'layer_1', 'layer_2', 'layer_4', 'layer_6', 'layer_3_maxpool', 'layer_5_maxpool', 'layer_7_maxpool', 'layer_8_concatenate', 'global_average_pooling3d', 'dense', 'dense_1', 'tf.math.multiply', 'layer_9', 'layer_10_instance_norm', 'layer_11', 'tf.identity', 'layer_13_maxpool', 'layer_14_conv', 'layer_15_instance_norm', 'layer_16_conv', 'tf.identity_1', 'layer_18_maxpool', 'global_average_pooling3d_1', 'dense_2', 'dense_3', 'tf.math.multiply_1', 'up_sampling3d', 'upsample_layer_1_conv', 'upsample_layer_1_instance_norm', 'upsample_layer_1_concatenate', 'upsample_layer_1_conv1', 'global_average_pooling3d_2', 'dense_4', 'dense_5', 'tf.math.multiply_2', 'tf.identity_2', 'upsample_layer_1_conv2', 'up_sampling3d_1', 'upsample_layer_2_conv', 'upsample_layer_2_instance_norm', 'upsample_layer_2_concatenate', 'upsample_layer_2_conv1', 'global_average_pooling3d_3', 'dense_6', 'dense_7', 'tf.math.multiply_3', 'tf.identity_3', 'upsample_layer_2_conv2', 'up_sampling3d_2', 'upsample_layer_3_conv', 'upsample_layer_3_instance_norm', 'upsample_layer_3_concatenate', 'upsample_layer_3_conv1', 'global_average_pooling3d_4', 'dense_8', 'dense_9', 'tf.math.multiply_4', 'tf.identity_4', 'upsample_layer_3_conv2', 'final_output']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "e9VK6lQclGez",
+        "outputId": "7e9e0074-6701-4d5e-91a0-6dba8421337c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": 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AAAAMoX/84x/r1q1rb28nhNTW1jo6OnK5XELIW2+9lZiYaO7sAIaWtbkTAAAAAAAYWuPHj797965arWY3q6urCSFCodDX19eseQEMBzzYCQAAAAAWTiAQLFy4kKKozo0URS1YsMBcKQEMG5R8AAAAAGD53nnnHTs7O8MmRVHz58+3sbExY0oAwwMlHwAAAABYvtDQ0M6bdnZ2a9asMVcyAMMJJR8AAAAAWD4rKyupVGplZcVuMgwzd+5c86YEMDxQ8gEAAADAS2HVqlU0TRNCOBzOW2+9ZW2NhQzhpYCSDwAAAABeCtOmTRMIBIQQW1vb1atXmzsdgGGCkg8AAAAAXhbR0dHW1tZcLnf69OnmzgVgmKDkAwAAAICXxapVq3Q6XXR0dJcPNgBYMIphGHPnAAAAADDI1qxZk5WVZe4sYKDUavWgf0ehoaHBzs5uUF7k02q1FEXhnUBLUllZyePxzJ3FIMMNCgAAABaoublZJpOFh4ebOxEYEBcXl6qqqsGNmZubO1g3xocffujs7Pzee+8NSjQwO09PT4ucD0PJBwAAAJbJxsam86e3YYQa9IsolUoHKxSfzxcIBLjN4AWHd/kAAAAAAAAsFko+AAAAAAAAi4WSDwAAAGCYbN68mcfj7dixY4j27weNRjN+/HihUEjT9MyZM2/evDl0fRm0tLRMnDiRpmmxWBweHl5aWjrAgOHh4V9++eWg5Da4tFptcnJyfHw8TdMURaWkpLDtV69e9fDw4PF4q1atGop+i4uLQ0ND8/LySPdnOycnh730EyZM+P7770duQON72GTAM2fO5Obm9ubsWR6UfAAAAADDZO/evcuWLRu6/ftBp9NNmTKltra2uro6MDAwKipqSLtjtbe3z5gxo6ampry8fNSoUStWrBhgwNzc3IGvobJr167KysoBBumso6MjMjJy7ty5MpksOTnZy8tr9+7dDQ0NhJBZs2YVFRVFR0cfOXJkEHtknTx58vjx43fv3mU3TZ5tpVIZFRW1fv16hUIRGxsrlUpVKtUIDWh8D5sMuGTJkqtXr6alpfXjlI54DAAAAIDFeeutt7KyssydhQl/+tOftm/fPnT7D0R+fj6Px9Pr9cPTnaFTDoej0+lM/srn84ctE39//4qKij4dkpiYmJKS0t2vSUlJsbGx7N8ymez48eNubm4bN25kWyoqKmJiYvqd7XO5u7vn5uZ2aTSc7ZKSEkJIfX09wzAKhYIQUlJSMtIDMqbu4c43mEql8vT0fPDgQXeH29nZaTSa5/Yy4mCWDwAAAF4ukZGRFEWVlpZWV1d7eXnRNJ2QkEBR1Ouvv07TtLe39+nTpwkh7e3tkZGRIpHIycmJnYqJjY0Vi8U2NjYrV67U6/WbNm2iKMrT01MkEnG5XD8/P19fX5FIZG9vn56eTggxGbaz7777zs/Pz97efvXq1e3t7c/NvEsCb775JkVRXl5e1dXV3377rb29fWBgYJeY8fHxFEXl5uYuWbJk27Zt3UXW6/V1dXXHjh2TSqXdfaP82rVr06ZNEwqF9vb227dvJ4RcvHhx0qRJNE0HBwdfuHCBELJlyxaKotauXRsQEEDTdFJS0tixYymKmjhxIiHk0qVLjo6O7u7uncMqlUpHR0crK6vnDr87aWlpAoFgx44dxr0TU1fB+AYghEil0pKSEk9Pz3Xr1hFCFi5cuGnTpn6nRAjp6OiQy+XR0dGGFrFYfODAgf3795eVlXXZucuZNDkQ0vcbxpjhbPv4+Pj7+587d06j0Zw9e3bcuHE+Pj4jOmB393DnG8zGxmbRokUHDx7sR2Ijm7lrTgAAAIDB1/MsHyHk0aNHDMPcuXNHJBIxDCMSifLz81UqlVwuFwgENTU1mZmZ8+bNU6lU9+/f//zzzxmGiY+Pr6mpefToEZfLvX//PsMw7u7up06dam9vz8zM5HK5paWlz5492759+5QpU9iOjMMaZu1qampsbGxycnLq6+snT56cmppqMtXOs3xdElAqlfb29n//+9/ZX+Pi4u7cuWMcUyKRpKenNzU1JScnd3dC5s2bRwiZO3duXV2dyR1+/fVXsVi8d+9epVL5yy+//PnPf1YoFCKRKCMjo6WlRS6Xi0Qi9liJRHLlyhW9Xn/06FGhUFhWVsbhcO7du8fG2bBhw88//9w5ckJCQnx8fHeJ9XKWLyYmhj1LXXpnfzW+CsY3gFarJYQM4ixfUVERIaSlpYXdlMlkOTk5DMNIpdIlS5YwnWb5TJ5J44H08oYxMDmH1vlsX79+nf2IvLW1dWFh4XMH+4IH7O4e7nKDpaWleXt7dxcEs3wAAAAAlszNzc3GxiYuLs7R0bGwsJCm6f/zf/5Pfn7+b37zmz//+c+EkNTUVFdX13Hjxjk4OLS2trJHubi4cLnc2bNna7Vad3d3Ho83bdq05ubm7sIa2gsLCz09PRcsWODg4BAREXH58uXnZtglAaFQGBUVdeLECUKIVqvVarX//ve/Tcb08vKyt7ffsmVLd5HPnz9fW1sbFhYWEhLS2NhovMOlS5eEQmFCQoJQKPT09Pz8888LCgokEsmyZctsbW3Z0f3www+G/SmKmj17tkqlGjNmTERERGpqKiFEpVJVVlZOmDDBsFtFRUVeXt5HH3303LH3iaF3nU7HtnR3FYbO48eP+Xy+ra1tl3aZTPbjjz/euHHD0NLDmew8kH7cMF10PtsKhWLRokXZ2dltbW2ZmZlSqZR9yXDkBjR5DxvfYE5OTk+ePGEs8XvrPUDJBwAAAPD/cHFxqa+v/6//+q+NGze+++67Xl5eFy9ebG1tXbp0qZOTE4/Hq6ur63dYw6ZCoXj48CFFURRF7dq1q6mpqefDTSYQExOTk5PT2tqan5//xz/+sa8xDaysrCQSSWJiIo/Hy87ONt6hurq6ywOZdXV1zs7Ohk2JRNLdadm4cWNGRkZyck/jAAAgAElEQVR9ff2JEydWrlzZeURr1qw5c+aMWCzuZZ4D1+UqDB21Ws3n843bnZ2dU1NTN2/ebGjp5Zns98VldTnb2dnZbm5u4eHhIpFo8eLFzs7OJq/7CApofA+bvMH4fL5er9doNH3KbaRDyQcAAADwfzEMU1VV5e7uTlHUtm3bKisr33nnnXXr1h0/fry4uPif//ynWq2WSCT9DmtoEYvFQUFBhievCgoKeo5gMoHf/va3fn5+Z8+evXbt2vz58/sa02SeJtslEgn7PKSBq6sru6gGq7a21tXV1eSxs2fPDggI+Prrr/Py8hYuXMg2tra2rl69et++fQEBAX1Nst+Mr8LQEQqF3b1ut3TpUolEYni3s5dnciAX1/hsG+emVqstIyB7D3d3g7W3t3M4HIFA0PvcLABKPgAAAHjp0DR97do1rVZbVVVlaGxra9NoNDKZrL29PTQ09Ouvvy4oKOjo6Hj11Vcpinr27Bmfz6dpuqSkpE9TBF3CGtrnzJlTUlJy4sQJpVKpUqmeO2nTXQIxMTFHjhxxcHCwsrLqa0xCSFZW1sGDBzUaTXNzc2pqqkKheO2114x3e+ONNxobG5OSkn799VetVltTUxMWFvb06dOMjIzW1la5XN7Y2BgWFtZdLxs3bvz000+nTZvG4XAIIS0tLTExMXv27Bm2eq/LVTC+ATgcDofDKS4u7uHTAn3i5eWl0Wja2tpM/rp//37DhwR7eSb7cXFZJs/29OnTHzx4UFBQoFars7KyiouLZ8yYMUIDGt/DkydP7u4GUygU7JJCvczNQgzTO4MAAAAAw6jn5Vt27twpEAj8/Pzi4uIIIatWrRKJRA4ODlwuNzg4mF0oIicnZ/To0dbW1n5+fgUFBU+ePPH39xeJRFFRUb6+vr6+vuvXryeEuLu7379/39/fnxASFBT0888/SyQSiqISExMZhukS9s9//jOXyxUKhX/9618ZhsnMzPTz8+Pz+VOnTr19+7Zxnp33N06go6ODYZiGhgahUPj48WP2kC4x4+PjCSFubm7Xrl0zeSpu377t7e3N5/OFQuGUKVMuXbrU3Un7n//5n9/+9rdCodDd3X3fvn0Mw+Tn5wcFBQmFwuDg4IKCAoZhPvjgA0LI2LFjm5qaxo8fTwhZvnw5wzDPnj3z9fVtaGhgQx06dKjLv0ivXLlistPeLN+yZcsWHo8nFArZUMa9G19c4xuAYZjIyEg+nx8VFcUwzJtvvrlhw4bndt3D8i06nW706NFFRUUMw+zbt4+maQcHh0OHDhl2OHLkiOEjDV3OZHenscvFVSgUzs7OMpmsS9ebNm3y9PQkhNA0PX36dMOCn13O9ldffeXr6ysQCF555ZXDhw8zDPOCB+wumvE93MMNtmnTph6urKUu30IxL9nLiwAAAPAyiIyMXLFiRURERC/3p2n61q1bgYGBg5vGEIXtjGGYjRs3fvHFF0PXhRkJBIKBv3Y1dFdh69atLi4u3X3O4ZNPPmloaNi7d++g98vSarXLly8PDg5mv5lh8QEHHk2n0wUEBGRnZ3c3vWxvb//06VOTL2GOaHiwEwAAAIAQQvR6vRnDVlZWUqZUVlZ2d8iVK1eUSuWHH374+9//fhDj9yOTF98QXdyeJSYmlpeX37x5c4jiy+VyJyenhISElyTgwKPt2LFj27Ztw/n66AvC2twJAAAAAJjZypUrlUrl/Pnzs7OzQ0JCzBLWw8Ojr89e7d+//9y5c5s3b+78iuDA4/cjkxfZEF3c3rCysvrmm28+//xzFxcXb2/vQY/PPrX78gQcYLRTp07Nnj07PDx8sPIZQfBgJwAAAFigvj7YCS+mQXmwc+j0/GAnjDh4sBMAAAAAAABGGJR8AAAAAAAAFgslHwAAAAAAgMXC8i0AAABggTQazeHDhwsLC82dCAxIR0fHIC4gOeiuX78uEAgqKirMnQgMDp1OZ+4UhgRKPgAAALBAHA7H1dV13Lhx5k4EBoSiqBf5Iv7rX/+iafpFzhD6hKIoc6cwJLBiJwAAAFggrNhpGbBiJwwnrNgJAAAAAAAAIwxKPgAAAAAAAIuFkg8AAAAAAMBioeQDAAAAMJv8/Pw33nhDLBZbW1vb29sHBgZeuHBhIAE1Gs348eOFQiFN0zNnzrx582ZKSgpN0xRFURTF4XAcHBxCQ0MzMzMHawjQA61Wm5ycHB8fz16ClJQUtv3q1aseHh48Hm/VqlVD0W9xcXFoaGheXh4hpKWlZeLEiTRNi8Xi8PDw0tJSdp+cnBz2VpkwYcL3338/cgMa3/MmA545cyY3N7c3Z8/yoOQDAAAAMI/09PSFCxfOmzfvH//4h0qlevjw4datWx8/fjyQmDqdbsqUKbW1tdXV1YGBgVFRUQkJCcnJyRKJhGGY5ubm/Px8V1fXqKionTt3DtZAXmS7du2qrKwc6kNM6ujoiIyMnDt3rkwmS05O9vLy2r17d0NDAyFk1qxZRUVF0dHRR44cGXhHXZw8efL48eN3795lN9vb22fMmFFTU1NeXj5q1KgVK1YQQpRKZVRU1Pr16xUKRWxsrFQqValUIzSg8T1vMuCSJUuuXr2alpbWj1M64jEAAAAAFuett97KysoydxY9UalUTk5OO3fuHLou8vPzeTyeXq+XyWRsyWfw/vvvW1lZ/ec//xm63gcFn88fYAR/f/+KioohOiQxMTElJaW7X5OSkmJjY9m/ZTLZ8ePH3dzcNm7cyLZUVFTExMT0KbE+cXd3z83N7dKYn5/P4XB0Ol1JSQkhpL6+nmEYhUJBCCkpKRnpAZlO97xxQIZhVCqVp6fngwcPujvczs5Oo9E8t5cRB7N8AAAAAGZw48aNX3/9dfny5SZ/vXjx4qRJk2iaDg4OZh/13LJlC0VRa9euDQgIoGk6KSlp7NixFEVNnDiREHLp0iVHR0d3d3f2cL1eX1dXd+zYMalUavJTYx988IFerx/gQ6TmZXyKIiMjKYoqLS2trq728vKiaVoqlZaUlHh6eq5bty4hIYGiqNdff52maW9v79OnTz/3EELIwoUL+/cNho6ODrlcHh0dbWgRi8UHDhzYv39/WVlZz2Mxvtbsbt99952fn5+9vf3q1avb29v7kZVSqXR0dLSysvLx8fH39z937pxGozl79uy4ceN8fHxGdMDu7nlDQEKIjY3NokWLDh482I/ERjZz15wAAAAAg+/Fn+U7dOgQIcTklIJCoRCJRBkZGS0tLXK5XCQS1dXVMQwjkUiuXLmi1+uPHj0qFArLyso4HM69e/fYozZs2PDzzz+zf8+bN48QMnfuXPZA41k+Ntr27duHcISDobtZvu5OESHk0aNHDMPcuXNHJBJptVpCiGHKTiQS5efnq1QquVwuEAhqamqee0jPepjlKyoqIoS0tLSwmzKZLCcnh2EYqVS6ZMkSptMsn8mxdLnWDMPU1NTY2Njk5OTU19dPnjw5NTW159xMzqElJCTEx8ezf1+/ft3a2poQYm1tXVhY+NzBvuABu9zzJgMyDJOWlubt7d1dEMzyAQAAAMCgYRiGEGJyCq6goEAikSxbtszW1jYuLs7R0fGHH34w/EpR1OzZs1Uq1ZgxYyIiIlJTUwkhKpWqsrJywoQJ7D7nz5+vra0NCwsLCQlpbGw0mYBGo+FwRuo/BXs+RT1wc3OzsbFhDyksLBy6DB8/fszn821tbbu0y2SyH3/88caNG4aWHsZiuNY6na6wsNDT03PBggUODg4RERGXL1/ua0oVFRV5eXkfffQRIUShUCxatCg7O7utrS0zM1MqlbIvGY7cgCbv+c4BWU5OTk+ePGH/63t5jNT/zgEAAABGNC8vL0KIYXHCzurq6pydnQ2bEomkrq7OZJCNGzdmZGTU19efOHFi5cqVhnYrKyuJRJKYmMjj8bKzs40PbGlpaW5u9vb2HuAozKX3p6g7Li4u9fX1g53X/6VWq/l8vnG7s7Nzamrq5s2bDS29HItCoXj48CG78uquXbuampr6lE9ra+uaNWvOnDkjFosJIdnZ2W5ubuHh4SKRaPHixc7OzibvkxEU0Pie7xKQxefz9Xq9RqPpU24jHUo+AAAAADOYNWuWs7OzTCbr3NjR0bFz505XV1d2vQpWbW2tq6urySCzZ88OCAj4+uuv8/LyFi5caLxDd7MZR48etba2nj9//gBGYE69P0UmMQxTVVVlePVxKAiFwu5et1u6dKlEImFfJiS9HotYLA4KCjI8qldQUND7ZFpbW1evXr1v376AgAC2xTg3tVptGQHZe944oCEsh8MRCAS9z80CoOQDAAAAMAOBQPDll18ePnx427Ztjx8/1mq15eXlSUlJWq02LCzs6dOnGRkZra2tcrm8sbExLCysuzgbN2789NNPp02bxj6lmZWVdfDgQY1G09zcnJqaqlAoXnvtNUIIwzDPnj0jhFRVVcnl8q1bt+7YscPNzW3Yxju4ujtFNE1fu3ZNq9VWVVURQjgcDofDKS4uNqzv39bWptFoZDJZe3t7aGhobw7pHy8vL41G09bWZvLX/fv3f/nllz2PpYs5c+aUlJScOHFCqVSqVKrez/K1tLTExMTs2bOnc/Ezffr0Bw8eFBQUqNXqrKys4uLiGTNmjNCAxvf85MmTjQOyFAoFu+5RL3OzEGZ5gxAAAABgSL34y7ewLl++PH/+/FGjRnE4HLFYPHPmTHaRj/z8/KCgIKFQGBwcXFBQwDDMBx98QAgZO3ZsU1PT+PHjCSHLly9nGObZs2e+vr4NDQ1swNu3b3t7e/P5fKFQOGXKlEuXLv3tb3+TSCQ8Ho+tCUUi0bRp09LT08046t7r4SMNxqeIYZidO3cKBAI/P7+4uDhCyKpVqyIjI/l8flRUFMMwIpHIwcGBy+UGBwcblgPp+ZA333xzw4YN3eXQw/ItOp1u9OjRRUVFDMPs27ePpmkHB4dDhw4Zdjhy5IjhIw1dxtLdtc7MzPTz8+Pz+VOnTr19+7ZCoWAnirt0vWnTJk9PT0IITdPTp083LPhpcOXKFYZhvvrqK19fX4FA8Morrxw+fJhhmBc8YHfRjO95dm0k44Bs1z1cUEtdvoViXrKXFwEAAOBlEBkZuWLFioiICHMnAgMiEAgG8bUrmqZv3boVGBg4WAG3bt3q4uLS3VccPvnkk4aGhr179w5Wd11otdrly5cHBwdv3779ZQg48Gg6nS4gICA7O9t49o9lb2//9OlTky9hjmh4sBMAAAAAXhZ6vX7Y+kpMTCwvL7958+YQxZfL5U5OTgkJCS9JwIFH27Fjx7Zt27qr9yyYtbkTAAAAAAAYcitXrlQqlfPnz8/Ozg4JCRmGHq2srL755pvPP//cxcVlKBZHjY+Pf6kCDjDaqVOnZs+eHR4ePlj5jCAo+QAAAADA8qWnp6enpw9zp1wud9u2bcPcKZgklUrNnYLZ4MFOAAAAAAAAi4WSDwAAAAAAwGLhwU4AAACwTOXl5Xfu3DF3FjAgDMO8yBexrq5Oq9W+yBlCnwzn6j7DCR9pAAAAAAu0Y8eOS5cumTsLGKimpqZRo0YNbsyqqioXFxculzvwUGq1msPhWN6a/i+zy5cvD8q98UJByQcAAAAAL5HJkyenp6cP4tf5AF5weJcPAAAAAADAYqHkAwAAAAAAsFgo+QAAAAAAACwWSj4AAAAAAACLhZIPAAAAAADAYqHkAwAAAAAAsFgo+QAAAAAAACwWSj4AAAAAAACLhZIPAAAAAADAYqHkAwAAAAAAsFgo+QAAAAAAACwWSj4AAAAAAACLhZIPAAAAAADAYqHkAwAAAAAAsFgo+QAAAAAAACwWSj4AAAAAAACLhZIPAAAAAADAYqHkAwAAAAAAsFgo+QAAAAAAACwWSj4AAAAAAACLhZIPAAAAAADAYqHkAwAAAAAAsFgo+QAAAAAAACyWtbkTAAAAAAAYWs+ePVOpVOzfHR0dLS0tjY2NhBBra2tbW1uzpgYw5CiGYcydAwAAAADAELp58+a0adMEAgEhRK/XUxRFUZRWq127dq1MJjN3dgBDCyUfAAAAAFg+Nze32trazi12dnYXLlyYNm2auVICGB54lw8AAAAALN+f/vQnHo/XuYXH402dOtVc+QAMG5R8AAAAAGD53n777c4lH5fLXbVqFUVRZkwJYHig5AMAAAAAy/eb3/zG2dnZsCkQCKKjo82YD8CwQckHAAAAAC+Fd955h13BhRDi6Og4YcIE8+YDMDxQ8gEAAADAS2HlypUcDocQIhAIYmJizJ0OwDBByQcAAAAALwVPT09fX19CiJWV1YoVK8ydDsAwQckHAAAAAC+LuLg4Lpfr5eXl5eVl7lwAhom1uRMAAAAAGMFUKtW9e/fMnQX0lq+vb0dHx7x584qKisydC/TW6NGjx4wZY+4sRjB8ih0AAACg/+7duzdr1qyQkBBzJzJ8nj59SghxcXExdyLdun//fg9Ls9y/f9/Pz6/LN/rghVVVVfWHP/zhs88+M3ciIxhm+QAAAAAG5De/+U1hYaG5sxg+u3fvJoTs2LHD3ImY1tHRYWdn18MVKSsrY9/ogxEhNTW1srLS3FmMbHiXDwAAAABeIqj34GWDkg8AAAAAAMBioeQDAAAAAACwWCj5AAAAAIYKwzAxMTE0Tb/99tvh4eFffvllLw/cvHkzj8d7Yd+X640+jXc4abXa5OTkx48fp6Sk0DRNUVRKSgr709WrVz08PHg83qpVqwa93+Li4tDQ0Ly8PHazpaVl4sSJNE2LxeLw8PDS0lJCSE5Ozvjx44VC4YQJE77//vuRG1Cj0bC70TQ9c+bMmzdvmgx45syZ3Nzc3pw9GAiUfAAAAABDJTc39+7du42NjUePHs3NzX3vvfd6eeDevXuXLVs2KDns2rWrr6tf9OMQY30a71AnY9DR0REZGTl37lxvb++EhITk5GQvL6/du3c3NDQQQmbNmlVUVBQdHX3kyJHB6pF18uTJ48eP371719DS3t4+Y8aMmpqa8vLyUaNGrVixQqlURkVFrV+/XqFQxMbGSqVSlUo1QgPqdLopU6bU1tZWV1cHBgZGRUWZDLhkyZKrV6+mpaX16WRCX6HkAwAAABgqxcXFXl5eXC7XjDmcOnVqGA4ZOoObzKeffuri4vLqq68aWpKSkgQCwccffzyIvRiLioras2ePjY2NocXJyUkul9va2orF4tWrV//000+//PKLUql86623RCLRsmXL2traeqh1X/CANE0fOXLEzs7Ozs4uMjKyqqqKYRjjgB0dHTt27Pjwww//9a9/9e2EQl+g5AMAAAAYEocPH961a9e3335LUdTvfvc7gUDAPqi5ZcsWiqLWrl0bEBBA03RSUhK7f2xsrFgstrGxWblypV6vf278ixcvTpo0iabp4ODgCxcuREZGUhRVWlpaXV3t5eVF0zQhRCqVlpSUeHp6UhRFUdTrr79O07S3t/fp06cJIT0fsm7dun6PPS0trYfxJiQk9COZhQsXbtq0qd8pEUI6Ojrkcnl0dHTnRrFYfODAgf3795eVlXVu73J6TQ6EEPLdd9/5+fnZ29uvXr26vb29f4kplUpHR8dXXnnF39//3LlzGo3m7Nmz48aN8/HxGdEB9Xp9XV3dsWPHpFIpRVHGAa2srGxsbBYtWnTw4MH+JQa9wgAAAABAf929e/fVV1/t7tc9e/YsXryY/TsmJmb79u3s3xKJ5MqVK3q9/ujRo0KhkG2Mj4+vqal59OgRl8u9f//+n/70J8P+xhQKhUgkysjIaGlpkcvlIpGorq6OEPLo0SOGYe7cuSMSiRiG0Wq1hJCKigqGYUQiUX5+vkqlksvlAoGgpqaGYZieDzHp448//vjjj597Znoe72AlY0yn0xlOaRdFRUWEkJaWFkOLTCbLyclhGEYqlS5ZsoRhmIqKipiYGJOn13ggNTU1NjY2OTk59fX1kydPTk1N7Tk3d3f33Nxc4/aEhIT4+HiGYa5fv25tbU0Isba2LiwsfO5gX/CA8+bNI4TMnTuXPXsmAzIMk5aW5u3t3V2QL7744v33339u5tADzPIBAAAAmAdFUbNnz1apVDqdjhCSmprq6uo6btw4BweH1tbWno8tKCiQSCTLli2ztbWNi4tzdHT84Ycfntujm5ubjY0Nu//wfz6+y3iHP5nHjx/z+XxbW1vjn2Qy2Y8//njjxg12s+fTaxjIxYsXPT09FyxY4ODgEBERcfny5X5kVVFRkZeX99FHHykUikWLFmVnZ7e1tWVmZkqlUvYNw5Eb8Pz587W1tWFhYSEhIY2NjcYB2U0nJ6cnT54wDNOP3KA3UPIBAAAAmF9ra+vSpUudnJx4PB47X9ezuro6Z2dnw6ZEIunNUQYuLi719fX9SXQIDFsyarWaz+eb/MnZ2Tk1NXXz5s3sZi9Pr0KhePjwIfvQ7K5du5qamvqaUmtr65o1a86cOSMWi7Ozs93c3MLDw0Ui0eLFi52dnbOzs0d0QCsrK4lEkpiYyOPxDHt2Dsi28Pl8vV6v0Wj6mhv0Eko+AAAAAPM7fvx4cXHxP//5T7VaLZFInru/q6urQqEwbNbW1rq6uvayL4Zhqqqq3N3d+5nroBrOZIRCYQ+v2y1dulQikbAvFvby9IrF4qCgIMPjcwUFBX3Kp7W1dfXq1fv27QsICCCEGOemVqstI6BhBq9LQFZ7ezuHwxEIBH3KDXoPJR8AAACA+T179ozP59M0XVJS0pvpjrCwsKdPn2ZkZLS2tsrl8sbGxrCwMJqmr127ptVqq6qq2N04HA6HwykuLmYX029ra9NoNDKZrL29PTQ0lBDy3EOGzvAn4+XlpdFo2trautth//797LcETZ5e4/3nzJlTUlJy4sQJpVKpUqn6NMvX0tISExOzZ88eQ/Ezffr0Bw8eFBQUqNXqrKys4uLiGTNmjNCAWVlZBw8e1Gg0zc3NqampCoXitddeMw7IUigUY8eO7bK+CwwmM71DCAAAAGAJeli+JT8/f9SoUdbW1nPmzFm/fj2PxxMKhX/5y18++OADQsjYsWObmprGjx9PCFm+fPmTJ0/8/f1FIlFUVJSvry8hxMrKSigU/vWvf+2u6/z8/KCgIKFQGBwcXFBQwDDMzp07BQKBn59fXFwcIWTVqlUMw0RGRvL5/KioKJFI5ODgwOVyg4ODDQtv9HyIyX57s3zLli1beh5vP5J58803N2zY0HO/TI/Lt+h0utGjRxcVFbGb+/bto2nawcHh0KFDhn2OHDkSExNj8vSaHEhmZqafnx+fz586dert27cVCoWzs7NMJuvS9aZNmzw9PQkhNE1Pnz69qqrq0KFDXf5ZfuXKla+++srX11cgELzyyiuHDx9mGGaEBrx9+7a3tzefzxcKhVOmTLl06RLDMCYDsl33cGWxfMvAUQxelAQAAADor3v37r3zzju3bt0ydyLPR9P0rVu3AgMDBxhn9+7dhBD2AwxmT8ZYR0eHnZ2dUqk0+esnn3zS0NCwd+/eQe+XpdVqly9fHhwcvH37dgTsDZ1OFxAQkJ2d3WXqzyA1NbWysvKzzz4bWJovNTzYCQAAAPDiqqyspEzp4ZvaPejN5/6GjVmSSUxMLC8vv3nz5hDFl8vlTk5OCQkJCNhLO3bs2LZtW3f1HgwKa3MnAAAAAADd8vDwGJRnslauXKlUKufPn5+dnR0SEjLwgCM0GSsrq2+++ebzzz93cXHx9vYe9Pjx8fEI2HunTp2aPXt2eHj4IOYDxlDyAQAAAFi+9PT09PR0c2fx/zNvMlwud9u2bebqHTqTSqXmTuGlgAc7AQAAAAAALBZKPgAAAAAAAIuFkg8AAAAAAMBi4V0+AAAAgAH5z3/+M27cOHNnMXwaGxsJIUePHjV3It169uzZS3VFLFtzc/Py5cvNncXIhpIPAAAAYEA8PDyys7PNncXwSU1NJYSsX7/e3ImY1tHRERAQUFhYaO5EYHCkpaW1tbWZO4uRDSUfAAAAwIBYW1t7eHiYO4vhY2dnRwh5YYfc0dFBUdQLmx701ahRo1DyDRDe5QMAAAAAALBYKPkAAAAAAAAsFko+AAAAALPJz89/4403xGKxtbW1vb19YGDghQsXBhJQo9GMHz9eKBTSND1z5sybN2+mpKTQNE1RFEVRHA7HwcEhNDQ0MzNzsIYwomm12uTk5Pj4ePYUpaSksO1Xr1718PDg8XirVq0ain6Li4tDQ0Pz8vIIIS0tLRMnTqRpWiwWh4eHl5aWsvvk5OSwl3LChAnff//9SA/I2rBhQ2Ji4pkzZ3Jzc3uzPwwKlHwAAAAA5pGenr5w4cJ58+b94x//UKlUDx8+3Lp16+PHjwcSU6fTTZkypba2trq6OjAwMCoqKiEhITk5WSKRMAzT3Nycn5/v6uoaFRW1c+fOwRpIP+zatauysnKoD+lZR0dHZGTk3LlzZTJZcnKyl5fX7t27GxoaCCGzZs0qKiqKjo4+cuTIIPbIOnny5PHjx+/evctutre3z5gxo6ampry8fNSoUStWrCCEKJXKqKio9evXKxSK2NhYqVSqUqlGbkDWTz/9lJ6eTghZsmTJ1atX09LSen/SYEAYAAAAAOivu3fvvvrqq/04UKVSOTk57dy5c9BTMsjPz+fxeHq9XiaTsSWfwfvvv29lZfWf//ynH2E//vjjjz/+eIC5+fv7V1RUDMUhOp1OKBT2JmBSUlJsbCz7t0wmO378uJub28aNG9mWioqKmJiYPmXYJ+7u7rm5uV0a8/PzORyOTqcrKSkhhNTX1zMMo1AoCCElJSUjOqBWq3333XeXLVu2ZcsWhmFUKpWnp+eDBw96zoFhmC+++OL9999/7m7QA8zyAQAAAJjBjRs3fv311+4+OHbx4sVJkybRNB0cHMw+6rllyxaKotauXRsQEEDTdFJS0tixYymKmjhxIiHk0qVLjo6O7u7u7OF6vb6uru7YsWNSqZSiKOP4H3zwgV6vH+BDpL3JPDIykgZ+Ox8AAAgCSURBVKKo0tLS6upqLy8vmqYJIVKptKSkxNPTc926dQkJCRRFvf766zRNe3t7nz59+rmHLFy4cNOmTQPMs6OjQy6XR0dHG1rEYvGBAwf2799fVlbW86CMrwW723fffefn52dvb7969er29vZ+ZKVUKh0dHa2srHx8fPz9/c+dO6fRaM6ePTtu3DgfH58RHTA1NTUuLs5wN9rY2CxatOjgwYP9yAH6zNw1JwAAAMAI1u9ZvkOHDhFCNBqN8U8KhUIkEmVkZLS0tMjlcpFIVFdXxzCMRCK5cuWKXq8/evSoUCgsKyvjcDj37t1jj9qwYcPPP//M/j1v3jxCyNy5c9kDjWf52Gjbt2/vR+Y9zPKZzJwQ8ujRI4Zh7ty5IxKJGIbRarWEEMOUnUgkys/PV6lUcrlcIBDU1NQ895Ae9HKWr6ioiBDS0tLCbspkspycHIZhpFLpkiVLmE6zfCYH1eVaMAxTU1NjY2OTk5NTX18/efLk1NTUnhMwOYeWkJAQHx/P/n39+nVra2tCiLW1dWFh4XNH9CIHLC8v37p1K8Mwy5cvZ2f5GIZJS0vz9vZ+bhqY5Rs4zPIBAAAAmAHDMIQQk1NwBQUFEolk2bJltra2cXFxjo6OP/zwg+FXiqJmz56tUqnGjBkTERHBfhhdpVJVVlZOmDCB3ef8+fO1tbVhYWEhISGNjY0mE9BoNBzOIP9TsOfMe+Dm5mZjY8MeMjxfUX/8+DGfz7e1te3SLpPJfvzxxxs3bhhaehiU4VrodLrCwkJPT88FCxY4ODhERERcvny5rylVVFTk5eV99NFHhBCFQrFo0aLs7Oy2trbMzEypVMq+ZDhCAyYlJSUmJnZpdHJyevLkCfsfAgwplHwAAAAAZuDl5UUIMSx+2FldXZ2zs7NhUyKRsHNlxjZu3JiRkVFfX3/ixImVK1ca2q2srCQSSWJiIo/Hy87ONj6wpaWlubnZ29t7gKPod+bdcXFxqa+vH9ysTFKr1Xw+37jd2dk5NTV18+bN/1979xLSRheGAfj80TQhCaERo1EoKqIQSwi40GgFLwSJiKuCbZUuWi1diZeFaExoEVtREUGNRGtQiJHeNmYlDd1UClW6KkjQhW4aEAe8xWg0l/MvBoJoYtOYOhjfZ5fJzHfew6wOM+eb0JEoJ8UwzPr6OtsZ9dWrV3t7e3+Vx+12v3jx4vPnzzKZjBBit9szMjJqamrEYvHDhw/lcnnY+3gjCtpsturqaqlUeu64QCAIBoNer/evYkAMsOQDAAAA4EBZWZlcLh8bGzt7MBAIGI1GhULB9sNgbW1tKRSKsEXKy8uVSuW7d+8WFxfr6uounhDpEcrs7GxycrJOp7vCDMKIPnlYlFKXyxXakfhPiUSiSNvtHj9+nJ6e/unTJ/ZnlJOSyWQqlSr0Kp3D4Yg+jNvtfv78+cjIiFKpZI9czHZ8fHxDC87NzTU0NLCLYZvNNjAwoNFo2Ao8Hk8oFEYfA2KDJR8AAAAAB4RCoclkslgser1+c3PT5/NtbGz09vb6fD6tVru9vW2z2dxut9ls3t3d1Wq1keq0tbW9fftWo9Gwb2kuLCxMTU15vd79/f3R0VGGYSorKwkhlNKTkxNCiMvlMpvN3d3dBoMhIyMjvpMKm1wikXz//t3n87lcLvY0Ho/H4/GcTmeorf/h4aHX6x0bGzs9Pa2qqormkivKzs72er2Hh4dh/52YmDCZTJdM6uIlFRUVa2tr8/PzHo/n6Ogo+qd8BwcHTU1N/f39odUUIaSkpGR1ddXhcBwfHy8sLDidztLS0hta8OyGQHYvH7uRkmEYtgVRlDEgdte8dxAAAAAgkcTcvoX17ds3nU539+5dHo8nk8kePHjANhH58uWLSqUSiURqtdrhcFBKOzs7CSFZWVl7e3v3798nhDQ2NlJKT05OcnNzd3Z22II/f/7MyckRCAQikaioqOjr16/j4+Pp6el37txh14RisVij0Vit1pgzX/6RhovJjUajUCjMz89/+fIlIeTZs2eU0vr6eoFA8OTJE0qpWCxOSUnh8/lqtZrtAnL5JbW1ta2trZECRNm+xe/3Z2Zm/vjxg1I6MjIikUhSUlKmp6dDJ8zMzIQ+0nBuUpHuxfv37/Pz8wUCQXFx8eLiIvsU9+LQ7e3t9+7dI4RIJJKSkpJQw8+QpaUlSunk5GRubq5QKMzLy7NYLAzD3NyCIWfbt7S3t19yH0PQvuXq/qPYMQkAAAAQq1+/fjU3N6+srHAd5Pr09fURQgwGQ7wKSiSSlZWVgoKCuFQLBAJSqdTj8fzxzDdv3uzs7AwPD8dl3HN8Pl9jY6Nare7p6UHBi/x+v1KptNvtZ58chjU6Ovr79+/BwcG4hLyd8GInAAAAAHAsGAxe/6BdXV0bGxvLy8v/orjZbE5NTe3o6EDBsAwGg16v/+N6D+IimesAAAAAAHB7PX361OPx6HQ6u91eWFh4nUMnJSV9/PhxaGgoLS0t7s1LW1paUDCSDx8+lJeX19TUxDcARIIlHwAAAABwxmq1Wq1Wrkbn8/l6vZ6r0W+tR48ecR3hdsGLnQAAAAAAAAkLSz4AAAAAAICEhSUfAAAAAABAwsJePgAAAIArcblcr1+/5jrF9VlaWiKE+P1+roOERykNBAK36o4ktuXlZZVKxXWKmw3f5QMAAACIHcMwFouF6xQAiUyj0VRUVHCd4gbDkg8AAAAAACBhYS8fAAAAAABAwsKSDwAAAAAAIGH9D31nhRsz0GEmAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ],
+      "source": [
+        "from tensorflow.keras.utils import plot_model\n",
+        "plot_model(model,\n",
+        "           show_shapes = True,\n",
+        "           show_dtype = False,\n",
+        "           show_layer_names = True,\n",
+        "           rankdir = 'TB',\n",
+        "           expand_nested = False,\n",
+        "           dpi = 70)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print (\"computing class weights\")\n",
+        "images_path=\"/content/drive/MyDrive/Bratsdataset/BraTs2020/\"\n",
+        "val_list=os.listdir(images_path)\n",
+        "image_names=os.listdir(images_path)\n",
+        "\n",
+        "for i, dir_name in enumerate(image_names[:]):\n",
+        "    print(' ********* ', i, dir_name)\n",
+        "def get_class_weights(images_path, class_names=['backg','1','2','3']):\n",
+        "    num_classes = len(class_names)\n",
+        "    trainId_to_count0 = [0 for k in range(num_classes)]\n",
+        "    trainId_to_count=trainId_to_count0\n",
+        "    for trainId in range(num_classes):\n",
+        "        trainId_to_count[trainId] = 0\n",
+        "    image_names_list=os.listdir(images_dir_path)\n",
+        "    for i, dir_name in enumerate( image_names_list):\n",
+        "        print(' ********* ', i, dir_name)\n",
+        "        label_img  = mask  = np.load(glob.glob(images_path+dir_name+'/'+'mask_*.npy')[0])\n",
+        "        ## change the aboenline to read the masks from the directory\n",
+        "        for trainId in range(num_classes):\n",
+        "            # count how many pixels in label_img which are of object class trainId:\n",
+        "            trainId_mask = np.equal(label_img, trainId)\n",
+        "            trainId_count = np.sum(trainId_mask)\n",
+        "          # print(trainId,trainId_count)\n",
+        "            # add to the total count:\n",
+        "            trainId_to_count0[trainId] += trainId_count\n",
+        "        if i % 10 == 0:\n",
+        "              print (i+1,' -patients so far',trainId_to_count0)\n",
+        "# compute the class weights according to the ENet paper:\n",
+        "    trainId_to_count[1]=trainId_to_count0[1]+trainId_to_count0[2]+trainId_to_count0[3]\n",
+        "    trainId_to_count[2]=trainId_to_count0[1]+trainId_to_count0[3]\n",
+        "    trainId_to_count[3]=trainId_to_count0[3]\n",
+        "    class_weights = []\n",
+        "    total_count = sum(trainId_to_count[1:])\n",
+        "    for trainId, count in enumerate(trainId_to_count[1:]):\n",
+        "        trainId_prob = float(count)/float(total_count)\n",
+        "        trainId_weight = 1/np.log(1.22 + trainId_prob)\n",
+        "        class_weights.append(trainId_weight)\n",
+        "    s=sum(class_weights)\n",
+        "    for idx, w in enumerate(class_weights):\n",
+        "        class_weights[idx]=class_weights[idx]/s\n",
+        "    data_info=dict(class_seg=['WT','CT','ET'],pixel_count=trainId_to_count[1:],class_weights=class_weights)\n",
+        "    return data_info\n",
+        "images_dir_path=\"/content/drive/MyDrive/Bratsdataset/BraTs2020/\"\n",
+        "data_pixels_weights_info=get_class_weights(images_dir_path, class_names=['backg','1','2','3'])\n",
+        "print(data_pixels_weights_info)\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xcyZ-2xqdn77",
+        "outputId": "0a748d00-d832-4758-b563-55ffdf042a7d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "computing class weights\n",
+            " *********  0 train0\n",
+            " *********  1 train1\n",
+            " *********  2 train2\n",
+            " *********  3 train3\n",
+            " *********  4 train5\n",
+            " *********  5 train6\n",
+            " *********  6 train7\n",
+            " *********  7 train8\n",
+            " *********  8 train9\n",
+            " *********  9 train10\n",
+            " *********  10 train11\n",
+            " *********  11 train12\n",
+            " *********  12 train13\n",
+            " *********  13 train14\n",
+            " *********  14 train15\n",
+            " *********  15 train16\n",
+            " *********  16 train17\n",
+            " *********  17 train18\n",
+            " *********  18 train19\n",
+            " *********  19 train20\n",
+            " *********  20 train21\n",
+            " *********  21 train22\n",
+            " *********  22 train23\n",
+            " *********  23 train24\n",
+            " *********  24 train25\n",
+            " *********  25 train26\n",
+            " *********  26 train28\n",
+            " *********  27 train29\n",
+            " *********  28 train30\n",
+            " *********  29 train31\n",
+            " *********  30 train32\n",
+            " *********  31 train33\n",
+            " *********  32 train34\n",
+            " *********  33 train36\n",
+            " *********  34 train37\n",
+            " *********  35 train38\n",
+            " *********  36 train39\n",
+            " *********  37 train40\n",
+            " *********  38 train41\n",
+            " *********  39 train42\n",
+            " *********  40 train44\n",
+            " *********  41 train45\n",
+            " *********  42 train46\n",
+            " *********  43 train47\n",
+            " *********  44 train48\n",
+            " *********  45 train49\n",
+            " *********  46 train50\n",
+            " *********  47 train51\n",
+            " *********  48 train52\n",
+            " *********  49 train53\n",
+            " *********  50 train54\n",
+            " *********  51 train55\n",
+            " *********  52 train56\n",
+            " *********  53 train57\n",
+            " *********  54 train58\n",
+            " *********  55 train59\n",
+            " *********  56 train61\n",
+            " *********  57 train63\n",
+            " *********  58 train64\n",
+            " *********  59 train65\n",
+            " *********  60 train66\n",
+            " *********  61 train67\n",
+            " *********  62 train68\n",
+            " *********  63 train69\n",
+            " *********  64 train70\n",
+            " *********  65 train71\n",
+            " *********  66 train72\n",
+            " *********  67 train73\n",
+            " *********  68 train74\n",
+            " *********  69 train75\n",
+            " *********  70 train76\n",
+            " *********  71 train77\n",
+            " *********  72 train79\n",
+            " *********  73 train80\n",
+            " *********  74 train82\n",
+            " *********  75 train83\n",
+            " *********  76 train84\n",
+            " *********  77 train86\n",
+            " *********  78 train87\n",
+            " *********  79 train88\n",
+            " *********  80 train89\n",
+            " *********  81 train90\n",
+            " *********  82 train91\n",
+            " *********  83 train92\n",
+            " *********  84 train93\n",
+            " *********  85 train94\n",
+            " *********  86 train95\n",
+            " *********  87 train96\n",
+            " *********  88 train97\n",
+            " *********  89 train99\n",
+            " *********  90 train100\n",
+            " *********  91 train101\n",
+            " *********  92 train102\n",
+            " *********  93 train103\n",
+            " *********  94 train104\n",
+            " *********  95 train105\n",
+            " *********  96 train106\n",
+            " *********  97 train108\n",
+            " *********  98 train110\n",
+            " *********  99 train111\n",
+            " *********  100 train112\n",
+            " *********  101 train113\n",
+            " *********  102 train114\n",
+            " *********  103 train115\n",
+            " *********  104 train116\n",
+            " *********  105 train117\n",
+            " *********  106 train118\n",
+            " *********  107 train119\n",
+            " *********  108 train120\n",
+            " *********  109 train122\n",
+            " *********  110 train123\n",
+            " *********  111 train124\n",
+            " *********  112 train125\n",
+            " *********  113 train126\n",
+            " *********  114 train127\n",
+            " *********  115 train128\n",
+            " *********  116 train129\n",
+            " *********  117 train130\n",
+            " *********  118 train131\n",
+            " *********  119 train132\n",
+            " *********  120 train133\n",
+            " *********  121 train134\n",
+            " *********  122 train135\n",
+            " *********  123 train136\n",
+            " *********  124 train137\n",
+            " *********  125 train139\n",
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+            " *********  314 train338\n",
+            " *********  315 train339\n",
+            " *********  316 train341\n",
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+            " *********  320 train345\n",
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+            " *********  336 train361\n",
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+            " *********  341 train366\n",
+            " *********  342 train367\n",
+            " *********  343 train368\n",
+            " *********  0 train0\n",
+            "1  -patients so far [1886119, 15443, 167848, 27742]\n",
+            " *********  1 train1\n",
+            " *********  2 train2\n",
+            " *********  3 train3\n",
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+            " *********  6 train7\n",
+            " *********  7 train8\n",
+            " *********  8 train9\n",
+            " *********  9 train10\n",
+            " *********  10 train11\n",
+            "11  -patients so far [22150373, 91402, 674252, 152645]\n",
+            " *********  11 train12\n",
+            " *********  12 train13\n",
+            " *********  13 train14\n",
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+            " *********  15 train16\n",
+            " *********  16 train17\n",
+            " *********  17 train18\n",
+            " *********  18 train19\n",
+            " *********  19 train20\n",
+            " *********  20 train21\n",
+            "21  -patients so far [42279112, 271166, 1226705, 263209]\n",
+            " *********  21 train22\n",
+            " *********  22 train23\n",
+            " *********  23 train24\n",
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+            " *********  25 train26\n",
+            " *********  26 train28\n",
+            " *********  27 train29\n",
+            " *********  28 train30\n",
+            " *********  29 train31\n",
+            " *********  30 train32\n",
+            "31  -patients so far [62413781, 348529, 1836988, 412414]\n",
+            " *********  31 train33\n",
+            " *********  32 train34\n",
+            " *********  33 train36\n",
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+            " *********  35 train38\n",
+            " *********  36 train39\n",
+            " *********  37 train40\n",
+            " *********  38 train41\n",
+            " *********  39 train42\n",
+            " *********  40 train44\n",
+            "41  -patients so far [82484623, 482996, 2410129, 605484]\n",
+            " *********  41 train45\n",
+            " *********  42 train46\n",
+            " *********  43 train47\n",
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+            " *********  45 train49\n",
+            " *********  46 train50\n",
+            " *********  47 train51\n",
+            " *********  48 train52\n",
+            " *********  49 train53\n",
+            " *********  50 train54\n",
+            "51  -patients so far [102624852, 586071, 2997913, 745916]\n",
+            " *********  51 train55\n",
+            " *********  52 train56\n",
+            " *********  53 train57\n",
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+            " *********  55 train59\n",
+            " *********  56 train61\n",
+            " *********  57 train63\n",
+            " *********  58 train64\n",
+            " *********  59 train65\n",
+            " *********  60 train66\n",
+            "61  -patients so far [122817730, 712404, 3495830, 900308]\n",
+            " *********  61 train67\n",
+            " *********  62 train68\n",
+            " *********  63 train69\n",
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+            " *********  65 train71\n",
+            " *********  66 train72\n",
+            " *********  67 train73\n",
+            " *********  68 train74\n",
+            " *********  69 train75\n",
+            " *********  70 train76\n",
+            "71  -patients so far [142907317, 902845, 4019249, 1068381]\n",
+            " *********  71 train77\n",
+            " *********  72 train79\n",
+            " *********  73 train80\n",
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+            " *********  77 train86\n",
+            " *********  78 train87\n",
+            " *********  79 train88\n",
+            " *********  80 train89\n",
+            "81  -patients so far [163399014, 952026, 4367637, 1150635]\n",
+            " *********  81 train90\n",
+            " *********  82 train91\n",
+            " *********  83 train92\n",
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+            " *********  86 train95\n",
+            " *********  87 train96\n",
+            " *********  88 train97\n",
+            " *********  89 train99\n",
+            " *********  90 train100\n",
+            "91  -patients so far [183447270, 1090636, 4929742, 1373184]\n",
+            " *********  91 train101\n",
+            " *********  92 train102\n",
+            " *********  93 train103\n",
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+            " *********  95 train105\n",
+            " *********  96 train106\n",
+            " *********  97 train108\n",
+            " *********  98 train110\n",
+            " *********  99 train111\n",
+            " *********  100 train112\n",
+            "101  -patients so far [203601721, 1184352, 5450909, 1575370]\n",
+            " *********  101 train113\n",
+            " *********  102 train114\n",
+            " *********  103 train115\n",
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+            " *********  105 train117\n",
+            " *********  106 train118\n",
+            " *********  107 train119\n",
+            " *********  108 train120\n",
+            " *********  109 train122\n",
+            " *********  110 train123\n",
+            "111  -patients so far [223554017, 1340323, 6130320, 1759212]\n",
+            " *********  111 train124\n",
+            " *********  112 train125\n",
+            " *********  113 train126\n",
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+            " *********  115 train128\n",
+            " *********  116 train129\n",
+            " *********  117 train130\n",
+            " *********  118 train131\n",
+            " *********  119 train132\n",
+            " *********  120 train133\n",
+            "121  -patients so far [243709298, 1465683, 6665538, 1914873]\n",
+            " *********  121 train134\n",
+            " *********  122 train135\n",
+            " *********  123 train136\n",
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+            " *********  126 train140\n",
+            " *********  127 train142\n",
+            " *********  128 train143\n",
+            " *********  129 train144\n",
+            " *********  130 train145\n",
+            "131  -patients so far [263362138, 1701191, 7407278, 2256305]\n",
+            " *********  131 train146\n",
+            " *********  132 train147\n",
+            " *********  133 train148\n",
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+            " *********  136 train151\n",
+            " *********  137 train152\n",
+            " *********  138 train153\n",
+            " *********  139 train154\n",
+            " *********  140 train155\n",
+            "141  -patients so far [282869202, 1911721, 8360578, 2556931]\n",
+            " *********  141 train156\n",
+            " *********  142 train157\n",
+            " *********  143 train158\n",
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+            " *********  145 train160\n",
+            " *********  146 train161\n",
+            " *********  147 train162\n",
+            " *********  148 train163\n",
+            " *********  149 train164\n",
+            " *********  150 train165\n",
+            "151  -patients so far [302764037, 2042004, 9005420, 2858491]\n",
+            " *********  151 train166\n",
+            " *********  152 train167\n",
+            " *********  153 train168\n",
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+            " *********  155 train170\n",
+            " *********  156 train171\n",
+            " *********  157 train172\n",
+            " *********  158 train173\n",
+            " *********  159 train174\n",
+            " *********  160 train175\n",
+            "161  -patients so far [322642597, 2244249, 9617369, 3137257]\n",
+            " *********  161 train177\n",
+            " *********  162 train178\n",
+            " *********  163 train179\n",
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+            " *********  165 train181\n",
+            " *********  166 train182\n",
+            " *********  167 train183\n",
+            " *********  168 train184\n",
+            " *********  169 train185\n",
+            " *********  170 train186\n",
+            "171  -patients so far [342631109, 2341692, 10168956, 3471235]\n",
+            " *********  171 train187\n",
+            " *********  172 train188\n",
+            " *********  173 train189\n",
+            " *********  174 train190\n",
+            " *********  175 train191\n",
+            " *********  176 train192\n",
+            " *********  177 train193\n",
+            " *********  178 train194\n",
+            " *********  179 train195\n",
+            " *********  180 train196\n",
+            "181  -patients so far [362587571, 2478343, 10726479, 3792119]\n",
+            " *********  181 train197\n",
+            " *********  182 train198\n",
+            " *********  183 train199\n",
+            " *********  184 train200\n",
+            " *********  185 train201\n",
+            " *********  186 train202\n",
+            " *********  187 train204\n",
+            " *********  188 train205\n",
+            " *********  189 train206\n",
+            " *********  190 train207\n",
+            "191  -patients so far [382757332, 2561613, 11251512, 3985575]\n",
+            " *********  191 train208\n",
+            " *********  192 train209\n",
+            " *********  193 train210\n",
+            " *********  194 train211\n",
+            " *********  195 train212\n",
+            " *********  196 train213\n",
+            " *********  197 train214\n",
+            " *********  198 train215\n",
+            " *********  199 train217\n",
+            " *********  200 train218\n",
+            "201  -patients so far [402768295, 2750212, 11753200, 4255845]\n",
+            " *********  201 train219\n",
+            " *********  202 train220\n",
+            " *********  203 train221\n",
+            " *********  204 train222\n",
+            " *********  205 train223\n",
+            " *********  206 train224\n",
+            " *********  207 train225\n",
+            " *********  208 train226\n",
+            " *********  209 train227\n",
+            " *********  210 train228\n",
+            "211  -patients so far [422703910, 2842390, 12334709, 4618063]\n",
+            " *********  211 train229\n",
+            " *********  212 train230\n",
+            " *********  213 train231\n",
+            " *********  214 train233\n",
+            " *********  215 train234\n",
+            " *********  216 train235\n",
+            " *********  217 train236\n",
+            " *********  218 train237\n",
+            " *********  219 train238\n",
+            " *********  220 train239\n",
+            "221  -patients so far [442305168, 3137981, 13160024, 4867419]\n",
+            " *********  221 train240\n",
+            " *********  222 train241\n",
+            " *********  223 train242\n",
+            " *********  224 train243\n",
+            " *********  225 train244\n",
+            " *********  226 train245\n",
+            " *********  227 train246\n",
+            " *********  228 train247\n",
+            " *********  229 train248\n",
+            " *********  230 train249\n",
+            "231  -patients so far [462177466, 3297631, 13799755, 5167260]\n",
+            " *********  231 train250\n",
+            " *********  232 train251\n",
+            " *********  233 train252\n",
+            " *********  234 train253\n",
+            " *********  235 train254\n",
+            " *********  236 train255\n",
+            " *********  237 train256\n",
+            " *********  238 train257\n",
+            " *********  239 train258\n",
+            " *********  240 train259\n",
+            "241  -patients so far [481987134, 3486575, 14417986, 5521937]\n",
+            " *********  241 train260\n",
+            " *********  242 train261\n",
+            " *********  243 train262\n",
+            " *********  244 train263\n",
+            " *********  245 train264\n",
+            " *********  246 train265\n",
+            " *********  247 train266\n",
+            " *********  248 train268\n",
+            " *********  249 train269\n",
+            " *********  250 train270\n",
+            "251  -patients so far [502172055, 3772775, 14843509, 5596813]\n",
+            " *********  251 train271\n",
+            " *********  252 train272\n",
+            " *********  253 train273\n",
+            " *********  254 train274\n",
+            " *********  255 train275\n",
+            " *********  256 train277\n",
+            " *********  257 train278\n",
+            " *********  258 train279\n",
+            " *********  259 train280\n",
+            " *********  260 train281\n",
+            "261  -patients so far [521951010, 4256505, 15451640, 5697517]\n",
+            " *********  261 train282\n",
+            " *********  262 train283\n",
+            " *********  263 train284\n",
+            " *********  264 train285\n",
+            " *********  265 train286\n",
+            " *********  266 train287\n",
+            " *********  267 train288\n",
+            " *********  268 train289\n",
+            " *********  269 train290\n",
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+            "271  -patients so far [541722987, 4652258, 16180271, 5772676]\n",
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+            "281  -patients so far [561551506, 5179742, 16731169, 5837295]\n",
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+            "291  -patients so far [581292434, 5906058, 17224503, 5848237]\n",
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+            "301  -patients so far [601017771, 6581184, 17777903, 5865894]\n",
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+            "311  -patients so far [620687091, 7284919, 18334376, 5907886]\n",
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+            "321  -patients so far [640585543, 7523374, 18871075, 6205800]\n",
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+            "331  -patients so far [660600199, 7689805, 19346447, 6520861]\n",
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+            "341  -patients so far [680621557, 7837601, 19890691, 6778983]\n",
+            " *********  341 train366\n",
+            " *********  342 train367\n",
+            " *********  343 train368\n",
+            "{'class_seg': ['WT', 'CT', 'ET'], 'pixel_count': [34921136, 41778544, 6857408], 'class_weights': [0.26460319433243357, 0.24074940490721086, 0.49464740076035557]}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hh_9D3FvlGlC"
+      },
+      "outputs": [],
+      "source": [
+        "#Define loss, metrics and optimizer to be used for training\n",
+        "import keras\n",
+        "import tensorflow as tf\n",
+        "import keras.backend as K\n",
+        "def dice_coef_class(y_true, y_pred,i, epsilon=0.00001):\n",
+        "    \"\"\"\n",
+        "    Dice = (2*|X & Y|)/ (|X|+ |Y|)\n",
+        "         =  2*sum(|A*B|)/(sum(A^2)+sum(B^2))\n",
+        "    ref: https://arxiv.org/pdf/1606.04797v1.pdf\n",
+        "\n",
+        "    \"\"\"\n",
+        "    axis = (0,1,2,3)\n",
+        "    dice_numerator = 2. * K.sum(y_true[:,:,:,:,i:i+1] * y_pred[:,:,:,:,i:i+1], axis=axis) + epsilon\n",
+        "    dice_denominator = K.sum(y_true[:,:,:,:,i:i+1]*y_true[:,:,:,:,i:i+1], axis=axis) + K.sum(y_pred[:,:,:,:,i:i+1]*y_pred[:,:,:,:,i:i+1], axis=axis) + epsilon\n",
+        "    return K.mean((dice_numerator)/(dice_denominator))\n",
+        "\n",
+        "\n",
+        "def dice_coef_loss_3classes(y_true, y_pred):\n",
+        "    core=2-dice_coef_class(y_true, y_pred,1) -dice_coef_class(y_true, y_pred,3)\n",
+        "    whole=3-dice_coef_class(y_true, y_pred,1)-dice_coef_class(y_true, y_pred,2) -dice_coef_class(y_true, y_pred,3)\n",
+        "    enhance=1-dice_coef_class(y_true, y_pred,3)\n",
+        "    return 0.6*core + 0.5*whole +0.7*enhance\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Define metrics and optimizer to be used for training\n",
+        "import tensorflow.keras\n",
+        "import tensorflow as tf\n",
+        "import tensorflow.keras.backend as K\n",
+        "#from segmentation_models_3D.losses import DiceLoss\n",
+        "from segmentation_models_3D.metrics import IOUScore, FScore\n",
+        "#dice_loss = DiceLoss()\n",
+        "FScores = FScore()\n",
+        "IOUScores = IOUScore(threshold=0.5)\n",
+        "\n",
+        "\n",
+        "\n",
+        "LR=0.0001\n",
+        "metrics = ['accuracy',FScores,IOUScores]\n",
+        "\n",
+        "optim = tf.keras.optimizers.Adam(LR)"
+      ],
+      "metadata": {
+        "id": "y9odgt8JhYCx"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0UtiReA5oJlg"
+      },
+      "outputs": [],
+      "source": [
+        "import pickle\n",
+        "import keras\n",
+        "import keras.backend as K\n",
+        "import pandas as pd\n",
+        "from keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
+        "from sklearn.model_selection import KFold # import KFold\n",
+        "import time\n",
+        "\n",
+        "'load histories'\n",
+        "with open('folds_dic.pkl', 'rb') as m:\n",
+        "      folds_dict = pickle.load(m)\n",
+        "\n",
+        "\n",
+        "\n",
+        "kf = KFold(n_splits=5, random_state=1, shuffle=True)\n",
+        "#kfold = kf.split(train_img_list)\n",
+        "train_img_list=  os.listdir('/content/drive/MyDrive/Bratsdataset/BraTs2020/')\n",
+        "Kfolds=kf.split(train_img_list,train_img_list)\n",
+        "trainlist=[]\n",
+        "validationlist=[]\n",
+        "Histories=[]\n",
+        "#nb_fold=0\n",
+        "batch_size=1\n",
+        "for nb_fold  in range(0,5):\n",
+        "\n",
+        "    print(' Training for the fold' +str(nb_fold)+' started ...')\n",
+        "\n",
+        "    train_img_fold =  folds_dict['train'][nb_fold]\n",
+        "    valid_img_fold =  folds_dict['validation'][nb_fold]\n",
+        "    steps_per_epoch = len( train_img_fold)//batch_size\n",
+        "    val_steps_per_epoch = len(valid_img_fold)//batch_size\n",
+        "    train_img_datagen1=imageLoader(train_img_fold,batch_size)\n",
+        "    val_img_datagen1=imageLoader(valid_img_fold,batch_size)\n",
+        "    LR=0.0001\n",
+        "    metrics = ['accuracy',FScores, IOUScores]\n",
+        "\n",
+        "    optim = tf.keras.optimizers.Adam(LR)\n",
+        "    model =  CNN_Model(IMG_HEIGHT=128,\n",
+        "                          IMG_WIDTH=128,\n",
+        "                          IMG_DEPTH=128,\n",
+        "                          IMG_CHANNELS=3,\n",
+        "                          num_classes=4)\n",
+        "\n",
+        "    print(model.summary())\n",
+        "    checkpoint_filepath = '/content/drive/MyDrive/2020/2020fold_'+str(nb_fold)+'-{epoch:03d}-{val_f1-score:.04f}.hdf5' # use your path\n",
+        "\n",
+        "    model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_filepath,\n",
+        "                                                                monitor='val_f1-score', verbose=1,\n",
+        "                                                                save_best_only=True, mode='max')\n",
+        "\n",
+        "    model.compile(optimizer = optim, loss=dice_coef_loss_3classes, metrics=metrics)\n",
+        "\n",
+        "\n",
+        "\n",
+        "    start_time = time.time()\n",
+        "    model_history=model.fit(train_img_datagen1,\n",
+        "          steps_per_epoch=steps_per_epoch,\n",
+        "          epochs=200,\n",
+        "          verbose=1,\n",
+        "          validation_data=val_img_datagen1,\n",
+        "          validation_steps=val_steps_per_epoch,\n",
+        "          callbacks = [model_checkpoint_callback] )\n",
+        "\n",
+        "    end_time = time.time()\n",
+        "\n",
+        "    training_time = end_time - start_time\n",
+        "    # Print the training time of the model\n",
+        "\n",
+        "    print(\"Training time: \", training_time, \"seconds\")\n",
+        "    model.save('last_model_fold'+str(nb_fold)+'.hdf5')\n",
+        "    history_df = pd.DataFrame(model_history.history)\n",
+        "    with open('fold_'+str(nb_fold)+'_history.csv', mode='w') as f:\n",
+        "                  history_df.to_csv(f)\n",
+        "            # 'Save the model'\n",
+        "    with open('model_history_fold_'+str(nb_fold)+'.pkl', 'wb') as f:\n",
+        "         pickle.dump(model_history.history, f)\n",
+        "\n",
+        "    Histories.append(model_history.history)\n",
+        "\n",
+        "\n",
+        "'save all histories'\n",
+        "import pickle\n",
+        "with open('all_models_history.pkl', 'wb') as f:\n",
+        "      pickle.dump(Histories, f)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MmtPHCvXTEq-"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Load model data from CSV\n",
+        "def plot_cruves_for_onefold(nb_fold):\n",
+        "    # Load the CSV file into a pandas DataFrame\n",
+        "    df = pd.read_csv('2020/2020fold_' + str(nb_fold) + '.csv')\n",
+        "\n",
+        "    loss = df['loss']\n",
+        "    val_loss = df['val_loss']\n",
+        "    epochs = range(1, len(loss) + 1)\n",
+        "    f, ax = plt.subplots(1, 3, figsize=(16, 8))\n",
+        "\n",
+        "    ax[0].plot(epochs, loss, 'y', label='Training loss')\n",
+        "    ax[0].plot(epochs, val_loss, 'r', label='Validation loss')\n",
+        "    ax[0].legend()\n",
+        "\n",
+        "    acc = df['accuracy']\n",
+        "    val_acc = df['val_accuracy']\n",
+        "    ax[1].plot(epochs, acc, 'y', label='Training acc')\n",
+        "    ax[1].plot(epochs, val_acc, 'r', label='Validation acc')\n",
+        "    ax[1].set_title('Training and validation acc')\n",
+        "    ax[1].set_xlabel('Epochs')\n",
+        "    ax[1].set_ylabel('acc')\n",
+        "    ax[1].legend()\n",
+        "\n",
+        "    FScore = df['f1-score']\n",
+        "    val_FScore = df['val_f1-score']\n",
+        "    ax[2].plot(epochs, FScore, 'y', label='Training f_score')\n",
+        "    ax[2].plot(epochs, val_FScore, 'r', label='Validation f_score')\n",
+        "    ax[2].set_title('Training and validation FScore')\n",
+        "    ax[2].set_xlabel('Epochs')\n",
+        "    ax[2].set_ylabel('FScore')\n",
+        "    ax[2].legend()\n",
+        "    plt.show()\n",
+        "\n",
+        "    iou_score = df['iou_score']\n",
+        "    val_iou_score = df['val_iou_score']\n",
+        "\n",
+        "    plt.plot(epochs, iou_score, 'y', label='Training iou_score')\n",
+        "    plt.plot(epochs, val_iou_score, 'r', label='Validation iou_score')\n",
+        "    plt.title('Training and validation iou_score')\n",
+        "    plt.xlabel('Epochs')\n",
+        "    plt.ylabel('iou_score')\n",
+        "    plt.legend()\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "zZAXlXyLITYF",
+        "outputId": "c16b6fe8-2a8c-4dcb-b369-194593ae7e1a"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plot_cruves_for_onefold(2)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Load model data from CSV\n",
+        "def plot_cruves_for_onefold(nb_fold):\n",
+        "    # Load the CSV file into a pandas DataFrame\n",
+        "    df = pd.read_csv('2021/2021fold_' + str(nb_fold) + '.csv')\n",
+        "\n",
+        "    loss = df['loss']\n",
+        "    val_loss = df['val_loss']\n",
+        "    epochs = range(1, len(loss) + 1)\n",
+        "    f, ax = plt.subplots(1, 3, figsize=(16, 8))\n",
+        "\n",
+        "    ax[0].plot(epochs, loss, 'y', label='Training loss')\n",
+        "    ax[0].plot(epochs, val_loss, 'r', label='Validation loss')\n",
+        "    ax[0].legend()\n",
+        "\n",
+        "    acc = df['accuracy']\n",
+        "    val_acc = df['val_accuracy']\n",
+        "    ax[1].plot(epochs, acc, 'y', label='Training acc')\n",
+        "    ax[1].plot(epochs, val_acc, 'r', label='Validation acc')\n",
+        "    ax[1].set_title('Training and validation acc')\n",
+        "    ax[1].set_xlabel('Epochs')\n",
+        "    ax[1].set_ylabel('acc')\n",
+        "    ax[1].legend()\n",
+        "\n",
+        "    FScore = df['f1-score']\n",
+        "    val_FScore = df['val_f1-score']\n",
+        "    ax[2].plot(epochs, FScore, 'y', label='Training f_score')\n",
+        "    ax[2].plot(epochs, val_FScore, 'r', label='Validation f_score')\n",
+        "    ax[2].set_title('Training and validation FScore')\n",
+        "    ax[2].set_xlabel('Epochs')\n",
+        "    ax[2].set_ylabel('FScore')\n",
+        "    ax[2].legend()\n",
+        "    plt.show()\n",
+        "\n",
+        "    iou_score = df['iou_score']\n",
+        "    val_iou_score = df['val_iou_score']\n",
+        "\n",
+        "    plt.plot(epochs, iou_score, 'y', label='Training iou_score')\n",
+        "    plt.plot(epochs, val_iou_score, 'r', label='Validation iou_score')\n",
+        "    plt.title('Training and validation iou_score')\n",
+        "    plt.xlabel('Epochs')\n",
+        "    plt.ylabel('iou_score')\n",
+        "    plt.legend()\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "qR_0KrjaQ3UF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plot_cruves_for_onefold(4)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "YTJWtuElQ9MO",
+        "outputId": "4528d4df-f8f2-4a7d-f6f6-b25b06b7f63d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IZsEflmSodDM",
+        "outputId": "f1ef8de3-cc56-4996-dcb0-9e9c8e3d1fd3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-12-de4130f88f9f>:3: DeprecationWarning: Please use `distance_transform_edt` from the `scipy.ndimage` namespace, the `scipy.ndimage.morphology` namespace is deprecated.\n",
+            "  from scipy.ndimage.morphology import distance_transform_edt, binary_erosion,\\\n",
+            "<ipython-input-12-de4130f88f9f>:3: DeprecationWarning: Please use `binary_erosion` from the `scipy.ndimage` namespace, the `scipy.ndimage.morphology` namespace is deprecated.\n",
+            "  from scipy.ndimage.morphology import distance_transform_edt, binary_erosion,\\\n",
+            "<ipython-input-12-de4130f88f9f>:3: DeprecationWarning: Please use `generate_binary_structure` from the `scipy.ndimage` namespace, the `scipy.ndimage.morphology` namespace is deprecated.\n",
+            "  from scipy.ndimage.morphology import distance_transform_edt, binary_erosion,\\\n",
+            "<ipython-input-12-de4130f88f9f>:5: DeprecationWarning: Please use `label` from the `scipy.ndimage` namespace, the `scipy.ndimage.measurements` namespace is deprecated.\n",
+            "  from scipy.ndimage.measurements import label, find_objects\n",
+            "<ipython-input-12-de4130f88f9f>:5: DeprecationWarning: Please use `find_objects` from the `scipy.ndimage` namespace, the `scipy.ndimage.measurements` namespace is deprecated.\n",
+            "  from scipy.ndimage.measurements import label, find_objects\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy\n",
+        "from scipy.ndimage import _ni_support\n",
+        "from scipy.ndimage.morphology import distance_transform_edt, binary_erosion,\\\n",
+        "    generate_binary_structure\n",
+        "from scipy.ndimage.measurements import label, find_objects\n",
+        "from scipy.stats import pearsonr"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bSlsPzknodNW"
+      },
+      "outputs": [],
+      "source": [
+        "import difflib\n",
+        "import scipy.spatial\n",
+        "import numpy as np\n",
+        "import os\n",
+        "import glob\n",
+        "import SimpleITK as sitk\n",
+        "import medpy\n",
+        "import scipy.spatial\n",
+        "import medpy.metric.binary as medpyMetrics\n",
+        "\n",
+        "\n",
+        "def getHausdorff(testImage, resultImage):\n",
+        "    \"\"\"Compute the Hausdorff distance.\"\"\"\n",
+        "\n",
+        "    # Hausdorff distance is only defined when something is detected\n",
+        "    resultStatistics = sitk.StatisticsImageFilter()\n",
+        "    resultStatistics.Execute(resultImage)\n",
+        "    if resultStatistics.GetSum() == 0:\n",
+        "        return float('nan')\n",
+        "\n",
+        "    # Edge detection is done by ORIGINAL - ERODED, keeping the outer boundaries of lesions. Erosion is performed in 2D\n",
+        "    eTestImage   = sitk.BinaryErode(testImage, (1,1,0) )\n",
+        "    eResultImage = sitk.BinaryErode(resultImage, (1,1,0) )\n",
+        "\n",
+        "    hTestImage   = sitk.Subtract(testImage, eTestImage)\n",
+        "    hResultImage = sitk.Subtract(resultImage, eResultImage)\n",
+        "\n",
+        "    hTestArray   = sitk.GetArrayFromImage(hTestImage)\n",
+        "    hResultArray = sitk.GetArrayFromImage(hResultImage)\n",
+        "\n",
+        "    # Convert voxel location to world coordinates. Use the coordinate system of the test image\n",
+        "\n",
+        "    testCoordinates   = [testImage.TransformIndexToPhysicalPoint(x.tolist()) for x in np.transpose( np.flipud( np.nonzero(hTestArray) ))]\n",
+        "    resultCoordinates = [testImage.TransformIndexToPhysicalPoint(x.tolist()) for x in np.transpose( np.flipud( np.nonzero(hResultArray) ))]\n",
+        "\n",
+        "\n",
+        "    # Use a kd-tree for fast spatial search\n",
+        "    def getDistancesFromAtoB(a, b):\n",
+        "        kdTree = scipy.spatial.KDTree(a, leafsize=100)\n",
+        "        return kdTree.query(b, k=1, eps=0, p=2)[0]\n",
+        "\n",
+        "    # Compute distances from test to result; and result to test\n",
+        "    dTestToResult = getDistancesFromAtoB(testCoordinates, resultCoordinates)\n",
+        "    dResultToTest = getDistancesFromAtoB(resultCoordinates, testCoordinates)\n",
+        "\n",
+        "    return max(np.percentile(dTestToResult, 95), np.percentile(dResultToTest, 95))\n",
+        "def hd95(result, reference, voxelspacing=None, connectivity=1):\n",
+        "    try:\n",
+        "        hd1 = medpyMetrics.__surface_distances(result, reference, voxelspacing, connectivity)\n",
+        "        hd2 = medpyMetrics.__surface_distances(reference, result, voxelspacing, connectivity)\n",
+        "        hd95 = numpy.percentile(numpy.hstack((hd1, hd2)), 95)\n",
+        "    except:\n",
+        "        hd95 = 95\n",
+        "    return hd95\n",
+        "def getDSC(testImage, resultImage):\n",
+        "    \"\"\"Compute the Dice Similarity Coefficient.\"\"\"\n",
+        "    testArray   = sitk.GetArrayFromImage(testImage).flatten()\n",
+        "    resultArray = sitk.GetArrayFromImage(resultImage).flatten()\n",
+        "\n",
+        "    # similarity = 1.0 - dissimilarity\n",
+        "    return 1.0 - scipy.spatial.distance.dice(testArray, resultArray)\n",
+        "\n",
+        "def recall(result, reference):\n",
+        "    result = np.atleast_1d(result.astype(np.bool))\n",
+        "    reference = np.atleast_1d(reference.astype(np.bool))\n",
+        "\n",
+        "    tp = np.count_nonzero(result & reference)\n",
+        "    fn = np.count_nonzero(~result & reference)\n",
+        "\n",
+        "    try:\n",
+        "        recall = tp / float(tp + fn)\n",
+        "    except ZeroDivisionError:\n",
+        "        recall = 0.0\n",
+        "\n",
+        "    return recall\n",
+        "\n",
+        "\n",
+        "def sensitivity(result, reference):\n",
+        "\n",
+        "    return recall(result, reference)\n",
+        "def specificity(result, reference):\n",
+        "\n",
+        "    result = np.atleast_1d(result.astype(np.bool))\n",
+        "    reference = np.atleast_1d(reference.astype(np.bool))\n",
+        "\n",
+        "    tn = np.count_nonzero(~result & ~reference)\n",
+        "    fp = np.count_nonzero(result & ~reference)\n",
+        "\n",
+        "    try:\n",
+        "        specificity = tn / float(tn + fp)\n",
+        "    except ZeroDivisionError:\n",
+        "        specificity = 0.0\n",
+        "\n",
+        "    return specificity\n",
+        "Dice_all=[]\n",
+        "H95_all=[]\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YdhUusgCXBZ1"
+      },
+      "outputs": [],
+      "source": [
+        "import math\n",
+        "def get_score_per_sampe(my_model,image, maks):\n",
+        "           Disces=[list() for i in range(0,3)]\n",
+        "           Haus95s=[list() for i in range(0,3)]\n",
+        "           Specs=[list() for i in range(0,3)]\n",
+        "           Senss=[list() for i in range(0,3)]\n",
+        "           domains=['whole','Core','Enhance'] #[0,1,2]\n",
+        "           predcition=my_model.predict(image)\n",
+        "           prediction=np.argmax(predcition, axis=4)[0,:,:,:]\n",
+        "\n",
+        "           for k, dom in enumerate(domains):\n",
+        "                if k==0:\n",
+        "                    test_prediction = (prediction>0.4).astype(int)\n",
+        "                    msk=(mask >0.4).astype(int)\n",
+        "                elif k==1:\n",
+        "                    test_prediction1=(prediction ==1).astype(int)\n",
+        "                    test_prediction2=(prediction ==3).astype(int)\n",
+        "                    test_prediction1=(test_prediction1 >0.4).astype(float)\n",
+        "                    test_prediction2=(test_prediction2 >0.4).astype(float)\n",
+        "                    test_prediction=test_prediction1 + test_prediction2\n",
+        "                    test_prediction=(test_prediction >0.4).astype(int)\n",
+        "\n",
+        "                    msk1=(mask ==1).astype(int)\n",
+        "                    msk2=(mask ==3).astype(int)\n",
+        "                    msk1=(msk1 >0.4).astype(float)\n",
+        "                    msk2=(msk2 >0.4).astype(float)\n",
+        "                    msk=msk1+msk2\n",
+        "                    msk=(msk > 0.3).astype(int)\n",
+        "                   # print(np.unique(msk),np.unique(test_prediction))\n",
+        "                else:\n",
+        "                    test_prediction =(prediction ==3).astype(int)\n",
+        "                    test_prediction =(test_prediction >0.4).astype(int)\n",
+        "                    msk=(mask ==3).astype(int)\n",
+        "\n",
+        "                Spec=specificity(test_prediction , msk)\n",
+        "                Sens=sensitivity(test_prediction , msk)\n",
+        "                image_sitk = sitk.GetImageFromArray(msk)\n",
+        "                image_sitk1 = sitk.GetImageFromArray(test_prediction)\n",
+        "                Dice=getDSC(image_sitk, image_sitk1)\n",
+        "                if math.isnan(Dice):\n",
+        "                    Dice=1\n",
+        "                print('dice ---- ',Dice)\n",
+        "\n",
+        "                Haus=hd95(test_prediction , msk, voxelspacing=None, connectivity=1)\n",
+        "                Senss[k].append(100*Sens)\n",
+        "                Specs[k].append(100*Spec)\n",
+        "                Disces[k].append(100*Dice)\n",
+        "                Haus95s[k].append(Haus)\n",
+        "           return  Senss,Specs,Disces,Haus95s"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras.models import load_model\n",
+        "from tensorflow_addons.layers import InstanceNormalization\n",
+        "import pickle\n",
+        "images_path='/content/drive/MyDrive/Bratsdataset/BraTs2020/'\n",
+        "models_list=os.listdir('/content/drive/MyDrive/2020/')\n",
+        "Dices_all=[]\n",
+        "H95s_all=[]\n",
+        "Senss_all=[]\n",
+        "Specs_all=[]\n",
+        "open_file = open('/content/drive/MyDrive/folds_dic.pkl', \"rb\")\n",
+        "trainvallist = pickle.load(open_file)\n",
+        "open_file.close()\n",
+        "for k in range(0,5):\n",
+        "\n",
+        "       fold=0\n",
+        "       model_for_this_fold=[m for m in models_list if '2020fold_'+str(k) in m]\n",
+        "       model = load_model('/content/drive/MyDrive/2020/'+model_for_this_fold[0],\n",
+        "                      compile=False)\n",
+        "# empty list to read list from a file\n",
+        "       val_list=trainvallist['validation'][k][:69]\n",
+        "\n",
+        " # open file and read the content in a list\n",
+        "\n",
+        "# =============================================================================\n",
+        "       print(' ****** ',len(val_list))\n",
+        "       Dice_fold=[]\n",
+        "       H95_fold=[]\n",
+        "       Sens_fold=[]\n",
+        "       Spec_fold=[]\n",
+        "       for i, dir_name in enumerate(val_list):\n",
+        "           print(' ********* ', i, dir_name)\n",
+        "           image = np.load(glob.glob(images_path+dir_name+'/'+'image_*.npy')[0])\n",
+        "           image=np.expand_dims(image,0)\n",
+        "           mask = np.load(glob.glob(images_path+dir_name+'/'+'mask_*.npy')[0])\n",
+        "      #  mask=np.expand_dims(mask,3)\n",
+        "           print(mask.shape)\n",
+        "           print(np.unique(mask))\n",
+        "           Senss,Specs,Disces,Haus95s=get_score_per_sampe(model,image, mask)\n",
+        "           Dice_fold.append(Disces)\n",
+        "           H95_fold.append(Haus95s)\n",
+        "           Spec_fold.append(Specs)\n",
+        "           Sens_fold.append(Senss)\n",
+        "\n",
+        "       Dices_all.append(Dice_fold)\n",
+        "       H95s_all.append(H95_fold)\n",
+        "       Specs_all.append(Spec_fold)\n",
+        "       Senss_all.append(Sens_fold)\n",
+        "\n",
+        "import pickle\n",
+        "open_file = open('dscs2020_paper1.pkl', \"wb\")\n",
+        "pickle.dump(Dices_all, open_file)\n",
+        "open_file.close()\n",
+        "open_file = open('hds2020_paper1.pkl', \"wb\")\n",
+        "pickle.dump(H95s_all, open_file)\n",
+        "open_file.close()\n",
+        "open_file = open('specs2020_paper1.pkl', \"wb\")\n",
+        "pickle.dump(Specs_all, open_file)\n",
+        "open_file.close()\n",
+        "open_file = open('sensis2020_paper1.pkl', \"wb\")\n",
+        "pickle.dump(Senss_all, open_file)\n",
+        "open_file.close()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2QS6a_P-KdeV",
+        "outputId": "ae684a79-a4a6-4e44-b7d4-4e78afb80376"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " ******  69\n",
+            " *********  0 train10\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 1s 695ms/step\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-23-48be69b7e0e7>:83: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+            "  result = np.atleast_1d(result.astype(np.bool))\n",
+            "<ipython-input-23-48be69b7e0e7>:84: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+            "  reference = np.atleast_1d(reference.astype(np.bool))\n",
+            "<ipython-input-23-48be69b7e0e7>:64: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+            "  result = np.atleast_1d(result.astype(np.bool))\n",
+            "<ipython-input-23-48be69b7e0e7>:65: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+            "  reference = np.atleast_1d(reference.astype(np.bool))\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "dice ----  0.8857212816566391\n",
+            "dice ----  0.9203531330966318\n",
+            "dice ----  0.7668969253690912\n",
+            " *********  1 train100\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 50ms/step\n",
+            "dice ----  0.9051978449648906\n",
+            "dice ----  0.8776826248452332\n",
+            "dice ----  0.8116623092960916\n",
+            " *********  2 train104\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 48ms/step\n",
+            "dice ----  0.8818878432488465\n",
+            "dice ----  0.9493638676844783\n",
+            "dice ----  0.9032967032967033\n",
+            " *********  3 train11\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 49ms/step\n",
+            "dice ----  0.8316564251468701\n",
+            "dice ----  0.9281927710843374\n",
+            "dice ----  0.848693700913608\n",
+            " *********  4 train110\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.8786012833343144\n",
+            "dice ----  0.934295588534255\n",
+            "dice ----  0.8573911532110889\n",
+            " *********  5 train111\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9364614774840696\n",
+            "dice ----  0.9734994493392071\n",
+            "dice ----  0.9341551561958513\n",
+            " *********  6 train117\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 48ms/step\n",
+            "dice ----  0.8870832989047107\n",
+            "dice ----  0.9521169117992575\n",
+            "dice ----  0.9129707595593748\n",
+            " *********  7 train125\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 49ms/step\n",
+            "dice ----  0.8759522782808682\n",
+            "dice ----  0.9172706465763876\n",
+            "dice ----  0.8845760980592441\n",
+            " *********  8 train137\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 49ms/step\n",
+            "dice ----  0.9233580921599315\n",
+            "dice ----  0.8619840351345738\n",
+            "dice ----  0.8872559990053462\n",
+            " *********  9 train139\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 50ms/step\n",
+            "dice ----  0.941652439113368\n",
+            "dice ----  0.9336212976022567\n",
+            "dice ----  0.8637039889380919\n",
+            " *********  10 train142\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9254715378079864\n",
+            "dice ----  0.9353546547778675\n",
+            "dice ----  0.7689049720887966\n",
+            " *********  11 train15\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8736663789614678\n",
+            "dice ----  0.8042068946328705\n",
+            "dice ----  0.7451211951226713\n",
+            " *********  12 train153\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9146399414808131\n",
+            "dice ----  0.9544381186517436\n",
+            "dice ----  0.8757348891898731\n",
+            " *********  13 train173\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 48ms/step\n",
+            "dice ----  0.9554468060763338\n",
+            "dice ----  0.9365096790719801\n",
+            "dice ----  0.9094678645473393\n",
+            " *********  14 train175\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9489458307571045\n",
+            "dice ----  0.8472926728636264\n",
+            "dice ----  0.611326479015675\n",
+            " *********  15 train18\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8790558385210337\n",
+            "dice ----  0.9315239978968979\n",
+            "dice ----  0.7550494890158526\n",
+            " *********  16 train181\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8787760480210824\n",
+            "dice ----  0.9009464904882392\n",
+            "dice ----  0.8784508097515993\n",
+            " *********  17 train185\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9012128448438383\n",
+            "dice ----  0.9453229884055444\n",
+            "dice ----  0.9134221258043993\n",
+            " *********  18 train186\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9480072219677167\n",
+            "dice ----  0.9576454967913255\n",
+            "dice ----  0.886199421965318\n",
+            " *********  19 train196\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9544873623775223\n",
+            "dice ----  0.9301194939081537\n",
+            "dice ----  0.8929330109347926\n",
+            " *********  20 train198\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9579642369516321\n",
+            "dice ----  0.9388670610727242\n",
+            "dice ----  0.9088648556586902\n",
+            " *********  21 train2\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.7664009638149138\n",
+            "dice ----  0.9015237020316027\n",
+            "dice ----  0.8278921220723918\n",
+            " *********  22 train201\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9214165312831308\n",
+            "dice ----  0.9409317367362993\n",
+            "dice ----  0.9253441581362514\n",
+            " *********  23 train21\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9128401272164134\n",
+            "dice ----  0.9356693391254352\n",
+            "dice ----  0.8343543543543543\n",
+            " *********  24 train215\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9162054739384182\n",
+            "dice ----  0.9221378380296685\n",
+            "dice ----  0.7918221161020743\n",
+            " *********  25 train214\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.920318813061621\n",
+            "dice ----  0.897012088055608\n",
+            "dice ----  0.8476449950018621\n",
+            " *********  26 train225\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 49ms/step\n",
+            "dice ----  0.9046742285775723\n",
+            "dice ----  0.939860197708566\n",
+            "dice ----  0.9058778969296688\n",
+            " *********  27 train234\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9336376848224441\n",
+            "dice ----  0.9556083121852016\n",
+            "dice ----  0.9003728525947894\n",
+            " *********  28 train235\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8476842408773935\n",
+            "dice ----  0.8694392089635642\n",
+            "dice ----  0.7775658600548337\n",
+            " *********  29 train246\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.9002970873691372\n",
+            "dice ----  0.9572739068084041\n",
+            "dice ----  0.8537693216843179\n",
+            " *********  30 train254\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 48ms/step\n",
+            "dice ----  0.934318431372549\n",
+            "dice ----  0.9488146422309226\n",
+            "dice ----  0.8971204659914428\n",
+            " *********  31 train258\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.922814362475167\n",
+            "dice ----  0.8988348855623846\n",
+            "dice ----  0.8869132290184922\n",
+            " *********  32 train268\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8754808192119763\n",
+            "dice ----  0.845217116856474\n",
+            "dice ----  0.0\n",
+            " *********  33 train270\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9091505949938449\n",
+            "dice ----  0.8800468363762523\n",
+            "dice ----  0.7740740740740741\n",
+            " *********  34 train271\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8978466291367277\n",
+            "dice ----  0.6197557169437609\n",
+            "dice ----  0.0\n",
+            " *********  35 train28\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.90500781147787\n",
+            "dice ----  0.9365996149505411\n",
+            "dice ----  0.872567690651652\n",
+            " *********  36 train290\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 47ms/step\n",
+            "dice ----  0.9021248193351533\n",
+            "dice ----  0.7697702391388554\n",
+            "dice ----  0.31334622823984526\n",
+            " *********  37 train293\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9160143804986409\n",
+            "dice ----  0.8173406493181719\n",
+            "dice ----  0.0\n",
+            " *********  38 train295\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9565667664405966\n",
+            "dice ----  0.8303365118118573\n",
+            "dice ----  0.6000816826628548\n",
+            " *********  39 train303\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8625999970750399\n",
+            "dice ----  0.849823773800382\n",
+            "dice ----  0.0\n",
+            " *********  40 train306\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 47ms/step\n",
+            "dice ----  0.926873294619744\n",
+            "dice ----  0.8333884180457518\n",
+            "dice ----  0.006868918145392144\n",
+            " *********  41 train31\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8161751778150723\n",
+            "dice ----  0.8731211688638845\n",
+            "dice ----  0.7349587791615506\n",
+            " *********  42 train328\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8638724318067592\n",
+            "dice ----  0.5863363818060687\n",
+            "dice ----  0.0\n",
+            " *********  43 train330\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9092083462046112\n",
+            "dice ----  0.4151045178691841\n",
+            "dice ----  0.7831780407957694\n",
+            " *********  44 train333\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 47ms/step\n",
+            "dice ----  0.883312888811005\n",
+            "dice ----  0.8393932533393706\n",
+            "dice ----  0.8617117762184352\n",
+            " *********  45 train334\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9116765787442684\n",
+            "dice ----  0.7153703742225366\n",
+            "dice ----  0.0\n",
+            " *********  46 train335\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9633524110627307\n",
+            "dice ----  0.9680502883266663\n",
+            "dice ----  0.8736229984847864\n",
+            " *********  47 train344\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8378492466000649\n",
+            "dice ----  0.8455271815760614\n",
+            "dice ----  0.8291519289298347\n",
+            " *********  48 train353\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9329543273843792\n",
+            "dice ----  0.9644779449191548\n",
+            "dice ----  0.9081273341632581\n",
+            " *********  49 train348\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9367698796820029\n",
+            "dice ----  0.9373309934549628\n",
+            "dice ----  0.8412771969482905\n",
+            " *********  50 train354\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9361622602099708\n",
+            "dice ----  0.9664026732765775\n",
+            "dice ----  0.9197318566015739\n",
+            " *********  51 train356\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8586482611844342\n",
+            "dice ----  0.8341217114004783\n",
+            "dice ----  0.7559040923021453\n",
+            " *********  52 train358\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9158262893617531\n",
+            "dice ----  0.9694934168875344\n",
+            "dice ----  0.9009080964700571\n",
+            " *********  53 train360\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 47ms/step\n",
+            "dice ----  0.914994165211418\n",
+            "dice ----  0.6580269769902143\n",
+            "dice ----  0.13879250520471897\n",
+            " *********  54 train361\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9345936520838523\n",
+            "dice ----  0.9554580442723016\n",
+            "dice ----  0.8858598509259372\n",
+            " *********  55 train362\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9349196937499189\n",
+            "dice ----  0.9667023478198815\n",
+            "dice ----  0.9260648531292656\n",
+            " *********  56 train363\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.896629081397741\n",
+            "dice ----  0.9577320400599322\n",
+            "dice ----  0.8901001904702913\n",
+            " *********  57 train364\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.942755806675582\n",
+            "dice ----  0.9711739181423713\n",
+            "dice ----  0.9196561288444152\n",
+            " *********  58 train367\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9312661868786327\n",
+            "dice ----  0.9446749269107615\n",
+            "dice ----  0.9327529270489343\n",
+            " *********  59 train40\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9135265765704951\n",
+            "dice ----  0.880237972064149\n",
+            "dice ----  0.8201620162016201\n",
+            " *********  60 train57\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.7101805110879771\n",
+            "dice ----  0.85350532809871\n",
+            "dice ----  0.7478885135135135\n",
+            " *********  61 train56\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9205587179663032\n",
+            "dice ----  0.9157825479069003\n",
+            "dice ----  0.8488029564565369\n",
+            " *********  62 train66\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9298995126060533\n",
+            "dice ----  0.9569678450281741\n",
+            "dice ----  0.8700156178953403\n",
+            " *********  63 train7\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.7979985071738589\n",
+            "dice ----  0.7972389991371872\n",
+            "dice ----  0.7735941320293398\n",
+            " *********  64 train71\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.7490049272746104\n",
+            "dice ----  0.9237682373314922\n",
+            "dice ----  0.8638162533999165\n",
+            " *********  65 train75\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9100313969854082\n",
+            "dice ----  0.8998559077809798\n",
+            "dice ----  0.7563232082588842\n",
+            " *********  66 train80\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8916851714779176\n",
+            "dice ----  0.9285791757049892\n",
+            "dice ----  0.7999789783476982\n",
+            " *********  67 train82\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9141712710740183\n",
+            "dice ----  0.9465670117450443\n",
+            "dice ----  0.8610301263362488\n",
+            " *********  68 train9\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.7549311328568361\n",
+            "dice ----  0.9178519593613933\n",
+            "dice ----  0.8973713206359201\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " ******  69\n",
+            " *********  0 train0\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 1s 671ms/step\n",
+            "dice ----  0.8861974228998182\n",
+            "dice ----  0.9363777333303369\n",
+            "dice ----  0.8681893717073861\n",
+            " *********  1 train6\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8540261050507598\n",
+            "dice ----  0.9148123631549467\n",
+            "dice ----  0.8548150322959484\n",
+            " *********  2 train10\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8775684186718166\n",
+            "dice ----  0.9036183752148266\n",
+            "dice ----  0.7280377406604616\n",
+            " *********  3 train15\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.8694309297661732\n",
+            "dice ----  0.8226930465386935\n",
+            "dice ----  0.6404059689494046\n",
+            " *********  4 train17\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7257074004975124\n",
+            "dice ----  0.5177901410272998\n",
+            "dice ----  0.5974968256847452\n",
+            " *********  5 train18\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8685375938929804\n",
+            "dice ----  0.9021707746016747\n",
+            "dice ----  0.7718931686046512\n",
+            " *********  6 train20\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7421456566394878\n",
+            "dice ----  0.8492617231867596\n",
+            "dice ----  0.6237201365187713\n",
+            " *********  7 train37\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9011250383853047\n",
+            "dice ----  0.8748884240817468\n",
+            "dice ----  0.7989650334359797\n",
+            " *********  8 train41\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9234913416127339\n",
+            "dice ----  0.9522379092820666\n",
+            "dice ----  0.8333294003822829\n",
+            " *********  9 train42\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.7776348966212809\n",
+            "dice ----  0.452651873133482\n",
+            "dice ----  0.48650495387769044\n",
+            " *********  10 train45\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.5810315627405697\n",
+            "dice ----  0.1651376146788991\n",
+            "dice ----  0.39841986455981937\n",
+            " *********  11 train46\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8807211699987452\n",
+            "dice ----  0.9279109220807149\n",
+            "dice ----  0.7988706566404831\n",
+            " *********  12 train48\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9113197011194725\n",
+            "dice ----  0.5331966471687872\n",
+            "dice ----  0.6268602209440336\n",
+            " *********  13 train55\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8437348015599908\n",
+            "dice ----  0.9235802981453698\n",
+            "dice ----  0.866861741038771\n",
+            " *********  14 train68\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.7403798987238603\n",
+            "dice ----  0.9277029749746412\n",
+            "dice ----  0.7535888860303576\n",
+            " *********  15 train76\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9052693094380053\n",
+            "dice ----  0.9359324559225229\n",
+            "dice ----  0.8511544883975355\n",
+            " *********  16 train87\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.8246269960515742\n",
+            "dice ----  0.8981003278394036\n",
+            "dice ----  0.8293743918493504\n",
+            " *********  17 train97\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9534405586863322\n",
+            "dice ----  0.9502546343450805\n",
+            "dice ----  0.8393250771512164\n",
+            " *********  18 train112\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9364329682873482\n",
+            "dice ----  0.39809922781129836\n",
+            "dice ----  0.5986365267312522\n",
+            " *********  19 train117\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 47ms/step\n",
+            "dice ----  0.8819635552249907\n",
+            "dice ----  0.9397761088281139\n",
+            "dice ----  0.890988707075363\n",
+            " *********  20 train123\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9037547846163193\n",
+            "dice ----  0.9074030974245759\n",
+            "dice ----  0.7974445209125969\n",
+            " *********  21 train125\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8860395627671244\n",
+            "dice ----  0.8926615293420272\n",
+            "dice ----  0.8680362016951587\n",
+            " *********  22 train132\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9256634149967425\n",
+            "dice ----  0.7010086209834054\n",
+            "dice ----  0.8195372450617104\n",
+            " *********  23 train154\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.920663860597998\n",
+            "dice ----  0.9363350671868227\n",
+            "dice ----  0.8966316497813003\n",
+            " *********  24 train162\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9467091187816917\n",
+            "dice ----  0.9280760206820974\n",
+            "dice ----  0.8500682081322435\n",
+            " *********  25 train165\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8945216639920998\n",
+            "dice ----  0.9556010306459765\n",
+            "dice ----  0.9094201354863448\n",
+            " *********  26 train173\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9564583105386831\n",
+            "dice ----  0.9295300149936139\n",
+            "dice ----  0.8970359810200651\n",
+            " *********  27 train177\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 51ms/step\n",
+            "dice ----  0.9378059665161064\n",
+            "dice ----  0.9128703550574376\n",
+            "dice ----  0.8734918879664626\n",
+            " *********  28 train178\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8856836487063926\n",
+            "dice ----  0.8625582412280041\n",
+            "dice ----  0.7546635346137902\n",
+            " *********  29 train179\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8469684162906701\n",
+            "dice ----  0.9547916935379853\n",
+            "dice ----  0.9120992414400066\n",
+            " *********  30 train183\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8581944740037808\n",
+            "dice ----  0.9202315598061895\n",
+            "dice ----  0.8970776009351678\n",
+            " *********  31 train187\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.6905798247293125\n",
+            "dice ----  0.8856243441762854\n",
+            "dice ----  0.8077811731385556\n",
+            " *********  32 train188\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.918166053053387\n",
+            "dice ----  0.9246839129046197\n",
+            "dice ----  0.9042709867452136\n",
+            " *********  33 train191\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9318490107114458\n",
+            "dice ----  0.9717514124293786\n",
+            "dice ----  0.9243251581058152\n",
+            " *********  34 train193\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9222473725920823\n",
+            "dice ----  0.9529346511363712\n",
+            "dice ----  0.8461015546491695\n",
+            " *********  35 train195\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9195831694848604\n",
+            "dice ----  0.9412615674297917\n",
+            "dice ----  0.9065449839950963\n",
+            " *********  36 train196\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9512256720894902\n",
+            "dice ----  0.9175620492918996\n",
+            "dice ----  0.8391624106230848\n",
+            " *********  37 train202\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8563052309754157\n",
+            "dice ----  0.9190708684716674\n",
+            "dice ----  0.8717401250571908\n",
+            " *********  38 train204\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8388380533329597\n",
+            "dice ----  0.8439595636072466\n",
+            "dice ----  0.8199398366996132\n",
+            " *********  39 train205\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.7958412484425108\n",
+            "dice ----  0.9238347859037515\n",
+            "dice ----  0.9084652443220922\n",
+            " *********  40 train208\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.8866440736878693\n",
+            "dice ----  0.9493239914609448\n",
+            "dice ----  0.8658972961444584\n",
+            " *********  41 train209\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9429842894718135\n",
+            "dice ----  0.955278586558982\n",
+            "dice ----  0.930645627659135\n",
+            " *********  42 train222\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9560436661059991\n",
+            "dice ----  0.9510685471454087\n",
+            "dice ----  0.9313925046610677\n",
+            " *********  43 train224\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9291819579675864\n",
+            "dice ----  0.9258829595256509\n",
+            "dice ----  0.8377349601417657\n",
+            " *********  44 train231\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.84289915183526\n",
+            "dice ----  0.3865038094086347\n",
+            "dice ----  0.44084888621312457\n",
+            " *********  45 train237\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8252417160423515\n",
+            "dice ----  0.622184742826329\n",
+            "dice ----  0.5822615595252213\n",
+            " *********  46 train243\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9276975494106507\n",
+            "dice ----  0.9173282632340725\n",
+            "dice ----  0.7169858555513832\n",
+            " *********  47 train244\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.7294841325318049\n",
+            "dice ----  0.7603232801103883\n",
+            "dice ----  0.6556533876349174\n",
+            " *********  48 train250\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9440837302582101\n",
+            "dice ----  0.9009991391229489\n",
+            "dice ----  0.8864713216957606\n",
+            " *********  49 train251\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9350005267380329\n",
+            "dice ----  0.9262751670762882\n",
+            "dice ----  0.8464550343221073\n",
+            " *********  50 train252\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9248855201137521\n",
+            "dice ----  0.9184383015104018\n",
+            "dice ----  0.769179619316765\n",
+            " *********  51 train253\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9413625624854243\n",
+            "dice ----  0.9369670779024651\n",
+            "dice ----  0.8991461135339173\n",
+            " *********  52 train261\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 46ms/step\n",
+            "dice ----  0.8401876216200517\n",
+            "dice ----  0.3926361143752448\n",
+            "dice ----  0.0\n",
+            " *********  53 train263\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.5886005660309765\n",
+            "dice ----  0.7912727709252423\n",
+            "dice ----  0.0\n",
+            " *********  54 train264\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9077711230266008\n",
+            "dice ----  0.7664707187222715\n",
+            "dice ----  0.0\n",
+            " *********  55 train269\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9238031937556453\n",
+            "dice ----  0.9659236783755397\n",
+            "dice ----  0.9226612688801022\n",
+            " *********  56 train278\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.852209776215926\n",
+            "dice ----  0.7473815447650118\n",
+            "dice ----  0.0\n",
+            " *********  57 train282\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8773158364472691\n",
+            "dice ----  0.7080380420812984\n",
+            "dice ----  0.7465623518255098\n",
+            " *********  58 train293\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8798976988210268\n",
+            "dice ----  0.6826568265682658\n",
+            "dice ----  0.0\n",
+            " *********  59 train299\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8512141638918216\n",
+            "dice ----  0.6612261979493618\n",
+            "dice ----  0.35516052965717393\n",
+            " *********  60 train304\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.6836701239090952\n",
+            "dice ----  0.5989645838625752\n",
+            "dice ----  0.0\n",
+            " *********  61 train309\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8077431626094826\n",
+            "dice ----  0.5119888674801969\n",
+            "dice ----  0.0\n",
+            " *********  62 train329\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9152113991630113\n",
+            "dice ----  0.6792223572296476\n",
+            "dice ----  0.0\n",
+            " *********  63 train347\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9413210199673117\n",
+            "dice ----  0.9564673609431933\n",
+            "dice ----  0.9013039619507088\n",
+            " *********  64 train350\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.909681291757274\n",
+            "dice ----  0.9678325248914986\n",
+            "dice ----  0.9525472536225225\n",
+            " *********  65 train353\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9185027629558571\n",
+            "dice ----  0.9426836452122593\n",
+            "dice ----  0.8889859185678419\n",
+            " *********  66 train355\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.7655880792785842\n",
+            "dice ----  0.8983847669074195\n",
+            "dice ----  0.8260390966027709\n",
+            " *********  67 train362\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9215866874088307\n",
+            "dice ----  0.9497647484618169\n",
+            "dice ----  0.8911330495426139\n",
+            " *********  68 train367\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9240084952035508\n",
+            "dice ----  0.9305960192773709\n",
+            "dice ----  0.9177481585408629\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " ******  69\n",
+            " *********  0 train9\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 1s 671ms/step\n",
+            "dice ----  0.6876034096383019\n",
+            "dice ----  0.8938153064106185\n",
+            "dice ----  0.8727605397575665\n",
+            " *********  1 train22\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.859367106048776\n",
+            "dice ----  0.9506967373663887\n",
+            "dice ----  0.7996312939278719\n",
+            " *********  2 train25\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 48ms/step\n",
+            "dice ----  0.8991620516686479\n",
+            "dice ----  0.778768222082723\n",
+            "dice ----  0.695413731921071\n",
+            " *********  3 train30\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8740008083348303\n",
+            "dice ----  0.8900778051446001\n",
+            "dice ----  0.8470469883053192\n",
+            " *********  4 train33\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9312021645459726\n",
+            "dice ----  0.9208760546537635\n",
+            "dice ----  0.7910513010587139\n",
+            " *********  5 train34\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8498558237485825\n",
+            "dice ----  0.8472366033441662\n",
+            "dice ----  0.6887982138732773\n",
+            " *********  6 train36\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.876008186937037\n",
+            "dice ----  0.787272295532413\n",
+            "dice ----  0.756497159636653\n",
+            " *********  7 train38\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.867498110996969\n",
+            "dice ----  0.9108543645522669\n",
+            "dice ----  0.8419330749262117\n",
+            " *********  8 train44\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9101701897449387\n",
+            "dice ----  0.9337998529449051\n",
+            "dice ----  0.845773492459488\n",
+            " *********  9 train50\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8945433604442937\n",
+            "dice ----  0.931640625\n",
+            "dice ----  0.858652857092186\n",
+            " *********  10 train51\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.8682294264339152\n",
+            "dice ----  0.9348226103237887\n",
+            "dice ----  0.8205866820298622\n",
+            " *********  11 train52\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9320139564913408\n",
+            "dice ----  0.8738968711253738\n",
+            "dice ----  0.7543805717798955\n",
+            " *********  12 train57\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.6796463874851464\n",
+            "dice ----  0.7722669049746798\n",
+            "dice ----  0.5720789937965967\n",
+            " *********  13 train59\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8985309585177822\n",
+            "dice ----  0.8974358974358975\n",
+            "dice ----  0.7359888190076869\n",
+            " *********  14 train61\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9083743102736384\n",
+            "dice ----  0.9408647114652734\n",
+            "dice ----  0.8520794831019614\n",
+            " *********  15 train67\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9056512169860176\n",
+            "dice ----  0.914104361945531\n",
+            "dice ----  0.7672594142259415\n",
+            " *********  16 train69\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8018700836805083\n",
+            "dice ----  0.860910911592801\n",
+            "dice ----  0.620051774350124\n",
+            " *********  17 train72\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8874317400746379\n",
+            "dice ----  0.9264137196659754\n",
+            "dice ----  0.7945329471397538\n",
+            " *********  18 train75\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.8665631209885567\n",
+            "dice ----  0.8055672711935892\n",
+            "dice ----  0.6621100032164683\n",
+            " *********  19 train88\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.7063289715421244\n",
+            "dice ----  0.9548225647578734\n",
+            "dice ----  0.9520656708383798\n",
+            " *********  20 train93\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8709812809535802\n",
+            "dice ----  0.8201936376210235\n",
+            "dice ----  0.7860194174757281\n",
+            " *********  21 train111\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9277530145920331\n",
+            "dice ----  0.9576728527685202\n",
+            "dice ----  0.9102698486215051\n",
+            " *********  22 train113\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9060444381209836\n",
+            "dice ----  0.9411699432227997\n",
+            "dice ----  0.8195775337483389\n",
+            " *********  23 train120\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.9458555823124978\n",
+            "dice ----  0.9581744333860416\n",
+            "dice ----  0.8780529953917051\n",
+            " *********  24 train126\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9143339553896739\n",
+            "dice ----  0.8422953451043339\n",
+            "dice ----  0.8759260591239301\n",
+            " *********  25 train127\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 47ms/step\n",
+            "dice ----  0.748001414927485\n",
+            "dice ----  0.31598652190190935\n",
+            "dice ----  0.5647668393782384\n",
+            " *********  26 train129\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8931262783937991\n",
+            "dice ----  0.9536230407390426\n",
+            "dice ----  0.8711695568985939\n",
+            " *********  27 train143\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.928607780359802\n",
+            "dice ----  0.933483172476606\n",
+            "dice ----  0.92078485251814\n",
+            " *********  28 train149\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8818366947611007\n",
+            "dice ----  0.9492322186501423\n",
+            "dice ----  0.915620656945578\n",
+            " *********  29 train152\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9309421266253896\n",
+            "dice ----  0.961029438721267\n",
+            "dice ----  0.9381919553962564\n",
+            " *********  30 train157\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.5766722636573081\n",
+            "dice ----  0.21523260601070404\n",
+            "dice ----  0.31601222666353157\n",
+            " *********  31 train172\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8624059834446826\n",
+            "dice ----  0.8987815433783873\n",
+            "dice ----  0.7055335435702541\n",
+            " *********  32 train180\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8709831219605225\n",
+            "dice ----  0.8445297504798465\n",
+            "dice ----  0.8861863240018357\n",
+            " *********  33 train184\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8612199066239006\n",
+            "dice ----  0.92070775811783\n",
+            "dice ----  0.8562892815873284\n",
+            " *********  34 train189\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8840102050872141\n",
+            "dice ----  0.7444168734491314\n",
+            "dice ----  0.7640066347217103\n",
+            " *********  35 train197\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9158485133270681\n",
+            "dice ----  0.8938705035971223\n",
+            "dice ----  0.8202387315161233\n",
+            " *********  36 train200\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9278011145700658\n",
+            "dice ----  0.9471242490929638\n",
+            "dice ----  0.8528104970599912\n",
+            " *********  37 train201\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8928855224500806\n",
+            "dice ----  0.9396890901778376\n",
+            "dice ----  0.8917549236791794\n",
+            " *********  38 train217\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9022221831388446\n",
+            "dice ----  0.9281516034985423\n",
+            "dice ----  0.8602487798160927\n",
+            " *********  39 train219\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8299766033913023\n",
+            "dice ----  0.8528675841179957\n",
+            "dice ----  0.7417281806339557\n",
+            " *********  40 train223\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.9261009854364699\n",
+            "dice ----  0.9421479392169743\n",
+            "dice ----  0.8960074363397478\n",
+            " *********  41 train225\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8866372035907303\n",
+            "dice ----  0.9296631699721357\n",
+            "dice ----  0.8796184552543631\n",
+            " *********  42 train238\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8464555617177947\n",
+            "dice ----  0.7573699529815658\n",
+            "dice ----  0.5914925973967885\n",
+            " *********  43 train240\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9056506556559253\n",
+            "dice ----  0.9047145470098548\n",
+            "dice ----  0.7946757081513083\n",
+            " *********  44 train242\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8980788214746753\n",
+            "dice ----  0.9012875536480687\n",
+            "dice ----  0.8210713851884844\n",
+            " *********  45 train246\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8943644369243785\n",
+            "dice ----  0.9470202152796009\n",
+            "dice ----  0.8387984355102783\n",
+            " *********  46 train247\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9274617985812718\n",
+            "dice ----  0.9073365801759165\n",
+            "dice ----  0.8527542008865675\n",
+            " *********  47 train281\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 45ms/step\n",
+            "dice ----  0.9326022634590083\n",
+            "dice ----  0.5823033951778633\n",
+            "dice ----  0.8250201126307322\n",
+            " *********  48 train289\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.64128015955826\n",
+            "dice ----  0.7478805750092149\n",
+            "dice ----  0.8553047140016377\n",
+            " *********  49 train290\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8802957603258065\n",
+            "dice ----  0.7536190012772945\n",
+            "dice ----  0.050720100187852224\n",
+            " *********  50 train291\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8797474495901878\n",
+            "dice ----  0.812529383025599\n",
+            "dice ----  0.322247882986913\n",
+            " *********  51 train292\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.6437885239935371\n",
+            "dice ----  0.6954202651181343\n",
+            "dice ----  0.7630672713014423\n",
+            " *********  52 train295\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9358382794361928\n",
+            "dice ----  0.8608741138909433\n",
+            "dice ----  0.6096032202415181\n",
+            " *********  53 train296\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.6678660279211299\n",
+            "dice ----  0.6729698329393854\n",
+            "dice ----  0.0\n",
+            " *********  54 train298\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.6919703789748802\n",
+            "dice ----  0.449343563211287\n",
+            "dice ----  0.792332268370607\n",
+            " *********  55 train305\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8197916297415835\n",
+            "dice ----  0.5803044343211141\n",
+            "dice ----  0.0\n",
+            " *********  56 train306\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.878026674526564\n",
+            "dice ----  0.8188870228833214\n",
+            "dice ----  0.0\n",
+            " *********  57 train311\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8190179828321638\n",
+            "dice ----  0.6459733015243743\n",
+            "dice ----  0.0\n",
+            " *********  58 train315\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.7749255171835004\n",
+            "dice ----  0.43813767842973816\n",
+            "dice ----  0.297939465641329\n",
+            " *********  59 train318\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9064047721500831\n",
+            "dice ----  0.8612736618698857\n",
+            "dice ----  0.0\n",
+            " *********  60 train322\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.7904393136956858\n",
+            "dice ----  0.6921831446476987\n",
+            "dice ----  0.0003129400719762421\n",
+            " *********  61 train323\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8402386501632988\n",
+            "dice ----  0.3640243251893738\n",
+            "dice ----  0.0\n",
+            " *********  62 train328\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8570835741704089\n",
+            "dice ----  0.7124004372950179\n",
+            "dice ----  0.0\n",
+            " *********  63 train335\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9227834869608894\n",
+            "dice ----  0.9198456020782717\n",
+            "dice ----  0.847828296172842\n",
+            " *********  64 train338\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8935198623687582\n",
+            "dice ----  0.9417020090546822\n",
+            "dice ----  0.9085597065953875\n",
+            " *********  65 train341\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 47ms/step\n",
+            "dice ----  0.8378175882691355\n",
+            "dice ----  0.9156692105955697\n",
+            "dice ----  0.8362904048128887\n",
+            " *********  66 train351\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9035444864223795\n",
+            "dice ----  0.7465909723381756\n",
+            "dice ----  0.8133191640099185\n",
+            " *********  67 train352\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.881764460682147\n",
+            "dice ----  0.9415077074681495\n",
+            "dice ----  0.8753680078508341\n",
+            " *********  68 train361\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9129191068071466\n",
+            "dice ----  0.932077870753793\n",
+            "dice ----  0.8487969827025621\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " ******  69\n",
+            " *********  0 train2\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 1s 679ms/step\n",
+            "dice ----  0.7173069978489897\n",
+            "dice ----  0.8972472063232488\n",
+            "dice ----  0.8293261090416526\n",
+            " *********  1 train11\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8127074126573834\n",
+            "dice ----  0.9086371023914558\n",
+            "dice ----  0.8235957772584415\n",
+            " *********  2 train14\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 47ms/step\n",
+            "dice ----  0.9283071530572504\n",
+            "dice ----  0.7887702330579622\n",
+            "dice ----  0.838052338052338\n",
+            " *********  3 train21\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9220189930321421\n",
+            "dice ----  0.9348529288527613\n",
+            "dice ----  0.8560952279732171\n",
+            " *********  4 train24\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9140604605022712\n",
+            "dice ----  0.9562843681509771\n",
+            "dice ----  0.8649458698292122\n",
+            " *********  5 train39\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.9288309146008621\n",
+            "dice ----  0.9563982724960066\n",
+            "dice ----  0.8762599637299144\n",
+            " *********  6 train49\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9038839739285948\n",
+            "dice ----  0.9534248225295282\n",
+            "dice ----  0.7972595199814597\n",
+            " *********  7 train56\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9170734515311997\n",
+            "dice ----  0.8863132511376454\n",
+            "dice ----  0.8105939830290563\n",
+            " *********  8 train58\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7413220010939539\n",
+            "dice ----  0.8488038277511962\n",
+            "dice ----  0.7615732683605987\n",
+            " *********  9 train63\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8209195993824472\n",
+            "dice ----  0.9029297754129969\n",
+            "dice ----  0.7592300589530506\n",
+            " *********  10 train66\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 48ms/step\n",
+            "dice ----  0.9337008254883797\n",
+            "dice ----  0.9539778209735837\n",
+            "dice ----  0.8897982685228979\n",
+            " *********  11 train82\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9183230747115171\n",
+            "dice ----  0.9236172861461853\n",
+            "dice ----  0.8333644568938686\n",
+            " *********  12 train83\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.911541154622855\n",
+            "dice ----  0.9250454605934271\n",
+            "dice ----  0.82701970813901\n",
+            " *********  13 train86\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.668923858258628\n",
+            "dice ----  0.3887684054331697\n",
+            "dice ----  0.137855579868709\n",
+            " *********  14 train90\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8654077259566852\n",
+            "dice ----  0.9341577767207991\n",
+            "dice ----  0.9117208031115316\n",
+            " *********  15 train91\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.860843488802399\n",
+            "dice ----  0.9413964801174823\n",
+            "dice ----  0.90805371180375\n",
+            " *********  16 train104\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8560884136610278\n",
+            "dice ----  0.9486122222906025\n",
+            "dice ----  0.9024228343843346\n",
+            " *********  17 train106\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8975025363779133\n",
+            "dice ----  0.9365236306110342\n",
+            "dice ----  0.9322930874570353\n",
+            " *********  18 train108\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9063070234839831\n",
+            "dice ----  0.9591847327715971\n",
+            "dice ----  0.9198213853632784\n",
+            " *********  19 train110\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8677576293675364\n",
+            "dice ----  0.9346712831463936\n",
+            "dice ----  0.860719312430749\n",
+            " *********  20 train115\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9336482256453984\n",
+            "dice ----  0.9681621414103972\n",
+            "dice ----  0.8871929190751445\n",
+            " *********  21 train131\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 48ms/step\n",
+            "dice ----  0.8923171042569462\n",
+            "dice ----  0.9054767920155347\n",
+            "dice ----  0.9193159865486895\n",
+            " *********  22 train137\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8914880275477994\n",
+            "dice ----  0.7995967867971119\n",
+            "dice ----  0.8493154316160585\n",
+            " *********  23 train145\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9055787687710501\n",
+            "dice ----  0.9377857835665789\n",
+            "dice ----  0.8513622626710038\n",
+            " *********  24 train150\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9229749684855033\n",
+            "dice ----  0.9032651935877742\n",
+            "dice ----  0.8497263227732625\n",
+            " *********  25 train155\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.923601429731643\n",
+            "dice ----  0.9277412724271017\n",
+            "dice ----  0.9211622670945095\n",
+            " *********  26 train160\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 47ms/step\n",
+            "dice ----  0.9094366998019724\n",
+            "dice ----  0.9074308055403247\n",
+            "dice ----  0.827197761743484\n",
+            " *********  27 train163\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.7398478128843593\n",
+            "dice ----  0.9458613296493321\n",
+            "dice ----  0.9145239913079227\n",
+            " *********  28 train164\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9218692558873776\n",
+            "dice ----  0.9091206054089369\n",
+            "dice ----  0.8224483098129307\n",
+            " *********  29 train166\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8658467166823365\n",
+            "dice ----  0.8120812417298524\n",
+            "dice ----  0.7252438946630109\n",
+            " *********  30 train167\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8717831928945571\n",
+            "dice ----  0.9369866973637344\n",
+            "dice ----  0.8582998344762355\n",
+            " *********  31 train175\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9478841656017167\n",
+            "dice ----  0.780150307215762\n",
+            "dice ----  0.5154399178363573\n",
+            " *********  32 train186\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9442283469373034\n",
+            "dice ----  0.9292415298928631\n",
+            "dice ----  0.852958812477514\n",
+            " *********  33 train192\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9547454675498336\n",
+            "dice ----  0.9515674846338794\n",
+            "dice ----  0.8983708301008534\n",
+            " *********  34 train198\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 48ms/step\n",
+            "dice ----  0.9510321524734703\n",
+            "dice ----  0.9172402215880476\n",
+            "dice ----  0.84186206025162\n",
+            " *********  35 train199\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9363064380344721\n",
+            "dice ----  0.8772413793103448\n",
+            "dice ----  0.8807531380753137\n",
+            " *********  36 train207\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.916111016304938\n",
+            "dice ----  0.9224789267124165\n",
+            "dice ----  0.8574570245207638\n",
+            " *********  37 train210\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8739237090641482\n",
+            "dice ----  0.9404700909440246\n",
+            "dice ----  0.8496456118407338\n",
+            " *********  38 train211\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9195251686907769\n",
+            "dice ----  0.8817187492367139\n",
+            "dice ----  0.6068489021817254\n",
+            " *********  39 train215\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9135131736150411\n",
+            "dice ----  0.884059689357882\n",
+            "dice ----  0.731045911641746\n",
+            " *********  40 train218\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.7793408446276622\n",
+            "dice ----  0.9352996296461559\n",
+            "dice ----  0.8791079042308954\n",
+            " *********  41 train220\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9446707574898159\n",
+            "dice ----  0.9006502945451763\n",
+            "dice ----  0.8619064354081822\n",
+            " *********  42 train228\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.9218401990145217\n",
+            "dice ----  0.9453469482478534\n",
+            "dice ----  0.9135340337247285\n",
+            " *********  43 train233\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8449350699173268\n",
+            "dice ----  0.7956156149755417\n",
+            "dice ----  0.7171667855935704\n",
+            " *********  44 train239\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8611461879602784\n",
+            "dice ----  0.8820206497648522\n",
+            "dice ----  0.51246783625731\n",
+            " *********  45 train249\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7864139422465335\n",
+            "dice ----  0.7298041120284928\n",
+            "dice ----  0.7471550497866287\n",
+            " *********  46 train257\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.885533222024408\n",
+            "dice ----  0.9104858013742272\n",
+            "dice ----  0.7938942395057241\n",
+            " *********  47 train259\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.665323205704542\n",
+            "dice ----  0.5443889403440579\n",
+            "dice ----  0.7669541687806528\n",
+            " *********  48 train265\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9253273119813021\n",
+            "dice ----  0.7459883708984816\n",
+            "dice ----  0.0\n",
+            " *********  49 train268\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9091873482035677\n",
+            "dice ----  0.5889125416004557\n",
+            "dice ----  0.0\n",
+            " *********  50 train270\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.9298951522420397\n",
+            "dice ----  0.7414177133803301\n",
+            "dice ----  0.10687593423019437\n",
+            " *********  51 train273\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.7694008344994601\n",
+            "dice ----  0.517676941308753\n",
+            "dice ----  0.5472478166813557\n",
+            " *********  52 train279\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8900458447132267\n",
+            "dice ----  0.14041353383458643\n",
+            "dice ----  0.6807017543859649\n",
+            " *********  53 train280\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8442409319337049\n",
+            "dice ----  0.39738708568372527\n",
+            "dice ----  0.0\n",
+            " *********  54 train283\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8936027971862484\n",
+            "dice ----  0.7361577294446895\n",
+            "dice ----  0.19709160699785933\n",
+            " *********  55 train319\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7108813406717881\n",
+            "dice ----  0.7524003491416933\n",
+            "dice ----  0.6941021467875688\n",
+            " *********  56 train321\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9416646439428089\n",
+            "dice ----  0.7871395240317312\n",
+            "dice ----  0.07077036490969402\n",
+            " *********  57 train326\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8659850317508315\n",
+            "dice ----  0.5913977986008296\n",
+            "dice ----  0.6367849576271186\n",
+            " *********  58 train327\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.7986442153813187\n",
+            "dice ----  0.5736885865457294\n",
+            "dice ----  0.15427369686044767\n",
+            " *********  59 train331\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9212165073380908\n",
+            "dice ----  0.5015008081274532\n",
+            "dice ----  0.6351578554081823\n",
+            " *********  60 train332\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9431997895687133\n",
+            "dice ----  0.30649605300968574\n",
+            "dice ----  0.5833333333333333\n",
+            " *********  61 train333\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8713322320196234\n",
+            "dice ----  0.8136812861502026\n",
+            "dice ----  0.8342829315802289\n",
+            " *********  62 train334\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9047957975846943\n",
+            "dice ----  0.7428854334751357\n",
+            "dice ----  0.0\n",
+            " *********  63 train336\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9431900946831755\n",
+            "dice ----  0.959344623097145\n",
+            "dice ----  0.8982052819012023\n",
+            " *********  64 train345\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9197794959738433\n",
+            "dice ----  0.9572060575123362\n",
+            "dice ----  0.8673354584661541\n",
+            " *********  65 train349\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.9239995914218035\n",
+            "dice ----  0.9612312964719562\n",
+            "dice ----  0.9218581299935219\n",
+            " *********  66 train363\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9004617191076084\n",
+            "dice ----  0.9460239699063949\n",
+            "dice ----  0.8774452949818294\n",
+            " *********  67 train364\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9208181953914298\n",
+            "dice ----  0.9623298219675969\n",
+            "dice ----  0.907079976684054\n",
+            " *********  68 train365\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.8679848738547213\n",
+            "dice ----  0.8643673577884105\n",
+            "dice ----  0.7713192095424604\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " ******  68\n",
+            " *********  0 train1\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 1s 713ms/step\n",
+            "dice ----  0.9054425756414303\n",
+            "dice ----  0.9525828475231475\n",
+            "dice ----  0.843493298746217\n",
+            " *********  1 train3\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9371916761535543\n",
+            "dice ----  0.9175483005560243\n",
+            "dice ----  0.8375881523272215\n",
+            " *********  2 train8\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8353066614175811\n",
+            "dice ----  0.8141784633772831\n",
+            "dice ----  0.6997688614946957\n",
+            " *********  3 train16\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9205745795095681\n",
+            "dice ----  0.8777157486754614\n",
+            "dice ----  0.7824614666043905\n",
+            " *********  4 train23\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8550073822666315\n",
+            "dice ----  0.9152494842056768\n",
+            "dice ----  0.8854728647796418\n",
+            " *********  5 train26\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8122970069454855\n",
+            "dice ----  0.9127353098087037\n",
+            "dice ----  0.8551616952597286\n",
+            " *********  6 train28\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9181542013001066\n",
+            "dice ----  0.9390365780531681\n",
+            "dice ----  0.8753728869738151\n",
+            " *********  7 train32\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9427483411237865\n",
+            "dice ----  0.9592752728021412\n",
+            "dice ----  0.872654079491977\n",
+            " *********  8 train40\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9199470976408595\n",
+            "dice ----  0.8443705712536086\n",
+            "dice ----  0.7684650362541477\n",
+            " *********  9 train47\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.5253982566877067\n",
+            "dice ----  0.8833718244803695\n",
+            "dice ----  0.7283093703520079\n",
+            " *********  10 train53\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 47ms/step\n",
+            "dice ----  0.8800711244209443\n",
+            "dice ----  0.9189999862408673\n",
+            "dice ----  0.8685670872696043\n",
+            " *********  11 train54\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9321818051370354\n",
+            "dice ----  0.9451581524838157\n",
+            "dice ----  0.8405568689256633\n",
+            " *********  12 train70\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9294133016400025\n",
+            "dice ----  0.7039526141929604\n",
+            "dice ----  0.8468027669984816\n",
+            " *********  13 train74\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9354148313794584\n",
+            "dice ----  0.9536298326268745\n",
+            "dice ----  0.7017932636968987\n",
+            " *********  14 train77\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.7497908908622171\n",
+            "dice ----  0.9144604266555486\n",
+            "dice ----  0.7496498272481091\n",
+            " *********  15 train79\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8679161747343566\n",
+            "dice ----  0.8446639179211813\n",
+            "dice ----  0.8137746101192577\n",
+            " *********  16 train84\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8922985840446002\n",
+            "dice ----  0.7765765765765766\n",
+            "dice ----  0.6211880839062225\n",
+            " *********  17 train92\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.946531695129176\n",
+            "dice ----  0.9648463895654907\n",
+            "dice ----  0.931012481556805\n",
+            " *********  18 train95\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9417549110176285\n",
+            "dice ----  0.9322615152277876\n",
+            "dice ----  0.8464234005497618\n",
+            " *********  19 train96\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 47ms/step\n",
+            "dice ----  0.939683378636867\n",
+            "dice ----  0.960211517379248\n",
+            "dice ----  0.8783000152601862\n",
+            " *********  20 train116\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9374707320880379\n",
+            "dice ----  0.8822319093286836\n",
+            "dice ----  0.871024337577039\n",
+            " *********  21 train122\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9314350109376957\n",
+            "dice ----  0.8547242692381448\n",
+            "dice ----  0.8184768238451985\n",
+            " *********  22 train128\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8802498614249776\n",
+            "dice ----  0.647343602640679\n",
+            "dice ----  0.6530199252801993\n",
+            " *********  23 train134\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8380848371845242\n",
+            "dice ----  0.7736593897057709\n",
+            "dice ----  0.8371921369413962\n",
+            " *********  24 train140\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9136583465126407\n",
+            "dice ----  0.7233286353330474\n",
+            "dice ----  0.5382932166301969\n",
+            " *********  25 train144\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 47ms/step\n",
+            "dice ----  0.9116852970201779\n",
+            "dice ----  0.9234257086689074\n",
+            "dice ----  0.8021294064676748\n",
+            " *********  26 train148\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8902031439421847\n",
+            "dice ----  0.8608152082529698\n",
+            "dice ----  0.838937483723709\n",
+            " *********  27 train151\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9372164437840516\n",
+            "dice ----  0.9580624868945271\n",
+            "dice ----  0.9142117089002622\n",
+            " *********  28 train156\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8820089262812292\n",
+            "dice ----  0.9260488733078935\n",
+            "dice ----  0.9018576849624247\n",
+            " *********  29 train158\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.656743860263538\n",
+            "dice ----  0.6173818968626037\n",
+            "dice ----  0.595003111940962\n",
+            " *********  30 train159\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8969245993824565\n",
+            "dice ----  0.8768231010095212\n",
+            "dice ----  0.6657036149672327\n",
+            " *********  31 train168\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9122154154013586\n",
+            "dice ----  0.9531189461449051\n",
+            "dice ----  0.9121761320245124\n",
+            " *********  32 train170\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8457004214815621\n",
+            "dice ----  0.832725377800938\n",
+            "dice ----  0.7081315703640515\n",
+            " *********  33 train171\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8657128761716824\n",
+            "dice ----  0.9213320647002854\n",
+            "dice ----  0.8442495402461452\n",
+            " *********  34 train182\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9318341792316527\n",
+            "dice ----  0.9429188345968363\n",
+            "dice ----  0.8943578254309359\n",
+            " *********  35 train194\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.897827502515835\n",
+            "dice ----  0.9094388175909736\n",
+            "dice ----  0.7721338578188245\n",
+            " *********  36 train212\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.922247715495075\n",
+            "dice ----  0.8996899113002091\n",
+            "dice ----  0.836026936026936\n",
+            " *********  37 train213\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9326394911824226\n",
+            "dice ----  0.9211465162372313\n",
+            "dice ----  0.889568358187605\n",
+            " *********  38 train221\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8686837517164766\n",
+            "dice ----  0.9180673231051065\n",
+            "dice ----  0.9206069936221685\n",
+            " *********  39 train227\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8517511551597134\n",
+            "dice ----  0.9443896944952836\n",
+            "dice ----  0.8888268676155922\n",
+            " *********  40 train234\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9349559489328566\n",
+            "dice ----  0.9581211528899405\n",
+            "dice ----  0.8951309983991254\n",
+            " *********  41 train235\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.7552112353547303\n",
+            "dice ----  0.6819848655064968\n",
+            "dice ----  0.6537564492190261\n",
+            " *********  42 train245\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.7449830859318018\n",
+            "dice ----  0.8263300478413742\n",
+            "dice ----  0.7309334256374755\n",
+            " *********  43 train254\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9374363386750852\n",
+            "dice ----  0.929212565313432\n",
+            "dice ----  0.8689242408539746\n",
+            " *********  44 train256\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9434376918354819\n",
+            "dice ----  0.9065760354094214\n",
+            "dice ----  0.8978736148547469\n",
+            " *********  45 train258\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9332481914642361\n",
+            "dice ----  0.5305414989839423\n",
+            "dice ----  0.8313838980666426\n",
+            " *********  46 train260\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9181800819886922\n",
+            "dice ----  0.7790143969702314\n",
+            "dice ----  0.5172457762193179\n",
+            " *********  47 train262\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8444502724597138\n",
+            "dice ----  0.8237075928917609\n",
+            "dice ----  0.0\n",
+            " *********  48 train272\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8412234333047991\n",
+            "dice ----  0.8643396772992087\n",
+            "dice ----  0.5970528455284553\n",
+            " *********  49 train274\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.3174618528280898\n",
+            "dice ----  0.0\n",
+            "dice ----  1\n",
+            " *********  50 train275\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/scipy/spatial/distance.py:1374: RuntimeWarning: invalid value encountered in divide\n",
+            "  return float((ntf + nft) / np.array(2.0 * ntt + ntf + nft))\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8745813978610781\n",
+            "dice ----  0.8539325842696629\n",
+            "dice ----  0.7516425755584757\n",
+            " *********  51 train285\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.872595281306715\n",
+            "dice ----  0.3908962029196521\n",
+            "dice ----  0.0\n",
+            " *********  52 train286\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.9267007561220997\n",
+            "dice ----  0.7652710799357008\n",
+            "dice ----  0.667242555126165\n",
+            " *********  53 train287\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.9008017089058256\n",
+            "dice ----  0.6909942321969048\n",
+            "dice ----  0.8724124731309443\n",
+            " *********  54 train297\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.8652009939329726\n",
+            "dice ----  0.5888930301900857\n",
+            "dice ----  0.0\n",
+            " *********  55 train300\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8112077464313207\n",
+            "dice ----  0.7437865610201492\n",
+            "dice ----  0.6223061192386345\n",
+            " *********  56 train301\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8427046336829933\n",
+            "dice ----  0.7654294817040856\n",
+            "dice ----  0.7721936903888481\n",
+            " *********  57 train302\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.820843640361514\n",
+            "dice ----  0.6114272104162808\n",
+            "dice ----  0.20352781546811394\n",
+            " *********  58 train303\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8139295896041902\n",
+            "dice ----  0.5758602586741756\n",
+            "dice ----  1\n",
+            " *********  59 train307\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.7759853862023212\n",
+            "dice ----  0.5706473715134328\n",
+            "dice ----  0.7178086030545047\n",
+            " *********  60 train320\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9170871689048671\n",
+            "dice ----  0.7622977700043725\n",
+            "dice ----  0.0\n",
+            " *********  61 train337\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9098059485151185\n",
+            "dice ----  0.9627474264503496\n",
+            "dice ----  0.9152745369318769\n",
+            " *********  62 train342\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9078059793864335\n",
+            "dice ----  0.944118014751844\n",
+            "dice ----  0.8915498470593352\n",
+            " *********  63 train343\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8719130216888938\n",
+            "dice ----  0.8024349414092223\n",
+            "dice ----  0.6450896163531329\n",
+            " *********  64 train344\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.7189555283557731\n",
+            "dice ----  0.3135026737967914\n",
+            "dice ----  0.8572625230356844\n",
+            " *********  65 train346\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9489872090200909\n",
+            "dice ----  0.9587915004625908\n",
+            "dice ----  0.9333751126082018\n",
+            " *********  66 train359\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9337422153141705\n",
+            "dice ----  0.9515945585298359\n",
+            "dice ----  0.9084875588753033\n",
+            " *********  67 train360\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 47ms/step\n",
+            "dice ----  0.9163897974777002\n",
+            "dice ----  0.07529900612050089\n",
+            "dice ----  0.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pickle\n",
+        "from keras.models import load_model\n",
+        "from tensorflow_addons.layers import InstanceNormalization\n",
+        "images_path='/content/drive/MyDrive/Bratsdataset2021/BraTs2021/'\n",
+        "models_list=os.listdir('/content/drive/MyDrive/2021/')\n",
+        "Dices_all=[]\n",
+        "H95s_all=[]\n",
+        "Senss_all=[]\n",
+        "Specs_all=[]\n",
+        "open_file = open('/content/drive/MyDrive/folds_dic1.pkl', \"rb\")\n",
+        "trainvallist = pickle.load(open_file)\n",
+        "open_file.close()\n",
+        "for k in range(0,5):\n",
+        "       # the fold Number\n",
+        "       fold=0\n",
+        "       model_for_this_fold=[m for m in models_list if '2021fold_'+str(k) in m]\n",
+        "       model = load_model('/content/drive/MyDrive/2021/'+model_for_this_fold[0],\n",
+        "                      compile=False)\n",
+        "# empty list to read list from a file\n",
+        "       val_list=trainvallist['validation'][k][:230]\n",
+        "\n",
+        " # open file and read the content in a list\n",
+        "\n",
+        "# =============================================================================\n",
+        "       print(' ****** ',len(val_list))\n",
+        "       Dice_fold=[]\n",
+        "       H95_fold=[]\n",
+        "       Sens_fold=[]\n",
+        "       Spec_fold=[]\n",
+        "       for i, dir_name in enumerate(val_list):\n",
+        "           print(' ********* ', i, dir_name)\n",
+        "           image = np.load(glob.glob(images_path+dir_name+'/'+'image_*.npy')[0])\n",
+        "           image=np.expand_dims(image,0)\n",
+        "           mask = np.load(glob.glob(images_path+dir_name+'/'+'mask_*.npy')[0])\n",
+        "           print(mask.shape)\n",
+        "           print(np.unique(mask))\n",
+        "           Senss,Specs,Disces,Haus95s=get_score_per_sampe(model,image, mask)\n",
+        "           Dice_fold.append(Disces)\n",
+        "           H95_fold.append(Haus95s)\n",
+        "           Spec_fold.append(Specs)\n",
+        "           Sens_fold.append(Senss)\n",
+        "\n",
+        "       Dices_all.append(Dice_fold)\n",
+        "       H95s_all.append(H95_fold)\n",
+        "       Specs_all.append(Spec_fold)\n",
+        "       Senss_all.append(Sens_fold)\n",
+        "\n",
+        "import pickle\n",
+        "open_file = open('dscs2021_paper1.pkl', \"wb\")\n",
+        "pickle.dump(Dices_all, open_file)\n",
+        "open_file.close()\n",
+        "open_file = open('hds2021_paper1.pkl', \"wb\")\n",
+        "pickle.dump(H95s_all, open_file)\n",
+        "open_file.close()\n",
+        "open_file = open('specs2021_paper1.pkl', \"wb\")\n",
+        "pickle.dump(Specs_all, open_file)\n",
+        "open_file.close()\n",
+        "open_file = open('sensis2021_paper1.pkl', \"wb\")\n",
+        "pickle.dump(Senss_all, open_file)\n",
+        "open_file.close()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RX3V6Vv_RmES",
+        "outputId": "9f50a594-9c3e-4a39-f74c-f46d9bff3aae"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "metadata": {
+            "tags": null
+          },
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n"
+          ]
+        },
+        {
+          "metadata": {
+            "tags": null
+          },
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            " ******  230\n",
+            " *********  0 train115\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 7s 7s/step\n",
+            "dice ----  0.9116307455837976\n"
+          ]
+        },
+        {
+          "metadata": {
+            "tags": null
+          },
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "<ipython-input-14-48be69b7e0e7>:83: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+            "  result = np.atleast_1d(result.astype(np.bool))\n",
+            "<ipython-input-14-48be69b7e0e7>:84: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+            "  reference = np.atleast_1d(reference.astype(np.bool))\n",
+            "<ipython-input-14-48be69b7e0e7>:64: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+            "  result = np.atleast_1d(result.astype(np.bool))\n",
+            "<ipython-input-14-48be69b7e0e7>:65: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
+            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+            "  reference = np.atleast_1d(reference.astype(np.bool))\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "dice ----  0.9565662380861\n",
+            "dice ----  0.9243845252051582\n",
+            " *********  1 train1151\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 46ms/step\n",
+            "dice ----  0.9152323112060035\n",
+            "dice ----  0.9536888950698839\n",
+            "dice ----  0.8795124961431657\n",
+            " *********  2 train1153\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.9412043883138164\n",
+            "dice ----  0.9569449138222372\n",
+            "dice ----  0.9296288736537515\n",
+            " *********  3 train1161\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 46ms/step\n",
+            "dice ----  0.930585368538505\n",
+            "dice ----  0.9745264863769878\n",
+            "dice ----  0.947581386794188\n",
+            " *********  4 train1168\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.911218710968912\n",
+            "dice ----  0.9378214118747078\n",
+            "dice ----  0.8791798662442802\n",
+            " *********  5 train1176\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.8240813279385064\n",
+            "dice ----  0.9551505804042536\n",
+            "dice ----  0.8781772475160609\n",
+            " *********  6 train1177\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9569845848336884\n",
+            "dice ----  0.9816374663072777\n",
+            "dice ----  0.9558257657380329\n",
+            " *********  7 train1184\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9251769744067782\n",
+            "dice ----  0.9765584302830352\n",
+            "dice ----  0.8789411937234198\n",
+            " *********  8 train1188\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7800248976663713\n",
+            "dice ----  0.922266139657444\n",
+            "dice ----  0.8795013850415513\n",
+            " *********  9 train1190\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8128883930654472\n",
+            "dice ----  0.8779390717644653\n",
+            "dice ----  0.8495730238668711\n",
+            " *********  10 train1201\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9072815040252862\n",
+            "dice ----  0.9380118975773774\n",
+            "dice ----  0.9355072673996925\n",
+            " *********  11 train1203\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9527718103953352\n",
+            "dice ----  0.9691276594716572\n",
+            "dice ----  0.8922962946333359\n",
+            " *********  12 train1208\n",
+            "(128, 128, 128)\n",
+            "[0. 2.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7573804029783286\n",
+            "dice ----  0.0\n",
+            "dice ----  0.0\n",
+            " *********  13 train1218\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.9606969448718204\n",
+            "dice ----  0.9808946356991903\n",
+            "dice ----  0.9269251101321586\n",
+            " *********  14 train1219\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 48ms/step\n",
+            "dice ----  0.9569907497973899\n",
+            "dice ----  0.904109589041096\n",
+            "dice ----  0.8035346862749345\n",
+            " *********  15 train1220\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.6563897006318594\n",
+            "dice ----  0.5363262691506967\n",
+            "dice ----  0.38627681982078466\n",
+            " *********  16 train1223\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9479545977131629\n",
+            "dice ----  0.9695669725433962\n",
+            "dice ----  0.9148737627000393\n",
+            " *********  17 train1225\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9309037044604206\n",
+            "dice ----  0.9513454183745219\n",
+            "dice ----  0.9253403797921892\n",
+            " *********  18 train1229\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7842116122055507\n",
+            "dice ----  0.9568848758465012\n",
+            "dice ----  0.9213137996219282\n",
+            " *********  19 train1230\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8281960221579112\n",
+            "dice ----  0.7598784194528876\n",
+            "dice ----  0.7621951219512195\n",
+            " *********  20 train1233\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8407593523171413\n",
+            "dice ----  0.9512443603551157\n",
+            "dice ----  0.9042187077065852\n",
+            " *********  21 train1238\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9002131977963875\n",
+            "dice ----  0.9662638167132724\n",
+            "dice ----  0.9448259670129717\n",
+            " *********  22 train1242\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.8840476987392779\n",
+            "dice ----  0.8969456170846785\n",
+            "dice ----  0.8487434192520085\n",
+            " *********  23 train1244\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8365782406735376\n",
+            "dice ----  0.9355336996265373\n",
+            "dice ----  0.8944671649508766\n",
+            " *********  24 train1247\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9424199294250415\n",
+            "dice ----  0.7185606756012484\n",
+            "dice ----  0.7972554227534308\n",
+            " *********  25 train1250\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9128286959536797\n",
+            "dice ----  0.5540855160062682\n",
+            "dice ----  0.0\n",
+            " *********  26 train128\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9369777001490672\n",
+            "dice ----  0.9755185854636221\n",
+            "dice ----  0.9512542279496166\n",
+            " *********  27 train135\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9211854795068903\n",
+            "dice ----  0.9683863956015855\n",
+            "dice ----  0.948775146009085\n",
+            " *********  28 train136\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8774719831191056\n",
+            "dice ----  0.9599574769666903\n",
+            "dice ----  0.9221899591802382\n",
+            " *********  29 train141\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9265103358526453\n",
+            "dice ----  0.9697475783747673\n",
+            "dice ----  0.9136754913107032\n",
+            " *********  30 train148\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.8672259099861405\n",
+            "dice ----  0.9449211502782932\n",
+            "dice ----  0.9078886053478309\n",
+            " *********  31 train15\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.7301666075859624\n",
+            "dice ----  0.5653202636471184\n",
+            "dice ----  0.48588908624432603\n",
+            " *********  32 train153\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9479211862892729\n",
+            "dice ----  0.9670654746252958\n",
+            "dice ----  0.9569960844683912\n",
+            " *********  33 train16\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8933604629094264\n",
+            "dice ----  0.8484023398387408\n",
+            "dice ----  0.7613103324532848\n",
+            " *********  34 train164\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8813711499021822\n",
+            "dice ----  0.8913333801925916\n",
+            "dice ----  0.800208011325061\n",
+            " *********  35 train170\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9494522801362709\n",
+            "dice ----  0.9442925447636674\n",
+            "dice ----  0.8718718092975766\n",
+            " *********  36 train172\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.6361779482638162\n",
+            "dice ----  0.984116797689716\n",
+            "dice ----  0.9404910114038145\n",
+            " *********  37 train175\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8937533291541503\n",
+            "dice ----  0.8909427980332626\n",
+            "dice ----  0.882582376435347\n",
+            " *********  38 train182\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.917961963975496\n",
+            "dice ----  0.9838224917021666\n",
+            "dice ----  0.9447344903814496\n",
+            " *********  39 train198\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8585698488757832\n",
+            "dice ----  0.9525021204410518\n",
+            "dice ----  0.8955242449690523\n",
+            " *********  40 train204\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9227845134346168\n",
+            "dice ----  0.9401781627407486\n",
+            "dice ----  0.8848298622965745\n",
+            " *********  41 train207\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7435739255603537\n",
+            "dice ----  0.6145682585624699\n",
+            "dice ----  0.6215512753774076\n",
+            " *********  42 train208\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9588701382267708\n",
+            "dice ----  0.7812089356110381\n",
+            "dice ----  0.7205018686599038\n",
+            " *********  43 train211\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8292982483074136\n",
+            "dice ----  0.9630949969617176\n",
+            "dice ----  0.9424902289223898\n",
+            " *********  44 train212\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8010193777948742\n",
+            "dice ----  0.6334336055006446\n",
+            "dice ----  0.622513708203419\n",
+            " *********  45 train217\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8840017807276701\n",
+            "dice ----  0.9561522466323117\n",
+            "dice ----  0.9156756756756756\n",
+            " *********  46 train218\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9298525697239524\n",
+            "dice ----  0.9716904319505626\n",
+            "dice ----  0.9152487243601471\n",
+            " *********  47 train219\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.8807900404475495\n",
+            "dice ----  0.9234247814290021\n",
+            "dice ----  0.884472049689441\n",
+            " *********  48 train220\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.9110209394398071\n",
+            "dice ----  0.9547366383330693\n",
+            "dice ----  0.872832089913768\n",
+            " *********  49 train222\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9553679131483716\n",
+            "dice ----  0.966690290574061\n",
+            "dice ----  0.9387961132377066\n",
+            " *********  50 train233\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.91552452463825\n",
+            "dice ----  0.9024921872948344\n",
+            "dice ----  0.8796288533020318\n",
+            " *********  51 train237\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7891848391026859\n",
+            "dice ----  0.9055567003228244\n",
+            "dice ----  0.8149208337060561\n",
+            " *********  52 train245\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7368989457444073\n",
+            "dice ----  0.9455247142645095\n",
+            "dice ----  0.8752500142865307\n",
+            " *********  53 train252\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8443656731032863\n",
+            "dice ----  0.8767217448777264\n",
+            "dice ----  0.7782727782727783\n",
+            " *********  54 train26\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9328141568307056\n",
+            "dice ----  0.9391381704565179\n",
+            "dice ----  0.8407294708681357\n",
+            " *********  55 train264\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9382141331735242\n",
+            "dice ----  0.8705876163922353\n",
+            "dice ----  0.8444026733500418\n",
+            " *********  56 train271\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9177915282987315\n",
+            "dice ----  0.9567192208586278\n",
+            "dice ----  0.9165055018339446\n",
+            " *********  57 train277\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.9128035720768142\n",
+            "dice ----  0.9397853003677653\n",
+            "dice ----  0.8055779107709866\n",
+            " *********  58 train281\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9285406378328506\n",
+            "dice ----  0.9724551790972455\n",
+            "dice ----  0.9307493881870494\n",
+            " *********  59 train286\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9371052209054853\n",
+            "dice ----  0.6916756068519152\n",
+            "dice ----  0.7889693853704136\n",
+            " *********  60 train292\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8805527106080858\n",
+            "dice ----  0.004570848146267181\n",
+            "dice ----  0.0017536168347216385\n",
+            " *********  61 train298\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7565358828896735\n",
+            "dice ----  0.9293259728753492\n",
+            "dice ----  0.8455917020273456\n",
+            " *********  62 train318\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9545489960941338\n",
+            "dice ----  0.9271710405015058\n",
+            "dice ----  0.8942013860211582\n",
+            " *********  63 train320\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9477711127207263\n",
+            "dice ----  0.9206284708113233\n",
+            "dice ----  0.9017072048916426\n",
+            " *********  64 train322\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9633490098789009\n",
+            "dice ----  0.9409787316568621\n",
+            "dice ----  0.9185096632795378\n",
+            " *********  65 train325\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9343828707415146\n",
+            "dice ----  0.7043952220804824\n",
+            "dice ----  0.6949946835742133\n",
+            " *********  66 train327\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9519782437055883\n",
+            "dice ----  0.8910464797075693\n",
+            "dice ----  0.8789102999535233\n",
+            " *********  67 train336\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9304415193246159\n",
+            "dice ----  0.9608681251084368\n",
+            "dice ----  0.8739222524122291\n",
+            " *********  68 train337\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.881329128195569\n",
+            "dice ----  0.9669487103872241\n",
+            "dice ----  0.8817908725242274\n",
+            " *********  69 train344\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8167433484500763\n",
+            "dice ----  0.802404207362885\n",
+            "dice ----  0.7719568567026194\n",
+            " *********  70 train35\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.9085119201308202\n",
+            "dice ----  0.8835581912527798\n",
+            "dice ----  0.8013417815877749\n",
+            " *********  71 train350\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.9634814603480674\n",
+            "dice ----  0.8367433111972669\n",
+            "dice ----  0.8298917568692756\n",
+            " *********  72 train354\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8964732906945321\n",
+            "dice ----  0.9294913774285091\n",
+            "dice ----  0.847290347513303\n",
+            " *********  73 train359\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9448045335282207\n",
+            "dice ----  0.9737773232635338\n",
+            "dice ----  0.8730742662532731\n",
+            " *********  74 train360\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9399569453059438\n",
+            "dice ----  0.892200328407225\n",
+            "dice ----  0.8739266031529029\n",
+            " *********  75 train385\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9467148705544525\n",
+            "dice ----  0.9523365433350228\n",
+            "dice ----  0.8745795021305225\n",
+            " *********  76 train394\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9431819619919428\n",
+            "dice ----  0.9751850036183674\n",
+            "dice ----  0.9374664171499509\n",
+            " *********  77 train4\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9529680192615563\n",
+            "dice ----  0.9791515997905185\n",
+            "dice ----  0.929518798640798\n",
+            " *********  78 train403\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9382857680996879\n",
+            "dice ----  0.9493210457303296\n",
+            "dice ----  0.8972682510579906\n",
+            " *********  79 train405\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9296335346806854\n",
+            "dice ----  0.9758658844652289\n",
+            "dice ----  0.9223058571137949\n",
+            " *********  80 train411\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9175950386651331\n",
+            "dice ----  0.8820297469291105\n",
+            "dice ----  0.8177762377095334\n",
+            " *********  81 train415\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7925062884487749\n",
+            "dice ----  0.4847456028669779\n",
+            "dice ----  0.8797127468581688\n",
+            " *********  82 train429\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8796406761409524\n",
+            "dice ----  0.9254472265196689\n",
+            "dice ----  0.856612361470173\n",
+            " *********  83 train437\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9471332670590995\n",
+            "dice ----  0.9827257684400541\n",
+            "dice ----  0.9444580111246778\n",
+            " *********  84 train439\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8891023364045612\n",
+            "dice ----  0.9610055932169892\n",
+            "dice ----  0.9310424591343682\n",
+            " *********  85 train443\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8634556904906712\n",
+            "dice ----  0.9561146636387179\n",
+            "dice ----  0.9037617116487204\n",
+            " *********  86 train445\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9509087611101034\n",
+            "dice ----  0.9811676534272953\n",
+            "dice ----  0.932114079511205\n",
+            " *********  87 train454\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8179180313578003\n",
+            "dice ----  0.9730853258890642\n",
+            "dice ----  0.9234792879439804\n",
+            " *********  88 train465\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9368930396237268\n",
+            "dice ----  0.9700025546905463\n",
+            "dice ----  0.9025326170376056\n",
+            " *********  89 train467\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8086885691012762\n",
+            "dice ----  0.8993784449396036\n",
+            "dice ----  0.8456841311722578\n",
+            " *********  90 train469\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9454021148851955\n",
+            "dice ----  0.9735425882618128\n",
+            "dice ----  0.9505921658492708\n",
+            " *********  91 train472\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9096078182510471\n",
+            "dice ----  0.9356857949748159\n",
+            "dice ----  0.8702930986521002\n",
+            " *********  92 train474\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8423988174674893\n",
+            "dice ----  0.9382246450510557\n",
+            "dice ----  0.8517937043723998\n",
+            " *********  93 train480\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7931995944380069\n",
+            "dice ----  0.4836879432624114\n",
+            "dice ----  0.42760690172543137\n",
+            " *********  94 train481\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7920517560073937\n",
+            "dice ----  0.9595228088325628\n",
+            "dice ----  0.8588248119481086\n",
+            " *********  95 train483\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9450840701397282\n",
+            "dice ----  0.9265439596374848\n",
+            "dice ----  0.8768740210337883\n",
+            " *********  96 train489\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9033971317133525\n",
+            "dice ----  0.858991521642124\n",
+            "dice ----  0.8177689346425019\n",
+            " *********  97 train49\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8937168885093146\n",
+            "dice ----  0.9208450704225353\n",
+            "dice ----  0.8470805995802277\n",
+            " *********  98 train491\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9062917374809016\n",
+            "dice ----  0.9597451162658681\n",
+            "dice ----  0.9056197598005892\n",
+            " *********  99 train496\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 52ms/step\n",
+            "dice ----  0.9216786280979664\n",
+            "dice ----  0.9343293862089062\n",
+            "dice ----  0.8484151783381579\n",
+            " *********  100 train501\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.9089978913892633\n",
+            "dice ----  0.9632714487733365\n",
+            "dice ----  0.9486952354675224\n",
+            " *********  101 train505\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9346810138730972\n",
+            "dice ----  0.9582107706246664\n",
+            "dice ----  0.8974748880940653\n",
+            " *********  102 train513\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9349834201800095\n",
+            "dice ----  0.9633745136850727\n",
+            "dice ----  0.8967656631407649\n",
+            " *********  103 train518\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9349891332200784\n",
+            "dice ----  0.937850135451461\n",
+            "dice ----  0.9262240992477234\n",
+            " *********  104 train52\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9367251740512978\n",
+            "dice ----  0.9713473479464353\n",
+            "dice ----  0.9382321906874814\n",
+            " *********  105 train523\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8971849513285978\n",
+            "dice ----  0.9484226445424453\n",
+            "dice ----  0.9001683983096622\n",
+            " *********  106 train528\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7561192671145864\n",
+            "dice ----  0.9068620811096362\n",
+            "dice ----  0.8318679995182464\n",
+            " *********  107 train537\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8504377774445793\n",
+            "dice ----  0.9058393150832321\n",
+            "dice ----  0.8402398004713635\n",
+            " *********  108 train542\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9645922834581236\n",
+            "dice ----  0.9616499782835812\n",
+            "dice ----  0.9114723220203007\n",
+            " *********  109 train548\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9681145852054016\n",
+            "dice ----  0.9463797998553432\n",
+            "dice ----  0.8911783238815375\n",
+            " *********  110 train549\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9357235968950728\n",
+            "dice ----  0.9583787121765003\n",
+            "dice ----  0.9293161587059598\n",
+            " *********  111 train561\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9086758849904368\n",
+            "dice ----  0.8296469898930716\n",
+            "dice ----  0.7700690156193244\n",
+            " *********  112 train566\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9491112114021167\n",
+            "dice ----  0.8783553388049641\n",
+            "dice ----  0.8322682505074807\n",
+            " *********  113 train57\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9426724050013258\n",
+            "dice ----  0.965888926487459\n",
+            "dice ----  0.9174099908405552\n",
+            " *********  114 train571\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9456615380688308\n",
+            "dice ----  0.9706039164656022\n",
+            "dice ----  0.9065860951516195\n",
+            " *********  115 train578\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8499422031310205\n",
+            "dice ----  0.9159357359965262\n",
+            "dice ----  0.9043106556110625\n",
+            " *********  116 train580\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7635107221992714\n",
+            "dice ----  0.956298828125\n",
+            "dice ----  0.9098697738176833\n",
+            " *********  117 train589\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8119748080407212\n",
+            "dice ----  0.9611533990775807\n",
+            "dice ----  0.9074869938927844\n",
+            " *********  118 train593\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.923428592929844\n",
+            "dice ----  0.9306511902419831\n",
+            "dice ----  0.8436910093644743\n",
+            " *********  119 train602\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.9429074474885333\n",
+            "dice ----  0.9831393528926325\n",
+            "dice ----  0.9377983337780544\n",
+            " *********  120 train605\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9648847891184084\n",
+            "dice ----  0.9754096948182868\n",
+            "dice ----  0.9424439220686083\n",
+            " *********  121 train608\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8704624331676203\n",
+            "dice ----  0.8924452391595887\n",
+            "dice ----  0.8719084533038022\n",
+            " *********  122 train614\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9160375036717714\n",
+            "dice ----  0.9094578768573968\n",
+            "dice ----  0.8601565875299002\n",
+            " *********  123 train621\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9166625368349224\n",
+            "dice ----  0.9114164503260939\n",
+            "dice ----  0.7671017949039789\n",
+            " *********  124 train628\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9041248248794208\n",
+            "dice ----  0.9412872772673011\n",
+            "dice ----  0.9139026015530443\n",
+            " *********  125 train631\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.8903211209119555\n",
+            "dice ----  0.8112655018572748\n",
+            "dice ----  0.7553472285428233\n",
+            " *********  126 train636\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.9147371343479468\n",
+            "dice ----  0.9685732454232069\n",
+            "dice ----  0.9327329407689643\n",
+            " *********  127 train64\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.6233186290091249\n",
+            "dice ----  0.5971514438782177\n",
+            "dice ----  0.4419849783377793\n",
+            " *********  128 train640\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8747578057999555\n",
+            "dice ----  0.8716951788491446\n",
+            "dice ----  0.739306853292633\n",
+            " *********  129 train648\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.614580535872911\n",
+            "dice ----  0.5070934885412877\n",
+            "dice ----  0.417029511369134\n",
+            " *********  130 train656\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9141798561781219\n",
+            "dice ----  0.956269062731844\n",
+            "dice ----  0.884884519919788\n",
+            " *********  131 train66\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9456710568842922\n",
+            "dice ----  0.9329235752010453\n",
+            "dice ----  0.8938181198910082\n",
+            " *********  132 train680\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.929797677884332\n",
+            "dice ----  0.9775662537881099\n",
+            "dice ----  0.9433934843163376\n",
+            " *********  133 train686\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.6636090271238951\n",
+            "dice ----  0.9230303488988117\n",
+            "dice ----  0.873666022969814\n",
+            " *********  134 train688\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7452181334404337\n",
+            "dice ----  0.8020432356114389\n",
+            "dice ----  0.7789248212330091\n",
+            " *********  135 train690\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.748909803004618\n",
+            "dice ----  0.9119991383024558\n",
+            "dice ----  0.862536198205284\n",
+            " *********  136 train694\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.7459180255238218\n",
+            "dice ----  0.7775152589092341\n",
+            "dice ----  0.44605235156416256\n",
+            " *********  137 train7\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9196846036796238\n",
+            "dice ----  0.8897977732333561\n",
+            "dice ----  0.8826022219677013\n",
+            " *********  138 train700\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.6955167218528954\n",
+            "dice ----  0.9580065246060943\n",
+            "dice ----  0.8593737676472908\n",
+            " *********  139 train705\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9425277806625818\n",
+            "dice ----  0.9621084797555386\n",
+            "dice ----  0.9246840191608472\n",
+            " *********  140 train708\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8982830249580909\n",
+            "dice ----  0.9616682266385146\n",
+            "dice ----  0.9106274522955162\n",
+            " *********  141 train719\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "dice ----  0.9433534371994075\n",
+            "dice ----  0.9810986804739199\n",
+            "dice ----  0.8902415778170312\n",
+            " *********  142 train73\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9391052195526097\n",
+            "dice ----  0.9086212628833022\n",
+            "dice ----  0.8932321699544765\n",
+            " *********  143 train730\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9347773066275293\n",
+            "dice ----  0.6805596425991813\n",
+            "dice ----  0.8148412132731448\n",
+            " *********  144 train737\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9227589563408594\n",
+            "dice ----  0.9653304129802012\n",
+            "dice ----  0.9083130913968749\n",
+            " *********  145 train740\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8896421642110192\n",
+            "dice ----  0.8606146572104019\n",
+            "dice ----  0.7636981906839471\n",
+            " *********  146 train747\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8673585790368747\n",
+            "dice ----  0.8957172138516307\n",
+            "dice ----  0.8341528208235267\n",
+            " *********  147 train748\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9423256552237804\n",
+            "dice ----  0.9793840872773185\n",
+            "dice ----  0.9431834006405793\n",
+            " *********  148 train749\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9162534943826152\n",
+            "dice ----  0.9636605842808017\n",
+            "dice ----  0.936788334726479\n",
+            " *********  149 train751\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7887416224184765\n",
+            "dice ----  0.36240016764849703\n",
+            "dice ----  0.4793488091649081\n",
+            " *********  150 train753\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.902154817217498\n",
+            "dice ----  0.18589612223057128\n",
+            "dice ----  0.011678832116788329\n",
+            " *********  151 train757\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.8589792161007092\n",
+            "dice ----  0.08588817040699881\n",
+            "dice ----  0.017671917018824423\n",
+            " *********  152 train761\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.8020952585663086\n",
+            "dice ----  0.8128809581334346\n",
+            "dice ----  0.7766390780353226\n",
+            " *********  153 train767\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9174359478467574\n",
+            "dice ----  0.9245407442298634\n",
+            "dice ----  0.8076740218698437\n",
+            " *********  154 train77\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8828736918317647\n",
+            "dice ----  0.778447370546894\n",
+            "dice ----  0.5110839402477274\n",
+            " *********  155 train776\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8681422515613371\n",
+            "dice ----  0.21495480690221858\n",
+            "dice ----  0.15290703492444713\n",
+            " *********  156 train778\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "dice ----  0.9196684818242137\n",
+            "dice ----  0.9573946711875448\n",
+            "dice ----  0.65932217643596\n",
+            " *********  157 train782\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8681981882321128\n",
+            "dice ----  0.9035456289781653\n",
+            "dice ----  0.8142718009404224\n",
+            " *********  158 train784\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9420375296570566\n",
+            "dice ----  0.9263572958927839\n",
+            "dice ----  0.8294307953489862\n",
+            " *********  159 train79\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "dice ----  0.7246999146200592\n",
+            "dice ----  0.679410842995926\n",
+            "dice ----  0.6806509945750452\n",
+            " *********  160 train795\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8610460179996604\n",
+            "dice ----  0.9093369418132612\n",
+            "dice ----  0.7784690667598742\n",
+            " *********  161 train797\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.8231057994330805\n",
+            "dice ----  0.9131039400962738\n",
+            "dice ----  0.6745470614228899\n",
+            " *********  162 train80\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8724115271638706\n",
+            "dice ----  0.9682517285170029\n",
+            "dice ----  0.9185093664262224\n",
+            " *********  163 train801\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9044539341658503\n",
+            "dice ----  0.8811199337673579\n",
+            "dice ----  0.8210406668350594\n",
+            " *********  164 train809\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8553697160630226\n",
+            "dice ----  0.942068257088713\n",
+            "dice ----  0.9151491501907735\n",
+            " *********  165 train811\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.889391220105945\n",
+            "dice ----  0.964764743453268\n",
+            "dice ----  0.885585144039645\n",
+            " *********  166 train822\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8941140903215046\n",
+            "dice ----  0.9154419224860133\n",
+            "dice ----  0.8253955440749112\n",
+            " *********  167 train832\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9238801330831309\n",
+            "dice ----  0.9511739099407693\n",
+            "dice ----  0.8300292492687683\n",
+            " *********  168 train844\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9227972973566251\n",
+            "dice ----  0.963186515958204\n",
+            "dice ----  0.8963442657196574\n",
+            " *********  169 train846\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7284883613608255\n",
+            "dice ----  0.9349333566913386\n",
+            "dice ----  0.8069818181818182\n",
+            " *********  170 train853\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7773865722059947\n",
+            "dice ----  0.7894847689980662\n",
+            "dice ----  0.6444323179148075\n",
+            " *********  171 train856\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.901882923191648\n",
+            "dice ----  0.9418885173741905\n",
+            "dice ----  0.8580379630198415\n",
+            " *********  172 train858\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8605720243596123\n",
+            "dice ----  0.8454306510311362\n",
+            "dice ----  0.8236149483786535\n",
+            " *********  173 train859\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8616812272708272\n",
+            "dice ----  0.9224756460362831\n",
+            "dice ----  0.7834136444331727\n",
+            " *********  174 train862\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8674792936396312\n",
+            "dice ----  0.9357023657602828\n",
+            "dice ----  0.9353202282815473\n",
+            " *********  175 train868\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8784569009748588\n",
+            "dice ----  0.9388265187067334\n",
+            "dice ----  0.8655673847145138\n",
+            " *********  176 train872\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9351837381888038\n",
+            "dice ----  0.9555724754925288\n",
+            "dice ----  0.844937961217031\n",
+            " *********  177 train876\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.9219903440816779\n",
+            "dice ----  0.9313589072572924\n",
+            "dice ----  0.875695207056573\n",
+            " *********  178 train890\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.9141258233322879\n",
+            "dice ----  0.8068861197147591\n",
+            "dice ----  0.7582184517497349\n",
+            " *********  179 train891\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9656840316360547\n",
+            "dice ----  0.9415128422916342\n",
+            "dice ----  0.8894761829403387\n",
+            " *********  180 train894\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.5677493459255951\n",
+            "dice ----  0.5084745762711864\n",
+            "dice ----  0.5077605321507761\n",
+            " *********  181 train897\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9436699089341866\n",
+            "dice ----  0.9337110238417133\n",
+            "dice ----  0.8597748707027685\n",
+            " *********  182 train9\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7539574654680992\n",
+            "dice ----  0.691661279896574\n",
+            "dice ----  0.6629001883239172\n",
+            " *********  183 train902\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8087251175048884\n",
+            "dice ----  0.9091526446842224\n",
+            "dice ----  0.8614497491429999\n",
+            " *********  184 train912\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.947412558380903\n",
+            "dice ----  0.8873103002906038\n",
+            "dice ----  0.8740701046526289\n",
+            " *********  185 train918\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8942525244856123\n",
+            "dice ----  0.9451502943910753\n",
+            "dice ----  0.9166211159231383\n",
+            " *********  186 train919\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.942457689477557\n",
+            "dice ----  0.9327606635071091\n",
+            "dice ----  0.9068668580260544\n",
+            " *********  187 train92\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.9374335638231687\n",
+            "dice ----  0.9339549735463215\n",
+            "dice ----  0.7395770392749245\n",
+            " *********  188 train921\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9092604423877195\n",
+            "dice ----  0.9585202742741048\n",
+            "dice ----  0.897448423584232\n",
+            " *********  189 train930\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9474082599402515\n",
+            "dice ----  0.9761787505699955\n",
+            "dice ----  0.9186753921536387\n",
+            " *********  190 train935\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8517803876785556\n",
+            "dice ----  0.9758452713803938\n",
+            "dice ----  0.9385663029948011\n",
+            " *********  191 train936\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8821320113904052\n",
+            "dice ----  0.8194147037830122\n",
+            "dice ----  0.8015941404566997\n",
+            " *********  192 train939\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9341921592599013\n",
+            "dice ----  0.9461843208698418\n",
+            "dice ----  0.9396741793238609\n",
+            " *********  193 train940\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9239368485251719\n",
+            "dice ----  0.9583315462148831\n",
+            "dice ----  0.8782479675295214\n",
+            " *********  194 train946\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9349856233994459\n",
+            "dice ----  0.9534004493605254\n",
+            "dice ----  0.9114733416483879\n",
+            " *********  195 train948\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.2669640106222215\n",
+            "dice ----  0.7874231032125769\n",
+            "dice ----  0.7295504789977892\n",
+            " *********  196 train953\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7688862194985392\n",
+            "dice ----  0.9626009035029253\n",
+            "dice ----  0.9213816486543759\n",
+            " *********  197 train955\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9321990446751395\n",
+            "dice ----  0.9546095051026944\n",
+            "dice ----  0.9337046782626525\n",
+            " *********  198 train956\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8141843971631206\n",
+            "dice ----  0.9362871372144943\n",
+            "dice ----  0.8748438670996752\n",
+            " *********  199 train966\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9097252015264073\n",
+            "dice ----  0.9569034901587766\n",
+            "dice ----  0.9262764341628695\n",
+            " *********  200 train97\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8801942993309504\n",
+            "dice ----  0.8548940841254642\n",
+            "dice ----  0.8469416947988785\n",
+            " *********  201 train979\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.88721985219011\n",
+            "dice ----  0.6795351585582037\n",
+            "dice ----  0.6878097125867195\n",
+            " *********  202 train98\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9452820967654352\n",
+            "dice ----  0.9024623135996099\n",
+            "dice ----  0.8556986077784139\n",
+            " *********  203 train981\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.903217787396451\n",
+            "dice ----  0.9194973493029649\n",
+            "dice ----  0.9165710399931999\n",
+            " *********  204 train986\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.8667153031092583\n",
+            "dice ----  0.697439140056908\n",
+            "dice ----  0.864208552807785\n",
+            " *********  205 train987\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9208226323438651\n",
+            "dice ----  0.957413884472708\n",
+            "dice ----  0.9290009494672433\n",
+            " *********  206 train996\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9093750796333891\n",
+            "dice ----  0.8742955721679126\n",
+            "dice ----  0.8681049122586326\n",
+            " *********  207 train10\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8910663208719364\n",
+            "dice ----  0.9627536846070537\n",
+            "dice ----  0.8760812248485129\n",
+            " *********  208 train1000\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.89975898413612\n",
+            "dice ----  0.77711986884963\n",
+            "dice ----  0.7796231948739201\n",
+            " *********  209 train1005\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9389229374433363\n",
+            "dice ----  0.9334216256257155\n",
+            "dice ----  0.8733431243144764\n",
+            " *********  210 train1009\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9326071909948636\n",
+            "dice ----  0.9401039098941939\n",
+            "dice ----  0.9366707606082098\n",
+            " *********  211 train1014\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8991378934993687\n",
+            "dice ----  0.796161208275428\n",
+            "dice ----  0.8235076665916616\n",
+            " *********  212 train1019\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.4730982604607429\n",
+            "dice ----  0.8041381952870554\n",
+            "dice ----  0.0\n",
+            " *********  213 train1021\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.7108056113957157\n",
+            "dice ----  0.5591971278002897\n",
+            "dice ----  0.0\n",
+            " *********  214 train1027\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8668868506030525\n",
+            "dice ----  0.8952301255230125\n",
+            "dice ----  0.7578671826096853\n",
+            " *********  215 train1032\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8904603606326181\n",
+            "dice ----  0.8992299229922992\n",
+            "dice ----  0.8879035925420646\n",
+            " *********  216 train1047\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9487476328843161\n",
+            "dice ----  0.9155836118331476\n",
+            "dice ----  0.8974865113830768\n",
+            " *********  217 train1057\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.783664535188523\n",
+            "dice ----  0.752204550351531\n",
+            "dice ----  0.6975773840158812\n",
+            " *********  218 train106\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9389803910328297\n",
+            "dice ----  0.9367092448193209\n",
+            "dice ----  0.8383393767815245\n",
+            " *********  219 train1072\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.878119123478803\n",
+            "dice ----  0.6797777867888569\n",
+            "dice ----  0.7058619943759463\n",
+            " *********  220 train1073\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9219169350989365\n",
+            "dice ----  0.7293646591046329\n",
+            "dice ----  0.25988577644819144\n",
+            " *********  221 train1075\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8478611235169615\n",
+            "dice ----  0.19630058795759842\n",
+            "dice ----  0.0\n",
+            " *********  222 train1087\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8160916014926136\n",
+            "dice ----  0.6490193389109862\n",
+            "dice ----  0.041634541249036205\n",
+            " *********  223 train1099\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.6716080173180796\n",
+            "dice ----  0.19927371360760904\n",
+            "dice ----  0.0\n",
+            " *********  224 train1100\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.6832281346723073\n",
+            "dice ----  0.23614457831325297\n",
+            "dice ----  0.24400234055002923\n",
+            " *********  225 train1107\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8657298195077011\n",
+            "dice ----  0.38499600636971476\n",
+            "dice ----  0.26214949428500944\n",
+            " *********  226 train111\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8660663845878922\n",
+            "dice ----  0.8917435048729871\n",
+            "dice ----  0.7460880640465793\n",
+            " *********  227 train1112\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8697500006099331\n",
+            "dice ----  0.6988552998132025\n",
+            "dice ----  0.0003924646781789942\n",
+            " *********  228 train1121\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9521519940203246\n",
+            "dice ----  0.959961020252521\n",
+            "dice ----  0.8995235403375595\n",
+            " *********  229 train1126\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9589798034867372\n",
+            "dice ----  0.9596593747963418\n",
+            "dice ----  0.943639733644539\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " ******  230\n",
+            " *********  0 train1146\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 492ms/step\n",
+            "dice ----  0.9574453202765597\n",
+            "dice ----  0.9641969986245407\n",
+            "dice ----  0.9303442754203363\n",
+            " *********  1 train1147\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9287539415026639\n",
+            "dice ----  0.9659834746217405\n",
+            "dice ----  0.9313665232534317\n",
+            " *********  2 train1149\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9591037114015342\n",
+            "dice ----  0.9637788656480245\n",
+            "dice ----  0.9338144914746762\n",
+            " *********  3 train1155\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9291174538053495\n",
+            "dice ----  0.961691752238946\n",
+            "dice ----  0.9308976933248445\n",
+            " *********  4 train1154\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9557981090100112\n",
+            "dice ----  0.978668317667586\n",
+            "dice ----  0.9622246666487806\n",
+            " *********  5 train1156\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9559026660863971\n",
+            "dice ----  0.8710700385989076\n",
+            "dice ----  0.07329907757072984\n",
+            " *********  6 train1159\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9668775586155564\n",
+            "dice ----  0.7555747853291619\n",
+            "dice ----  0.9407075236671649\n",
+            " *********  7 train116\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9510069861179523\n",
+            "dice ----  0.974756662478198\n",
+            "dice ----  0.9353494623655914\n",
+            " *********  8 train1166\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9564704635199958\n",
+            "dice ----  0.9397172283952896\n",
+            "dice ----  0.9036824180502341\n",
+            " *********  9 train1191\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9519923821703864\n",
+            "dice ----  0.9616037082168543\n",
+            "dice ----  0.9353954819460513\n",
+            " *********  10 train1196\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9131322168479324\n",
+            "dice ----  0.9383576490204252\n",
+            "dice ----  0.7541549953314659\n",
+            " *********  11 train1198\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9532238665481357\n",
+            "dice ----  0.9516201117318436\n",
+            "dice ----  0.9287902469521725\n",
+            " *********  12 train1200\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.6807205825986968\n",
+            "dice ----  0.10115333711476648\n",
+            "dice ----  0.6363636363636364\n",
+            " *********  13 train1211\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9574416261110024\n",
+            "dice ----  0.9819978336107369\n",
+            "dice ----  0.9506920041787901\n",
+            " *********  14 train1215\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9499534903275647\n",
+            "dice ----  0.9560977489978415\n",
+            "dice ----  0.9013365509560052\n",
+            " *********  15 train1216\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9559871400528618\n",
+            "dice ----  0.9355585815206638\n",
+            "dice ----  0.8044232437120555\n",
+            " *********  16 train1217\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8405289770517308\n",
+            "dice ----  0.9774033738163423\n",
+            "dice ----  0.9447889779088867\n",
+            " *********  17 train1228\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9001723007486696\n",
+            "dice ----  0.9311166762212232\n",
+            "dice ----  0.9311645592540061\n",
+            " *********  18 train1234\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9607629745846794\n",
+            "dice ----  0.9587220664457473\n",
+            "dice ----  0.9509522888345673\n",
+            " *********  19 train1246\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.955249811157872\n",
+            "dice ----  0.9456062837506136\n",
+            "dice ----  0.8757149811366679\n",
+            " *********  20 train1249\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9088821533404182\n",
+            "dice ----  0.8766267123287671\n",
+            "dice ----  0.9057448618636099\n",
+            " *********  21 train125\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9458984768650485\n",
+            "dice ----  0.9314295418620753\n",
+            "dice ----  0.877130642385209\n",
+            " *********  22 train133\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9448890671430915\n",
+            "dice ----  0.9826794849875718\n",
+            "dice ----  0.957513902075139\n",
+            " *********  23 train137\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.825436812840768\n",
+            "dice ----  0.9226987053668788\n",
+            "dice ----  0.929683658416653\n",
+            " *********  24 train140\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9457800941492939\n",
+            "dice ----  0.9781421730602725\n",
+            "dice ----  0.9656539717905629\n",
+            " *********  25 train151\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9572308832889206\n",
+            "dice ----  0.9805618397065606\n",
+            "dice ----  0.9679830854036094\n",
+            " *********  26 train168\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8795702704148296\n",
+            "dice ----  0.9679773303946808\n",
+            "dice ----  0.9623735928257966\n",
+            " *********  27 train176\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9586140301050838\n",
+            "dice ----  0.9645991877291905\n",
+            "dice ----  0.9322363894541142\n",
+            " *********  28 train19\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8748340183370218\n",
+            "dice ----  0.9433175586225786\n",
+            "dice ----  0.9258255379631772\n",
+            " *********  29 train192\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9504242398595036\n",
+            "dice ----  0.9591198226384601\n",
+            "dice ----  0.9096989966555185\n",
+            " *********  30 train194\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "dice ----  0.9424017932806649\n",
+            "dice ----  0.921869138845721\n",
+            "dice ----  0.867701714422731\n",
+            " *********  31 train197\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9277297874405512\n",
+            "dice ----  0.966610128422583\n",
+            "dice ----  0.9343522474276431\n",
+            " *********  32 train205\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9095910167920058\n",
+            "dice ----  0.8777712797065235\n",
+            "dice ----  0.8646536808617514\n",
+            " *********  33 train209\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9086489406398175\n",
+            "dice ----  0.9461223694466095\n",
+            "dice ----  0.9180498525244347\n",
+            " *********  34 train215\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9686593888985155\n",
+            "dice ----  0.9717862077001181\n",
+            "dice ----  0.9379810906312673\n",
+            " *********  35 train224\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8263393929686874\n",
+            "dice ----  0.951292643923241\n",
+            "dice ----  0.9372378496940779\n",
+            " *********  36 train232\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9272884713297681\n",
+            "dice ----  0.8991992957156032\n",
+            "dice ----  0.8441523570853204\n",
+            " *********  37 train24\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9340026820667842\n",
+            "dice ----  0.6296296296296297\n",
+            "dice ----  0.6577462518136385\n",
+            " *********  38 train242\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7963595382829042\n",
+            "dice ----  0.9321246355685131\n",
+            "dice ----  0.9207058713973088\n",
+            " *********  39 train25\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9306930693069306\n",
+            "dice ----  0.9747762408462164\n",
+            "dice ----  0.9279693944647021\n",
+            " *********  40 train254\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "dice ----  0.9501346768453519\n",
+            "dice ----  0.9712047799563176\n",
+            "dice ----  0.9149938907313667\n",
+            " *********  41 train257\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9673462404085339\n",
+            "dice ----  0.9818955344388547\n",
+            "dice ----  0.9541390596311966\n",
+            " *********  42 train258\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9572358458246237\n",
+            "dice ----  0.9381613868273017\n",
+            "dice ----  0.5234811165845649\n",
+            " *********  43 train266\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "dice ----  0.9198325566700892\n",
+            "dice ----  0.9112190349743196\n",
+            "dice ----  0.8766548506113\n",
+            " *********  44 train274\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9352479404851555\n",
+            "dice ----  0.8656297470444488\n",
+            "dice ----  0.7332502565606216\n",
+            " *********  45 train278\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9637708490486967\n",
+            "dice ----  0.9786729857819905\n",
+            "dice ----  0.9526035661828577\n",
+            " *********  46 train283\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "dice ----  0.9361231371464435\n",
+            "dice ----  0.9738780214421865\n",
+            "dice ----  0.8763563544916873\n",
+            " *********  47 train287\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9544930282737303\n",
+            "dice ----  0.956379340793424\n",
+            "dice ----  0.94225953229163\n",
+            " *********  48 train299\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.9703619579579049\n",
+            "dice ----  0.9720417005144869\n",
+            "dice ----  0.9595264520902701\n",
+            " *********  49 train3\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9601766019210066\n",
+            "dice ----  0.9654957970999472\n",
+            "dice ----  0.9051549347696971\n",
+            " *********  50 train30\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9621518336150511\n",
+            "dice ----  0.9606330107245619\n",
+            "dice ----  0.9454385450278674\n",
+            " *********  51 train31\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8895993037190775\n",
+            "dice ----  0.9681179775280899\n",
+            "dice ----  0.9217696014016645\n",
+            " *********  52 train315\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9647703832374335\n",
+            "dice ----  0.9570492720046336\n",
+            "dice ----  0.9159250101539843\n",
+            " *********  53 train316\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9302022959279064\n",
+            "dice ----  0.959648364317625\n",
+            "dice ----  0.9120702581128842\n",
+            " *********  54 train328\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7869663168305951\n",
+            "dice ----  0.8990243100691798\n",
+            "dice ----  0.8109719607864472\n",
+            " *********  55 train330\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9585866708508218\n",
+            "dice ----  0.974202769511938\n",
+            "dice ----  0.8879841356193953\n",
+            " *********  56 train339\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9254893706588087\n",
+            "dice ----  0.9360722454920833\n",
+            "dice ----  0.8545417114623601\n",
+            " *********  57 train340\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9464078254326561\n",
+            "dice ----  0.9574034836685837\n",
+            "dice ----  0.8804451078843805\n",
+            " *********  58 train346\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9576109330899986\n",
+            "dice ----  0.9837653998423481\n",
+            "dice ----  0.9376224076409196\n",
+            " *********  59 train356\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9259470103496868\n",
+            "dice ----  0.903639004316779\n",
+            "dice ----  0.8250705360741637\n",
+            " *********  60 train361\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9296994567685296\n",
+            "dice ----  0.9016010483733121\n",
+            "dice ----  0.7956403269754768\n",
+            " *********  61 train362\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9296799417930616\n",
+            "dice ----  0.9588991490132175\n",
+            "dice ----  0.9322354998542699\n",
+            " *********  62 train363\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9393378975860084\n",
+            "dice ----  0.982964842334179\n",
+            "dice ----  0.9229000613120785\n",
+            " *********  63 train367\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9480852524238216\n",
+            "dice ----  0.9641738615202302\n",
+            "dice ----  0.8869004694697493\n",
+            " *********  64 train369\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8331128408659714\n",
+            "dice ----  0.9003720736752098\n",
+            "dice ----  0.7587315010570824\n",
+            " *********  65 train377\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 43ms/step\n",
+            "dice ----  0.9173563970140501\n",
+            "dice ----  0.9797707093560469\n",
+            "dice ----  0.9546314021633175\n",
+            " *********  66 train380\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9447599023425343\n",
+            "dice ----  0.9566277212742709\n",
+            "dice ----  0.8843855909996157\n",
+            " *********  67 train381\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9266027561414021\n",
+            "dice ----  0.9670123787219806\n",
+            "dice ----  0.9033158240315168\n",
+            " *********  68 train383\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9620437156837855\n",
+            "dice ----  0.9789260969976905\n",
+            "dice ----  0.8211177278973889\n",
+            " *********  69 train387\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9456921728971963\n",
+            "dice ----  0.9824804469273744\n",
+            "dice ----  0.9304815472666272\n",
+            " *********  70 train388\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7804356761200164\n",
+            "dice ----  0.8449274071434933\n",
+            "dice ----  0.7912360781449699\n",
+            " *********  71 train395\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9449645458608652\n",
+            "dice ----  0.9699297453870405\n",
+            "dice ----  0.9471070488259092\n",
+            " *********  72 train397\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9244196695990534\n",
+            "dice ----  0.9614437160104202\n",
+            "dice ----  0.9392541775789742\n",
+            " *********  73 train398\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9312110918727448\n",
+            "dice ----  0.9647218977932817\n",
+            "dice ----  0.9144224652388597\n",
+            " *********  74 train400\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.8974373522458629\n",
+            "dice ----  0.889803094233474\n",
+            "dice ----  0.8392100279556317\n",
+            " *********  75 train414\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8456012493492973\n",
+            "dice ----  0.9392930848397542\n",
+            "dice ----  0.8513372976720621\n",
+            " *********  76 train419\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9360360105313819\n",
+            "dice ----  0.9660101098134913\n",
+            "dice ----  0.9154088121478196\n",
+            " *********  77 train424\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9567267113612609\n",
+            "dice ----  0.9624141997520487\n",
+            "dice ----  0.9346518463724669\n",
+            " *********  78 train431\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9386683412512059\n",
+            "dice ----  0.9658581060006238\n",
+            "dice ----  0.8486303030303031\n",
+            " *********  79 train440\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9642102501393828\n",
+            "dice ----  0.9753049847844235\n",
+            "dice ----  0.961882072662299\n",
+            " *********  80 train448\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8531863926395373\n",
+            "dice ----  0.7779825334508452\n",
+            "dice ----  0.7625553447185326\n",
+            " *********  81 train451\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7435120096014465\n",
+            "dice ----  0.9025531195401003\n",
+            "dice ----  0.892089646828369\n",
+            " *********  82 train462\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "dice ----  0.7996392144404987\n",
+            "dice ----  0.9667532180542512\n",
+            "dice ----  0.8952697095435684\n",
+            " *********  83 train463\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9476029544029871\n",
+            "dice ----  0.9589008770434851\n",
+            "dice ----  0.9222665932250069\n",
+            " *********  84 train482\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9401874951576664\n",
+            "dice ----  0.9644780657749764\n",
+            "dice ----  0.8896863273707604\n",
+            " *********  85 train487\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9172384647892735\n",
+            "dice ----  0.9786634508614617\n",
+            "dice ----  0.9315469170165589\n",
+            " *********  86 train488\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9567858780375974\n",
+            "dice ----  0.9611914443134518\n",
+            "dice ----  0.9201856891307828\n",
+            " *********  87 train492\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9547092145755336\n",
+            "dice ----  0.9726070025820234\n",
+            "dice ----  0.9417202524218367\n",
+            " *********  88 train494\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9620468084483541\n",
+            "dice ----  0.9736590354291746\n",
+            "dice ----  0.8967032967032967\n",
+            " *********  89 train500\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9572191958495461\n",
+            "dice ----  0.9771095908042335\n",
+            "dice ----  0.9204903029430517\n",
+            " *********  90 train502\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.918620130809747\n",
+            "dice ----  0.9720315181775628\n",
+            "dice ----  0.9007730863987766\n",
+            " *********  91 train503\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9412173594914711\n",
+            "dice ----  0.9590516723171992\n",
+            "dice ----  0.901696474375984\n",
+            " *********  92 train504\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9565845228795303\n",
+            "dice ----  0.9681343891067861\n",
+            "dice ----  0.9218224535864007\n",
+            " *********  93 train54\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8917557730486573\n",
+            "dice ----  0.9105859693310616\n",
+            "dice ----  0.8847457627118644\n",
+            " *********  94 train541\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9192616189181617\n",
+            "dice ----  0.9663806528443617\n",
+            "dice ----  0.9350628868824666\n",
+            " *********  95 train545\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9354833922051708\n",
+            "dice ----  0.9658323745848438\n",
+            "dice ----  0.9458974958524011\n",
+            " *********  96 train546\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9280515293642572\n",
+            "dice ----  0.9609694740807427\n",
+            "dice ----  0.9195750094556958\n",
+            " *********  97 train550\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.943271247007193\n",
+            "dice ----  0.9134258809824289\n",
+            "dice ----  0.8758018988965871\n",
+            " *********  98 train552\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9080700209181989\n",
+            "dice ----  0.8293855132166151\n",
+            "dice ----  0.8084019492522265\n",
+            " *********  99 train56\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9102617094872408\n",
+            "dice ----  0.8903171425561058\n",
+            "dice ----  0.819819495882924\n",
+            " *********  100 train564\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.9567620084904143\n",
+            "dice ----  0.9668522161302473\n",
+            "dice ----  0.9072892485242422\n",
+            " *********  101 train570\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9389473844085919\n",
+            "dice ----  0.9622576591670656\n",
+            "dice ----  0.91080709719816\n",
+            " *********  102 train583\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.920898184040656\n",
+            "dice ----  0.551702172836011\n",
+            "dice ----  0.6803043110735418\n",
+            " *********  103 train586\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9589940131760226\n",
+            "dice ----  0.9834430379746836\n",
+            "dice ----  0.9137847053093034\n",
+            " *********  104 train588\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9128488086618564\n",
+            "dice ----  0.8447965477986922\n",
+            "dice ----  0.6886764393456555\n",
+            " *********  105 train590\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9279588591132713\n",
+            "dice ----  0.9570822814284664\n",
+            "dice ----  0.9305370782821866\n",
+            " *********  106 train596\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9008874206211493\n",
+            "dice ----  0.9516714118480193\n",
+            "dice ----  0.9382057039659264\n",
+            " *********  107 train597\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9507373020207537\n",
+            "dice ----  0.8907660937955141\n",
+            "dice ----  0.8832844574780059\n",
+            " *********  108 train598\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9509721422560786\n",
+            "dice ----  0.9817452122581777\n",
+            "dice ----  0.9482462044723655\n",
+            " *********  109 train601\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9574088036311563\n",
+            "dice ----  0.9642956987100124\n",
+            "dice ----  0.9267737876294647\n",
+            " *********  110 train61\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9678597665496694\n",
+            "dice ----  0.9772467463946536\n",
+            "dice ----  0.9080591868984341\n",
+            " *********  111 train611\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8445734273798313\n",
+            "dice ----  0.9135564895933559\n",
+            "dice ----  0.8898424707808706\n",
+            " *********  112 train612\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9009300556650417\n",
+            "dice ----  0.9542942611981422\n",
+            "dice ----  0.9144581260702928\n",
+            " *********  113 train62\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9301051500099689\n",
+            "dice ----  0.9751628078969534\n",
+            "dice ----  0.9416632188663633\n",
+            " *********  114 train625\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9264624606751126\n",
+            "dice ----  0.8827325791310627\n",
+            "dice ----  0.6036833650864806\n",
+            " *********  115 train637\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9219832864582632\n",
+            "dice ----  0.703833390167425\n",
+            "dice ----  0.8472759492576665\n",
+            " *********  116 train644\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9543891881358999\n",
+            "dice ----  0.9741452843672914\n",
+            "dice ----  0.9206232017805765\n",
+            " *********  117 train646\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.9572506467375683\n",
+            "dice ----  0.9703726358784124\n",
+            "dice ----  0.9100207476431406\n",
+            " *********  118 train647\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9588323331284898\n",
+            "dice ----  0.9265893347542731\n",
+            "dice ----  0.8924273554446728\n",
+            " *********  119 train649\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9529230848754264\n",
+            "dice ----  0.9591403423080374\n",
+            "dice ----  0.8880215704092121\n",
+            " *********  120 train654\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8660226335797101\n",
+            "dice ----  0.9065203796496394\n",
+            "dice ----  0.7530619226821759\n",
+            " *********  121 train658\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9395711893134301\n",
+            "dice ----  0.9238485758219228\n",
+            "dice ----  0.8827339432919649\n",
+            " *********  122 train662\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9516874109852003\n",
+            "dice ----  0.9731549866984168\n",
+            "dice ----  0.8542324602142548\n",
+            " *********  123 train665\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9485092506898013\n",
+            "dice ----  0.9699114501945645\n",
+            "dice ----  0.9388863237602734\n",
+            " *********  124 train667\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.962263347861618\n",
+            "dice ----  0.9794443401539328\n",
+            "dice ----  0.9366815590818056\n",
+            " *********  125 train668\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9296298357918175\n",
+            "dice ----  0.9468526550228578\n",
+            "dice ----  0.9102994048158555\n",
+            " *********  126 train67\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.9661915005817506\n",
+            "dice ----  0.96990910962732\n",
+            "dice ----  0.9368177307310619\n",
+            " *********  127 train670\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9450496741352743\n",
+            "dice ----  0.9656037928790555\n",
+            "dice ----  0.9195501264596675\n",
+            " *********  128 train682\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8941633837262287\n",
+            "dice ----  0.960627989697044\n",
+            "dice ----  0.9178818112049117\n",
+            " *********  129 train687\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9213897104674512\n",
+            "dice ----  0.510854870775348\n",
+            "dice ----  0.26042428675932694\n",
+            " *********  130 train70\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9395788912579958\n",
+            "dice ----  0.9540163062743708\n",
+            "dice ----  0.9290050674828157\n",
+            " *********  131 train703\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8139974570180772\n",
+            "dice ----  0.8965653375444137\n",
+            "dice ----  0.888628131217795\n",
+            " *********  132 train707\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9521399662194461\n",
+            "dice ----  0.9450482400945734\n",
+            "dice ----  0.8406606187485461\n",
+            " *********  133 train706\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9069603286339705\n",
+            "dice ----  0.9537332719414202\n",
+            "dice ----  0.9120427937967763\n",
+            " *********  134 train712\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8997772001485332\n",
+            "dice ----  0.9237230109097548\n",
+            "dice ----  0.8430751511661387\n",
+            " *********  135 train714\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8756195421288647\n",
+            "dice ----  0.9763798079754963\n",
+            "dice ----  0.955062764087222\n",
+            " *********  136 train724\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9342673039338997\n",
+            "dice ----  0.9582273883036687\n",
+            "dice ----  0.8935085112131856\n",
+            " *********  137 train75\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8853102358325978\n",
+            "dice ----  0.9238594487984637\n",
+            "dice ----  0.7152492359574244\n",
+            " *********  138 train752\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9217250217171246\n",
+            "dice ----  0.9485143652287795\n",
+            "dice ----  0.8462975815493032\n",
+            " *********  139 train759\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9480425150715445\n",
+            "dice ----  0.9454751805792\n",
+            "dice ----  0.9358681818946329\n",
+            " *********  140 train765\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8916176967240796\n",
+            "dice ----  0.9647485838939708\n",
+            "dice ----  0.9453931691818904\n",
+            " *********  141 train766\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8431314310584651\n",
+            "dice ----  0.16166460396039606\n",
+            "dice ----  0.24619289340101524\n",
+            " *********  142 train770\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.6786431342044128\n",
+            "dice ----  0.7518856504330012\n",
+            "dice ----  0.8330099599016945\n",
+            " *********  143 train769\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9037725507464994\n",
+            "dice ----  0.9310892232204062\n",
+            "dice ----  0.8779713425913145\n",
+            " *********  144 train771\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8906346957651825\n",
+            "dice ----  0.7861402826396993\n",
+            "dice ----  0.8081953410047713\n",
+            " *********  145 train774\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9321419769943452\n",
+            "dice ----  0.9560418773034147\n",
+            "dice ----  0.9210377545875414\n",
+            " *********  146 train775\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.949264518417523\n",
+            "dice ----  0.9012739662250815\n",
+            "dice ----  0.7520682748410694\n",
+            " *********  147 train780\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8600092585555271\n",
+            "dice ----  0.883805291677058\n",
+            "dice ----  0.8475656463522996\n",
+            " *********  148 train781\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9462709132661066\n",
+            "dice ----  0.9681160733500209\n",
+            "dice ----  0.9366902591170825\n",
+            " *********  149 train785\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9280626669779265\n",
+            "dice ----  0.9399262332872291\n",
+            "dice ----  0.8243942755230762\n",
+            " *********  150 train787\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.908923246250065\n",
+            "dice ----  0.9555360170822081\n",
+            "dice ----  0.8508246971245074\n",
+            " *********  151 train791\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8925536435096665\n",
+            "dice ----  0.9290663845543508\n",
+            "dice ----  0.8740234375\n",
+            " *********  152 train792\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.8074303051119125\n",
+            "dice ----  0.8441666666666667\n",
+            "dice ----  0.838468720821662\n",
+            " *********  153 train8\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9027257297460803\n",
+            "dice ----  0.671766103131838\n",
+            "dice ----  0.09430122116689277\n",
+            " *********  154 train802\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7468839792809848\n",
+            "dice ----  0.8705501618122977\n",
+            "dice ----  0.8083961248654468\n",
+            " *********  155 train803\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8705970020922301\n",
+            "dice ----  0.9664264657713724\n",
+            "dice ----  0.9659393440373267\n",
+            " *********  156 train808\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8828529632704701\n",
+            "dice ----  0.9537801545558723\n",
+            "dice ----  0.8617600720234075\n",
+            " *********  157 train812\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9266374842535764\n",
+            "dice ----  0.8655273807097732\n",
+            "dice ----  0.7417139567398634\n",
+            " *********  158 train816\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.787121518570123\n",
+            "dice ----  0.8069994188674372\n",
+            "dice ----  0.7701669758812616\n",
+            " *********  159 train824\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.6364930659307824\n",
+            "dice ----  0.8525155065472088\n",
+            "dice ----  0.8112994350282485\n",
+            " *********  160 train826\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8923062383128675\n",
+            "dice ----  0.9300377504064369\n",
+            "dice ----  0.8787987007465605\n",
+            " *********  161 train829\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7588833916072981\n",
+            "dice ----  0.7594924678040204\n",
+            "dice ----  0.6044365796625166\n",
+            " *********  162 train84\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.945377074389697\n",
+            "dice ----  0.9588553819062293\n",
+            "dice ----  0.8696221431032503\n",
+            " *********  163 train841\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7712490878517719\n",
+            "dice ----  0.9366179112137542\n",
+            "dice ----  0.88538729474208\n",
+            " *********  164 train842\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9127842861480925\n",
+            "dice ----  0.923287845938018\n",
+            "dice ----  0.8179813948486315\n",
+            " *********  165 train848\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8947818300227939\n",
+            "dice ----  0.9272495433711273\n",
+            "dice ----  0.8590116279069767\n",
+            " *********  166 train85\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8874400116341074\n",
+            "dice ----  0.9151168335717692\n",
+            "dice ----  0.9385749385749386\n",
+            " *********  167 train850\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9229739325744281\n",
+            "dice ----  0.7885092163414897\n",
+            "dice ----  0.8347320035423608\n",
+            " *********  168 train852\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9242167558898602\n",
+            "dice ----  0.9521136852161453\n",
+            "dice ----  0.8346456692913385\n",
+            " *********  169 train864\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9554350851446066\n",
+            "dice ----  0.9793684210526316\n",
+            "dice ----  0.9503301455729602\n",
+            " *********  170 train867\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9384691045772298\n",
+            "dice ----  0.9446151834172497\n",
+            "dice ----  0.919917985632735\n",
+            " *********  171 train870\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9183381016279717\n",
+            "dice ----  0.9648779304489457\n",
+            "dice ----  0.9316071662089709\n",
+            " *********  172 train873\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9255576404009592\n",
+            "dice ----  0.9252012479297462\n",
+            "dice ----  0.815637245754805\n",
+            " *********  173 train877\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8278565822160006\n",
+            "dice ----  0.7685646163539106\n",
+            "dice ----  0.4244328469485569\n",
+            " *********  174 train88\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8693098573036121\n",
+            "dice ----  0.9415194101044432\n",
+            "dice ----  0.5277705488364448\n",
+            " *********  175 train881\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "dice ----  0.7367395300423006\n",
+            "dice ----  0.8841896580208162\n",
+            "dice ----  0.806936253861275\n",
+            " *********  176 train888\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9380195276421691\n",
+            "dice ----  0.9776877098174077\n",
+            "dice ----  0.9482560367957071\n",
+            " *********  177 train89\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.913312155483418\n",
+            "dice ----  0.9290274338842683\n",
+            "dice ----  0.8730876005592818\n",
+            " *********  178 train895\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.8941010397155035\n",
+            "dice ----  0.9666093143973638\n",
+            "dice ----  0.9647435897435898\n",
+            " *********  179 train898\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9182881123903139\n",
+            "dice ----  0.5928262807763528\n",
+            "dice ----  0.3843807199511897\n",
+            " *********  180 train900\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9569810488797436\n",
+            "dice ----  0.9622418879056047\n",
+            "dice ----  0.9399664673056229\n",
+            " *********  181 train901\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9716576127338474\n",
+            "dice ----  0.9712102564876965\n",
+            "dice ----  0.9207004461016046\n",
+            " *********  182 train904\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "dice ----  0.9321276315656233\n",
+            "dice ----  0.948991614110472\n",
+            "dice ----  0.9279686082738494\n",
+            " *********  183 train924\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9444799386659852\n",
+            "dice ----  0.9644871794871794\n",
+            "dice ----  0.945278226737474\n",
+            " *********  184 train931\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8387703435804702\n",
+            "dice ----  0.8850574712643678\n",
+            "dice ----  0.8593272171253823\n",
+            " *********  185 train932\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9046271140634112\n",
+            "dice ----  0.9681334732251229\n",
+            "dice ----  0.9388231631382317\n",
+            " *********  186 train937\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9324209521814522\n",
+            "dice ----  0.9683271030745603\n",
+            "dice ----  0.937285826349036\n",
+            " *********  187 train938\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9028374018848274\n",
+            "dice ----  0.9751006503561475\n",
+            "dice ----  0.9319463851368835\n",
+            " *********  188 train942\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.968364620593022\n",
+            "dice ----  0.9747028450635894\n",
+            "dice ----  0.9512174470504275\n",
+            " *********  189 train954\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8233261339092872\n",
+            "dice ----  0.5511577506558686\n",
+            "dice ----  0.7775910364145658\n",
+            " *********  190 train957\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9123626284037176\n",
+            "dice ----  0.9871955257929207\n",
+            "dice ----  0.9621935071457924\n",
+            " *********  191 train958\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9697071664717808\n",
+            "dice ----  0.9866622276784786\n",
+            "dice ----  0.9673908662179048\n",
+            " *********  192 train961\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9232822639044684\n",
+            "dice ----  0.9507437196026404\n",
+            "dice ----  0.943271823264137\n",
+            " *********  193 train977\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9612945002869799\n",
+            "dice ----  0.9776789737735452\n",
+            "dice ----  0.9519723414004249\n",
+            " *********  194 train980\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9659581323181536\n",
+            "dice ----  0.9350926554768604\n",
+            "dice ----  0.8972828073595711\n",
+            " *********  195 train990\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9255645907261119\n",
+            "dice ----  0.9604647867365737\n",
+            "dice ----  0.9383931887267376\n",
+            " *********  196 train993\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8951708805129001\n",
+            "dice ----  0.91576529856275\n",
+            "dice ----  0.9002434077079108\n",
+            " *********  197 train100\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9038652130822596\n",
+            "dice ----  0.7921132995839397\n",
+            "dice ----  0.7913727839655783\n",
+            " *********  198 train1015\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.6217488605660326\n",
+            "dice ----  0.7346440189787967\n",
+            "dice ----  0.6304163680198085\n",
+            " *********  199 train102\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9123556835244431\n",
+            "dice ----  0.9422016610666321\n",
+            "dice ----  0.8740876434725363\n",
+            " *********  200 train1020\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9026042785104446\n",
+            "dice ----  0.9073234811165846\n",
+            "dice ----  0.0\n",
+            " *********  201 train1026\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9356060045117756\n",
+            "dice ----  0.9667614716174948\n",
+            "dice ----  0.8926627963908512\n",
+            " *********  202 train1028\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9232311272736384\n",
+            "dice ----  0.9479449059903804\n",
+            "dice ----  0.9206465927099842\n",
+            " *********  203 train1039\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9455646790358397\n",
+            "dice ----  0.9485410646033374\n",
+            "dice ----  0.9103541235120964\n",
+            " *********  204 train1046\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.8124122381978918\n",
+            "dice ----  0.8846269605056349\n",
+            "dice ----  0.7829361818679814\n",
+            " *********  205 train1049\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9635321131184101\n",
+            "dice ----  0.9632074168444642\n",
+            "dice ----  0.9454093459199785\n",
+            " *********  206 train1054\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.866079982481592\n",
+            "dice ----  0.9534614980565186\n",
+            "dice ----  0.9017271635725501\n",
+            " *********  207 train1056\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8727700733433686\n",
+            "dice ----  0.8921970201218575\n",
+            "dice ----  0.8370519742405516\n",
+            " *********  208 train1067\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7746113989637305\n",
+            "dice ----  0.0183816489870946\n",
+            "dice ----  0.0\n",
+            " *********  209 train1071\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9447300550697951\n",
+            "dice ----  0.6594712117094382\n",
+            "dice ----  0.8809863920006015\n",
+            " *********  210 train1080\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9381718750750866\n",
+            "dice ----  0.7336461684528323\n",
+            "dice ----  0.01552795031055898\n",
+            " *********  211 train1086\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7257687581814823\n",
+            "dice ----  0.6135339243604668\n",
+            "dice ----  0.0\n",
+            " *********  212 train1088\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9278150711029782\n",
+            "dice ----  0.7939901686135631\n",
+            "dice ----  0.44213886671987235\n",
+            " *********  213 train1091\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8755500756823946\n",
+            "dice ----  0.7294590708386883\n",
+            "dice ----  0.6793721973094171\n",
+            " *********  214 train1092\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8494109708437596\n",
+            "dice ----  0.7243216857177724\n",
+            "dice ----  0.09221902017291062\n",
+            " *********  215 train1093\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.847842905571732\n",
+            "dice ----  0.8277246063599877\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/scipy/spatial/distance.py:1415: RuntimeWarning: invalid value encountered in divide\n",
+            "  return float((ntf + nft) / np.array(2.0 * ntt + ntf + nft))\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "dice ----  1\n",
+            " *********  216 train1094\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.887228111204318\n",
+            "dice ----  0.8248955879203256\n",
+            "dice ----  0.0\n",
+            " *********  217 train1096\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9275600289270779\n",
+            "dice ----  0.8768188417486691\n",
+            "dice ----  0.0012582573136206143\n",
+            " *********  218 train1108\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9288782533696369\n",
+            "dice ----  0.9031707747514559\n",
+            "dice ----  0.0\n",
+            " *********  219 train1110\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9319040326697294\n",
+            "dice ----  0.7929546971398805\n",
+            "dice ----  0.0\n",
+            " *********  220 train1115\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9083186653401699\n",
+            "dice ----  0.6357720150733837\n",
+            "dice ----  0.17738324971053643\n",
+            " *********  221 train1116\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9158530944524962\n",
+            "dice ----  0.708836272574298\n",
+            "dice ----  0.7849544674673353\n",
+            " *********  222 train1117\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8396541805690242\n",
+            "dice ----  0.6136113926832207\n",
+            "dice ----  0.6228097141100523\n",
+            " *********  223 train112\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9312939603359235\n",
+            "dice ----  0.864149808873427\n",
+            "dice ----  0.7959673547767643\n",
+            " *********  224 train1122\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.928667726422118\n",
+            "dice ----  0.9720906061810289\n",
+            "dice ----  0.9486808179987991\n",
+            " *********  225 train1124\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9478257503918874\n",
+            "dice ----  0.9208272104942475\n",
+            "dice ----  0.9077303973775571\n",
+            " *********  226 train1129\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9317899958660604\n",
+            "dice ----  0.932007335308224\n",
+            "dice ----  0.905702276151955\n",
+            " *********  227 train1133\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9399672973604298\n",
+            "dice ----  0.9111852965992883\n",
+            "dice ----  0.8463758988085865\n",
+            " *********  228 train1134\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.881650423390318\n",
+            "dice ----  0.8981727049319935\n",
+            "dice ----  0.8458187454900179\n",
+            " *********  229 train1137\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7795682125905756\n",
+            "dice ----  0.8376214193335352\n",
+            "dice ----  0.911233917623694\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " ******  230\n",
+            " *********  0 train1144\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 1s 541ms/step\n",
+            "dice ----  0.7734340008957707\n",
+            "dice ----  0.8839200761179828\n",
+            "dice ----  0.8854368932038835\n",
+            " *********  1 train1148\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9654411688190058\n",
+            "dice ----  0.9805074101984426\n",
+            "dice ----  0.9528516569478026\n",
+            " *********  2 train1150\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8187865396123923\n",
+            "dice ----  0.7459646323627177\n",
+            "dice ----  0.5603587328214723\n",
+            " *********  3 train1152\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9621406302936557\n",
+            "dice ----  0.7010597148154082\n",
+            "dice ----  0.9325932286555446\n",
+            " *********  4 train1157\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9415498954865681\n",
+            "dice ----  0.9384798736153583\n",
+            "dice ----  0.9259322665431365\n",
+            " *********  5 train1165\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9681470765922304\n",
+            "dice ----  0.9781086459792143\n",
+            "dice ----  0.9438426672812475\n",
+            " *********  6 train1169\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9421283413956959\n",
+            "dice ----  0.937942800274862\n",
+            "dice ----  0.8914639325365528\n",
+            " *********  7 train1171\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.972811149106394\n",
+            "dice ----  0.9622948766128022\n",
+            "dice ----  0.9204348114270564\n",
+            " *********  8 train1172\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8630343625015569\n",
+            "dice ----  0.6649495755641007\n",
+            "dice ----  0.7979015334947539\n",
+            " *********  9 train1173\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9394286714900866\n",
+            "dice ----  0.9436866292216792\n",
+            "dice ----  0.9553525116705901\n",
+            " *********  10 train1175\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9031967243905188\n",
+            "dice ----  0.4720946144071938\n",
+            "dice ----  0.7210287443267775\n",
+            " *********  11 train1180\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9274108442503639\n",
+            "dice ----  0.975578733146782\n",
+            "dice ----  0.9634546947714208\n",
+            " *********  12 train1181\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9143920482911996\n",
+            "dice ----  0.9529460657554488\n",
+            "dice ----  0.8691376073629824\n",
+            " *********  13 train1182\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9283094705223309\n",
+            "dice ----  0.9609029590620332\n",
+            "dice ----  0.9298778608476651\n",
+            " *********  14 train1187\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9638507615862448\n",
+            "dice ----  0.9704545454545455\n",
+            "dice ----  0.943125654925452\n",
+            " *********  15 train1189\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.943969286934838\n",
+            "dice ----  0.3328224515513777\n",
+            "dice ----  0.8432959531935641\n",
+            " *********  16 train1193\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9641026081061949\n",
+            "dice ----  0.9689453030376435\n",
+            "dice ----  0.9569917325984532\n",
+            " *********  17 train12\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.949669643175707\n",
+            "dice ----  0.8809592382519764\n",
+            "dice ----  0.7684044919437965\n",
+            " *********  18 train120\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9638900452549451\n",
+            "dice ----  0.9717012487536204\n",
+            "dice ----  0.9239584185970369\n",
+            " *********  19 train1202\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9640560115886045\n",
+            "dice ----  0.9668935724571279\n",
+            "dice ----  0.9163341473529005\n",
+            " *********  20 train1209\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9638692885191148\n",
+            "dice ----  0.9579506021637069\n",
+            "dice ----  0.9134009610250935\n",
+            " *********  21 train121\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.930900407188562\n",
+            "dice ----  0.9380678458147411\n",
+            "dice ----  0.9096414234197455\n",
+            " *********  22 train1210\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9117828500925355\n",
+            "dice ----  0.9490297285225073\n",
+            "dice ----  0.9270256081199323\n",
+            " *********  23 train1214\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9587173519524179\n",
+            "dice ----  0.9499451634130291\n",
+            "dice ----  0.9229630763734722\n",
+            " *********  24 train1226\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9634393372968166\n",
+            "dice ----  0.9683859400408623\n",
+            "dice ----  0.9449307375074902\n",
+            " *********  25 train1231\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8270605798695686\n",
+            "dice ----  0.8402064296198097\n",
+            "dice ----  0.8254666275774455\n",
+            " *********  26 train1236\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9644635271547983\n",
+            "dice ----  0.9823810054047573\n",
+            "dice ----  0.9390576988733356\n",
+            " *********  27 train1239\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9258830538769257\n",
+            "dice ----  0.829025389775407\n",
+            "dice ----  0.917520551681147\n",
+            " *********  28 train1245\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9282065106615206\n",
+            "dice ----  0.921906265489325\n",
+            "dice ----  0.9231357406380823\n",
+            " *********  29 train127\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9660871062144948\n",
+            "dice ----  0.9837380011293054\n",
+            "dice ----  0.9251844359828653\n",
+            " *********  30 train130\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9458376156217883\n",
+            "dice ----  0.9269834270385477\n",
+            "dice ----  0.9029222154251565\n",
+            " *********  31 train132\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9556406159029817\n",
+            "dice ----  0.7770147949310194\n",
+            "dice ----  0.8290421229160501\n",
+            " *********  32 train134\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9328075145651171\n",
+            "dice ----  0.9760409696844923\n",
+            "dice ----  0.9440597092515921\n",
+            " *********  33 train138\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9454983697932919\n",
+            "dice ----  0.9653453247959334\n",
+            "dice ----  0.9028779157830961\n",
+            " *********  34 train14\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9163335173937052\n",
+            "dice ----  0.972555342806275\n",
+            "dice ----  0.9198576990952128\n",
+            " *********  35 train147\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9326358087872929\n",
+            "dice ----  0.9461400359066428\n",
+            "dice ----  0.8759887644077099\n",
+            " *********  36 train149\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.968175924394977\n",
+            "dice ----  0.9843962848297214\n",
+            "dice ----  0.935672514619883\n",
+            " *********  37 train163\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.959202676138911\n",
+            "dice ----  0.9578056413164472\n",
+            "dice ----  0.9478855939638533\n",
+            " *********  38 train165\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9601640370088946\n",
+            "dice ----  0.9833109203130926\n",
+            "dice ----  0.9355962887531115\n",
+            " *********  39 train173\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8396804012325094\n",
+            "dice ----  0.7028301886792453\n",
+            "dice ----  0.7456637472093423\n",
+            " *********  40 train177\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9128186914939496\n",
+            "dice ----  0.9539588654389031\n",
+            "dice ----  0.9444792755137583\n",
+            " *********  41 train179\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9088367705698238\n",
+            "dice ----  0.951937984496124\n",
+            "dice ----  0.9290144727773949\n",
+            " *********  42 train190\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7623635003739716\n",
+            "dice ----  0.9150351644174112\n",
+            "dice ----  0.9134495041879273\n",
+            " *********  43 train195\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8158863942205539\n",
+            "dice ----  0.9229496058190688\n",
+            "dice ----  0.8457065432168464\n",
+            " *********  44 train196\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.967000174782694\n",
+            "dice ----  0.9607072158798167\n",
+            "dice ----  0.9195527679301881\n",
+            " *********  45 train201\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7768621680330741\n",
+            "dice ----  0.9256789407557746\n",
+            "dice ----  0.9204984381822456\n",
+            " *********  46 train203\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8874629332555539\n",
+            "dice ----  0.8782668726782603\n",
+            "dice ----  0.86918426951029\n",
+            " *********  47 train21\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9452249639819038\n",
+            "dice ----  0.9665526863970444\n",
+            "dice ----  0.9245745852085063\n",
+            " *********  48 train210\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.886271171064293\n",
+            "dice ----  0.952016349558159\n",
+            "dice ----  0.8481396830154746\n",
+            " *********  49 train214\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9617543488045752\n",
+            "dice ----  0.9834292389564314\n",
+            "dice ----  0.9571915645914888\n",
+            " *********  50 train231\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.930222911556774\n",
+            "dice ----  0.9362426822778074\n",
+            "dice ----  0.8613283674736188\n",
+            " *********  51 train235\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8785537437035772\n",
+            "dice ----  0.9434960963345242\n",
+            "dice ----  0.9086326402016383\n",
+            " *********  52 train238\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9326398501750672\n",
+            "dice ----  0.9688831766217954\n",
+            "dice ----  0.9266657288882637\n",
+            " *********  53 train240\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "dice ----  0.9337967053852192\n",
+            "dice ----  0.8517131623291518\n",
+            "dice ----  0.8383483626008543\n",
+            " *********  54 train244\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.861219361570732\n",
+            "dice ----  0.9001923076923077\n",
+            "dice ----  0.8723882273646372\n",
+            " *********  55 train255\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9535726890621491\n",
+            "dice ----  0.97605569653198\n",
+            "dice ----  0.9631386366716357\n",
+            " *********  56 train260\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9596335096240144\n",
+            "dice ----  0.9169619544945916\n",
+            "dice ----  0.8770412444329697\n",
+            " *********  57 train261\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9511874848018157\n",
+            "dice ----  0.9760369308246485\n",
+            "dice ----  0.9179033283866864\n",
+            " *********  58 train262\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9569206684137086\n",
+            "dice ----  0.974333855799373\n",
+            "dice ----  0.8816296112284189\n",
+            " *********  59 train270\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8943410183701147\n",
+            "dice ----  0.9279887534451823\n",
+            "dice ----  0.801826846703733\n",
+            " *********  60 train280\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.940118103851254\n",
+            "dice ----  0.9698635112402508\n",
+            "dice ----  0.9264640372819792\n",
+            " *********  61 train284\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9476700802505025\n",
+            "dice ----  0.9798485065027869\n",
+            "dice ----  0.9510525415026527\n",
+            " *********  62 train291\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.868712693891109\n",
+            "dice ----  0.9600262553331146\n",
+            "dice ----  0.9451864218141347\n",
+            " *********  63 train294\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9441682057110385\n",
+            "dice ----  0.9020914249243055\n",
+            "dice ----  0.7793134471900351\n",
+            " *********  64 train295\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9527869181884404\n",
+            "dice ----  0.9841800513569307\n",
+            "dice ----  0.9450657380745106\n",
+            " *********  65 train300\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9215983136903916\n",
+            "dice ----  0.9590700377797152\n",
+            "dice ----  0.9249698505503229\n",
+            " *********  66 train301\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8617250519651405\n",
+            "dice ----  0.8905295315682281\n",
+            "dice ----  0.8904179408766565\n",
+            " *********  67 train302\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.96005494654952\n",
+            "dice ----  0.9786702491220627\n",
+            "dice ----  0.9634305317324185\n",
+            " *********  68 train306\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9039393427654684\n",
+            "dice ----  0.19496104548881632\n",
+            "dice ----  0.6170275882527441\n",
+            " *********  69 train308\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9511802459331259\n",
+            "dice ----  0.977131649121776\n",
+            "dice ----  0.9391680148431402\n",
+            " *********  70 train310\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9351762231083924\n",
+            "dice ----  0.9644301314801811\n",
+            "dice ----  0.9381164402863101\n",
+            " *********  71 train312\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.945742401498531\n",
+            "dice ----  0.9544008030584566\n",
+            "dice ----  0.9388984718544253\n",
+            " *********  72 train314\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9535634556328363\n",
+            "dice ----  0.928316220993644\n",
+            "dice ----  0.9282074483284122\n",
+            " *********  73 train321\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.946487040100843\n",
+            "dice ----  0.9773370110682977\n",
+            "dice ----  0.9315387469454075\n",
+            " *********  74 train323\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9639924795925653\n",
+            "dice ----  0.9632132407849474\n",
+            "dice ----  0.9155784377237547\n",
+            " *********  75 train334\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9226576576576576\n",
+            "dice ----  0.9819445554046496\n",
+            "dice ----  0.9366259350462296\n",
+            " *********  76 train349\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.6902401842023695\n",
+            "dice ----  0.9457386635988879\n",
+            "dice ----  0.8857820883202642\n",
+            " *********  77 train353\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9645473993849445\n",
+            "dice ----  0.9556962025316456\n",
+            "dice ----  0.9058266989731164\n",
+            " *********  78 train358\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.813154319507712\n",
+            "dice ----  0.9735630953655143\n",
+            "dice ----  0.9645731807479604\n",
+            " *********  79 train36\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9172823439454322\n",
+            "dice ----  0.9198037157982987\n",
+            "dice ----  0.7744569446097233\n",
+            " *********  80 train366\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9557911790057428\n",
+            "dice ----  0.8103699388290125\n",
+            "dice ----  0.7631160572337043\n",
+            " *********  81 train371\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9574686652620149\n",
+            "dice ----  0.7567290695805882\n",
+            "dice ----  0.7134806531771388\n",
+            " *********  82 train372\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9480609418282548\n",
+            "dice ----  0.9709809708958295\n",
+            "dice ----  0.8936972526485905\n",
+            " *********  83 train374\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8303919921331633\n",
+            "dice ----  0.883486617753436\n",
+            "dice ----  0.7685660018993352\n",
+            " *********  84 train375\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9540690925103078\n",
+            "dice ----  0.9778195878068147\n",
+            "dice ----  0.8825458608137817\n",
+            " *********  85 train379\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8819266217939405\n",
+            "dice ----  0.9672894665583537\n",
+            "dice ----  0.8996224194576571\n",
+            " *********  86 train384\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8376405799024649\n",
+            "dice ----  0.7796664786916943\n",
+            "dice ----  0.6255436931593515\n",
+            " *********  87 train390\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.963432986680443\n",
+            "dice ----  0.9649258452238701\n",
+            "dice ----  0.9251896667627004\n",
+            " *********  88 train393\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9382013274057656\n",
+            "dice ----  0.9675819104753115\n",
+            "dice ----  0.9336587350010169\n",
+            " *********  89 train399\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8141587995074474\n",
+            "dice ----  0.8085609883417435\n",
+            "dice ----  0.7652907887585126\n",
+            " *********  90 train40\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9550467928540688\n",
+            "dice ----  0.9464262573452198\n",
+            "dice ----  0.8768985002741947\n",
+            " *********  91 train402\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9195742021134847\n",
+            "dice ----  0.9655276762072879\n",
+            "dice ----  0.9493306117548178\n",
+            " *********  92 train404\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9097866985142093\n",
+            "dice ----  0.7778853662345775\n",
+            "dice ----  0.7081091346458188\n",
+            " *********  93 train413\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.942666163918692\n",
+            "dice ----  0.9787536595566708\n",
+            "dice ----  0.9251039919855392\n",
+            " *********  94 train421\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9535756286050289\n",
+            "dice ----  0.9767637054379997\n",
+            "dice ----  0.9220609527845464\n",
+            " *********  95 train422\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9321394263757377\n",
+            "dice ----  0.03322520993118505\n",
+            "dice ----  0.0\n",
+            " *********  96 train425\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9020618832975126\n",
+            "dice ----  0.9421718836173848\n",
+            "dice ----  0.8384825537688627\n",
+            " *********  97 train428\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9650810738074413\n",
+            "dice ----  0.972394607318639\n",
+            "dice ----  0.9493006083926993\n",
+            " *********  98 train430\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7734877551311492\n",
+            "dice ----  0.9417947446703024\n",
+            "dice ----  0.9357409462322204\n",
+            " *********  99 train435\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7548172037182579\n",
+            "dice ----  0.9053551265201858\n",
+            "dice ----  0.8487230745041665\n",
+            " *********  100 train436\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8680828645078744\n",
+            "dice ----  0.8609292419342116\n",
+            "dice ----  0.750994431185362\n",
+            " *********  101 train438\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8982462313419015\n",
+            "dice ----  0.9828552250076914\n",
+            "dice ----  0.9334968784890944\n",
+            " *********  102 train441\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.6192857054676988\n",
+            "dice ----  0.39809710790606356\n",
+            "dice ----  0.5465024296530682\n",
+            " *********  103 train442\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8382109466303078\n",
+            "dice ----  0.9152617898254735\n",
+            "dice ----  0.8878755801644818\n",
+            " *********  104 train444\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8919127119247525\n",
+            "dice ----  0.9614984047213635\n",
+            "dice ----  0.8636696441645921\n",
+            " *********  105 train457\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9181209411636531\n",
+            "dice ----  0.965183915474752\n",
+            "dice ----  0.9583754724011284\n",
+            " *********  106 train458\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9413065914739425\n",
+            "dice ----  0.9807693389354797\n",
+            "dice ----  0.9141246797608882\n",
+            " *********  107 train459\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9434736162604042\n",
+            "dice ----  0.9637294471381748\n",
+            "dice ----  0.9579827218669359\n",
+            " *********  108 train476\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9432339880521089\n",
+            "dice ----  0.8961815235008104\n",
+            "dice ----  0.8381321887363099\n",
+            " *********  109 train477\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9237070529459377\n",
+            "dice ----  0.9478672985781991\n",
+            "dice ----  0.9328194522779971\n",
+            " *********  110 train490\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8055458464406384\n",
+            "dice ----  0.9201316183069099\n",
+            "dice ----  0.8866758241758241\n",
+            " *********  111 train515\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8539252778434618\n",
+            "dice ----  0.8613539845116163\n",
+            "dice ----  0.8550615423260487\n",
+            " *********  112 train520\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9223786879256727\n",
+            "dice ----  0.9094795864727527\n",
+            "dice ----  0.8068900933624678\n",
+            " *********  113 train529\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8481084993714046\n",
+            "dice ----  0.965973051338905\n",
+            "dice ----  0.9510929091861517\n",
+            " *********  114 train532\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8130828971227206\n",
+            "dice ----  0.9102347870318315\n",
+            "dice ----  0.8843488542648885\n",
+            " *********  115 train534\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9534914477164521\n",
+            "dice ----  0.9652459737866348\n",
+            "dice ----  0.9275818232746547\n",
+            " *********  116 train539\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9325843703245805\n",
+            "dice ----  0.9217939081570564\n",
+            "dice ----  0.898884946781551\n",
+            " *********  117 train540\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9512989557841199\n",
+            "dice ----  0.9751554755816523\n",
+            "dice ----  0.8936388071163446\n",
+            " *********  118 train554\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9528798690343785\n",
+            "dice ----  0.9402404189478696\n",
+            "dice ----  0.889861318790864\n",
+            " *********  119 train557\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9427471461738367\n",
+            "dice ----  0.9369301249833207\n",
+            "dice ----  0.8565529126766059\n",
+            " *********  120 train562\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9283216783216783\n",
+            "dice ----  0.8813201272864796\n",
+            "dice ----  0.7779570737001442\n",
+            " *********  121 train563\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9155067072862841\n",
+            "dice ----  0.9493387004025302\n",
+            "dice ----  0.8793714802197038\n",
+            " *********  122 train565\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9660745968369492\n",
+            "dice ----  0.965515416637268\n",
+            "dice ----  0.9394152619287994\n",
+            " *********  123 train573\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9687809921715094\n",
+            "dice ----  0.9272657245281901\n",
+            "dice ----  0.7863744388698178\n",
+            " *********  124 train577\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7732223538414769\n",
+            "dice ----  0.9133806457677304\n",
+            "dice ----  0.8928322085175704\n",
+            " *********  125 train581\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.788659114857745\n",
+            "dice ----  0.9225142085420066\n",
+            "dice ----  0.8693687912313577\n",
+            " *********  126 train584\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8968921510525754\n",
+            "dice ----  0.8500606950709608\n",
+            "dice ----  0.6941053054482959\n",
+            " *********  127 train587\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8752256816156138\n",
+            "dice ----  0.8929168308490836\n",
+            "dice ----  0.8071241850650374\n",
+            " *********  128 train591\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9053839497979653\n",
+            "dice ----  0.9608205406874066\n",
+            "dice ----  0.9296003846367217\n",
+            " *********  129 train600\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9344495706496545\n",
+            "dice ----  0.9719679285496803\n",
+            "dice ----  0.9361570327925974\n",
+            " *********  130 train607\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9723113343215558\n",
+            "dice ----  0.9595996420640971\n",
+            "dice ----  0.924138504385418\n",
+            " *********  131 train615\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.89801329248849\n",
+            "dice ----  0.9312325136209689\n",
+            "dice ----  0.9069288092595529\n",
+            " *********  132 train633\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7513091319939087\n",
+            "dice ----  0.8167363108482119\n",
+            "dice ----  0.7657721129827708\n",
+            " *********  133 train643\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8405944939328881\n",
+            "dice ----  0.9130867238194478\n",
+            "dice ----  0.8799163652802893\n",
+            " *********  134 train651\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9213602289285652\n",
+            "dice ----  0.9582860216338971\n",
+            "dice ----  0.932211022203748\n",
+            " *********  135 train653\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9687808433423933\n",
+            "dice ----  0.9795118360946887\n",
+            "dice ----  0.9428818450275653\n",
+            " *********  136 train657\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9362811914123048\n",
+            "dice ----  0.9553839732888147\n",
+            "dice ----  0.9220829968478912\n",
+            " *********  137 train666\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9668026319707029\n",
+            "dice ----  0.9494807131812646\n",
+            "dice ----  0.8787016545473706\n",
+            " *********  138 train679\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.964385704152453\n",
+            "dice ----  0.975717769286108\n",
+            "dice ----  0.948009318085557\n",
+            " *********  139 train681\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9562832084532658\n",
+            "dice ----  0.9763459817552858\n",
+            "dice ----  0.9597820753599648\n",
+            " *********  140 train683\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7937834224598931\n",
+            "dice ----  0.8649343967069719\n",
+            "dice ----  0.8130563798219584\n",
+            " *********  141 train684\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9558200557698793\n",
+            "dice ----  0.9814248972324195\n",
+            "dice ----  0.9351233258398506\n",
+            " *********  142 train691\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9554040326917587\n",
+            "dice ----  0.8157984337759618\n",
+            "dice ----  0.7078365433608478\n",
+            " *********  143 train693\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9489852883226061\n",
+            "dice ----  0.9618133095131756\n",
+            "dice ----  0.9016526686534815\n",
+            " *********  144 train696\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9606618435738559\n",
+            "dice ----  0.8817106912681626\n",
+            "dice ----  0.7486677750778019\n",
+            " *********  145 train699\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9145692678388243\n",
+            "dice ----  0.9628919891828\n",
+            "dice ----  0.9303086221947203\n",
+            " *********  146 train710\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9699645750813475\n",
+            "dice ----  0.9773031852733374\n",
+            "dice ----  0.9634515627880033\n",
+            " *********  147 train715\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9378172028774439\n",
+            "dice ----  0.9720532561997532\n",
+            "dice ----  0.9262591311034217\n",
+            " *********  148 train713\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.95928923988154\n",
+            "dice ----  0.977253900317548\n",
+            "dice ----  0.9345758230419502\n",
+            " *********  149 train717\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8973866700238148\n",
+            "dice ----  0.9378509196515005\n",
+            "dice ----  0.9085237577076533\n",
+            " *********  150 train725\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9545662813012556\n",
+            "dice ----  0.9498481490601658\n",
+            "dice ----  0.9245589998287378\n",
+            " *********  151 train726\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9364256379945549\n",
+            "dice ----  0.9503928043347836\n",
+            "dice ----  0.8699690803006586\n",
+            " *********  152 train734\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7625802607642916\n",
+            "dice ----  0.9422611540952316\n",
+            "dice ----  0.8092344178462907\n",
+            " *********  153 train735\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8851786642385466\n",
+            "dice ----  0.7863123407353476\n",
+            "dice ----  0.8799727351592515\n",
+            " *********  154 train741\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9290299572039943\n",
+            "dice ----  0.8832149069788012\n",
+            "dice ----  0.7984216383933088\n",
+            " *********  155 train742\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9381987308056049\n",
+            "dice ----  0.804544455527566\n",
+            "dice ----  0.6996679746453365\n",
+            " *********  156 train743\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8957454322410364\n",
+            "dice ----  0.7473363774733638\n",
+            "dice ----  0.7230411171450737\n",
+            " *********  157 train750\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8949679563256587\n",
+            "dice ----  0.9703191251952689\n",
+            "dice ----  0.9385074284403143\n",
+            " *********  158 train763\n",
+            "(128, 128, 128)\n",
+            "[0. 2.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.793814007693815\n",
+            "dice ----  0.0\n",
+            "dice ----  0.0\n",
+            " *********  159 train768\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.7392634767914503\n",
+            "dice ----  0.9214772836423014\n",
+            "dice ----  0.8975162768266216\n",
+            " *********  160 train777\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9244382091609442\n",
+            "dice ----  0.9374256523609857\n",
+            "dice ----  0.8411662315056571\n",
+            " *********  161 train798\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9019186187436683\n",
+            "dice ----  0.923447838303272\n",
+            "dice ----  0.8247958240715303\n",
+            " *********  162 train804\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9257290708730146\n",
+            "dice ----  0.9719303228249551\n",
+            "dice ----  0.9213462759684297\n",
+            " *********  163 train805\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8962898273779276\n",
+            "dice ----  0.859715644331249\n",
+            "dice ----  0.6563121272365805\n",
+            " *********  164 train807\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9345465176807832\n",
+            "dice ----  0.92719350704827\n",
+            "dice ----  0.8622305529522024\n",
+            " *********  165 train815\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9225986210370557\n",
+            "dice ----  0.9419953596287703\n",
+            "dice ----  0.8838763575605681\n",
+            " *********  166 train818\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8464619936503714\n",
+            "dice ----  0.8829858190992663\n",
+            "dice ----  0.770593705643385\n",
+            " *********  167 train823\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8870645122014854\n",
+            "dice ----  0.9302072256658972\n",
+            "dice ----  0.8703974777615133\n",
+            " *********  168 train825\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8977190866794821\n",
+            "dice ----  0.9436591835926674\n",
+            "dice ----  0.8337914812820586\n",
+            " *********  169 train83\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9571064711150918\n",
+            "dice ----  0.9692469983814209\n",
+            "dice ----  0.9303074020641439\n",
+            " *********  170 train834\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9489572393098274\n",
+            "dice ----  0.9419597626284556\n",
+            "dice ----  0.8814711443233997\n",
+            " *********  171 train835\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9279893372664567\n",
+            "dice ----  0.9577429635602657\n",
+            "dice ----  0.8843502909145159\n",
+            " *********  172 train838\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.934383895648414\n",
+            "dice ----  0.9617433262313573\n",
+            "dice ----  0.8952388297598848\n",
+            " *********  173 train840\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9518027651324791\n",
+            "dice ----  0.9645889501428514\n",
+            "dice ----  0.8207333561387219\n",
+            " *********  174 train851\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7543987912462835\n",
+            "dice ----  0.9355614492493033\n",
+            "dice ----  0.8780165099088216\n",
+            " *********  175 train86\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.928192244675041\n",
+            "dice ----  0.9220732017591684\n",
+            "dice ----  0.7386591526413322\n",
+            " *********  176 train865\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9453646177275239\n",
+            "dice ----  0.9350527988038501\n",
+            "dice ----  0.8492903311054097\n",
+            " *********  177 train869\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9361237167088231\n",
+            "dice ----  0.9645802082701095\n",
+            "dice ----  0.9287816500046716\n",
+            " *********  178 train874\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.868886080175518\n",
+            "dice ----  0.9143979156439981\n",
+            "dice ----  0.847210381737236\n",
+            " *********  179 train875\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.955240718630428\n",
+            "dice ----  0.8017557054261655\n",
+            "dice ----  0.5023483765570758\n",
+            " *********  180 train884\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8916883443819753\n",
+            "dice ----  0.8354417360896842\n",
+            "dice ----  0.8382929153870592\n",
+            " *********  181 train886\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9112457413501283\n",
+            "dice ----  0.930557687780814\n",
+            "dice ----  0.8888863583308186\n",
+            " *********  182 train893\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9390316796174537\n",
+            "dice ----  0.9268341407381511\n",
+            "dice ----  0.8027119654298912\n",
+            " *********  183 train899\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9181519520633916\n",
+            "dice ----  0.9232672127372611\n",
+            "dice ----  0.8508734892067346\n",
+            " *********  184 train907\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9543200601051841\n",
+            "dice ----  0.9612040841518684\n",
+            "dice ----  0.9069267826917234\n",
+            " *********  185 train920\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.953464759281413\n",
+            "dice ----  0.9574901209435995\n",
+            "dice ----  0.8826846225433842\n",
+            " *********  186 train922\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9420261524237221\n",
+            "dice ----  0.9816969696969697\n",
+            "dice ----  0.9471929931269387\n",
+            " *********  187 train94\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9251112896082155\n",
+            "dice ----  0.9574761827177856\n",
+            "dice ----  0.9270230186302595\n",
+            " *********  188 train941\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9649538799926258\n",
+            "dice ----  0.9791693074745426\n",
+            "dice ----  0.9189055188869264\n",
+            " *********  189 train944\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9486821278417292\n",
+            "dice ----  0.9690284895103399\n",
+            "dice ----  0.9343743341551761\n",
+            " *********  190 train945\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9573515629307018\n",
+            "dice ----  0.9652232640137528\n",
+            "dice ----  0.9288915704188933\n",
+            " *********  191 train96\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9351413946463036\n",
+            "dice ----  0.8439948489597071\n",
+            "dice ----  0.681069851986497\n",
+            " *********  192 train962\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9535299268690981\n",
+            "dice ----  0.9681667876441649\n",
+            "dice ----  0.8939455505891913\n",
+            " *********  193 train963\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9501500369200798\n",
+            "dice ----  0.7981415929203539\n",
+            "dice ----  0.8056508342538224\n",
+            " *********  194 train969\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9595282502909871\n",
+            "dice ----  0.9782245192673101\n",
+            "dice ----  0.9448806091520663\n",
+            " *********  195 train970\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9661074709715952\n",
+            "dice ----  0.9798049182580024\n",
+            "dice ----  0.9324589175335444\n",
+            " *********  196 train971\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8716731142804961\n",
+            "dice ----  0.814239897370109\n",
+            "dice ----  0.8791413905739618\n",
+            " *********  197 train975\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9233037180679502\n",
+            "dice ----  0.9243609631078438\n",
+            "dice ----  0.8630202037644621\n",
+            " *********  198 train978\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8343231095274692\n",
+            "dice ----  0.9639024390243902\n",
+            "dice ----  0.9497607655502392\n",
+            " *********  199 train982\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9558617981032471\n",
+            "dice ----  0.9670409272002898\n",
+            "dice ----  0.9614237422946907\n",
+            " *********  200 train983\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8758558893641482\n",
+            "dice ----  0.9431507560292032\n",
+            "dice ----  0.9325605900948367\n",
+            " *********  201 train984\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9548206650104241\n",
+            "dice ----  0.906003037158707\n",
+            "dice ----  0.9297916595628601\n",
+            " *********  202 train99\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.922754598114399\n",
+            "dice ----  0.9180593594544744\n",
+            "dice ----  0.8389566085618005\n",
+            " *********  203 train995\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9018893038358337\n",
+            "dice ----  0.9689965891157155\n",
+            "dice ----  0.8940704138357011\n",
+            " *********  204 train998\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9351994147211484\n",
+            "dice ----  0.8153115828112537\n",
+            "dice ----  0.8401801638402708\n",
+            " *********  205 train1001\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8267674119615884\n",
+            "dice ----  0.9115108046656931\n",
+            "dice ----  0.8586831792495724\n",
+            " *********  206 train1002\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9339655320352498\n",
+            "dice ----  0.959322245063228\n",
+            "dice ----  0.8764184672780974\n",
+            " *********  207 train1003\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9214087722158812\n",
+            "dice ----  0.7909576059065221\n",
+            "dice ----  0.8940198966949789\n",
+            " *********  208 train1008\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9376099726591051\n",
+            "dice ----  0.9466653809226332\n",
+            "dice ----  0.6837085485605486\n",
+            " *********  209 train1010\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9178230663989624\n",
+            "dice ----  0.9244756753424869\n",
+            "dice ----  0.817590766308715\n",
+            " *********  210 train1012\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9276141172003636\n",
+            "dice ----  0.7422249952006144\n",
+            "dice ----  0.41565217391304343\n",
+            " *********  211 train1018\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8367215321201245\n",
+            "dice ----  0.6465124430424115\n",
+            "dice ----  0.0\n",
+            " *********  212 train1024\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7767395466688068\n",
+            "dice ----  0.688598683209839\n",
+            "dice ----  0.0\n",
+            " *********  213 train1025\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9534317654031528\n",
+            "dice ----  0.9546223993884126\n",
+            "dice ----  0.8754100794180955\n",
+            " *********  214 train103\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8309198703889746\n",
+            "dice ----  0.958139417079827\n",
+            "dice ----  0.9305736325830926\n",
+            " *********  215 train1034\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8913973548016101\n",
+            "dice ----  0.9074734327944892\n",
+            "dice ----  0.8584945829587927\n",
+            " *********  216 train1036\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8609423692789339\n",
+            "dice ----  0.9151267013389399\n",
+            "dice ----  0.8884342279456011\n",
+            " *********  217 train1040\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9547608395949931\n",
+            "dice ----  0.9127836384565653\n",
+            "dice ----  0.8873103958564558\n",
+            " *********  218 train1042\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9124493130787388\n",
+            "dice ----  0.9038454882143228\n",
+            "dice ----  0.7390235052211427\n",
+            " *********  219 train1052\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8718531401947467\n",
+            "dice ----  0.9373041712168686\n",
+            "dice ----  0.8926999266324285\n",
+            " *********  220 train1055\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9472474635969024\n",
+            "dice ----  0.9621049743651298\n",
+            "dice ----  0.9002777669105277\n",
+            " *********  221 train1060\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9239169401318148\n",
+            "dice ----  0.8690671031096563\n",
+            "dice ----  0.45890410958904104\n",
+            " *********  222 train107\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7397446115755736\n",
+            "dice ----  0.8566772716691239\n",
+            "dice ----  0.8083749224817648\n",
+            " *********  223 train1074\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.757827422971514\n",
+            "dice ----  0.7239309282246578\n",
+            "dice ----  0.6586460032626427\n",
+            " *********  224 train1077\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "dice ----  0.9148730350665054\n",
+            "dice ----  0.718258474473894\n",
+            "dice ----  0.8846681137895579\n",
+            " *********  225 train1079\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8191366510860599\n",
+            "dice ----  0.7920886680680298\n",
+            "dice ----  0.9036334913112164\n",
+            " *********  226 train1085\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9614427822260404\n",
+            "dice ----  0.8750303439374293\n",
+            "dice ----  0.41925644312806687\n",
+            " *********  227 train1097\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8543807121711727\n",
+            "dice ----  0.6746539599979469\n",
+            "dice ----  0.675483098671835\n",
+            " *********  228 train1123\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9298824027221919\n",
+            "dice ----  0.962833666822768\n",
+            "dice ----  0.928624155357447\n",
+            " *********  229 train114\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9677099038780971\n",
+            "dice ----  0.9910106370090378\n",
+            "dice ----  0.9731906429608659\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " ******  230\n",
+            " *********  0 train1145\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 497ms/step\n",
+            "dice ----  0.8281085373116096\n",
+            "dice ----  0.9736366165831942\n",
+            "dice ----  0.9080115576569477\n",
+            " *********  1 train1160\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9508207920318617\n",
+            "dice ----  0.9696338297581637\n",
+            "dice ----  0.915060522624552\n",
+            " *********  2 train1179\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9506302670285788\n",
+            "dice ----  0.9825512253133956\n",
+            "dice ----  0.9604753444208196\n",
+            " *********  3 train118\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9478896197288434\n",
+            "dice ----  0.5618213292766092\n",
+            "dice ----  0.802724126381601\n",
+            " *********  4 train1183\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7885955719679958\n",
+            "dice ----  0.894669070306464\n",
+            "dice ----  0.8625624976145949\n",
+            " *********  5 train1185\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9717722843464486\n",
+            "dice ----  0.9772937954843316\n",
+            "dice ----  0.9636023849781971\n",
+            " *********  6 train1192\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9601257284321497\n",
+            "dice ----  0.9653275675675675\n",
+            "dice ----  0.9234932223725525\n",
+            " *********  7 train1207\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9246012529305507\n",
+            "dice ----  0.9504748289594609\n",
+            "dice ----  0.8814564870777559\n",
+            " *********  8 train1213\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8915962396733453\n",
+            "dice ----  0.8827444956477215\n",
+            "dice ----  0.890870185449358\n",
+            " *********  9 train1227\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.910166560480568\n",
+            "dice ----  0.9438647624599242\n",
+            "dice ----  0.8501551245532232\n",
+            " *********  10 train1237\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7968687108459848\n",
+            "dice ----  0.9395987081879612\n",
+            "dice ----  0.8359387855849926\n",
+            " *********  11 train1240\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9187393526405452\n",
+            "dice ----  0.1760516848983188\n",
+            "dice ----  0.6510487605557069\n",
+            " *********  12 train1241\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9583765457473328\n",
+            "dice ----  0.963536817013669\n",
+            "dice ----  0.9444849073399693\n",
+            " *********  13 train1248\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9467830585192369\n",
+            "dice ----  0.6218203470097068\n",
+            "dice ----  0.5053556715188134\n",
+            " *********  14 train129\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9525621839199092\n",
+            "dice ----  0.976021119014446\n",
+            "dice ----  0.9510361739297678\n",
+            " *********  15 train131\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7926839239285072\n",
+            "dice ----  0.8597133119486768\n",
+            "dice ----  0.8649765695491457\n",
+            " *********  16 train139\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9403803078682743\n",
+            "dice ----  0.9761929089608664\n",
+            "dice ----  0.9403421882035119\n",
+            " *********  17 train142\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9506667978938045\n",
+            "dice ----  0.9534751773049646\n",
+            "dice ----  0.8972557194975109\n",
+            " *********  18 train143\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9528175918576964\n",
+            "dice ----  0.9030637056248987\n",
+            "dice ----  0.8468743747840163\n",
+            " *********  19 train146\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9122348812275319\n",
+            "dice ----  0.8257909758237365\n",
+            "dice ----  0.7189826672225862\n",
+            " *********  20 train152\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9690084896634411\n",
+            "dice ----  0.9857722820216768\n",
+            "dice ----  0.9508818566030973\n",
+            " *********  21 train156\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8823357522037696\n",
+            "dice ----  0.9137288342063589\n",
+            "dice ----  0.8833106388017288\n",
+            " *********  22 train160\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9251441798422849\n",
+            "dice ----  0.9237531155361403\n",
+            "dice ----  0.8972543312155392\n",
+            " *********  23 train167\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9413435281267067\n",
+            "dice ----  0.9080843245334107\n",
+            "dice ----  0.8769208591610206\n",
+            " *********  24 train17\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9460850717962612\n",
+            "dice ----  0.9371363003032538\n",
+            "dice ----  0.9281380842519013\n",
+            " *********  25 train174\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9436151489472655\n",
+            "dice ----  0.963971340839304\n",
+            "dice ----  0.9367215657648615\n",
+            " *********  26 train18\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7235115066209117\n",
+            "dice ----  0.8355534598608982\n",
+            "dice ----  0.8052238805970149\n",
+            " *********  27 train180\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9501758320167167\n",
+            "dice ----  0.9646592429052584\n",
+            "dice ----  0.942183449240772\n",
+            " *********  28 train184\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9463851384423075\n",
+            "dice ----  0.989081332928188\n",
+            "dice ----  0.9556903533681717\n",
+            " *********  29 train186\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9651902315370164\n",
+            "dice ----  0.9687067659266091\n",
+            "dice ----  0.9234402344655998\n",
+            " *********  30 train189\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9479131046318818\n",
+            "dice ----  0.9572041287646699\n",
+            "dice ----  0.9248829110123694\n",
+            " *********  31 train191\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9525289564438214\n",
+            "dice ----  0.9285231474731945\n",
+            "dice ----  0.9267578125\n",
+            " *********  32 train2\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9668475417739437\n",
+            "dice ----  0.9759227985524729\n",
+            "dice ----  0.939859624710962\n",
+            " *********  33 train22\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8664767144822165\n",
+            "dice ----  0.9437417760949542\n",
+            "dice ----  0.8687145662726498\n",
+            " *********  34 train225\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9467785560718497\n",
+            "dice ----  0.9755716517540122\n",
+            "dice ----  0.9592971435847375\n",
+            " *********  35 train23\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9225972650231125\n",
+            "dice ----  0.9618171456484572\n",
+            "dice ----  0.9249506571029702\n",
+            " *********  36 train230\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9643678237963416\n",
+            "dice ----  0.9776987775233813\n",
+            "dice ----  0.927417948449653\n",
+            " *********  37 train234\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9148371978293044\n",
+            "dice ----  0.9178247072983915\n",
+            "dice ----  0.8044493112788889\n",
+            " *********  38 train241\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9562572819884683\n",
+            "dice ----  0.9673921584033943\n",
+            "dice ----  0.9377314035678223\n",
+            " *********  39 train247\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9532056278410869\n",
+            "dice ----  0.9767079911984115\n",
+            "dice ----  0.9202347417840375\n",
+            " *********  40 train249\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9265616723569177\n",
+            "dice ----  0.9529358794395741\n",
+            "dice ----  0.9129939773946044\n",
+            " *********  41 train253\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9406017435039737\n",
+            "dice ----  0.9557891045808293\n",
+            "dice ----  0.948584689018347\n",
+            " *********  42 train256\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9179900142750788\n",
+            "dice ----  0.9774270050215552\n",
+            "dice ----  0.9687564435087415\n",
+            " *********  43 train265\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9333083443861369\n",
+            "dice ----  0.9574935873946501\n",
+            "dice ----  0.924972972972973\n",
+            " *********  44 train272\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9445771752416935\n",
+            "dice ----  0.9792483755780601\n",
+            "dice ----  0.9003633905180428\n",
+            " *********  45 train279\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9403942220711351\n",
+            "dice ----  0.9520966292477114\n",
+            "dice ----  0.9262352392790553\n",
+            " *********  46 train289\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9176170770098664\n",
+            "dice ----  0.9449549416457379\n",
+            "dice ----  0.8842910680824485\n",
+            " *********  47 train290\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9380137357822971\n",
+            "dice ----  0.974658507121902\n",
+            "dice ----  0.943594662080598\n",
+            " *********  48 train293\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9662383366350855\n",
+            "dice ----  0.9882858772430132\n",
+            "dice ----  0.9719470261805059\n",
+            " *********  49 train309\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9125878655239887\n",
+            "dice ----  0.9220527904636582\n",
+            "dice ----  0.8867730001819395\n",
+            " *********  50 train311\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9053279458748356\n",
+            "dice ----  0.9282511210762332\n",
+            "dice ----  0.826904618009883\n",
+            " *********  51 train317\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9361356797237995\n",
+            "dice ----  0.9409412775816004\n",
+            "dice ----  0.8876525425132051\n",
+            " *********  52 train324\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9081533754378852\n",
+            "dice ----  0.9627179410572142\n",
+            "dice ----  0.9019134360481902\n",
+            " *********  53 train329\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9331766499396558\n",
+            "dice ----  0.9649938962024481\n",
+            "dice ----  0.8708308780200353\n",
+            " *********  54 train331\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9339954899029569\n",
+            "dice ----  0.9446518834616069\n",
+            "dice ----  0.829673590504451\n",
+            " *********  55 train341\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.18127207046312455\n",
+            "dice ----  0.9227105101413645\n",
+            "dice ----  0.8818316100443131\n",
+            " *********  56 train343\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.94516036312247\n",
+            "dice ----  0.9763078773301263\n",
+            "dice ----  0.933210332103321\n",
+            " *********  57 train347\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9198140886525152\n",
+            "dice ----  0.8668229031044759\n",
+            "dice ----  0.8271633579690452\n",
+            " *********  58 train348\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7755920942125007\n",
+            "dice ----  0.8480410659017136\n",
+            "dice ----  0.843679525222552\n",
+            " *********  59 train351\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9392443697318693\n",
+            "dice ----  0.9817409979359389\n",
+            "dice ----  0.9300480585857045\n",
+            " *********  60 train352\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9556192160066247\n",
+            "dice ----  0.9815481696841549\n",
+            "dice ----  0.9313984168865436\n",
+            " *********  61 train355\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9411605981619096\n",
+            "dice ----  0.9814355640633632\n",
+            "dice ----  0.9287667665060555\n",
+            " *********  62 train357\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.863054471365098\n",
+            "dice ----  0.9826318045435062\n",
+            "dice ----  0.9225176784649318\n",
+            " *********  63 train364\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9003076631798173\n",
+            "dice ----  0.7823213548120611\n",
+            "dice ----  0.749459108610991\n",
+            " *********  64 train365\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9522502640115849\n",
+            "dice ----  0.9733285736141568\n",
+            "dice ----  0.8819547889716451\n",
+            " *********  65 train368\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8245778084361667\n",
+            "dice ----  0.7210355254434617\n",
+            "dice ----  0.7924127755782233\n",
+            " *********  66 train373\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8363270082150138\n",
+            "dice ----  0.8984812508400162\n",
+            "dice ----  0.845667715882195\n",
+            " *********  67 train378\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9516206877405191\n",
+            "dice ----  0.2797691687178109\n",
+            "dice ----  0.371725313904821\n",
+            " *********  68 train38\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8825914197543314\n",
+            "dice ----  0.9662240184757506\n",
+            "dice ----  0.9181974362362891\n",
+            " *********  69 train386\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8838823799338331\n",
+            "dice ----  0.8951610113797517\n",
+            "dice ----  0.9059338604097594\n",
+            " *********  70 train389\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9324862730861733\n",
+            "dice ----  0.8836389891696751\n",
+            "dice ----  0.7727925644275454\n",
+            " *********  71 train39\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9352778604532506\n",
+            "dice ----  0.9601610474853247\n",
+            "dice ----  0.9284380553868304\n",
+            " *********  72 train391\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9508344099593363\n",
+            "dice ----  0.982521870618989\n",
+            "dice ----  0.9060278483274412\n",
+            " *********  73 train392\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.7918964841197229\n",
+            "dice ----  0.9674402140246019\n",
+            "dice ----  0.904978672280578\n",
+            " *********  74 train406\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9507664609219472\n",
+            "dice ----  0.9544473901703393\n",
+            "dice ----  0.8597953603246009\n",
+            " *********  75 train407\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9569142306680106\n",
+            "dice ----  0.9750002570932015\n",
+            "dice ----  0.9507968598209363\n",
+            " *********  76 train409\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9278065557513161\n",
+            "dice ----  0.9687169197225601\n",
+            "dice ----  0.9381147628051242\n",
+            " *********  77 train410\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9377826486779285\n",
+            "dice ----  0.5646891829833424\n",
+            "dice ----  0.8331772784019975\n",
+            " *********  78 train417\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.92822741901267\n",
+            "dice ----  0.9170519262981575\n",
+            "dice ----  0.9426177174780527\n",
+            " *********  79 train418\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9151210591920339\n",
+            "dice ----  0.9587606640179864\n",
+            "dice ----  0.9204964520969505\n",
+            " *********  80 train42\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9554460142312056\n",
+            "dice ----  0.9737535369304322\n",
+            "dice ----  0.9612514057867294\n",
+            " *********  81 train420\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.950062893081761\n",
+            "dice ----  0.9611512890519164\n",
+            "dice ----  0.9409980477888386\n",
+            " *********  82 train427\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9620670769450143\n",
+            "dice ----  0.910000814177545\n",
+            "dice ----  0.5215949924655152\n",
+            " *********  83 train432\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9037210549338902\n",
+            "dice ----  0.9185628011956323\n",
+            "dice ----  0.8660200060624432\n",
+            " *********  84 train434\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9615709218069636\n",
+            "dice ----  0.6252522269796742\n",
+            "dice ----  0.7456314926189174\n",
+            " *********  85 train447\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8541531536459758\n",
+            "dice ----  0.6827276064610867\n",
+            "dice ----  0.8965383119408791\n",
+            " *********  86 train449\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9507999901992013\n",
+            "dice ----  0.9841059328180367\n",
+            "dice ----  0.9360144828794457\n",
+            " *********  87 train45\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.94682479435519\n",
+            "dice ----  0.9721045909793641\n",
+            "dice ----  0.9101144680052543\n",
+            " *********  88 train450\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8427383929174833\n",
+            "dice ----  0.9736666221093436\n",
+            "dice ----  0.9534452624331339\n",
+            " *********  89 train453\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8533154206207268\n",
+            "dice ----  0.965806985444078\n",
+            "dice ----  0.8811737282325518\n",
+            " *********  90 train464\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9513421678559293\n",
+            "dice ----  0.14549934242288032\n",
+            "dice ----  0.6791501620453727\n",
+            " *********  91 train468\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.930751653206075\n",
+            "dice ----  0.9650199447683339\n",
+            "dice ----  0.9291979203873477\n",
+            " *********  92 train47\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9709328739945153\n",
+            "dice ----  0.9653173564623054\n",
+            "dice ----  0.9333766454746547\n",
+            " *********  93 train475\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9367356319246073\n",
+            "dice ----  0.8569471521154961\n",
+            "dice ----  0.8291820143768154\n",
+            " *********  94 train485\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9554515098722416\n",
+            "dice ----  0.9852887788331807\n",
+            "dice ----  0.9441385397839519\n",
+            " *********  95 train497\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9452079024708828\n",
+            "dice ----  0.9581322551193426\n",
+            "dice ----  0.9170884722321238\n",
+            " *********  96 train50\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.963204422081314\n",
+            "dice ----  0.9499040450614361\n",
+            "dice ----  0.8991686971477713\n",
+            " *********  97 train507\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9488557034091679\n",
+            "dice ----  0.969335197148866\n",
+            "dice ----  0.8792385868688498\n",
+            " *********  98 train510\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.95915385592682\n",
+            "dice ----  0.9623737723059897\n",
+            "dice ----  0.9264293805520778\n",
+            " *********  99 train514\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9438041695947529\n",
+            "dice ----  0.9560631513844662\n",
+            "dice ----  0.8527270368074278\n",
+            " *********  100 train517\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8602671755725191\n",
+            "dice ----  0.9367819822158759\n",
+            "dice ----  0.9047003018542475\n",
+            " *********  101 train521\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.966307301776915\n",
+            "dice ----  0.973405885959534\n",
+            "dice ----  0.9374962617381423\n",
+            " *********  102 train525\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9134372570074425\n",
+            "dice ----  0.9675742172086438\n",
+            "dice ----  0.9310467102930273\n",
+            " *********  103 train53\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9404254924337314\n",
+            "dice ----  0.9730444848685299\n",
+            "dice ----  0.968838663140178\n",
+            " *********  104 train530\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9342189005873475\n",
+            "dice ----  0.9684285164153377\n",
+            "dice ----  0.9285726864488861\n",
+            " *********  105 train533\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9266062484234803\n",
+            "dice ----  0.8940771083490093\n",
+            "dice ----  0.8521594251945956\n",
+            " *********  106 train535\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9573370574654272\n",
+            "dice ----  0.3164378187524519\n",
+            "dice ----  0.8531093221327732\n",
+            " *********  107 train538\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9509482362303892\n",
+            "dice ----  0.9463326121850792\n",
+            "dice ----  0.9316084017733847\n",
+            " *********  108 train544\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9557307752867508\n",
+            "dice ----  0.9704376927619922\n",
+            "dice ----  0.954433521046101\n",
+            " *********  109 train55\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8670107481905843\n",
+            "dice ----  0.7235326385079539\n",
+            "dice ----  0.7870261821023837\n",
+            " *********  110 train551\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9023397593421947\n",
+            "dice ----  0.9659245285675069\n",
+            "dice ----  0.8676991050697684\n",
+            " *********  111 train555\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "dice ----  0.9348435493681152\n",
+            "dice ----  0.9651555320409231\n",
+            "dice ----  0.9316988530094419\n",
+            " *********  112 train560\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9684950638092945\n",
+            "dice ----  0.9772363495114421\n",
+            "dice ----  0.9438223305064892\n",
+            " *********  113 train567\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9579325629935661\n",
+            "dice ----  0.9464478828716413\n",
+            "dice ----  0.8706916089889991\n",
+            " *********  114 train569\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9630563350819477\n",
+            "dice ----  0.9730166371565884\n",
+            "dice ----  0.944145294305394\n",
+            " *********  115 train575\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9376379074750504\n",
+            "dice ----  0.8771222434902765\n",
+            "dice ----  0.8628404540709123\n",
+            " *********  116 train576\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9496410182029154\n",
+            "dice ----  0.9631466599698644\n",
+            "dice ----  0.9533081661037155\n",
+            " *********  117 train58\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9337448674618027\n",
+            "dice ----  0.972338843173476\n",
+            "dice ----  0.9488936008264014\n",
+            " *********  118 train592\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8925376093583484\n",
+            "dice ----  0.9581903672639471\n",
+            "dice ----  0.9378877558297083\n",
+            " *********  119 train594\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9251151849763798\n",
+            "dice ----  0.9734008751384425\n",
+            "dice ----  0.9236205974936152\n",
+            " *********  120 train599\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9572290895616918\n",
+            "dice ----  0.9516956412194507\n",
+            "dice ----  0.9322827369009837\n",
+            " *********  121 train606\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9559693473090813\n",
+            "dice ----  0.9575507642655431\n",
+            "dice ----  0.9351616272530062\n",
+            " *********  122 train609\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9374719399208196\n",
+            "dice ----  0.9710899565550728\n",
+            "dice ----  0.8939807809460176\n",
+            " *********  123 train610\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.961836804962643\n",
+            "dice ----  0.9604710141680631\n",
+            "dice ----  0.8646726885632195\n",
+            " *********  124 train617\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9320186408993971\n",
+            "dice ----  0.9595234718479648\n",
+            "dice ----  0.9366777125233767\n",
+            " *********  125 train622\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9452194624021776\n",
+            "dice ----  0.9686680310149022\n",
+            "dice ----  0.9250606558660093\n",
+            " *********  126 train626\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9500539266015229\n",
+            "dice ----  0.9676029379034053\n",
+            "dice ----  0.8989570005040518\n",
+            " *********  127 train627\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9385415104061361\n",
+            "dice ----  0.9758900463259195\n",
+            "dice ----  0.9139846801295831\n",
+            " *********  128 train630\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9626490488402081\n",
+            "dice ----  0.9602391118701964\n",
+            "dice ----  0.9179550608122037\n",
+            " *********  129 train632\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8872581721147431\n",
+            "dice ----  0.8937878273124206\n",
+            "dice ----  0.7931200709589094\n",
+            " *********  130 train642\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9071286805544587\n",
+            "dice ----  0.9479443951493641\n",
+            "dice ----  0.865114118895966\n",
+            " *********  131 train645\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9553547193090685\n",
+            "dice ----  0.9804911026873023\n",
+            "dice ----  0.9506415396952687\n",
+            " *********  132 train650\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9376362066366041\n",
+            "dice ----  0.9648799406286658\n",
+            "dice ----  0.8888381068454194\n",
+            " *********  133 train652\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8978477391170151\n",
+            "dice ----  0.9189990408928416\n",
+            "dice ----  0.8645880403675252\n",
+            " *********  134 train659\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.660517622431863\n",
+            "dice ----  0.8050947233222734\n",
+            "dice ----  0.8016337937335412\n",
+            " *********  135 train660\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9586972666839806\n",
+            "dice ----  0.9787377657779278\n",
+            "dice ----  0.958793519089533\n",
+            " *********  136 train669\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9320665756510971\n",
+            "dice ----  0.9657128555935597\n",
+            "dice ----  0.9412035893636289\n",
+            " *********  137 train671\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9090841949778434\n",
+            "dice ----  0.9502727320870624\n",
+            "dice ----  0.9125442948263642\n",
+            " *********  138 train674\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8803377947737412\n",
+            "dice ----  0.9357201815930665\n",
+            "dice ----  0.9159546989866878\n",
+            " *********  139 train677\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9517360545104815\n",
+            "dice ----  0.9770772928902436\n",
+            "dice ----  0.9530163792259382\n",
+            " *********  140 train685\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9534514097072029\n",
+            "dice ----  0.9676910257057725\n",
+            "dice ----  0.9275470260158658\n",
+            " *********  141 train689\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9400931910177523\n",
+            "dice ----  0.9663468097685206\n",
+            "dice ----  0.9057950053032494\n",
+            " *********  142 train69\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9220721430624184\n",
+            "dice ----  0.9445077298616762\n",
+            "dice ----  0.8854166666666666\n",
+            " *********  143 train698\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 46ms/step\n",
+            "dice ----  0.8961658672443138\n",
+            "dice ----  0.9372612942758218\n",
+            "dice ----  0.8791532642095995\n",
+            " *********  144 train704\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9544071865354895\n",
+            "dice ----  0.9779951454552042\n",
+            "dice ----  0.9202418238764659\n",
+            " *********  145 train709\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.954627360640742\n",
+            "dice ----  0.9573731259238404\n",
+            "dice ----  0.9199387071901931\n",
+            " *********  146 train71\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9157027874574936\n",
+            "dice ----  0.9108257995897592\n",
+            "dice ----  0.8276507060467158\n",
+            " *********  147 train721\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9003878767114907\n",
+            "dice ----  0.8374720714969678\n",
+            "dice ----  0.7030502111684656\n",
+            " *********  148 train722\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8015950535418254\n",
+            "dice ----  0.9696833595328992\n",
+            "dice ----  0.9381687971020527\n",
+            " *********  149 train723\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.9034632678621987\n",
+            "dice ----  0.948240507755286\n",
+            "dice ----  0.8411918240059659\n",
+            " *********  150 train729\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9141030656518956\n",
+            "dice ----  0.931203433070334\n",
+            "dice ----  0.8249872208414856\n",
+            " *********  151 train732\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9573403021881959\n",
+            "dice ----  0.9702083883613919\n",
+            "dice ----  0.9527933163821286\n",
+            " *********  152 train739\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9719554085210823\n",
+            "dice ----  0.9541940372067705\n",
+            "dice ----  0.8929013868333059\n",
+            " *********  153 train74\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8487763045055743\n",
+            "dice ----  0.7225640291887252\n",
+            "dice ----  0.8056872037914692\n",
+            " *********  154 train755\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9439122309129924\n",
+            "dice ----  0.9663104080179595\n",
+            "dice ----  0.9301930572195514\n",
+            " *********  155 train760\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9251903053635488\n",
+            "dice ----  0.839347597251298\n",
+            "dice ----  0.0\n",
+            " *********  156 train762\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8634669614136519\n",
+            "dice ----  0.9569631517210695\n",
+            "dice ----  0.9141252457174951\n",
+            " *********  157 train772\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9377595651034121\n",
+            "dice ----  0.5262303757276416\n",
+            "dice ----  0.8224011803036824\n",
+            " *********  158 train773\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9420524873046832\n",
+            "dice ----  0.8439068688040764\n",
+            "dice ----  0.6917626347832465\n",
+            " *********  159 train779\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9315597869285122\n",
+            "dice ----  0.950875726606132\n",
+            "dice ----  0.8711722488038278\n",
+            " *********  160 train78\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8480677075928512\n",
+            "dice ----  0.5435021737991723\n",
+            "dice ----  0.4447125050261359\n",
+            " *********  161 train783\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9517468798263555\n",
+            "dice ----  0.9495108150559204\n",
+            "dice ----  0.8401114437791084\n",
+            " *********  162 train786\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9155972666509006\n",
+            "dice ----  0.9491226483357453\n",
+            "dice ----  0.8732551064754455\n",
+            " *********  163 train793\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8726688889376688\n",
+            "dice ----  0.8130358910049632\n",
+            "dice ----  0.7057673273302272\n",
+            " *********  164 train796\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8184529092264546\n",
+            "dice ----  0.9264187140293335\n",
+            "dice ----  0.8386332882273342\n",
+            " *********  165 train799\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9378888637742538\n",
+            "dice ----  0.7817427949972812\n",
+            "dice ----  0.8587528965662523\n",
+            " *********  166 train806\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7741048138487077\n",
+            "dice ----  0.8580015026296018\n",
+            "dice ----  0.7523834745762712\n",
+            " *********  167 train813\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8019539393341311\n",
+            "dice ----  0.9351572426937739\n",
+            "dice ----  0.8574681518862699\n",
+            " *********  168 train817\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8973286242533421\n",
+            "dice ----  0.8958262881552016\n",
+            "dice ----  0.8575658194406486\n",
+            " *********  169 train819\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9375061978144027\n",
+            "dice ----  0.9547871781397793\n",
+            "dice ----  0.8884650913316845\n",
+            " *********  170 train82\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8564341419683601\n",
+            "dice ----  0.7219205644827871\n",
+            "dice ----  0.6548979260302161\n",
+            " *********  171 train820\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9339634650881548\n",
+            "dice ----  0.9433215579305102\n",
+            "dice ----  0.8301024364363547\n",
+            " *********  172 train827\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8586046686605346\n",
+            "dice ----  0.9349980567431014\n",
+            "dice ----  0.8784644517128594\n",
+            " *********  173 train831\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7377386945347182\n",
+            "dice ----  0.8367626886145405\n",
+            "dice ----  0.8412570507655117\n",
+            " *********  174 train837\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8528966760132815\n",
+            "dice ----  0.9146992541718088\n",
+            "dice ----  0.7665415613738654\n",
+            " *********  175 train845\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9332668894826831\n",
+            "dice ----  0.9049664678431252\n",
+            "dice ----  0.8229735616392454\n",
+            " *********  176 train855\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9082496747747969\n",
+            "dice ----  0.7892787099585966\n",
+            "dice ----  0.6959277302180391\n",
+            " *********  177 train861\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9281323508388891\n",
+            "dice ----  0.946800382043935\n",
+            "dice ----  0.8649470990941153\n",
+            " *********  178 train879\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9134252821726985\n",
+            "dice ----  0.953679967123931\n",
+            "dice ----  0.8775792576550563\n",
+            " *********  179 train887\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.951079992208884\n",
+            "dice ----  0.9686149832884976\n",
+            "dice ----  0.9222169885382321\n",
+            " *********  180 train908\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9523026254163685\n",
+            "dice ----  0.9792246351534424\n",
+            "dice ----  0.9393203465440327\n",
+            " *********  181 train910\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9355746068074835\n",
+            "dice ----  0.9736545602398424\n",
+            "dice ----  0.9589172720881182\n",
+            " *********  182 train915\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8297057638049173\n",
+            "dice ----  0.955989214386911\n",
+            "dice ----  0.890151281259648\n",
+            " *********  183 train916\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9546639227181337\n",
+            "dice ----  0.9602968897266729\n",
+            "dice ----  0.9335476441800211\n",
+            " *********  184 train926\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7975477616196179\n",
+            "dice ----  0.9293491558600441\n",
+            "dice ----  0.9232422816055743\n",
+            " *********  185 train927\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9619083143041695\n",
+            "dice ----  0.9807862407862408\n",
+            "dice ----  0.9548585811750679\n",
+            " *********  186 train93\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9342193562710343\n",
+            "dice ----  0.9232603846918269\n",
+            "dice ----  0.8655050270134889\n",
+            " *********  187 train943\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9266324579937619\n",
+            "dice ----  0.9609012774556074\n",
+            "dice ----  0.9498276987536624\n",
+            " *********  188 train949\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7074141381610729\n",
+            "dice ----  0.8250407830342578\n",
+            "dice ----  0.8215309046254605\n",
+            " *********  189 train951\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8227672706987458\n",
+            "dice ----  0.9319408589532974\n",
+            "dice ----  0.7737686925504185\n",
+            " *********  190 train952\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9348759461967642\n",
+            "dice ----  0.9735544217687074\n",
+            "dice ----  0.9395580775513713\n",
+            " *********  191 train959\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.899776130316905\n",
+            "dice ----  0.828428927680798\n",
+            "dice ----  0.8754208754208754\n",
+            " *********  192 train965\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9504972680421255\n",
+            "dice ----  0.9488451978893999\n",
+            "dice ----  0.9492957222335932\n",
+            " *********  193 train967\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9021822729220852\n",
+            "dice ----  0.9455176040822708\n",
+            "dice ----  0.9275276831916777\n",
+            " *********  194 train985\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9621673245498436\n",
+            "dice ----  0.9864404406273338\n",
+            "dice ----  0.9606397118724006\n",
+            " *********  195 train988\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8742407920973619\n",
+            "dice ----  0.925463368069366\n",
+            "dice ----  0.8274160383824537\n",
+            " *********  196 train992\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9427132589370153\n",
+            "dice ----  0.9560692078940254\n",
+            "dice ----  0.8952034341172079\n",
+            " *********  197 train994\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9712141114655658\n",
+            "dice ----  0.9804596858894506\n",
+            "dice ----  0.9144444006775121\n",
+            " *********  198 train997\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9072307687283866\n",
+            "dice ----  0.9231612942538541\n",
+            "dice ----  0.9203712528649557\n",
+            " *********  199 train0\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9421463800986427\n",
+            "dice ----  0.9760382672448478\n",
+            "dice ----  0.9412425263285684\n",
+            " *********  200 train1006\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9051117922827263\n",
+            "dice ----  0.6252691218130312\n",
+            "dice ----  0.4383561643835616\n",
+            " *********  201 train1013\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8991585934569849\n",
+            "dice ----  0.7780557541100787\n",
+            "dice ----  0.7236328380892487\n",
+            " *********  202 train1016\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9525556339922011\n",
+            "dice ----  0.7757762347700773\n",
+            "dice ----  0.5400087663760775\n",
+            " *********  203 train1022\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8791978803814238\n",
+            "dice ----  0.25772956475583864\n",
+            "dice ----  0.8155751611808619\n",
+            " *********  204 train1030\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9343088806793648\n",
+            "dice ----  0.9069177903939392\n",
+            "dice ----  0.8372396257782704\n",
+            " *********  205 train1033\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9162343375504384\n",
+            "dice ----  0.9366834877709985\n",
+            "dice ----  0.9020746386015848\n",
+            " *********  206 train1038\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.788386048039843\n",
+            "dice ----  0.8428392438851664\n",
+            "dice ----  0.8403486306745425\n",
+            " *********  207 train104\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8869965378577727\n",
+            "dice ----  0.9589516962381549\n",
+            "dice ----  0.8853573840069451\n",
+            " *********  208 train1045\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7914379645511721\n",
+            "dice ----  0.9483736198149806\n",
+            "dice ----  0.888972499657956\n",
+            " *********  209 train1051\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9463897244803431\n",
+            "dice ----  0.9318566416729724\n",
+            "dice ----  0.8498311070574448\n",
+            " *********  210 train1059\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.945955095414913\n",
+            "dice ----  0.9378946482193472\n",
+            "dice ----  0.8666229976776038\n",
+            " *********  211 train1065\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8783753755784633\n",
+            "dice ----  0.876705991209808\n",
+            "dice ----  0.7688828584350973\n",
+            " *********  212 train1068\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9296463347805927\n",
+            "dice ----  0.8748482103274802\n",
+            "dice ----  0.0\n",
+            " *********  213 train1070\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8956331469125406\n",
+            "dice ----  0.5019967417787581\n",
+            "dice ----  0.0\n",
+            " *********  214 train108\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.78356919612525\n",
+            "dice ----  0.686730855065995\n",
+            "dice ----  0.6167804527488322\n",
+            " *********  215 train1081\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9158646460973209\n",
+            "dice ----  0.8620947107923326\n",
+            "dice ----  0.5289509536784741\n",
+            " *********  216 train1082\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8369439858890573\n",
+            "dice ----  0.6486813136056412\n",
+            "dice ----  0.7805065234075211\n",
+            " *********  217 train1084\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8093695087939901\n",
+            "dice ----  0.7100442198357548\n",
+            "dice ----  0.0\n",
+            " *********  218 train1089\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.8817481555312395\n",
+            "dice ----  0.7649137321819467\n",
+            "dice ----  0.48254189944134074\n",
+            " *********  219 train1095\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8737322945317517\n",
+            "dice ----  0.6774858470024677\n",
+            "dice ----  0.0\n",
+            " *********  220 train110\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.873034579261275\n",
+            "dice ----  0.9016774004223632\n",
+            "dice ----  0.8638247334108253\n",
+            " *********  221 train1109\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7732301188613467\n",
+            "dice ----  0.7914433686106732\n",
+            "dice ----  0.7902256125153533\n",
+            " *********  222 train1118\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8766221308141543\n",
+            "dice ----  0.7758278980937624\n",
+            "dice ----  0.0\n",
+            " *********  223 train1119\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9470703469533651\n",
+            "dice ----  0.8216972100656456\n",
+            "dice ----  0.0\n",
+            " *********  224 train1130\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9602833258050367\n",
+            "dice ----  0.962712618739656\n",
+            "dice ----  0.9497575636145061\n",
+            " *********  225 train1131\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9336842532211471\n",
+            "dice ----  0.9709099089744921\n",
+            "dice ----  0.9225093266887846\n",
+            " *********  226 train1132\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9193616631617503\n",
+            "dice ----  0.8380202474690663\n",
+            "dice ----  0.9187045604758758\n",
+            " *********  227 train1136\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9547329526368827\n",
+            "dice ----  0.963571644956074\n",
+            "dice ----  0.90364418364285\n",
+            " *********  228 train1138\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9623869284660349\n",
+            "dice ----  0.9828919733128819\n",
+            "dice ----  0.9434813343656377\n",
+            " *********  229 train1142\n",
+            "(128, 128, 128)\n",
+            "[0. 2.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8625956032515116\n",
+            "dice ----  0.0\n",
+            "dice ----  1\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " ******  230\n",
+            " *********  0 train1158\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 490ms/step\n",
+            "dice ----  0.8083218088320198\n",
+            "dice ----  0.8337001746638076\n",
+            "dice ----  0.6821125170795461\n",
+            " *********  1 train1162\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.6801414536321826\n",
+            "dice ----  0.9616847105193345\n",
+            "dice ----  0.9206982779701662\n",
+            " *********  2 train1163\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9082721659694533\n",
+            "dice ----  0.8013964515540735\n",
+            "dice ----  0.7202985395959669\n",
+            " *********  3 train1167\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9551573154034892\n",
+            "dice ----  0.9473864375449286\n",
+            "dice ----  0.9060228452751817\n",
+            " *********  4 train117\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9239070565469372\n",
+            "dice ----  0.9491488648243486\n",
+            "dice ----  0.8822250295110485\n",
+            " *********  5 train1170\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9274192026866404\n",
+            "dice ----  0.9680254408788668\n",
+            "dice ----  0.9485846331600231\n",
+            " *********  6 train1174\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9400420103322709\n",
+            "dice ----  0.9222357229647631\n",
+            "dice ----  0.8932963109877032\n",
+            " *********  7 train1186\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9452654717619209\n",
+            "dice ----  0.9407458905696865\n",
+            "dice ----  0.8455862450425429\n",
+            " *********  8 train119\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.907296083022211\n",
+            "dice ----  0.8624338624338624\n",
+            "dice ----  0.7890504704875962\n",
+            " *********  9 train1195\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.8354023747692031\n",
+            "dice ----  0.9287347182188649\n",
+            "dice ----  0.8463988812058115\n",
+            " *********  10 train1197\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.948611979441544\n",
+            "dice ----  0.9599409057332803\n",
+            "dice ----  0.95406195207481\n",
+            " *********  11 train1205\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9611958129065625\n",
+            "dice ----  0.9725554401099882\n",
+            "dice ----  0.901221820684908\n",
+            " *********  12 train122\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9235122307411464\n",
+            "dice ----  0.6658871674948845\n",
+            "dice ----  0.7063267233238905\n",
+            " *********  13 train1222\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8817324239913316\n",
+            "dice ----  0.9582041659868386\n",
+            "dice ----  0.9044536978365167\n",
+            " *********  14 train123\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.3862071549681033\n",
+            "dice ----  0.06370168964847545\n",
+            "dice ----  0.6835978835978835\n",
+            " *********  15 train1232\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9439953828932389\n",
+            "dice ----  0.9754996266526113\n",
+            "dice ----  0.9585143028306126\n",
+            " *********  16 train124\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8143918374653182\n",
+            "dice ----  0.7835190373797267\n",
+            "dice ----  0.7463913758450575\n",
+            " *********  17 train1243\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9187761586688761\n",
+            "dice ----  0.9665339171378908\n",
+            "dice ----  0.9255119301615906\n",
+            " *********  18 train126\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9280175187981727\n",
+            "dice ----  0.9757233388810652\n",
+            "dice ----  0.9041398031896845\n",
+            " *********  19 train13\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8757111782060086\n",
+            "dice ----  0.9043770151495075\n",
+            "dice ----  0.8668750000000001\n",
+            " *********  20 train144\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.6323742720443377\n",
+            "dice ----  0.789238599771476\n",
+            "dice ----  0.7204488778054863\n",
+            " *********  21 train145\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8154012834402867\n",
+            "dice ----  0.9477902086804729\n",
+            "dice ----  0.9076973715145531\n",
+            " *********  22 train150\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.7310905038037141\n",
+            "dice ----  0.9348396207383498\n",
+            "dice ----  0.8451961475217289\n",
+            " *********  23 train159\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.6195168101160671\n",
+            "dice ----  0.8340160576765525\n",
+            "dice ----  0.6735063367531684\n",
+            " *********  24 train161\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9314476830200321\n",
+            "dice ----  0.9600803543834346\n",
+            "dice ----  0.8489934668295533\n",
+            " *********  25 train162\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9196370052936728\n",
+            "dice ----  0.9478759798507663\n",
+            "dice ----  0.837817595206545\n",
+            " *********  26 train166\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8892954362608417\n",
+            "dice ----  0.9368498112044387\n",
+            "dice ----  0.9000886648933688\n",
+            " *********  27 train169\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.8403807575817174\n",
+            "dice ----  0.8944509974205758\n",
+            "dice ----  0.5640040444893832\n",
+            " *********  28 train171\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9195866572419132\n",
+            "dice ----  0.9401751776565856\n",
+            "dice ----  0.9221887117621714\n",
+            " *********  29 train183\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8798821719478599\n",
+            "dice ----  0.9358342665173572\n",
+            "dice ----  0.8842736730199712\n",
+            " *********  30 train187\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9338578201887554\n",
+            "dice ----  0.9601635625978341\n",
+            "dice ----  0.8970646178092987\n",
+            " *********  31 train188\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9120483130365342\n",
+            "dice ----  0.9642883825177437\n",
+            "dice ----  0.9292658213982529\n",
+            " *********  32 train193\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9066606521764936\n",
+            "dice ----  0.9430098885355882\n",
+            "dice ----  0.8363191276287971\n",
+            " *********  33 train20\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7484215914908139\n",
+            "dice ----  0.7617774807885065\n",
+            "dice ----  0.6799054373522458\n",
+            " *********  34 train206\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8042076403362755\n",
+            "dice ----  0.8218599912676466\n",
+            "dice ----  0.5828571428571429\n",
+            " *********  35 train213\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9305827715355806\n",
+            "dice ----  0.9638720618187222\n",
+            "dice ----  0.9199163240279882\n",
+            " *********  36 train216\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9361350849136619\n",
+            "dice ----  0.9460024565647474\n",
+            "dice ----  0.8516879284908444\n",
+            " *********  37 train223\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8911529197317883\n",
+            "dice ----  0.7343929970052983\n",
+            "dice ----  0.6937119675456389\n",
+            " *********  38 train226\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9377685218589091\n",
+            "dice ----  0.9402136849283929\n",
+            "dice ----  0.9136555043112091\n",
+            " *********  39 train227\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9297232017056679\n",
+            "dice ----  0.953628453963094\n",
+            "dice ----  0.8684750700774715\n",
+            " *********  40 train228\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.761293531160081\n",
+            "dice ----  0.8963923673225999\n",
+            "dice ----  0.7891737891737891\n",
+            " *********  41 train236\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.5957639233305132\n",
+            "dice ----  0.05955642868221067\n",
+            "dice ----  0.0\n",
+            " *********  42 train239\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9540331789521466\n",
+            "dice ----  0.9736503027960315\n",
+            "dice ----  0.9208674698795181\n",
+            " *********  43 train246\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9346815546077538\n",
+            "dice ----  0.9388854360886679\n",
+            "dice ----  0.9052017501215363\n",
+            " *********  44 train248\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.8752251763429192\n",
+            "dice ----  0.9209419680403701\n",
+            "dice ----  0.8695214105793451\n",
+            " *********  45 train250\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9451432472886742\n",
+            "dice ----  0.9650900336058584\n",
+            "dice ----  0.9229931602409228\n",
+            " *********  46 train251\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8770645952575633\n",
+            "dice ----  0.9420577549720963\n",
+            "dice ----  0.8943653288946427\n",
+            " *********  47 train259\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.843091576698954\n",
+            "dice ----  0.6267105123581203\n",
+            "dice ----  0.6855156866428705\n",
+            " *********  48 train267\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8646607138390736\n",
+            "dice ----  0.8802946593001841\n",
+            "dice ----  0.7850906648757555\n",
+            " *********  49 train268\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8655636814130999\n",
+            "dice ----  0.8779424098406485\n",
+            "dice ----  0.8174318507890961\n",
+            " *********  50 train27\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9562454991649515\n",
+            "dice ----  0.6811745909316291\n",
+            "dice ----  0.36217391304347823\n",
+            " *********  51 train282\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 43ms/step\n",
+            "dice ----  0.8883556846774403\n",
+            "dice ----  0.9052713448992873\n",
+            "dice ----  0.7701301667047271\n",
+            " *********  52 train285\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9211636803428467\n",
+            "dice ----  0.9662015693218942\n",
+            "dice ----  0.9363561417971971\n",
+            " *********  53 train288\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9227772736327218\n",
+            "dice ----  0.9583652461628014\n",
+            "dice ----  0.9400439627074964\n",
+            " *********  54 train29\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9128188259802914\n",
+            "dice ----  0.9632113167684747\n",
+            "dice ----  0.8979431929480901\n",
+            " *********  55 train296\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8700855062264601\n",
+            "dice ----  0.9281202876062679\n",
+            "dice ----  0.8282705258633042\n",
+            " *********  56 train297\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8462498186566081\n",
+            "dice ----  0.9743690722326146\n",
+            "dice ----  0.9560739642662089\n",
+            " *********  57 train303\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8826133197519398\n",
+            "dice ----  0.9415777841837225\n",
+            "dice ----  0.9337179984484096\n",
+            " *********  58 train304\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9138377836638271\n",
+            "dice ----  0.9462152100691367\n",
+            "dice ----  0.9024116930572472\n",
+            " *********  59 train307\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9271767438586612\n",
+            "dice ----  0.965187799877407\n",
+            "dice ----  0.9332815921501234\n",
+            " *********  60 train319\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9017959967096243\n",
+            "dice ----  0.9651351434081239\n",
+            "dice ----  0.9450621859813854\n",
+            " *********  61 train32\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9364333836312244\n",
+            "dice ----  0.9500763244163059\n",
+            "dice ----  0.9076881245326627\n",
+            " *********  62 train33\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9417778811406491\n",
+            "dice ----  0.9665869490946362\n",
+            "dice ----  0.9124068841510403\n",
+            " *********  63 train332\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9165840326988657\n",
+            "dice ----  0.9510164641171442\n",
+            "dice ----  0.9197325216307553\n",
+            " *********  64 train333\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.917013305012243\n",
+            "dice ----  0.9543974540571365\n",
+            "dice ----  0.9147454813721874\n",
+            " *********  65 train335\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 44ms/step\n",
+            "dice ----  0.9285497865307809\n",
+            "dice ----  0.9259697567389875\n",
+            "dice ----  0.8465393133997785\n",
+            " *********  66 train338\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8367078355942499\n",
+            "dice ----  0.6190329534955343\n",
+            "dice ----  0.5389264130821187\n",
+            " *********  67 train34\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9092863035926849\n",
+            "dice ----  0.9403179331110176\n",
+            "dice ----  0.8743765554370461\n",
+            " *********  68 train342\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8109416699913623\n",
+            "dice ----  0.9808210196004965\n",
+            "dice ----  0.9251550257174446\n",
+            " *********  69 train345\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8750625506876262\n",
+            "dice ----  0.7507191583792225\n",
+            "dice ----  0.6608776076456685\n",
+            " *********  70 train37\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.9484897111572588\n",
+            "dice ----  0.9315819003927522\n",
+            "dice ----  0.8966732380482997\n",
+            " *********  71 train382\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8903259257823479\n",
+            "dice ----  0.9623016757783585\n",
+            "dice ----  0.9066813914964108\n",
+            " *********  72 train396\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8384800094033886\n",
+            "dice ----  0.8846422095131817\n",
+            "dice ----  0.8429064592604916\n",
+            " *********  73 train401\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9072024627283366\n",
+            "dice ----  0.9354432553081897\n",
+            "dice ----  0.9150510443008336\n",
+            " *********  74 train408\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9129795241877007\n",
+            "dice ----  0.9385098399571177\n",
+            "dice ----  0.8931436196530579\n",
+            " *********  75 train416\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9110105266447557\n",
+            "dice ----  0.9582662335889948\n",
+            "dice ----  0.8983465559427597\n",
+            " *********  76 train423\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9013565151883113\n",
+            "dice ----  0.94582826342729\n",
+            "dice ----  0.8760535138014225\n",
+            " *********  77 train426\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8517681910757267\n",
+            "dice ----  0.9506041303017251\n",
+            "dice ----  0.9490623997065701\n",
+            " *********  78 train43\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8969636635141862\n",
+            "dice ----  0.9161422508415045\n",
+            "dice ----  0.890378633487075\n",
+            " *********  79 train433\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.7085909783744567\n",
+            "dice ----  0.917709419534237\n",
+            "dice ----  0.782612464245471\n",
+            " *********  80 train44\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9300459596968812\n",
+            "dice ----  0.8769010577269925\n",
+            "dice ----  0.7847523396484821\n",
+            " *********  81 train452\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9522403303876726\n",
+            "dice ----  0.9563277016257571\n",
+            "dice ----  0.9137431606050853\n",
+            " *********  82 train46\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9208576740498863\n",
+            "dice ----  0.9449597531792022\n",
+            "dice ----  0.9091916114901437\n",
+            " *********  83 train461\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9547167704291019\n",
+            "dice ----  0.9222455028618152\n",
+            "dice ----  0.814228396393287\n",
+            " *********  84 train466\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9261132152728018\n",
+            "dice ----  0.930375295697206\n",
+            "dice ----  0.8297967246543044\n",
+            " *********  85 train470\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9291631995948049\n",
+            "dice ----  0.9691906805640711\n",
+            "dice ----  0.9362801032970993\n",
+            " *********  86 train471\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8350724204180712\n",
+            "dice ----  0.03530866974024516\n",
+            "dice ----  0.7733990147783252\n",
+            " *********  87 train478\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.903749863468868\n",
+            "dice ----  0.9645728197195242\n",
+            "dice ----  0.9132850241545893\n",
+            " *********  88 train479\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9409320684163147\n",
+            "dice ----  0.9447939050595819\n",
+            "dice ----  0.8352149560815467\n",
+            " *********  89 train48\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9181484250560739\n",
+            "dice ----  0.9520793369285895\n",
+            "dice ----  0.9017965280581348\n",
+            " *********  90 train484\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8179429625376277\n",
+            "dice ----  0.9489486732164172\n",
+            "dice ----  0.8743477888974425\n",
+            " *********  91 train486\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9046621615217659\n",
+            "dice ----  0.9018418392017231\n",
+            "dice ----  0.829746835443038\n",
+            " *********  92 train493\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8828039660532728\n",
+            "dice ----  0.9432602541004703\n",
+            "dice ----  0.8739112483042016\n",
+            " *********  93 train499\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9187835883711394\n",
+            "dice ----  0.9027329459866581\n",
+            "dice ----  0.8396317163666346\n",
+            " *********  94 train506\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8113260727412117\n",
+            "dice ----  0.8963873370577281\n",
+            "dice ----  0.8861337187190994\n",
+            " *********  95 train508\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.924808774714966\n",
+            "dice ----  0.939877300613497\n",
+            "dice ----  0.9057087876844131\n",
+            " *********  96 train51\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9342893619880618\n",
+            "dice ----  0.9586918329654489\n",
+            "dice ----  0.9163184706084369\n",
+            " *********  97 train516\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8160156078740848\n",
+            "dice ----  0.8907661839336551\n",
+            "dice ----  0.8034482758620689\n",
+            " *********  98 train519\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9106579042094762\n",
+            "dice ----  0.9103158134114102\n",
+            "dice ----  0.8323146211999357\n",
+            " *********  99 train524\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9275638707131819\n",
+            "dice ----  0.9725888324873097\n",
+            "dice ----  0.9300535935138107\n",
+            " *********  100 train527\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.928427196782054\n",
+            "dice ----  0.9604213614945909\n",
+            "dice ----  0.8883474151661868\n",
+            " *********  101 train531\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9400011782105462\n",
+            "dice ----  0.9540475210861963\n",
+            "dice ----  0.8824611586531044\n",
+            " *********  102 train543\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9317214738906792\n",
+            "dice ----  0.974904552259299\n",
+            "dice ----  0.951200500620286\n",
+            " *********  103 train553\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.919972582642889\n",
+            "dice ----  0.9732070608685228\n",
+            "dice ----  0.9470684039087948\n",
+            " *********  104 train556\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8835700849711358\n",
+            "dice ----  0.8462443494953248\n",
+            "dice ----  0.7025065963060686\n",
+            " *********  105 train559\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.9437175942666944\n",
+            "dice ----  0.9517399242444886\n",
+            "dice ----  0.9173244341265235\n",
+            " *********  106 train568\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9474471300276388\n",
+            "dice ----  0.8865139003212873\n",
+            "dice ----  0.8379925793244627\n",
+            " *********  107 train572\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8271834524159081\n",
+            "dice ----  0.8595320959010054\n",
+            "dice ----  0.8207710634611219\n",
+            " *********  108 train574\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9483593264153934\n",
+            "dice ----  0.8765375854214124\n",
+            "dice ----  0.8296365450857934\n",
+            " *********  109 train579\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8743592949118639\n",
+            "dice ----  0.8855148342059337\n",
+            "dice ----  0.8749710473896326\n",
+            " *********  110 train585\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9421186639921164\n",
+            "dice ----  0.9768307631774089\n",
+            "dice ----  0.9097111336913921\n",
+            " *********  111 train59\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.938213261767387\n",
+            "dice ----  0.9394627978791587\n",
+            "dice ----  0.8887181278379322\n",
+            " *********  112 train595\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9271830653411326\n",
+            "dice ----  0.9601846508944027\n",
+            "dice ----  0.9370607527721381\n",
+            " *********  113 train60\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9591146323747007\n",
+            "dice ----  0.9284494704543205\n",
+            "dice ----  0.732066477986083\n",
+            " *********  114 train603\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9115605702635665\n",
+            "dice ----  0.9634139150943396\n",
+            "dice ----  0.9360838612029823\n",
+            " *********  115 train604\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9615915801504905\n",
+            "dice ----  0.968024674488063\n",
+            "dice ----  0.9312701237654027\n",
+            " *********  116 train613\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9304038729456222\n",
+            "dice ----  0.8975930632381521\n",
+            "dice ----  0.8953895071542131\n",
+            " *********  117 train616\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9325243834801077\n",
+            "dice ----  0.936589635124716\n",
+            "dice ----  0.8220177690029615\n",
+            " *********  118 train618\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7863693913473119\n",
+            "dice ----  0.7773540627750073\n",
+            "dice ----  0.7173766058147397\n",
+            " *********  119 train624\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9149609349777232\n",
+            "dice ----  0.9608222892399635\n",
+            "dice ----  0.8861903917745081\n",
+            " *********  120 train629\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9069854768347231\n",
+            "dice ----  0.9674606772922262\n",
+            "dice ----  0.9254616560047655\n",
+            " *********  121 train63\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.924708553051621\n",
+            "dice ----  0.9643004764287424\n",
+            "dice ----  0.9194817063220079\n",
+            " *********  122 train634\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.7101913339747938\n",
+            "dice ----  0.8474849094567405\n",
+            "dice ----  0.8212959838002025\n",
+            " *********  123 train635\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8827092599646964\n",
+            "dice ----  0.6203345785285042\n",
+            "dice ----  0.67105886813916\n",
+            " *********  124 train639\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9123529310433929\n",
+            "dice ----  0.9634735645252984\n",
+            "dice ----  0.90410314692166\n",
+            " *********  125 train641\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8659618699929014\n",
+            "dice ----  0.9560442562491462\n",
+            "dice ----  0.887992365087628\n",
+            " *********  126 train65\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8597798486699457\n",
+            "dice ----  0.9464091993924929\n",
+            "dice ----  0.8578493212898017\n",
+            " *********  127 train655\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8820935553770854\n",
+            "dice ----  0.9142373598476835\n",
+            "dice ----  0.808495145631068\n",
+            " *********  128 train663\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9107582337380189\n",
+            "dice ----  0.9165466363106024\n",
+            "dice ----  0.8497331534807343\n",
+            " *********  129 train664\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8918168533852898\n",
+            "dice ----  0.9528503043054634\n",
+            "dice ----  0.8740914419695194\n",
+            " *********  130 train673\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9132634224384177\n",
+            "dice ----  0.9731858868942699\n",
+            "dice ----  0.908878450068943\n",
+            " *********  131 train676\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 41ms/step\n",
+            "dice ----  0.9387735222184727\n",
+            "dice ----  0.9863006070056245\n",
+            "dice ----  0.9606942317508933\n",
+            " *********  132 train678\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7808789608396304\n",
+            "dice ----  0.9662986635676932\n",
+            "dice ----  0.903465059682466\n",
+            " *********  133 train695\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8781654922639545\n",
+            "dice ----  0.9550055195712025\n",
+            "dice ----  0.9029450734762707\n",
+            " *********  134 train697\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8872581922395264\n",
+            "dice ----  0.8619934564752557\n",
+            "dice ----  0.7359431970830934\n",
+            " *********  135 train701\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9406705721051976\n",
+            "dice ----  0.9706747687633406\n",
+            "dice ----  0.9276246277975219\n",
+            " *********  136 train702\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9051566412837941\n",
+            "dice ----  0.9608563815124506\n",
+            "dice ----  0.9280562234140929\n",
+            " *********  137 train711\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9389057047066187\n",
+            "dice ----  0.9533772847499111\n",
+            "dice ----  0.8703304933994944\n",
+            " *********  138 train718\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9491740724255715\n",
+            "dice ----  0.9858207517816663\n",
+            "dice ----  0.9336131589804302\n",
+            " *********  139 train72\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9287633137868456\n",
+            "dice ----  0.8988397064964505\n",
+            "dice ----  0.7871233538975712\n",
+            " *********  140 train720\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9364622994209055\n",
+            "dice ----  0.9618944500716007\n",
+            "dice ----  0.9229478615973915\n",
+            " *********  141 train727\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8483389764138364\n",
+            "dice ----  0.776014417775446\n",
+            "dice ----  0.601533174729051\n",
+            " *********  142 train728\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9448627170693592\n",
+            "dice ----  0.9127337104365474\n",
+            "dice ----  0.9204152249134948\n",
+            " *********  143 train733\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.6057993581587893\n",
+            "dice ----  0.3969465648854962\n",
+            "dice ----  0.6802325581395349\n",
+            " *********  144 train744\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 42ms/step\n",
+            "dice ----  0.9227613097611769\n",
+            "dice ----  0.9353530166880616\n",
+            "dice ----  0.8857806410979754\n",
+            " *********  145 train745\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9286566257137285\n",
+            "dice ----  0.9714040645795\n",
+            "dice ----  0.9337926165234972\n",
+            " *********  146 train746\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8663056670712352\n",
+            "dice ----  0.8978332548280735\n",
+            "dice ----  0.8579417141628132\n",
+            " *********  147 train754\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7825787125832382\n",
+            "dice ----  0.7187311344053564\n",
+            "dice ----  0.848133981184741\n",
+            " *********  148 train756\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.873041151739075\n",
+            "dice ----  0.8447905507186478\n",
+            "dice ----  0.6921873017215854\n",
+            " *********  149 train758\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8839159620995788\n",
+            "dice ----  0.855840300496789\n",
+            "dice ----  0.764286118179248\n",
+            " *********  150 train76\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9466303985788831\n",
+            "dice ----  0.9498429492478095\n",
+            "dice ----  0.9556887038353474\n",
+            " *********  151 train764\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8616819062204405\n",
+            "dice ----  0.7191726914981239\n",
+            "dice ----  0.7905324466107646\n",
+            " *********  152 train788\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.6946427776210904\n",
+            "dice ----  0.9009467288399039\n",
+            "dice ----  0.8266903914590747\n",
+            " *********  153 train790\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9082109044029704\n",
+            "dice ----  0.8954850692956137\n",
+            "dice ----  0.8085355587277185\n",
+            " *********  154 train794\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.7228769234108123\n",
+            "dice ----  0.8991021153553492\n",
+            "dice ----  0.86020964360587\n",
+            " *********  155 train800\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8751306887251514\n",
+            "dice ----  0.7881459497206704\n",
+            "dice ----  0.707761542307906\n",
+            " *********  156 train81\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.4979788617759535\n",
+            "dice ----  0.05576369534426828\n",
+            "dice ----  0.09442526114622019\n",
+            " *********  157 train810\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.8527597345803548\n",
+            "dice ----  0.8831245643311295\n",
+            "dice ----  0.8366186504927976\n",
+            " *********  158 train821\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.8544092663030395\n",
+            "dice ----  0.8588619371803585\n",
+            "dice ----  0.7623773173391494\n",
+            " *********  159 train828\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "dice ----  0.9281549974100973\n",
+            "dice ----  0.9145520455722423\n",
+            "dice ----  0.8523024975257611\n",
+            " *********  160 train830\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.9424718734870166\n",
+            "dice ----  0.7951298591644432\n",
+            "dice ----  0.8471298475391384\n",
+            " *********  161 train843\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.8950432975652655\n",
+            "dice ----  0.8973526900085397\n",
+            "dice ----  0.8567799545821947\n",
+            " *********  162 train847\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.8428156659253768\n",
+            "dice ----  0.8687305301754333\n",
+            "dice ----  0.6955914691612273\n",
+            " *********  163 train849\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.903056768558952\n",
+            "dice ----  0.8885019754905243\n",
+            "dice ----  0.8187808483451506\n",
+            " *********  164 train854\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.9281065314316855\n",
+            "dice ----  0.8983326762980313\n",
+            "dice ----  0.7126990249936818\n",
+            " *********  165 train866\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "dice ----  0.7307579931546874\n",
+            "dice ----  0.9412080586623213\n",
+            "dice ----  0.9021061083317893\n",
+            " *********  166 train87\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "dice ----  0.8976821559276922\n",
+            "dice ----  0.9474263367727846\n",
+            "dice ----  0.8789709521367902\n",
+            " *********  167 train871\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9400675186615614\n",
+            "dice ----  0.9696969696969697\n",
+            "dice ----  0.9426472665630738\n",
+            " *********  168 train878\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9057605816031051\n",
+            "dice ----  0.9642599478328431\n",
+            "dice ----  0.9376680054394232\n",
+            " *********  169 train880\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8200045357949821\n",
+            "dice ----  0.9548041988662588\n",
+            "dice ----  0.9148575002233539\n",
+            " *********  170 train882\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.9241028828799355\n",
+            "dice ----  0.9045105015494089\n",
+            "dice ----  0.8797519007603041\n",
+            " *********  171 train883\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.9145913572701314\n",
+            "dice ----  0.8881907668372079\n",
+            "dice ----  0.8388907709432318\n",
+            " *********  172 train889\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9347425725997656\n",
+            "dice ----  0.9689763433882681\n",
+            "dice ----  0.947448521812214\n",
+            " *********  173 train896\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9437029232884772\n",
+            "dice ----  0.9684056950244001\n",
+            "dice ----  0.9391304347826087\n",
+            " *********  174 train90\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.951643617519433\n",
+            "dice ----  0.9470805692003849\n",
+            "dice ----  0.8994030510722971\n",
+            " *********  175 train903\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9095634699859666\n",
+            "dice ----  0.9518559448742118\n",
+            "dice ----  0.8819119025304593\n",
+            " *********  176 train905\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9645370755829109\n",
+            "dice ----  0.9747091796113133\n",
+            "dice ----  0.9165635961799712\n",
+            " *********  177 train909\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8722217331102983\n",
+            "dice ----  0.8505930960948953\n",
+            "dice ----  0.8453984033740021\n",
+            " *********  178 train91\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.953725066614778\n",
+            "dice ----  0.8982534301066056\n",
+            "dice ----  0.8638268645837713\n",
+            " *********  179 train913\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.910197025775138\n",
+            "dice ----  0.9638579756226815\n",
+            "dice ----  0.8723252228197511\n",
+            " *********  180 train914\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9026334782389769\n",
+            "dice ----  0.8795146226736655\n",
+            "dice ----  0.807013741384542\n",
+            " *********  181 train923\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9285195952097178\n",
+            "dice ----  0.9828353855129426\n",
+            "dice ----  0.9461828559779839\n",
+            " *********  182 train929\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.898368454190018\n",
+            "dice ----  0.980125767588388\n",
+            "dice ----  0.9396354238051416\n",
+            " *********  183 train934\n",
+            "(128, 128, 128)\n",
+            "[0. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.7856161665496505\n",
+            "dice ----  0.0\n",
+            "dice ----  0.0\n",
+            " *********  184 train95\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8239095873366973\n",
+            "dice ----  0.952913844442263\n",
+            "dice ----  0.8794199077125906\n",
+            " *********  185 train960\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.936907703086397\n",
+            "dice ----  0.9347463163243086\n",
+            "dice ----  0.8111134586942742\n",
+            " *********  186 train964\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.7649794546133732\n",
+            "dice ----  0.9202502889482285\n",
+            "dice ----  0.890814703173407\n",
+            " *********  187 train968\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9429905565041642\n",
+            "dice ----  0.9728962978593703\n",
+            "dice ----  0.9195280859306216\n",
+            " *********  188 train972\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.948812921662081\n",
+            "dice ----  0.9825869333361493\n",
+            "dice ----  0.9365858736575463\n",
+            " *********  189 train974\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.931816447970705\n",
+            "dice ----  0.9467329866606375\n",
+            "dice ----  0.8878999932060602\n",
+            " *********  190 train976\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8994851007887817\n",
+            "dice ----  0.9756511570887008\n",
+            "dice ----  0.9466676908108662\n",
+            " *********  191 train999\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8272394261099617\n",
+            "dice ----  0.7891600779191017\n",
+            "dice ----  0.6181185233160622\n",
+            " *********  192 train1\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8208955223880597\n",
+            "dice ----  0.899045566502463\n",
+            "dice ----  0.7936522952486815\n",
+            " *********  193 train1011\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9108886693889189\n",
+            "dice ----  0.9319423900350331\n",
+            "dice ----  0.8437586238616502\n",
+            " *********  194 train1017\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.7785804971417891\n",
+            "dice ----  0.32700702220535205\n",
+            "dice ----  0.0\n",
+            " *********  195 train1029\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9429197074511264\n",
+            "dice ----  0.9114420120210478\n",
+            "dice ----  0.8413908496732025\n",
+            " *********  196 train1031\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.944373456001363\n",
+            "dice ----  0.8785992217898833\n",
+            "dice ----  0.8244269057620223\n",
+            " *********  197 train1037\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.7127321921015661\n",
+            "dice ----  0.9172109104878986\n",
+            "dice ----  0.9059085390530149\n",
+            " *********  198 train1041\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.907352303914488\n",
+            "dice ----  0.8657953458718521\n",
+            "dice ----  0.7813700808268186\n",
+            " *********  199 train1044\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9112719068974056\n",
+            "dice ----  0.9438031918068205\n",
+            "dice ----  0.8772082982809797\n",
+            " *********  200 train1048\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8631095395681279\n",
+            "dice ----  0.8270850884582982\n",
+            "dice ----  0.9159441233140655\n",
+            " *********  201 train105\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9093184725518529\n",
+            "dice ----  0.8619225404119143\n",
+            "dice ----  0.6048265271726239\n",
+            " *********  202 train1050\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.9269284658576724\n",
+            "dice ----  0.9121987239394045\n",
+            "dice ----  0.8264945504222061\n",
+            " *********  203 train1053\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.7560065453384419\n",
+            "dice ----  0.6100212980607556\n",
+            "dice ----  0.5873793199944033\n",
+            " *********  204 train1058\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.7271183725772865\n",
+            "dice ----  0.5588213778876681\n",
+            "dice ----  0.6685883259200358\n",
+            " *********  205 train1061\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.9049353613641841\n",
+            "dice ----  0.7498710191829652\n",
+            "dice ----  0.0\n",
+            " *********  206 train1062\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.8197012959058504\n",
+            "dice ----  0.6947287497705159\n",
+            "dice ----  0.48066298342541436\n",
+            " *********  207 train1063\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.6724758029800175\n",
+            "dice ----  0.34791257878127513\n",
+            "dice ----  0.467620075553157\n",
+            " *********  208 train1064\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.5333248358931872\n",
+            "dice ----  0.24740263063616352\n",
+            "dice ----  0.0\n",
+            " *********  209 train1069\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9088028637424567\n",
+            "dice ----  0.2456984273820536\n",
+            "dice ----  0.8444778362133734\n",
+            " *********  210 train1076\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8712856189840846\n",
+            "dice ----  0.774650380598336\n",
+            "dice ----  0.6878623796061143\n",
+            " *********  211 train1078\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.919395099334178\n",
+            "dice ----  0.7350593767507646\n",
+            "dice ----  0.0\n",
+            " *********  212 train1083\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8856048166392994\n",
+            "dice ----  0.6327830957531932\n",
+            "dice ----  0.0\n",
+            " *********  213 train109\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.9194576858787842\n",
+            "dice ----  0.8689712119506061\n",
+            "dice ----  0.8301070497531755\n",
+            " *********  214 train1090\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.804263429568666\n",
+            "dice ----  0.6758142954583706\n",
+            "dice ----  0.5426404581334159\n",
+            " *********  215 train1098\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8238478587154137\n",
+            "dice ----  0.539880666620945\n",
+            "dice ----  0.5552187093980078\n",
+            " *********  216 train11\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "dice ----  0.9314031877627907\n",
+            "dice ----  0.8656084656084656\n",
+            "dice ----  0.8389233954451346\n",
+            " *********  217 train1101\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8785365013317785\n",
+            "dice ----  0.7087119187600214\n",
+            "dice ----  0.0\n",
+            " *********  218 train1102\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.9437934400909237\n",
+            "dice ----  0.7852546106336111\n",
+            "dice ----  0.6277753591641271\n",
+            " *********  219 train1104\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.8914692313707092\n",
+            "dice ----  0.8813796114385506\n",
+            "dice ----  0.4316546762589928\n",
+            " *********  220 train1105\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.7890605609332341\n",
+            "dice ----  0.24317379641394887\n",
+            "dice ----  0.4090537223888737\n",
+            " *********  221 train1111\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.9423746336205712\n",
+            "dice ----  0.8264947245017585\n",
+            "dice ----  0.0\n",
+            " *********  222 train1113\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2.]\n",
+            "1/1 [==============================] - 0s 39ms/step\n",
+            "dice ----  0.8868282558742866\n",
+            "dice ----  0.38287084408741734\n",
+            "dice ----  0.0\n",
+            " *********  223 train1120\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8735091580292489\n",
+            "dice ----  0.34047709201060206\n",
+            "dice ----  0.8255842558425585\n",
+            " *********  224 train1125\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.8197285994412341\n",
+            "dice ----  0.8775556656446235\n",
+            "dice ----  0.6342826639871888\n",
+            " *********  225 train1127\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8946742707489791\n",
+            "dice ----  0.9594796271787795\n",
+            "dice ----  0.9527710843373494\n",
+            " *********  226 train113\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.7956202996836462\n",
+            "dice ----  0.29753181642884685\n",
+            "dice ----  0.5511580181764879\n",
+            " *********  227 train1139\n",
+            "(128, 128, 128)\n",
+            "[0. 2. 3.]\n",
+            "1/1 [==============================] - 0s 40ms/step\n",
+            "dice ----  0.7887478326723399\n",
+            "dice ----  0.3292705111879207\n",
+            "dice ----  0.3584158415841584\n",
+            " *********  228 train1140\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 38ms/step\n",
+            "dice ----  0.6318997890838006\n",
+            "dice ----  0.5647261886081908\n",
+            "dice ----  0.22655899506505162\n",
+            " *********  229 train1141\n",
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 0s 37ms/step\n",
+            "dice ----  0.8770073761854584\n",
+            "dice ----  0.9696617609088561\n",
+            "dice ----  0.9449996298763788\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HwLlUD0iodTy",
+        "outputId": "73ee2e6d-436b-48fa-894b-6da5e0b712a1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
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[7.529900612050088], [0.0]]]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import pickle\n",
+        "from os import listdir\n",
+        "from matplotlib import image\n",
+        "import matplotlib.pyplot as plt\n",
+        "import statistics\n",
+        "import pandas as pd\n",
+        "#model2\n",
+        "with open('dscs2020_paper1.pkl','rb') as f:\n",
+        "          dsc_scores = pickle.load(f)#[0]\n",
+        "with open('hds2020_paper1.pkl','rb') as f:\n",
+        "          hd95_scores = pickle.load(f)#[0]\n",
+        "with open('specs2020_paper1.pkl','rb') as f:\n",
+        "          spec_scores = pickle.load(f)#[0]\n",
+        "with open('sensis2020_paper1.pkl','rb') as f:\n",
+        "          sens_scores = pickle.load(f)#[0]\n",
+        "\n",
+        "#Z=np.transpose(np.asarray(dsc_scores[4]))[0]\n",
+        "\n",
+        "import copy\n",
+        "print(dsc_scores)\n",
+        "def plot_scores_per_region(k,metric='DSC'):\n",
+        "    domains=['Whole','Core','Enhance']\n",
+        "    'k=0 for whole, 1 for core and 1 for enhance'\n",
+        "    print(domains[k] , ' Tumor' )\n",
+        "\n",
+        "    if metric=='DSC':\n",
+        "        print('plotting scores for the dice')\n",
+        "        dsc_scoresc=copy.deepcopy(dsc_scores)\n",
+        "        Z=np.transpose(np.asarray(dsc_scoresc[4]))[0]\n",
+        "        lst=[[np.mean(Z,axis=1)[0]],[np.mean(Z,axis=1)[1]],[np.mean(Z,axis=1)[2]]]\n",
+        "        dsc_scoresc[4].append(lst)\n",
+        "        score_mat=np.transpose(np.asarray(dsc_scoresc))[0][k]\n",
+        "    elif  metric=='HD95':\n",
+        "        print('plotting scores for the hd95')\n",
+        "        hd95_scoresc=copy.deepcopy(hd95_scores)\n",
+        "        Z=np.transpose(np.asarray(hd95_scoresc[4]))[0]\n",
+        "        lst=[[np.mean(Z,axis=1)[0]],[np.mean(Z,axis=1)[1]],[np.mean(Z,axis=1)[2]]]\n",
+        "        hd95_scoresc[4].append(lst)\n",
+        "        score_mat=np.transpose(np.asarray(hd95_scoresc))[0][k]\n",
+        "    elif   metric=='spec':\n",
+        "        print('plotting scores for the spec')\n",
+        "        spec_scoresc=copy.deepcopy(spec_scores)\n",
+        "        Z=np.transpose(np.asarray(spec_scoresc[4]))[0]\n",
+        "        lst=[[np.mean(Z,axis=1)[0]],[np.mean(Z,axis=1)[1]],[np.mean(Z,axis=1)[2]]]\n",
+        "        spec_scoresc[4].append(lst)\n",
+        "        score_mat=np.transpose(np.asarray(spec_scoresc))[0][k]\n",
+        "    elif   metric=='sens':\n",
+        "        print('plotting scores for the sens')\n",
+        "        sens_scoresc=copy.deepcopy(sens_scores)\n",
+        "        Z=np.transpose(np.asarray(sens_scoresc[4]))[0]\n",
+        "        lst=[[np.mean(Z,axis=1)[0]],[np.mean(Z,axis=1)[1]],[np.mean(Z,axis=1)[2]]]\n",
+        "        sens_scoresc[4].append(lst)\n",
+        "        score_mat=np.transpose(np.asarray(sens_scoresc))[0][k]\n",
+        "    print(score_mat)\n",
+        "    avg=np.mean(score_mat,axis=1)\n",
+        "    print('shape score mat',score_mat.shape[0])\n",
+        "    avg=avg.reshape(score_mat.shape[0],1)\n",
+        "    score_mat=np.append(score_mat,avg,axis=1)\n",
+        "    m1 = score_mat.mean(axis=0)\n",
+        "    st1 = score_mat.std(axis=0)\n",
+        "    fig, ax = plt.subplots(figsize=(10, 5))\n",
+        "    bp = ax.boxplot(score_mat, showmeans=True,vert=True,patch_artist=False)\n",
+        "    plt.xticks([1, 2, 3, 4, 5, 6], ['Fold 1', 'Fold 2', 'Fold 3','Fold 4','Fold 5','Avg'])\n",
+        "    plt.ylabel('DSC Score')\n",
+        "    for i, line in enumerate(bp['medians']):\n",
+        "        x, y = line.get_xydata()[1]\n",
+        "        text = ' μ={:.2f}\\n σ={:.2f}'.format(m1[i], st1[i])\n",
+        "        ax.annotate(text, xy=(x, y))\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "h_IyLhjjMFu-",
+        "outputId": "1616b105-1ae4-4431-dd9a-8b9e02048ece"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
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[94.53469482478533], [91.35340337247285]], [[84.49350699173269], [79.56156149755417], [71.71667855935704]], [[86.11461879602784], [88.20206497648522], [51.246783625731]], [[78.64139422465335], [72.98041120284928], [74.71550497866288]], [[88.5533222024408], [91.04858013742272], [79.38942395057241]], [[66.53232057045419], [54.43889403440579], [76.69541687806529]], [[92.53273119813021], [74.59883708984816], [0.0]], [[90.91873482035678], [58.89125416004557], [0.0]], [[92.98951522420397], [74.14177133803301], [10.687593423019436]], [[76.940083449946], [51.7676941308753], [54.72478166813557]], [[89.00458447132267], [14.041353383458643], [68.0701754385965]], [[84.42409319337048], [39.73870856837253], [0.0]], [[89.36027971862484], [73.61577294446894], [19.709160699785933]], [[71.08813406717881], [75.24003491416933], [69.41021467875687]], [[94.1664643942809], [78.71395240317311], [7.077036490969402]], [[86.59850317508315], [59.13977986008297], [63.67849576271186]], [[79.86442153813186], [57.36885865457294], [15.427369686044766]], [[92.12165073380908], [50.15008081274532], [63.51578554081823]], [[94.31997895687132], [30.649605300968574], [58.33333333333333]], [[87.13322320196234], [81.36812861502027], [83.4282931580229]], [[90.47957975846943], [74.28854334751357], [0.0]], [[94.31900946831755], [95.93446230971449], [89.82052819012023]], [[91.97794959738434], [95.72060575123362], [86.73354584661541]], [[92.39995914218035], [96.12312964719561], [92.1858129993522]], [[90.04617191076085], [94.60239699063949], [87.74452949818294]], [[92.08181953914298], [96.23298219675969], [90.7079976684054]], [[86.79848738547213], [86.43673577884104], [77.13192095424604]]], [[[90.54425756414302], [95.25828475231475], [84.3493298746217]], [[93.71916761535543], [91.75483005560243], [83.75881523272216]], [[83.53066614175812], [81.4178463377283], [69.97688614946958]], [[92.05745795095682], [87.77157486754615], [78.24614666043905]], [[85.50073822666316], [91.52494842056768], [88.54728647796418]], [[81.22970069454854], [91.27353098087036], [85.51616952597286]], [[91.81542013001066], [93.90365780531681], [87.53728869738151]], [[94.27483411237864], [95.92752728021412], [87.26540794919771]], [[91.99470976408594], [84.43705712536087], [76.84650362541477]], [[52.53982566877067], [88.33718244803696], [72.83093703520079]], [[88.00711244209442], [91.89999862408673], [86.85670872696043]], [[93.21818051370354], [94.51581524838157], [84.05568689256633]], [[92.94133016400025], [70.39526141929603], [84.68027669984815]], [[93.54148313794583], [95.36298326268745], [70.17932636968986]], [[74.9790890862217], [91.44604266555486], [74.96498272481091]], [[86.79161747343565], [84.46639179211813], [81.37746101192576]], [[89.22985840446002], [77.65765765765767], [62.11880839062225]], [[94.6531695129176], [96.48463895654908], [93.10124815568051]], [[94.17549110176286], [93.22615152277875], [84.64234005497619]], [[93.9683378636867], [96.02115173792481], [87.83000152601862]], [[93.74707320880378], [88.22319093286836], [87.1024337577039]], [[93.14350109376957], [85.47242692381448], [81.84768238451984]], [[88.02498614249777], [64.7343602640679], [65.30199252801992]], [[83.80848371845242], [77.36593897057709], [83.71921369413961]], [[91.36583465126407], [72.33286353330473], [53.82932166301969]], [[91.16852970201779], [92.34257086689074], [80.21294064676748]], [[89.02031439421847], [86.08152082529698], [83.89374837237091]], [[93.72164437840516], [95.80624868945272], [91.42117089002622]], [[88.20089262812292], [92.60488733078935], [90.18576849624247]], [[65.6743860263538], [61.73818968626037], [59.5003111940962]], [[89.69245993824565], [87.68231010095212], [66.57036149672328]], [[91.22154154013586], [95.31189461449051], [91.21761320245125]], [[84.5700421481562], [83.2725377800938], [70.81315703640514]], [[86.57128761716824], [92.13320647002854], [84.42495402461452]], [[93.18341792316527], [94.29188345968363], [89.4357825430936]], [[89.7827502515835], [90.94388175909735], [77.21338578188245]], [[92.2247715495075], [89.96899113002091], [83.6026936026936]], [[93.26394911824227], [92.11465162372313], [88.95683581876051]], [[86.86837517164766], [91.80673231051065], [92.06069936221685]], [[85.17511551597134], [94.43896944952837], [88.88268676155921]], [[93.49559489328566], [95.81211528899405], [89.51309983991254]], [[75.52112353547304], [68.19848655064969], [65.3756449219026]], [[74.49830859318018], [82.63300478413743], [73.09334256374756]], [[93.74363386750852], [92.9212565313432], [86.89242408539745]], [[94.34376918354819], [90.65760354094215], [89.78736148547469]], [[93.32481914642361], [53.05414989839423], [83.13838980666426]], [[91.81800819886922], [77.90143969702315], [51.724577621931786]], [[84.44502724597139], [82.37075928917609], [0.0]], [[84.12234333047991], [86.43396772992087], [59.70528455284553]], [[31.74618528280898], [0.0], [100]], [[87.45813978610781], [85.39325842696628], [75.16425755584757]], [[87.2595281306715], [39.08962029196521], [0.0]], [[92.67007561220997], [76.52710799357007], [66.72425551261651]], [[90.08017089058255], [69.09942321969048], [87.24124731309443]], [[86.52009939329726], [58.889303019008565], [0.0]], [[81.12077464313207], [74.37865610201492], [62.23061192386346]], [[84.27046336829933], [76.54294817040856], [77.2193690388848]], [[82.0843640361514], [61.14272104162808], [20.352781546811393]], [[81.39295896041901], [57.586025867417554], [100]], [[77.59853862023212], [57.06473715134328], [71.78086030545046]], [[91.70871689048671], [76.22977700043725], [0.0]], [[90.98059485151185], [96.27474264503496], [91.52745369318768]], [[90.78059793864335], [94.4118014751844], [89.15498470593351]], [[87.19130216888938], [80.24349414092224], [64.50896163531328]], [[71.89555283557732], [31.350267379679142], [85.72625230356844]], [[94.89872090200909], [95.87915004625907], [93.33751126082018]], [[93.37422153141705], [95.15945585298358], [90.84875588753033]], [[91.63897974777002], [7.529900612050088], [0.0]]]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(dsc_scores)\n",
+        "#score_mat = np.transpose(np.asarray(dsc_scores, dtype=object))[0]\n",
+        "#print(score_mat.shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "D5GISJ9Dg0WM",
+        "outputId": "4f7f51a3-2514-47c9-ea60-50b7a4e2ea12"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Whole  Tumor\n",
+            "plotting scores for the dice\n",
+            "[[88.57212817 88.61974229 68.76034096 71.73069978 90.54425756]\n",
+            " [90.5197845  85.40261051 85.9367106  81.27074127 93.71916762]\n",
+            " [88.18878432 87.75684187 89.91620517 92.83071531 83.53066614]\n",
+            " [83.16564251 86.94309298 87.40008083 92.2018993  92.05745795]\n",
+            " [87.86012833 72.57074005 93.12021645 91.40604605 85.50073823]\n",
+            " [93.64614775 86.85375939 84.98558237 92.88309146 81.22970069]\n",
+            " [88.70832989 74.21456566 87.60081869 90.38839739 91.81542013]\n",
+            " [87.59522783 90.11250384 86.7498111  91.70734515 94.27483411]\n",
+            " [92.33580922 92.34913416 91.01701897 74.13220011 91.99470976]\n",
+            " [94.16524391 77.76348966 89.45433604 82.09195994 52.53982567]\n",
+            " [92.54715378 58.10315627 86.82294264 93.37008255 88.00711244]\n",
+            " [87.3666379  88.072117   93.20139565 91.83230747 93.21818051]\n",
+            " [91.46399415 91.13197011 67.96463875 91.15411546 92.94133016]\n",
+            " [95.54468061 84.37348016 89.85309585 66.89238583 93.54148314]\n",
+            " [94.89458308 74.03798987 90.83743103 86.5407726  74.97908909]\n",
+            " [87.90558385 90.52693094 90.5651217  86.08434888 86.79161747]\n",
+            " [87.8776048  82.46269961 80.18700837 85.60884137 89.2298584 ]\n",
+            " [90.12128448 95.34405587 88.74317401 89.75025364 94.65316951]\n",
+            " [94.8007222  93.64329683 86.6563121  90.63070235 94.1754911 ]\n",
+            " [95.44873624 88.19635552 70.63289715 86.77576294 93.96833786]\n",
+            " [95.7964237  90.37547846 87.0981281  93.36482256 93.74707321]\n",
+            " [76.64009638 88.60395628 92.77530146 89.23171043 93.14350109]\n",
+            " [92.14165313 92.5663415  90.60444381 89.14880275 88.02498614]\n",
+            " [91.28401272 92.06638606 94.58555823 90.55787688 83.80848372]\n",
+            " [91.62054739 94.67091188 91.43339554 92.29749685 91.36583465]\n",
+            " [92.03188131 89.4521664  74.80014149 92.36014297 91.1685297 ]\n",
+            " [90.46742286 95.64583105 89.31262784 90.94366998 89.02031439]\n",
+            " [93.36376848 93.78059665 92.86077804 73.98478129 93.72164438]\n",
+            " [84.76842409 88.56836487 88.18366948 92.18692559 88.20089263]\n",
+            " [90.02970874 84.69684163 93.09421266 86.58467167 65.67438603]\n",
+            " [93.43184314 85.8194474  57.66722637 87.17831929 89.69245994]\n",
+            " [92.28143625 69.05798247 86.24059834 94.78841656 91.22154154]\n",
+            " [87.54808192 91.81660531 87.0983122  94.42283469 84.57004215]\n",
+            " [90.9150595  93.18490107 86.12199066 95.47454675 86.57128762]\n",
+            " [89.78466291 92.22473726 88.40102051 95.10321525 93.18341792]\n",
+            " [90.50078115 91.95831695 91.58485133 93.6306438  89.78275025]\n",
+            " [90.21248193 95.12256721 92.78011146 91.61110163 92.22477155]\n",
+            " [91.60143805 85.6305231  89.28855225 87.39237091 93.26394912]\n",
+            " [95.65667664 83.88380533 90.22221831 91.95251687 86.86837517]\n",
+            " [86.25999971 79.58412484 82.99766034 91.35131736 85.17511552]\n",
+            " [92.68732946 88.66440737 92.61009854 77.93408446 93.49559489]\n",
+            " [81.61751778 94.29842895 88.66372036 94.46707575 75.52112354]\n",
+            " [86.38724318 95.60436661 84.64555617 92.1840199  74.49830859]\n",
+            " [90.92083462 92.9181958  90.56506557 84.49350699 93.74363387]\n",
+            " [88.33128888 84.28991518 89.80788215 86.1146188  94.34376918]\n",
+            " [91.16765787 82.5241716  89.43644369 78.64139422 93.32481915]\n",
+            " [96.33524111 92.76975494 92.74617986 88.5533222  91.8180082 ]\n",
+            " [83.78492466 72.94841325 93.26022635 66.53232057 84.44502725]\n",
+            " [93.29543274 94.40837303 64.12801596 92.5327312  84.12234333]\n",
+            " [93.67698797 93.50005267 88.02957603 90.91873482 31.74618528]\n",
+            " [93.61622602 92.48855201 87.97474496 92.98951522 87.45813979]\n",
+            " [85.86482612 94.13625625 64.3788524  76.94008345 87.25952813]\n",
+            " [91.58262894 84.01876216 93.58382794 89.00458447 92.67007561]\n",
+            " [91.49941652 58.8600566  66.78660279 84.42409319 90.08017089]\n",
+            " [93.45936521 90.7771123  69.1970379  89.36027972 86.52009939]\n",
+            " [93.49196937 92.38031938 81.97916297 71.08813407 81.12077464]\n",
+            " [89.66290814 85.22097762 87.80266745 94.16646439 84.27046337]\n",
+            " [94.27558067 87.73158364 81.90179828 86.59850318 82.08436404]\n",
+            " [93.12661869 87.98976988 77.49255172 79.86442154 81.39295896]\n",
+            " [91.35265766 85.12141639 90.64047722 92.12165073 77.59853862]\n",
+            " [71.01805111 68.36701239 79.04393137 94.31997896 91.70871689]\n",
+            " [92.0558718  80.77431626 84.02386502 87.1332232  90.98059485]\n",
+            " [92.98995126 91.52113992 85.70835742 90.47957976 90.78059794]\n",
+            " [79.79985072 94.132102   92.2783487  94.31900947 87.19130217]\n",
+            " [74.90049273 90.96812918 89.35198624 91.9779496  71.89555284]\n",
+            " [91.0031397  91.8502763  83.78175883 92.39995914 94.8987209 ]\n",
+            " [89.16851715 76.55880793 90.35444864 90.04617191 93.37422153]\n",
+            " [91.41712711 92.15866874 88.17644607 92.08181954 91.63897975]\n",
+            " [75.49311329 92.40084952 91.29191068 86.79848739 86.8988885 ]]\n",
+            "shape score mat 69\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "'for example, if you want to plot the dices for the whoel tumor, you run'\n",
+        "plot_scores_per_region(0,metric='DSC')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "l6NH99p-Fqu4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "6db6d313-c0ad-4a26-da14-c77ba371db04"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
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+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import pickle\n",
+        "from os import listdir\n",
+        "from matplotlib import image\n",
+        "import matplotlib.pyplot as plt\n",
+        "import statistics\n",
+        "import pandas as pd\n",
+        "\n",
+        "# Load data\n",
+        "with open('dscs2020_paper1.pkl', 'rb') as f:\n",
+        "    dsc_scores = pickle.load(f)\n",
+        "with open('hds2020_paper1.pkl', 'rb') as f:\n",
+        "    hd95_scores = pickle.load(f)\n",
+        "with open('specs2020_paper1.pkl', 'rb') as f:\n",
+        "    spec_scores = pickle.load(f)\n",
+        "with open('sensis2020_paper1.pkl', 'rb') as f:\n",
+        "    sens_scores = pickle.load(f)\n",
+        "\n",
+        "print(dsc_scores)\n",
+        "\n",
+        "\n",
+        "def calculate_average(metric_scores):\n",
+        "    avg_scores = []\n",
+        "    for scores in metric_scores:\n",
+        "        Z = np.transpose(np.asarray(scores))[0]\n",
+        "        avg_region = [np.mean(Z, axis=1)[i] for i in range(3)]\n",
+        "        avg_scores.append(avg_region)\n",
+        "    return np.asarray(avg_scores)\n",
+        "\n",
+        "\n",
+        "def plot_average_scores(averages, metric='DSC'):\n",
+        "    domains = ['Whole', 'Core', 'Enhance']\n",
+        "    fig, ax = plt.subplots(figsize=(10, 5))\n",
+        "    bp = ax.boxplot(averages, showmeans=True, vert=True, patch_artist=False)\n",
+        "    plt.xticks([1, 2, 3], domains)\n",
+        "    plt.ylabel(f'{metric.capitalize()} score')\n",
+        "\n",
+        "    m1 = averages.mean(axis=0)\n",
+        "    st1 = averages.std(axis=0)\n",
+        "    for i, line in enumerate(bp['medians']):\n",
+        "        x, y = line.get_xydata()[1]\n",
+        "        text = ' μ={:.2f}\\n σ={:.2f}'.format(m1[i], st1[i])\n",
+        "        ax.annotate(text, xy=(x, y))\n",
+        "    plt.show()\n",
+        "\n",
+        "\n",
+        "dsc_avg = calculate_average(dsc_scores)\n",
+        "hd95_avg = calculate_average(hd95_scores)\n",
+        "spec_avg = calculate_average(spec_scores)\n",
+        "sens_avg = calculate_average(sens_scores)\n",
+        "\n",
+        "plot_average_scores(dsc_avg, metric='dice')\n",
+        "plot_average_scores(hd95_avg, metric='hd95')\n",
+        "plot_average_scores(spec_avg, metric='spec')\n",
+        "plot_average_scores(sens_avg, metric='sens')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZlWeJcpUFvkL",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "a46fec94-1f89-49e6-ad36-da9b3434e6eb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Whole Tumor DSC\n",
+            "plotting scores for the dice\n",
+            "[[88.57212817 88.61974229 68.76034096 71.73069978 90.54425756]\n",
+            " [90.5197845  85.40261051 85.9367106  81.27074127 93.71916762]\n",
+            " [88.18878432 87.75684187 89.91620517 92.83071531 83.53066614]\n",
+            " [83.16564251 86.94309298 87.40008083 92.2018993  92.05745795]\n",
+            " [87.86012833 72.57074005 93.12021645 91.40604605 85.50073823]\n",
+            " [93.64614775 86.85375939 84.98558237 92.88309146 81.22970069]\n",
+            " [88.70832989 74.21456566 87.60081869 90.38839739 91.81542013]\n",
+            " [87.59522783 90.11250384 86.7498111  91.70734515 94.27483411]\n",
+            " [92.33580922 92.34913416 91.01701897 74.13220011 91.99470976]\n",
+            " [94.16524391 77.76348966 89.45433604 82.09195994 52.53982567]\n",
+            " [92.54715378 58.10315627 86.82294264 93.37008255 88.00711244]\n",
+            " [87.3666379  88.072117   93.20139565 91.83230747 93.21818051]\n",
+            " [91.46399415 91.13197011 67.96463875 91.15411546 92.94133016]\n",
+            " [95.54468061 84.37348016 89.85309585 66.89238583 93.54148314]\n",
+            " [94.89458308 74.03798987 90.83743103 86.5407726  74.97908909]\n",
+            " [87.90558385 90.52693094 90.5651217  86.08434888 86.79161747]\n",
+            " [87.8776048  82.46269961 80.18700837 85.60884137 89.2298584 ]\n",
+            " [90.12128448 95.34405587 88.74317401 89.75025364 94.65316951]\n",
+            " [94.8007222  93.64329683 86.6563121  90.63070235 94.1754911 ]\n",
+            " [95.44873624 88.19635552 70.63289715 86.77576294 93.96833786]\n",
+            " [95.7964237  90.37547846 87.0981281  93.36482256 93.74707321]\n",
+            " [76.64009638 88.60395628 92.77530146 89.23171043 93.14350109]\n",
+            " [92.14165313 92.5663415  90.60444381 89.14880275 88.02498614]\n",
+            " [91.28401272 92.06638606 94.58555823 90.55787688 83.80848372]\n",
+            " [91.62054739 94.67091188 91.43339554 92.29749685 91.36583465]\n",
+            " [92.03188131 89.4521664  74.80014149 92.36014297 91.1685297 ]\n",
+            " [90.46742286 95.64583105 89.31262784 90.94366998 89.02031439]\n",
+            " [93.36376848 93.78059665 92.86077804 73.98478129 93.72164438]\n",
+            " [84.76842409 88.56836487 88.18366948 92.18692559 88.20089263]\n",
+            " [90.02970874 84.69684163 93.09421266 86.58467167 65.67438603]\n",
+            " [93.43184314 85.8194474  57.66722637 87.17831929 89.69245994]\n",
+            " [92.28143625 69.05798247 86.24059834 94.78841656 91.22154154]\n",
+            " [87.54808192 91.81660531 87.0983122  94.42283469 84.57004215]\n",
+            " [90.9150595  93.18490107 86.12199066 95.47454675 86.57128762]\n",
+            " [89.78466291 92.22473726 88.40102051 95.10321525 93.18341792]\n",
+            " [90.50078115 91.95831695 91.58485133 93.6306438  89.78275025]\n",
+            " [90.21248193 95.12256721 92.78011146 91.61110163 92.22477155]\n",
+            " [91.60143805 85.6305231  89.28855225 87.39237091 93.26394912]\n",
+            " [95.65667664 83.88380533 90.22221831 91.95251687 86.86837517]\n",
+            " [86.25999971 79.58412484 82.99766034 91.35131736 85.17511552]\n",
+            " [92.68732946 88.66440737 92.61009854 77.93408446 93.49559489]\n",
+            " [81.61751778 94.29842895 88.66372036 94.46707575 75.52112354]\n",
+            " [86.38724318 95.60436661 84.64555617 92.1840199  74.49830859]\n",
+            " [90.92083462 92.9181958  90.56506557 84.49350699 93.74363387]\n",
+            " [88.33128888 84.28991518 89.80788215 86.1146188  94.34376918]\n",
+            " [91.16765787 82.5241716  89.43644369 78.64139422 93.32481915]\n",
+            " [96.33524111 92.76975494 92.74617986 88.5533222  91.8180082 ]\n",
+            " [83.78492466 72.94841325 93.26022635 66.53232057 84.44502725]\n",
+            " [93.29543274 94.40837303 64.12801596 92.5327312  84.12234333]\n",
+            " [93.67698797 93.50005267 88.02957603 90.91873482 31.74618528]\n",
+            " [93.61622602 92.48855201 87.97474496 92.98951522 87.45813979]\n",
+            " [85.86482612 94.13625625 64.3788524  76.94008345 87.25952813]\n",
+            " [91.58262894 84.01876216 93.58382794 89.00458447 92.67007561]\n",
+            " [91.49941652 58.8600566  66.78660279 84.42409319 90.08017089]\n",
+            " [93.45936521 90.7771123  69.1970379  89.36027972 86.52009939]\n",
+            " [93.49196937 92.38031938 81.97916297 71.08813407 81.12077464]\n",
+            " [89.66290814 85.22097762 87.80266745 94.16646439 84.27046337]\n",
+            " [94.27558067 87.73158364 81.90179828 86.59850318 82.08436404]\n",
+            " [93.12661869 87.98976988 77.49255172 79.86442154 81.39295896]\n",
+            " [91.35265766 85.12141639 90.64047722 92.12165073 77.59853862]\n",
+            " [71.01805111 68.36701239 79.04393137 94.31997896 91.70871689]\n",
+            " [92.0558718  80.77431626 84.02386502 87.1332232  90.98059485]\n",
+            " [92.98995126 91.52113992 85.70835742 90.47957976 90.78059794]\n",
+            " [79.79985072 94.132102   92.2783487  94.31900947 87.19130217]\n",
+            " [74.90049273 90.96812918 89.35198624 91.9779496  71.89555284]\n",
+            " [91.0031397  91.8502763  83.78175883 92.39995914 94.8987209 ]\n",
+            " [89.16851715 76.55880793 90.35444864 90.04617191 93.37422153]\n",
+            " [91.41712711 92.15866874 88.17644607 92.08181954 91.63897975]\n",
+            " [75.49311329 92.40084952 91.29191068 86.79848739 86.8988885 ]]\n",
+            "shape score mat 69\n",
+            "Whole Tumor HD95\n",
+            "plotting scores for the hd95\n",
+            "[[ 5.83095189  7.28010989  7.81024968  9.49734479  4.24264069]\n",
+            " [ 3.74165739  3.74165739  3.60555128 19.00262976  2.23606798]\n",
+            " [ 4.12310563  7.14142843  5.09901951  3.          7.54983444]\n",
+            " [19.20937271  4.12310563  3.60555128  2.44948974  2.23606798]\n",
+            " [ 3.60555128  7.28010989  2.23606798  3.          4.12310563]\n",
+            " [ 2.44948974  5.47722558  4.24264069  2.44948974  5.74456265]\n",
+            " [ 4.24264069  6.          5.83095189  3.          2.        ]\n",
+            " [ 5.47722558  3.16227766  4.47213595  3.          2.        ]\n",
+            " [ 4.12310563  2.82842712  3.          7.07106781  2.        ]\n",
+            " [ 2.23606798  9.05538514  4.12310563 27.640544   13.45362405]\n",
+            " [ 3.16227766 17.1464282   3.          2.23606798  3.74165739]\n",
+            " [ 4.58257569  8.60232527  2.82842712  2.23606798  3.        ]\n",
+            " [ 3.          2.44948974 14.31782106  4.          8.06225775]\n",
+            " [ 1.41421356  5.47722558  4.24264069 64.37779085  3.31662479]\n",
+            " [ 2.23606798 23.79075451  3.74165739 10.04987562 10.81665383]\n",
+            " [ 4.58257569  2.44948974  3.74165739  3.31662479  3.46410162]\n",
+            " [44.24929378  7.07106781  8.06225775  4.          3.        ]\n",
+            " [ 5.38516481  2.          2.82842712  3.16227766  1.73205081]\n",
+            " [ 2.23606798  9.2736185   4.          5.91607978  1.73205081]\n",
+            " [ 2.23606798  3.74165739 14.89966443  4.12310563  2.23606798]\n",
+            " [ 1.73205081  5.09901951  4.12310563  2.23606798  2.23606798]\n",
+            " [ 5.38516481  5.          2.82842712 13.60147051  2.82842712]\n",
+            " [ 3.          4.24264069 26.69737692  5.47722558  4.58257569]\n",
+            " [ 2.82842712  2.82842712  2.          3.16227766  8.24621125]\n",
+            " [10.39230485  2.23606798  2.23606798  2.23606798  2.23606798]\n",
+            " [ 2.44948974  7.81024968  8.36660027  2.82842712  3.        ]\n",
+            " [ 3.60555128  1.41421356  3.74165739  3.16227766  6.40312424]\n",
+            " [ 3.46410162  2.82842712  3.          9.48683298  2.23606798]\n",
+            " [ 5.          4.12310563  4.89897949  4.24264069  4.        ]\n",
+            " [ 5.         35.14896798  2.44948974  5.47722558 30.4244957 ]\n",
+            " [ 2.44948974  7.07106781 35.28455753  5.91607978 11.04536102]\n",
+            " [ 3.31662479 13.          5.          2.23606798  4.95959179]\n",
+            " [ 4.12310563  3.          3.          2.44948974  8.36660027]\n",
+            " [ 3.16227766  2.44948974  5.38516481  2.23606798  4.69041576]\n",
+            " [ 5.83095189  4.12310563  4.58257569  2.23606798  2.44948974]\n",
+            " [ 2.23606798  3.60555128  3.74165739  1.73205081  3.46410162]\n",
+            " [ 5.65685425  2.23606798  2.82842712  3.16227766 10.47136479]\n",
+            " [ 3.60555128  6.08276253  3.74165739  4.58257569  2.23606798]\n",
+            " [ 2.23606798 54.64476184 30.28200786  5.47722558  8.35460498]\n",
+            " [ 4.12310563  3.74165739  6.70820393 11.         15.92952652]\n",
+            " [ 9.2736185   3.74165739  2.82842712 14.62873884  3.31662479]\n",
+            " [ 4.89897949  3.          4.57705371  3.16227766  9.2736185 ]\n",
+            " [ 5.47722558  1.73205081 10.81665383  2.82842712 20.04993766]\n",
+            " [ 2.44948974  4.          3.60555128 32.69556545  2.44948974]\n",
+            " [ 7.61577311  6.92820323  7.          5.          2.        ]\n",
+            " [ 4.35889894 11.3137085   5.91607978 26.15052492  3.        ]\n",
+            " [ 1.41421356  2.44948974  3.16227766  5.47722558  4.12310563]\n",
+            " [14.89966443 12.97303698  3.16227766 47.88475744  5.38516481]\n",
+            " [ 2.82842712  2.23606798 18.13835715  2.23606798 16.79285562]\n",
+            " [ 3.          3.60555128  6.4807407   3.55604389 13.56465997]\n",
+            " [ 3.          2.23606798  4.24264069  7.34846923  7.07106781]\n",
+            " [ 4.12310563  2.44948974 14.31782106 12.72792206  7.07106781]\n",
+            " [ 3.          6.40312424  4.12310563 63.72989288  4.12310563]\n",
+            " [ 3.         69.89313269 20.42057786 10.21760372  5.65685425]\n",
+            " [ 3.          3.         14.76482306  8.60232527  3.46410162]\n",
+            " [ 3.          2.82842712 10.60173435 38.66393454 12.08304597]\n",
+            " [ 3.16227766 11.         10.          2.44948974  7.        ]\n",
+            " [ 2.44948974  5.         23.72024239 13.92838828 10.34408043]\n",
+            " [ 2.44948974  4.35889894 12.42978662 15.16575089  4.69041576]\n",
+            " [ 2.23606798  7.28010989  5.74456265 89.03819147 10.86278049]\n",
+            " [12.68857754 10.34408043  9.16515139  3.          2.82842712]\n",
+            " [ 3.         53.99073995  8.         13.74772708  3.74165739]\n",
+            " [ 2.44948974  4.24264069  5.19615242  4.69041576  3.60555128]\n",
+            " [ 7.07106781  3.16227766  3.16227766  2.23606798  4.58257569]\n",
+            " [ 7.14142843  2.44948974  9.43398113  4.89897949 11.04536102]\n",
+            " [ 2.44948974  3.31662479  9.48683298 17.49285568  2.        ]\n",
+            " [ 3.74165739 62.91263784  5.91607978  3.          2.82842712]\n",
+            " [ 2.23606798  3.31662479  4.12310563  4.12310563  3.60555128]\n",
+            " [ 4.58257569  2.82842712  3.74165739  6.          6.0501038 ]]\n",
+            "shape score mat 69\n",
+            "Core Tumor DSC\n",
+            "plotting scores for the dice\n",
+            "[[92.03531331 93.63777333 89.38153064 89.72472063 95.25828475]\n",
+            " [87.76826248 91.48123632 95.06967374 90.86371024 91.75483006]\n",
+            " [94.93638677 90.36183752 77.87682221 78.87702331 81.41784634]\n",
+            " [92.81927711 82.26930465 89.00778051 93.48529289 87.77157487]\n",
+            " [93.42955885 51.7790141  92.08760547 95.62843682 91.52494842]\n",
+            " [97.34994493 90.21707746 84.72366033 95.63982725 91.27353098]\n",
+            " [95.21169118 84.92617232 78.72722955 95.34248225 93.90365781]\n",
+            " [91.72706466 87.48884241 91.08543646 88.63132511 95.92752728]\n",
+            " [86.19840351 95.22379093 93.37998529 84.88038278 84.43705713]\n",
+            " [93.36212976 45.26518731 93.1640625  90.29297754 88.33718245]\n",
+            " [93.53546548 16.51376147 93.48226103 95.3977821  91.89999862]\n",
+            " [80.42068946 92.79109221 87.38968711 92.36172861 94.51581525]\n",
+            " [95.44381187 53.31966472 77.2266905  92.50454606 70.39526142]\n",
+            " [93.65096791 92.35802981 89.74358974 38.87684054 95.36298326]\n",
+            " [84.72926729 92.7702975  94.08647115 93.41577767 91.44604267]\n",
+            " [93.15239979 93.59324559 91.41043619 94.13964801 84.46639179]\n",
+            " [90.09464905 89.81003278 86.09109116 94.86122223 77.65765766]\n",
+            " [94.53229884 95.02546343 92.64137197 93.65236306 96.48463896]\n",
+            " [95.76454968 39.80992278 80.55672712 95.91847328 93.22615152]\n",
+            " [93.01194939 93.97761088 95.48225648 93.46712831 96.02115174]\n",
+            " [93.88670611 90.74030974 82.01936376 96.81621414 88.22319093]\n",
+            " [90.1523702  89.26615293 95.76728528 90.5476792  85.47242692]\n",
+            " [94.09317367 70.1008621  94.11699432 79.95967868 64.73436026]\n",
+            " [93.56693391 93.63350672 95.81744334 93.77857836 77.36593897]\n",
+            " [92.2137838  92.80760207 84.22953451 90.32651936 72.33286353]\n",
+            " [89.70120881 95.56010306 31.59865219 92.77412724 92.34257087]\n",
+            " [93.98601977 92.9530015  95.36230407 90.74308055 86.08152083]\n",
+            " [95.56083122 91.28703551 93.34831725 94.58613296 95.80624869]\n",
+            " [86.9439209  86.25582412 94.92322187 90.91206054 92.60488733]\n",
+            " [95.72739068 95.47916935 96.10294387 81.20812417 61.73818969]\n",
+            " [94.88146422 92.02315598 21.5232606  93.69866974 87.6823101 ]\n",
+            " [89.88348856 88.56243442 89.87815434 78.01503072 95.31189461]\n",
+            " [84.52171169 92.46839129 84.45297505 92.92415299 83.27253778]\n",
+            " [88.00468364 97.17514124 92.07077581 95.15674846 92.13320647]\n",
+            " [61.97557169 95.29346511 74.44168734 91.72402216 94.29188346]\n",
+            " [93.6599615  94.12615674 89.38705036 87.72413793 90.94388176]\n",
+            " [76.97702391 91.75620493 94.71242491 92.24789267 89.96899113]\n",
+            " [81.73406493 91.90708685 93.96890902 94.04700909 92.11465162]\n",
+            " [83.03365118 84.39595636 92.81516035 88.17187492 91.80673231]\n",
+            " [84.98237738 92.38347859 85.28675841 88.40596894 94.43896945]\n",
+            " [83.3388418  94.93239915 94.21479392 93.52996296 95.81211529]\n",
+            " [87.31211689 95.52785866 92.966317   90.06502945 68.19848655]\n",
+            " [58.63363818 95.10685471 75.7369953  94.53469482 82.63300478]\n",
+            " [41.51045179 92.58829595 90.4714547  79.5615615  92.92125653]\n",
+            " [83.93932533 38.65038094 90.12875536 88.20206498 90.65760354]\n",
+            " [71.53703742 62.21847428 94.70202153 72.9804112  53.0541499 ]\n",
+            " [96.80502883 91.73282632 90.73365802 91.04858014 77.9014397 ]\n",
+            " [84.55271816 76.03232801 58.23033952 54.43889403 82.37075929]\n",
+            " [96.44779449 90.09991391 74.7880575  74.59883709 86.43396773]\n",
+            " [93.73309935 92.62751671 75.36190013 58.89125416  0.        ]\n",
+            " [96.64026733 91.84383015 81.2529383  74.14177134 85.39325843]\n",
+            " [83.41217114 93.69670779 69.54202651 51.76769413 39.08962029]\n",
+            " [96.94934169 39.26361144 86.08741139 14.04135338 76.52710799]\n",
+            " [65.8026977  79.12727709 67.29698329 39.73870857 69.09942322]\n",
+            " [95.54580443 76.64707187 44.93435632 73.61577294 58.88930302]\n",
+            " [96.67023478 96.59236784 58.03044343 75.24003491 74.3786561 ]\n",
+            " [95.77320401 74.73815448 81.88870229 78.7139524  76.54294817]\n",
+            " [97.11739181 70.80380421 64.59733015 59.13977986 61.14272104]\n",
+            " [94.46749269 68.26568266 43.81376784 57.36885865 57.58602587]\n",
+            " [88.02379721 66.12261979 86.12736619 50.15008081 57.06473715]\n",
+            " [85.35053281 59.89645839 69.21831446 30.6496053  76.229777  ]\n",
+            " [91.57825479 51.19888675 36.40243252 81.36812862 96.27474265]\n",
+            " [95.6967845  67.92223572 71.24004373 74.28854335 94.41180148]\n",
+            " [79.72389991 95.64673609 91.98456021 95.93446231 80.24349414]\n",
+            " [92.37682373 96.78325249 94.17020091 95.72060575 31.35026738]\n",
+            " [89.98559078 94.26836452 91.56692106 96.12312965 95.87915005]\n",
+            " [92.85791757 89.83847669 74.65909723 94.60239699 95.15945585]\n",
+            " [94.65670117 94.97647485 94.15077075 96.2329822   7.52990061]\n",
+            " [91.78519594 93.05960193 93.20778708 86.43673578 80.65477887]]\n",
+            "shape score mat 69\n",
+            "Core Tumor HD95\n",
+            "plotting scores for the hd95\n",
+            "[[ 1.41421356  2.23606798  2.23606798  2.          1.        ]\n",
+            " [ 3.60555128 14.45683229  1.41421356  1.73205081  2.44948974]\n",
+            " [ 1.          2.          5.38516481  7.14142843 12.04159458]\n",
+            " [ 1.41421356  5.09901951  2.82842712  2.          4.        ]\n",
+            " [ 1.73205081 14.31607285  3.          1.41421356  2.        ]\n",
+            " [ 1.          2.82842712  4.47213595  1.41421356  3.74165739]\n",
+            " [ 1.          2.23606798 12.24744871  1.73205081  1.        ]\n",
+            " [ 2.          3.60555128  2.44948974  2.44948974  1.        ]\n",
+            " [ 6.32455532  1.73205081  2.23606798  2.          3.46410162]\n",
+            " [ 3.         12.16552506  2.          6.32455532  1.73205081]\n",
+            " [ 4.12310563 14.86269774  1.41421356  1.41421356  4.24264069]\n",
+            " [ 6.40312424  2.82842712  5.91607978  1.41421356  1.41421356]\n",
+            " [ 2.23606798 25.57342371  4.24264069  2.23606798 14.76482306]\n",
+            " [ 1.41421356  2.82842712  2.23606798 60.73384545  2.        ]\n",
+            " [ 4.58257569  2.          1.73205081  5.19615242  1.41421356]\n",
+            " [ 1.73205081  1.41421356  2.82842712  2.          3.16227766]\n",
+            " [ 2.          2.23606798  3.16227766  1.41421356  8.66025404]\n",
+            " [ 2.          2.          1.73205081  1.73205081  1.        ]\n",
+            " [ 1.73205081 12.84523258  4.70084395  1.41421356  2.23606798]\n",
+            " [ 2.82842712  1.          2.3107656   1.41421356  1.73205081]\n",
+            " [ 2.23606798  6.164414    1.41421356  1.          5.09901951]\n",
+            " [ 2.          3.          2.          2.82842712  4.89897949]\n",
+            " [ 1.73205081 10.04987562  2.          8.24621125  4.89897949]\n",
+            " [ 2.23606798  2.23606798  1.          2.         11.5758369 ]\n",
+            " [ 2.          3.          4.69041576  4.12310563  4.35889894]\n",
+            " [ 3.74165739  1.73205081  7.54983444  5.65685425  2.44948974]\n",
+            " [ 2.          1.73205081  1.73205081  3.          6.78232998]\n",
+            " [ 2.23606798  4.24264069  2.82842712  2.          1.73205081]\n",
+            " [ 4.          5.19615242  2.          2.23606798  2.        ]\n",
+            " [ 1.73205081  2.23606798  1.         12.18604034 32.74751172]\n",
+            " [ 2.23606798  6.50375005 68.91552712  2.44948974  5.09901951]\n",
+            " [ 1.41421356  2.23606798  3.          7.21110255  1.41421356]\n",
+            " [ 3.60555128  2.23606798  3.16227766  3.16227766  8.1240384 ]\n",
+            " [ 3.          1.          2.23606798  1.73205081  1.73205081]\n",
+            " [15.67162262  2.          9.21954446  3.          2.23606798]\n",
+            " [ 1.          2.          4.12310563  2.          3.31662479]\n",
+            " [10.44030651  3.31662479  1.41421356  2.44948974  2.82842712]\n",
+            " [ 7.68114575  2.82842712  2.          2.          2.82842712]\n",
+            " [ 6.70820393  8.77496439  2.23606798  4.58257569  1.41421356]\n",
+            " [ 4.12310563  1.41421356  5.83095189  3.          1.41421356]\n",
+            " [ 7.          1.73205081  2.44948974  2.          2.        ]\n",
+            " [ 3.          1.73205081  2.23606798  5.09901951  8.48528137]\n",
+            " [12.52996409  1.41421356  6.40312424  1.73205081  6.        ]\n",
+            " [11.          5.          3.46410162  8.77496439  3.74165739]\n",
+            " [ 2.82842712 13.19090596 44.30800713  3.74165739 20.61552813]\n",
+            " [ 9.2736185   9.          2.          6.4807407  15.58845727]\n",
+            " [ 1.41421356  3.60555128  5.          3.74165739  9.64365076]\n",
+            " [ 3.         10.44030651  8.77496439 35.          5.        ]\n",
+            " [ 1.73205081  2.23606798 10.07220881  8.06225775  5.09901951]\n",
+            " [ 3.31662479  3.          7.48331477 52.88712706 95.        ]\n",
+            " [ 1.73205081  2.23606798  5.91607978 41.0998777   2.23606798]\n",
+            " [ 5.          3.60555128 11.04536102 16.91153453 13.36598973]\n",
+            " [ 1.41421356 28.16734556  6.08276253 21.80136996  6.40312424]\n",
+            " [ 6.164414    5.38516481 10.         28.61817604 11.09053651]\n",
+            " [ 2.          7.28010989 22.29349681 11.83215957  7.87400787]\n",
+            " [ 1.41421356  1.41421356 20.54263858  3.74165739  9.43398113]\n",
+            " [ 1.41421356 12.08304597  7.81024968  6.78232998  9.        ]\n",
+            " [ 1.41421356  6.40312424 60.1817921  19.39071943 18.43908891]\n",
+            " [ 1.41421356  9.16515139 14.03566885 15.09966887 10.04987562]\n",
+            " [ 3.31662479  9.43398113  5.83095189 75.05331438 16.55294536]\n",
+            " [ 3.         11.78982612 12.08304597 17.67624966  6.63324958]\n",
+            " [ 1.73205081 74.69270379 25.33475775  5.83095189  1.73205081]\n",
+            " [ 1.41421356 12.04159458 11.18033989  8.30662386  2.        ]\n",
+            " [ 2.23606798  2.          5.          1.41421356  6.        ]\n",
+            " [ 3.16227766  1.          3.          1.73205081 21.77154106]\n",
+            " [ 2.          3.          6.4807407   2.23606798  1.41421356]\n",
+            " [ 1.73205081  4.35889894 10.44030651  2.23606798  2.23606798]\n",
+            " [ 1.          2.          1.41421356  1.73205081 18.8281657 ]\n",
+            " [ 1.41421356  1.73205081  3.          6.32455532  7.59176954]]\n",
+            "shape score mat 69\n",
+            "Enhance Tumor DSC\n",
+            "plotting scores for the dice\n",
+            "[[7.66896925e+01 8.68189372e+01 8.72760540e+01 8.29326109e+01\n",
+            "  8.43493299e+01]\n",
+            " [8.11662309e+01 8.54815032e+01 7.99631294e+01 8.23595777e+01\n",
+            "  8.37588152e+01]\n",
+            " [9.03296703e+01 7.28037741e+01 6.95413732e+01 8.38052338e+01\n",
+            "  6.99768861e+01]\n",
+            " [8.48693701e+01 6.40405969e+01 8.47046988e+01 8.56095228e+01\n",
+            "  7.82461467e+01]\n",
+            " [8.57391153e+01 5.97496826e+01 7.91051301e+01 8.64945870e+01\n",
+            "  8.85472865e+01]\n",
+            " [9.34155156e+01 7.71893169e+01 6.88798214e+01 8.76259964e+01\n",
+            "  8.55161695e+01]\n",
+            " [9.12970760e+01 6.23720137e+01 7.56497160e+01 7.97259520e+01\n",
+            "  8.75372887e+01]\n",
+            " [8.84576098e+01 7.98965033e+01 8.41933075e+01 8.10593983e+01\n",
+            "  8.72654079e+01]\n",
+            " [8.87255999e+01 8.33329400e+01 8.45773492e+01 7.61573268e+01\n",
+            "  7.68465036e+01]\n",
+            " [8.63703989e+01 4.86504954e+01 8.58652857e+01 7.59230059e+01\n",
+            "  7.28309370e+01]\n",
+            " [7.68904972e+01 3.98419865e+01 8.20586682e+01 8.89798269e+01\n",
+            "  8.68567087e+01]\n",
+            " [7.45121195e+01 7.98870657e+01 7.54380572e+01 8.33364457e+01\n",
+            "  8.40556869e+01]\n",
+            " [8.75734889e+01 6.26860221e+01 5.72078994e+01 8.27019708e+01\n",
+            "  8.46802767e+01]\n",
+            " [9.09467865e+01 8.66861741e+01 7.35988819e+01 1.37855580e+01\n",
+            "  7.01793264e+01]\n",
+            " [6.11326479e+01 7.53588886e+01 8.52079483e+01 9.11720803e+01\n",
+            "  7.49649827e+01]\n",
+            " [7.55049489e+01 8.51154488e+01 7.67259414e+01 9.08053712e+01\n",
+            "  8.13774610e+01]\n",
+            " [8.78450810e+01 8.29374392e+01 6.20051774e+01 9.02422834e+01\n",
+            "  6.21188084e+01]\n",
+            " [9.13422126e+01 8.39325077e+01 7.94532947e+01 9.32293087e+01\n",
+            "  9.31012482e+01]\n",
+            " [8.86199422e+01 5.98636527e+01 6.62110003e+01 9.19821385e+01\n",
+            "  8.46423401e+01]\n",
+            " [8.92933011e+01 8.90988707e+01 9.52065671e+01 8.60719312e+01\n",
+            "  8.78300015e+01]\n",
+            " [9.08864856e+01 7.97444521e+01 7.86019417e+01 8.87192919e+01\n",
+            "  8.71024338e+01]\n",
+            " [8.27892122e+01 8.68036202e+01 9.10269849e+01 9.19315987e+01\n",
+            "  8.18476824e+01]\n",
+            " [9.25344158e+01 8.19537245e+01 8.19577534e+01 8.49315432e+01\n",
+            "  6.53019925e+01]\n",
+            " [8.34354354e+01 8.96631650e+01 8.78052995e+01 8.51362263e+01\n",
+            "  8.37192137e+01]\n",
+            " [7.91822116e+01 8.50068208e+01 8.75926059e+01 8.49726323e+01\n",
+            "  5.38293217e+01]\n",
+            " [8.47644995e+01 9.09420135e+01 5.64766839e+01 9.21162267e+01\n",
+            "  8.02129406e+01]\n",
+            " [9.05877897e+01 8.97035981e+01 8.71169557e+01 8.27197762e+01\n",
+            "  8.38937484e+01]\n",
+            " [9.00372853e+01 8.73491888e+01 9.20784853e+01 9.14523991e+01\n",
+            "  9.14211709e+01]\n",
+            " [7.77565860e+01 7.54663535e+01 9.15620657e+01 8.22448310e+01\n",
+            "  9.01857685e+01]\n",
+            " [8.53769322e+01 9.12099241e+01 9.38191955e+01 7.25243895e+01\n",
+            "  5.95003112e+01]\n",
+            " [8.97120466e+01 8.97077601e+01 3.16012227e+01 8.58299834e+01\n",
+            "  6.65703615e+01]\n",
+            " [8.86913229e+01 8.07781173e+01 7.05533544e+01 5.15439918e+01\n",
+            "  9.12176132e+01]\n",
+            " [0.00000000e+00 9.04270987e+01 8.86186324e+01 8.52958812e+01\n",
+            "  7.08131570e+01]\n",
+            " [7.74074074e+01 9.24325158e+01 8.56289282e+01 8.98370830e+01\n",
+            "  8.44249540e+01]\n",
+            " [0.00000000e+00 8.46101555e+01 7.64006635e+01 8.41862060e+01\n",
+            "  8.94357825e+01]\n",
+            " [8.72567691e+01 9.06544984e+01 8.20238732e+01 8.80753138e+01\n",
+            "  7.72133858e+01]\n",
+            " [3.13346228e+01 8.39162411e+01 8.52810497e+01 8.57457025e+01\n",
+            "  8.36026936e+01]\n",
+            " [0.00000000e+00 8.71740125e+01 8.91754924e+01 8.49645612e+01\n",
+            "  8.89568358e+01]\n",
+            " [6.00081683e+01 8.19939837e+01 8.60248780e+01 6.06848902e+01\n",
+            "  9.20606994e+01]\n",
+            " [0.00000000e+00 9.08465244e+01 7.41728181e+01 7.31045912e+01\n",
+            "  8.88826868e+01]\n",
+            " [6.86891815e-01 8.65897296e+01 8.96007436e+01 8.79107904e+01\n",
+            "  8.95130998e+01]\n",
+            " [7.34958779e+01 9.30645628e+01 8.79618455e+01 8.61906435e+01\n",
+            "  6.53756449e+01]\n",
+            " [0.00000000e+00 9.31392505e+01 5.91492597e+01 9.13534034e+01\n",
+            "  7.30933426e+01]\n",
+            " [7.83178041e+01 8.37734960e+01 7.94675708e+01 7.17166786e+01\n",
+            "  8.68924241e+01]\n",
+            " [8.61711776e+01 4.40848886e+01 8.21071385e+01 5.12467836e+01\n",
+            "  8.97873615e+01]\n",
+            " [0.00000000e+00 5.82261560e+01 8.38798436e+01 7.47155050e+01\n",
+            "  8.31383898e+01]\n",
+            " [8.73622998e+01 7.16985856e+01 8.52754201e+01 7.93894240e+01\n",
+            "  5.17245776e+01]\n",
+            " [8.29151929e+01 6.55653388e+01 8.25020113e+01 7.66954169e+01\n",
+            "  0.00000000e+00]\n",
+            " [9.08127334e+01 8.86471322e+01 8.55304714e+01 0.00000000e+00\n",
+            "  5.97052846e+01]\n",
+            " [8.41277197e+01 8.46455034e+01 5.07201002e+00 0.00000000e+00\n",
+            "  1.00000000e+02]\n",
+            " [9.19731857e+01 7.69179619e+01 3.22247883e+01 1.06875934e+01\n",
+            "  7.51642576e+01]\n",
+            " [7.55904092e+01 8.99146114e+01 7.63067271e+01 5.47247817e+01\n",
+            "  0.00000000e+00]\n",
+            " [9.00908096e+01 0.00000000e+00 6.09603220e+01 6.80701754e+01\n",
+            "  6.67242555e+01]\n",
+            " [1.38792505e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
+            "  8.72412473e+01]\n",
+            " [8.85859851e+01 0.00000000e+00 7.92332268e+01 1.97091607e+01\n",
+            "  0.00000000e+00]\n",
+            " [9.26064853e+01 9.22661269e+01 0.00000000e+00 6.94102147e+01\n",
+            "  6.22306119e+01]\n",
+            " [8.90100190e+01 0.00000000e+00 0.00000000e+00 7.07703649e+00\n",
+            "  7.72193690e+01]\n",
+            " [9.19656129e+01 7.46562352e+01 0.00000000e+00 6.36784958e+01\n",
+            "  2.03527815e+01]\n",
+            " [9.32752927e+01 0.00000000e+00 2.97939466e+01 1.54273697e+01\n",
+            "  1.00000000e+02]\n",
+            " [8.20162016e+01 3.55160530e+01 0.00000000e+00 6.35157855e+01\n",
+            "  7.17808603e+01]\n",
+            " [7.47888514e+01 0.00000000e+00 3.12940072e-02 5.83333333e+01\n",
+            "  0.00000000e+00]\n",
+            " [8.48802956e+01 0.00000000e+00 0.00000000e+00 8.34282932e+01\n",
+            "  9.15274537e+01]\n",
+            " [8.70015618e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
+            "  8.91549847e+01]\n",
+            " [7.73594132e+01 9.01303962e+01 8.47828296e+01 8.98205282e+01\n",
+            "  6.45089616e+01]\n",
+            " [8.63816253e+01 9.52547254e+01 9.08559707e+01 8.67335458e+01\n",
+            "  8.57262523e+01]\n",
+            " [7.56323208e+01 8.88985919e+01 8.36290405e+01 9.21858130e+01\n",
+            "  9.33375113e+01]\n",
+            " [7.99978978e+01 8.26039097e+01 8.13319164e+01 8.77445295e+01\n",
+            "  9.08487559e+01]\n",
+            " [8.61030126e+01 8.91133050e+01 8.75368008e+01 9.07079977e+01\n",
+            "  0.00000000e+00]\n",
+            " [8.97371321e+01 9.17748159e+01 8.48796983e+01 7.71319210e+01\n",
+            "  7.36753205e+01]]\n",
+            "shape score mat 69\n",
+            "Enhance Tumor HD95\n",
+            "plotting scores for the hd95\n",
+            "[[ 1.73205081  1.41421356  2.          1.41421356  1.        ]\n",
+            " [ 2.82842712  1.41421356  2.44948974  1.41421356  1.41421356]\n",
+            " [ 1.          2.23606798  4.69041576  1.41421356  8.77496439]\n",
+            " [ 1.41421356  3.31662479  1.41421356  1.41421356  1.41421356]\n",
+            " [ 1.41421356 13.74772708  2.23606798  1.41421356  1.41421356]\n",
+            " [ 1.          1.41421356  3.          1.41421356  1.41421356]\n",
+            " [ 1.          3.          2.23606798  1.73205081  1.41421356]\n",
+            " [ 1.73205081  2.          2.23606798  2.          1.41421356]\n",
+            " [ 1.73205081  1.73205081  2.44948974  1.41421356  2.44948974]\n",
+            " [ 1.41421356  5.38516481  2.          3.          1.41421356]\n",
+            " [ 2.44948974 12.          1.73205081  1.41421356  2.44948974]\n",
+            " [ 2.23606798  2.          3.          1.41421356  1.41421356]\n",
+            " [ 1.41421356 24.85960579  5.65685425  2.23606798  5.        ]\n",
+            " [ 1.41421356  2.          2.23606798 82.45908026  2.23606798]\n",
+            " [ 4.47213595  1.41421356  1.73205081  1.73205081  2.23606798]\n",
+            " [ 2.          1.41421356  1.41421356  1.          2.23606798]\n",
+            " [ 1.41421356  2.23606798  2.44948974  1.41421356 11.5758369 ]\n",
+            " [ 1.41421356  1.73205081  1.73205081  1.          1.        ]\n",
+            " [ 2.         12.68857754  4.12310563  1.41421356  1.73205081]\n",
+            " [ 2.23606798  1.41421356  4.69041576  1.41421356  1.41421356]\n",
+            " [ 2.          3.74165739  1.41421356  1.          2.44948974]\n",
+            " [ 1.41421356  2.          1.41421356  2.          4.24264069]\n",
+            " [ 1.          2.44948974  1.83923048  2.44948974  4.12310563]\n",
+            " [ 2.          1.41421356  1.          1.73205081  4.89897949]\n",
+            " [ 2.23606798  2.23606798  3.74165739  2.23606798  3.        ]\n",
+            " [ 2.          2.          6.08276253  1.          2.        ]\n",
+            " [ 2.          1.73205081  1.41421356  1.73205081  2.82842712]\n",
+            " [ 1.41421356  3.          1.41421356  1.73205081  1.41421356]\n",
+            " [ 2.82842712  5.          2.23606798  2.82842712  2.        ]\n",
+            " [ 1.73205081  2.          1.41421356  7.6353849   4.69041576]\n",
+            " [ 2.          1.41421356 70.10741684  2.23606798  5.74456265]\n",
+            " [ 1.41421356  2.          3.          6.40312424  1.41421356]\n",
+            " [95.          1.41421356  3.74165739  2.44948974  3.16227766]\n",
+            " [ 1.          1.          1.41421356  1.41421356  2.        ]\n",
+            " [95.          1.41421356  8.69478035  3.          2.44948974]\n",
+            " [ 1.          2.          2.44948974  1.73205081  3.        ]\n",
+            " [19.60448025  3.16227766  3.          2.          2.44948974]\n",
+            " [95.          1.41421356  1.41421356  1.41421356  2.82842712]\n",
+            " [ 5.38516481  8.48528137  1.73205081  2.82842712  1.        ]\n",
+            " [95.          1.41421356  4.12310563  2.84558441  1.41421356]\n",
+            " [17.11724277  1.41421356  1.73205081  1.41421356  1.41421356]\n",
+            " [ 2.23606798  1.41421356  2.23606798  3.          7.61577311]\n",
+            " [95.          1.41421356  7.00355339  1.41421356  5.38516481]\n",
+            " [ 1.73205081  4.24264069  3.         19.10497317  2.44948974]\n",
+            " [ 3.         29.41768058  2.23606798  4.69041576 20.09850668]\n",
+            " [95.          7.07106781  2.23606798  6.32455532  2.23606798]\n",
+            " [ 1.41421356  3.31662479  2.23606798  2.44948974  3.60555128]\n",
+            " [ 2.82842712  9.16515139  5.74456265 33.18132005 95.        ]\n",
+            " [ 1.41421356  2.         11.87434209 95.         14.14213562]\n",
+            " [ 3.          2.23606798 32.78261412 95.         95.        ]\n",
+            " [ 1.41421356  2.82842712 12.08304597 75.29408932  2.23606798]\n",
+            " [ 4.24264069  2.23606798  8.71779789 10.29563014 95.        ]\n",
+            " [ 1.41421356 95.          5.09901951 19.05776782  6.40312424]\n",
+            " [12.40967365 95.         95.         95.          1.41421356]\n",
+            " [ 1.41421356 95.          1.41421356 20.92844954 17.54393242]\n",
+            " [ 1.          1.73205081 95.          4.12310563  8.83176087]\n",
+            " [ 1.41421356 95.         20.7159341  33.34815699  3.        ]\n",
+            " [ 1.          5.83095189 95.         22.30022185 15.13274595]\n",
+            " [ 1.41421356 95.         13.6381817  48.76884225 95.        ]\n",
+            " [ 2.         16.1245155  95.         74.60696773  5.09901951]\n",
+            " [ 4.12310563 95.         38.00328549 33.39161571 95.        ]\n",
+            " [ 1.41421356 95.         95.          5.83095189  1.41421356]\n",
+            " [ 1.41421356 95.         95.         95.          1.41421356]\n",
+            " [ 2.23606798  1.41421356  2.23606798  1.         12.11402823]\n",
+            " [ 2.44948974  1.          1.41421356  1.41421356  4.69041576]\n",
+            " [ 2.          1.41421356  2.23606798  1.41421356  1.        ]\n",
+            " [ 1.41421356  3.60555128  2.82842712  1.41421356  1.73205081]\n",
+            " [ 1.41421356  1.41421356  1.          1.         27.28919071]\n",
+            " [ 1.41421356  1.73205081  2.23606798  5.38516481 11.05573211]]\n",
+            "shape score mat 69\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x1000 with 6 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import pickle\n",
+        "from matplotlib import pyplot as plt\n",
+        "from matplotlib.ticker import MaxNLocator\n",
+        "\n",
+        "def plot_scores_per_region(metrics=['DSC', 'HD95']):\n",
+        "    with open('dscs2020_paper1.pkl', 'rb') as f:\n",
+        "        dsc_scores = pickle.load(f)\n",
+        "    with open('hds2020_paper1.pkl', 'rb') as f:\n",
+        "        hd95_scores = pickle.load(f)\n",
+        "    with open('specs2020_paper1.pkl', 'rb') as f:\n",
+        "        spec_scores = pickle.load(f)\n",
+        "    with open('sensis2020_paper1.pkl', 'rb') as f:\n",
+        "        sens_scores = pickle.load(f)\n",
+        "\n",
+        "    domains = ['Whole', 'Core', 'Enhance']\n",
+        "    fig, axs = plt.subplots(2, 3, figsize=(15, 10))\n",
+        "\n",
+        "    for k in range(3):\n",
+        "        for j, metric in enumerate(metrics):\n",
+        "            print(domains[k], 'Tumor', metric)\n",
+        "            if metric == 'DSC':\n",
+        "                print('plotting scores for the dice')\n",
+        "                scores = copy.deepcopy(dsc_scores)\n",
+        "                text_va = 'bottom'  # Align text at the bottom for 'dice'\n",
+        "            elif metric == 'HD95':\n",
+        "                print('plotting scores for the hd95')\n",
+        "                scores = copy.deepcopy(hd95_scores)\n",
+        "                text_va = 'top'  # Align text at the top for 'hd95'\n",
+        "            elif metric == 'spec':\n",
+        "                print('plotting scores for the spec')\n",
+        "                scores = copy.deepcopy(spec_scores)\n",
+        "                text_va = 'bottom'  # Align text at the bottom for 'spec'\n",
+        "            elif metric == 'sens':\n",
+        "                print('plotting scores for the sens')\n",
+        "                scores = copy.deepcopy(sens_scores)\n",
+        "                text_va = 'bottom'  # Align text at the bottom for 'sens'\n",
+        "\n",
+        "            Z = np.transpose(np.asarray(scores[4]))[0]\n",
+        "            lst = [[np.mean(Z, axis=1)[0]], [np.mean(Z, axis=1)[1]], [np.mean(Z, axis=1)[2]]]\n",
+        "            scores[4].append(lst)\n",
+        "            score_mat = np.transpose(np.asarray(scores))[0][k]\n",
+        "\n",
+        "            print(score_mat)\n",
+        "            avg = np.mean(score_mat, axis=1)\n",
+        "            print('shape score mat', score_mat.shape[0])\n",
+        "            avg = avg.reshape(score_mat.shape[0], 1)\n",
+        "            score_mat = np.append(score_mat, avg, axis=1)\n",
+        "            m1 = score_mat.mean(axis=0)\n",
+        "            st1 = score_mat.std(axis=0)\n",
+        "\n",
+        "            ax = axs[j, k]\n",
+        "            bp = ax.boxplot(score_mat, showmeans=True, vert=True, patch_artist=False)\n",
+        "            ax.set_title(domains[k] + ' tumor' + ' ' + metric)\n",
+        "            ax.set_xticklabels(['Fold 1', 'Fold 2', 'Fold 3', 'Fold 4', 'Fold 5', 'Avg'])\n",
+        "            ax.yaxis.set_major_locator(MaxNLocator(nbins=6))\n",
+        "            ax.set_ylabel(metric + ' score')\n",
+        "            for i in range(len(m1)):\n",
+        "                text_y = ax.get_ylim()[0] if text_va == 'bottom' else ax.get_ylim()[1]\n",
+        "                ax.text(i+1, text_y, ' μ={:.2f}\\n σ={:.2f}'.format(m1[i], st1[i]), ha='center', va=text_va, color='red', fontsize=10)\n",
+        "\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "plot_scores_per_region(metrics=['DSC', 'HD95'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pickle\n",
+        "from os import listdir\n",
+        "from matplotlib import image\n",
+        "import matplotlib.pyplot as plt\n",
+        "import statistics\n",
+        "import pandas as pd\n",
+        "#model2\n",
+        "with open('dscs2021_paper1.pkl','rb') as f:\n",
+        "          dsc_scores = pickle.load(f)#[0]\n",
+        "with open('hds2021_paper1.pkl','rb') as f:\n",
+        "          hd95_scores = pickle.load(f)#[0]\n",
+        "with open('specs2021_paper1.pkl','rb') as f:\n",
+        "          spec_scores = pickle.load(f)#[0]\n",
+        "with open('sensis2021_paper1.pkl','rb') as f:\n",
+        "          sens_scores = pickle.load(f)#[0]\n",
+        "\n",
+        "\n",
+        "import copy\n",
+        "print(dsc_scores)\n",
+        "def plot_scores_per_region(k,metric='DSC'):\n",
+        "    domains=['Whole','Core','Enhance']\n",
+        "    'k=0 for whole, 1 for core and 1 for enhance'\n",
+        "    print(domains[k] , ' tumor' )\n",
+        "\n",
+        "    if metric=='DSC':\n",
+        "        print('plotting scores for the dice')\n",
+        "        dsc_scoresc=copy.deepcopy(dsc_scores)\n",
+        "        Z=np.transpose(np.asarray(dsc_scoresc))[0][k]\n",
+        "\n",
+        "    elif  metric=='HD95':\n",
+        "        print('plotting scores for the hd95')\n",
+        "        hd95_scoresc=copy.deepcopy(hd95_scores)\n",
+        "        Z=np.transpose(np.asarray(hd95_scoresc))[0][k]\n",
+        "\n",
+        "    elif   metric=='spec':\n",
+        "        print('plotting scores for the spec')\n",
+        "        spec_scoresc=copy.deepcopy(spec_scores)\n",
+        "        Z=np.transpose(np.asarray(spec_scoresc))[0][k]\n",
+        "\n",
+        "    elif   metric=='sens':\n",
+        "        print('plotting scores for the sens')\n",
+        "        sens_scoresc=copy.deepcopy(sens_scores)\n",
+        "        Z=np.transpose(np.asarray(sens_scoresc))[0][k]\n",
+        "\n",
+        "    score_mat=Z\n",
+        "    avg=np.mean(score_mat,axis=1)\n",
+        "    print('shape score mat',score_mat.shape[0])\n",
+        "    avg=avg.reshape(score_mat.shape[0],1)\n",
+        "\n",
+        "    m1 = score_mat.mean(axis=0)\n",
+        "    st1 = score_mat.std(axis=0)\n",
+        "    fig, ax = plt.subplots(figsize=(10, 5))\n",
+        "\n",
+        "    bp = ax.boxplot(score_mat, showmeans=True,vert=True,patch_artist=False)\n",
+        "    plt.xticks([1, 2, 3, 4, 5], ['Fold 1', 'Fold 2', 'Fold 3','Fold 4','Fold 5'])\n",
+        "    plt.ylabel('DSC score')\n",
+        "    for i, line in enumerate(bp['medians']):\n",
+        "        x, y = line.get_xydata()[1]\n",
+        "        text = ' μ={:.2f}\\n σ={:.2f}'.format(m1[i], st1[i])\n",
+        "        ax.annotate(text, xy=(x, y))\n",
+        "\n",
+        "plot_scores_per_region(1,metric='HD95')\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 534
+        },
+        "id": "PVTg9xFg5qlY",
+        "outputId": "374f4a7d-4771-4457-8ea9-8dbe246d1d21"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
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[[67.24758029800175], [34.79125787812751], [46.7620075553157]], [[53.33248358931873], [24.740263063616354], [0.0]], [[90.88028637424567], [24.56984273820536], [84.44778362133734]], [[87.12856189840846], [77.4650380598336], [68.78623796061143]], [[91.9395099334178], [73.50593767507647], [0.0]], [[88.56048166392993], [63.27830957531933], [0.0]], [[91.94576858787842], [86.8971211950606], [83.01070497531755]], [[80.4263429568666], [67.58142954583705], [54.26404581334159]], [[82.38478587154137], [53.988066662094504], [55.52187093980078]], [[93.14031877627907], [86.56084656084656], [83.89233954451346]], [[87.85365013317785], [70.87119187600214], [0.0]], [[94.37934400909236], [78.5254610633611], [62.77753591641271]], [[89.14692313707093], [88.13796114385507], [43.16546762589928]], [[78.9060560933234], [24.317379641394886], [40.905372238887374]], [[94.23746336205711], [82.64947245017585], [0.0]], [[88.68282558742865], [38.287084408741734], [0.0]], [[87.35091580292489], [34.04770920106021], [82.55842558425584]], [[81.97285994412341], [87.75556656446236], [63.42826639871888]], [[89.46742707489791], [95.94796271787796], [95.27710843373494]], [[79.56202996836463], [29.753181642884684], [55.11580181764879]], [[78.874783267234], [32.92705111879207], [35.84158415841584]], [[63.18997890838006], [56.47261886081908], [22.65589950650516]], [[87.70073761854584], [96.96617609088561], [94.49996298763787]]]]\n",
+            "Core  tumor\n",
+            "plotting scores for the hd95\n",
+            "shape score mat 230\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(dsc_scores)\n",
+        "#score_mat = np.transpose(np.asarray(dsc_scores, dtype=object))[0]\n",
+        "#print(score_mat.shape)\n",
+        "plot_scores_per_region(0,metric='DSC')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 534
+        },
+        "id": "5gbDwFAvAJG8",
+        "outputId": "ce5e7866-25a5-4d69-f643-20a59fb85782"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
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+            "Whole  tumor\n",
+            "plotting scores for the dice\n",
+            "shape score mat 230\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plot_scores_per_region(1,metric='DSC')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 497
+        },
+        "id": "ZZIzhEjTATPQ",
+        "outputId": "7ba2d712-8e48-43e5-e926-4945b45aec47"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Core  tumor\n",
+            "plotting scores for the dice\n",
+            "shape score mat 230\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pickle\n",
+        "from matplotlib import pyplot as plt\n",
+        "import copy\n",
+        "\n",
+        "# Load the pickle files\n",
+        "with open('dscs2021_paper1.pkl', 'rb') as f:\n",
+        "    dsc_scores = pickle.load(f)\n",
+        "with open('hds2021_paper1.pkl', 'rb') as f:\n",
+        "    hd95_scores = pickle.load(f)\n",
+        "\n",
+        "def plot_scores(scores, metric):\n",
+        "    domains = ['Whole', 'Core', 'Enhance']\n",
+        "    fig, ax = plt.subplots(1, 3, figsize=(18, 6))  # 1 row, 3 columns\n",
+        "\n",
+        "    for k, domain in enumerate(domains):\n",
+        "        Z = np.transpose(np.asarray(scores))[0][k]\n",
+        "        score_mat = Z\n",
+        "        avg = np.mean(score_mat, axis=1)\n",
+        "        avg = avg.reshape(score_mat.shape[0], 1)\n",
+        "        score_mat = np.append(score_mat, avg, axis=1)\n",
+        "        m1 = score_mat.mean(axis=0)\n",
+        "        st1 = score_mat.std(axis=0)\n",
+        "        position = np.arange(6) + 1  # To separate different folds\n",
+        "\n",
+        "        bp = ax[k].boxplot(score_mat, positions=position, widths=0.6, showmeans=True, vert=True, patch_artist=False)\n",
+        "        ax[k].set_title(domain)\n",
+        "        ax[k].set_xticks([1, 2, 3, 4, 5, 6])\n",
+        "        ax[k].set_xticklabels(['Fold 1', 'Fold 2', 'Fold 3', 'Fold 4', 'Fold 5', 'Avg'])\n",
+        "        for i, line in enumerate(bp['medians']):\n",
+        "            x, y = line.get_xydata()[1]\n",
+        "            text = ' μ={:.2f}\\n σ={:.2f}'.format(m1[i], st1[i])\n",
+        "            text_y = ax[k].get_ylim()[0] if metric == 'dice' else ax[k].get_ylim()[1]\n",
+        "            ax[k].annotate(text, xy=(x, text_y), color='red', ha='center', va='bottom' if metric == 'dice' else 'top')  # set the color to red, align text at the bottom for 'dice' and at the top for 'hd95'\n",
+        "\n",
+        "        ax[k].set_ylabel(metric + ' score')\n",
+        "\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "# Step 1: Plot dice scores\n",
+        "plot_scores(dsc_scores, 'DSC')\n",
+        "\n",
+        "# Step 2: Plot hd95 scores\n",
+        "plot_scores(hd95_scores, 'HD95')\n",
+        "\n",
+        "# Step 3: Plot both metrics\n",
+        "fig, ax = plt.subplots(2, 3, figsize=(18, 12))  # 2 rows (for two metrics), 3 columns (for domains)\n",
+        "\n",
+        "def plot_scores_together(scores, metric, row):\n",
+        "    domains = ['Whole tumor', 'Core tumor', 'Enhance tumor']\n",
+        "\n",
+        "    for k, domain in enumerate(domains):\n",
+        "        Z = np.transpose(np.asarray(scores))[0][k]\n",
+        "        score_mat = Z\n",
+        "        avg = np.mean(score_mat, axis=1)\n",
+        "        avg = avg.reshape(score_mat.shape[0], 1)\n",
+        "        score_mat = np.append(score_mat, avg, axis=1)\n",
+        "        m1 = score_mat.mean(axis=0)\n",
+        "        st1 = score_mat.std(axis=0)\n",
+        "        position = np.arange(6) + 1  # To separate different folds\n",
+        "\n",
+        "        bp = ax[row][k].boxplot(score_mat, positions=position, widths=0.6, showmeans=True, vert=True, patch_artist=False)\n",
+        "        ax[row][k].set_title(domain + '  ' + metric)\n",
+        "        ax[row][k].set_xticks([1, 2, 3, 4, 5, 6])\n",
+        "        ax[row][k].set_xticklabels(['Fold 1', 'Fold 2', 'Fold 3', 'Fold 4', 'Fold 5', 'Avg'])\n",
+        "        for i, line in enumerate(bp['medians']):\n",
+        "            x, y = line.get_xydata()[1]\n",
+        "            text = ' μ={:.2f}\\n σ={:.2f}'.format(m1[i], st1[i])\n",
+        "            text_y = ax[row][k].get_ylim()[0] if metric == 'DSC' else ax[row][k].get_ylim()[1]\n",
+        "            ax[row][k].annotate(text, xy=(x, text_y), color='red', ha='center', va='bottom' if metric == 'DSC' else 'top')  # set the color to red, align text at the bottom for 'dice' and at the top for 'hd95'\n",
+        "\n",
+        "        ax[row][k].set_ylabel(metric + ' score')\n",
+        "\n",
+        "plot_scores_together(dsc_scores, 'DSC', 0)  # Plotting 'dice' scores in the first row\n",
+        "plot_scores_together(hd95_scores, 'HD95', 1)  # Plotting 'hd95' scores in the second row\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "lyTLCY9NAsZ6",
+        "outputId": "99565077-d837-4bb0-9618-f784b4bef9e2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x600 with 3 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x600 with 3 Axes>"
+            ],
+            "image/png": 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PD2lwycnJOvbYY4O2jR8/Xp9++qkkadSoUZKk+vr6oDT19fWBfW0pLCxUQ0ND4PXZZ5+FNO7uYLPZlJ2drZkzZyo7O5vlOgEAiEI92Y4CAADoLWhDAQAA7F+/jiQ2xqiurk5Lly7VzTffrPXr12vbtm068cQTNW7cuJAHd8opp+jDDz8M2vbRRx9pzJgxkiS73a5Ro0Zp9erVOuGEEyRJjY2NWrdunS6//PL9Hjc+Pl7x8fEhjxcAAGB/erodBQAA0BvQhgIAADiwDg/0paWl6f3339e4ceO6fcnLa6+9Vg6HQ0VFRTr//PP1+uuv6/7779f9998vSYqJidGcOXO0cOFCjRs3Tna7XQsWLFBKSoqcTme3xgYAANARPd2OAgAA6A1oQwEAABxYh5bujI2N1bhx4/T11193VzxBTj75ZD3xxBNauXKl0tPTdfvtt2vJkiW64IILAmluuOEG/eY3v9Hs2bN18skna9u2bVq1apUGDBjQIzECAAC0R0+3owAAAHoD2lAAAAAHFmOMMR15w9NPP63Fixfr3nvvVXp6enfF1aMaGxuVmJiohoYGJSQkhDscIGr5fD55PB7V1tYqOTlZWVlZPE8SQMSIhPq+t7WjIiFPAQBA9wt3nd/b2lBS+PMUAAB0v56q7zs80HfIIYdox44d2rNnj+Li4jRw4MCg/Vu2bAlpgD2BxhXQdW63WwUFBaqpqQlsS01NVWlpqVwuV/gCA4D/Ewn1fW9rR0VCngIAgO4X7jq/t7WhpPDnKQAA6H49Vd936Bl9krRkyZJuCANANHO73crPz1dubm5gqd3KykoVFRUpPz9fZWVlDPYBgGhHAQAAdEZPt6E2bdqkG2+8Uc8++6x27NihtLQ0LV++XCeddJIk67mBt9xyix544AFt3bpVp5xyiu69916NGzeuR+MEAACQOjGjrzfiLiqg83w+n9LS0pSRkaHy8nLFxu599Gdzc7OcTqcqKytVVVXFMp4Awor6PvTIUwAA+oa+VOd/8803OvHEE5WTk6PLL79cI0aMUFVVlcaOHauxY8dKkhYtWqTi4mI9/PDDstvtWrBggdavX6///Oc/GjBgQLt+T1/KUwAA+qqIndEnWR375eXl+uCDDyRJEyZM0DnnnEMnPtAHeTwe1dTUaOXKlUGDfJL10PTCwkI5HA55PB5lZ2eHJ8goxTMPgd6JdhQAAEDH9VQbatGiRRo9erSWL18e2Ga32wP/N8ZoyZIlmj9/vvLy8iRJjzzyiJKSklReXq4ZM2aENB4AAICDiT14kmAbNmzQ+PHjdeGFF8rtdsvtdutnP/uZJkyYoI0bN3ZHjAAiWG1trSTt94Ho/u3+dGgft9uttLQ05eTkaNasWcrJyVFaWprcbne4QwPQBbSjAAAAOq4n21BPPfWUTjrpJJ133nkaOXKkTjzxRD3wwAOB/dXV1aqrq9PUqVMD2xITEzVp0iR5vd6QxgIAANAeHR7ou/rqqzV27Fh99tln+ve//61///vf+vTTT2W323X11Vd3R4wAIlhycrIkqbKyss39/u3+dDg4/zMPMzIy5PV61dTUJK/Xq4yMDOXn5zPYB0Qx2lEAAAAd15NtqI8//jjwvL3nnntOl19+ua6++mo9/PDDkqS6ujpJUlJSUtD7kpKSAvvasmvXLjU2Nga9AAAAQqHDz+gbPHiwXnvtNWVkZARtf/fdd3XKKado27ZtIQ2wJ7AuOtB5PKMvtMhPoPtEQn3f29pRkZCnAACg+4W7zu/JNlRcXJxOOukkvfrqq4FtV199td544w15vV69+uqrOuWUU/TFF18E3dB6/vnnKyYmRn//+9/bPO5vf/tb3Xrrra22044CAKD36qk2VIdn9MXHx6upqanV9m3btikuLi4kQQGIHjabTaWlpaqoqJDT6QyageZ0OlVRUaGSkhIGpdrJ/8zDefPm7feZh9XV1fJ4PGGKEEBX0I4CAADouJ5sQyUnJ+vYY48N2jZ+/Hh9+umnkqRRo0ZJkurr64PS1NfXB/a1pbCwUA0NDYHXZ599FtK4AQBA39Xhgb7c3FzNnj1b69atkzFGxhi99tpr+vWvf61zzjmnO2IEEOFcLpfKysq0fv16ORwOJSQkyOFwqLKyUmVlZXK5XOEOMWrwzEOgd6MdBQAA0HE92YY65ZRT9OGHHwZt++ijjzRmzBhJkt1u16hRo7R69erA/sbGRq1bt06TJ0/e73Hj4+OVkJAQ9ELf4/P5tHbtWq1cuVJr166Vz+cLd0gAgF6gX0ffsHTpUl100UWaPHmy+vfvL0nas2ePzjnnHN11110hDxBAdHC5XMrLy5PH41Ftba2Sk5OVlZXFTL4OavnMw8zMzFb7eeYhEN1oRwEAopXP56Otj7DpyTbUtddeK4fDoaKiIp1//vl6/fXXdf/99+v++++XJMXExGjOnDlauHChxo0bJ7vdrgULFiglJUVOpzOksaB3cbvdKigoUE1NTWBbamqqSktLuUEaANAlHR7oGzZsmJ588klt2LBBH3zwgSRrCYO0tLSQBwcAfU1WVpZSU1NVVFTU5jP6iouLZbfblZWVFcYoAXQW7SgAQDSicxrh1pNtqJNPPllPPPGECgsLddttt8lut2vJkiW64IILAmluuOEGbd++XbNnz9bWrVs1ZcoUrVq1SgMGDAh5POgd3G638vPzlZubq5UrVyo9PV2VlZUqKipSfn4+qyEBQAdwA1prMcYYE+4gwi3cD5UGegMu/kOn5QVAYWFh4AKguLhYFRUVXAAAnUR9H3rkKQD0fv626VlnnaXp06dr4MCB+vbbb/Xss8/qmWeeoW3aR1Dnhx552nf4fD6lpaUpIyOjzRt6nU6nKisrVVVV1ec7qgHgYKKtD7qn6vsOP6Pv3HPP1aJFi1ptX7x4sc4777yQBAUguvgv/jMyMuT1etXU1CSv16uMjAzl5+fL7XaHO8SowjMPgd6LdhQAIJr4fD4VFBRo4sSJWr9+va688kpdfPHFuvLKK7V+/XpNnDhRc+fO5RlT6Ha0oRDNPB6PampqNG/evKBBPkmKjY1VYWGhqqur5fF4whQhAEQH+qD3r8Mz+kaMGKEXX3xRGRkZQdvXr1+vqVOnqr6+PqQB9gTuogI6jzvTug/T0IHQioT6vre1oyIhTwEA3Wft2rXKycmRJJ199tmaN29e0HJzTz/9tCRpzZo1ys7ODmOk6G7hrvN7WxtKCn+eouesXLlSs2bNUlNTk4YMGdJqf1NTkxISErRixQrNnDkzDBECQOSL1j7oiJ3Rt23bNsXFxbXa3r9/fzU2NoYkKADRgzvTuo/NZlN2drZmzpyp7OzsiKqkAHQO7SgAQDTZtGmTJGn69OkqLy9XZmamhgwZoszMTJWXl2v69OlB6YDuQhsK0Sw5OVmSVFlZ2eZ+/3Z/OgBAa/RBH1iHB/oyMjL097//vdX2v/3tbzr22GNDEhSA6FFbWytJSk9Pb3O/f7s/HQD0ZbSjAADR5Msvv5RkLS3fVoeK0+kMSgd0F9pQiGZZWVlKTU1VUVGRmpubg/Y1NzeruLhYdrtdWVlZYYoQACIffdAH1q+jb1iwYIFcLpc2btyo0047TZK0evVqrVy5Uo8//njIAwQQ2VremZaZmdlqP3emAcBetKMAANFkxIgRkqznoVx88cWtlkgqLy8PSgd0F9pQiGY2m02lpaXKz8+X0+lUYWFhYBnk4uJiVVRUqKysjFV8AOAA6IM+sA7P6Dv77LNVXl6uDRs26IorrlBBQYE+//xzvfDCC4G7+QD0HdyZBgDtRzsKABBNDj/8cEnSqlWr5HQ65fV61dTUJK/XK6fTqVWrVgWlA7oLbShEO5fLpbKyMq1fv14Oh0MJCQlyOByqrKxUWVmZXC5XuEMEgIhGH/SBxRhjTLiDCDcegAx0jdvtVn5+vnJzc/d7ZxqNVgDhRn0feuQpAPRuPp9PaWlpOuyww/TVV1+ppqYmsM9ut+vQQw/V119/raqqKmai9HLU+aFHnvZNPp9PHo9HtbW1Sk5OVlZWFudPAGinaOyD7qn6vsNLd3722WeKiYnREUccIUl6/fXXtWLFCh177LGaPXt2yAOEhYYAIpn/zrSCggI5HI7AdrvdHpEnWAAIF9pRAIBo0nK5ubPOOktz587VwIED9e2332rVqlV65plnWG4OPYI2FHoLm82m7OzscIcBAFGJPuj96/CMvqysLM2ePVs///nPVVdXp6OOOkrp6emqqqrSb37zG918883dFWu3ifS7qNxutwoKCoLunkxNTVVpaWmfLryIPAxIA4hkkVDf97Z2VCTkKQCg+7V1TWq321VSUsI1aR8R7jq/t7WhpPDnKQAA0Sqa+qB7qr7v8DP6Kisr9YMf/ECS9NhjjykjI0OvvvqqHn30UT300EOhjq/P809HzcjICHoeQkZGhvLz8+V2u8MdIhDgvzNt5syZys7OjtgTLACEC+0oAEA0crlc2rBhg9asWaMVK1ZozZo1qqqqYpAPPYY2FAAA8KMPurUOL9353XffKT4+XpL0wgsv6JxzzpEkHXPMMaqtrQ1tdH2cz+dTQUGBcnNzVV5erthYa1w2MzNT5eXlcjqdmjt3rvLy8ijMAABEAdpRAIBoxXJzCCfaUAAAAPvX4Rl9EyZM0H333SePx6Pnn39eZ555piTpiy++0KGHHhryAPsyj8ejmpoazZs3LzDI5xcbG6vCwkJVV1fL4/GEKUIgmM/n09q1a7Vy5UqtXbtWPp8v3CEBQEShHQUAANBxtKEAAAD2r8MDfYsWLdKf/vSnwNTI448/XpL01FNPBZZRQGj470pLT09vc79/O3evIRK43W6lpaUpJydHs2bNUk5OjtLS0lheFgBaoB0FAADQcbShAAAA9q/DS3dmZ2frq6++UmNjow455JDA9tmzZ2vQoEEhDa6vS05OlmStRZ+Zmdlqf2VlZVA6IFz8z5LMzc3VypUrlZ6ersrKShUVFSk/P19lZWU8vwMARDsKAACgM2hDAQAA7F+MMcaEO4hwa2xsVGJiohoaGpSQkBDucAJ8Pp/S0tKUkZER9Iw+SWpubpbT6VRlZaWqqqp4Rh/ChnIKIFpEan0fzchTAAD6Bur80CNPAQDo/Xqqvu/w0p3oOTabTaWlpaqoqJDT6ZTX61VTU5O8Xq+cTqcqKipUUlLC4AnCimdJAgAAdA3POQYAAAAAdFaHl+5Ez3K5XCorK9N1110nh8MR2J6amspyiIgIPEsSAACg89xutwoKClRTUxPYlpqaqtLSUtr6AAAAALAPn88nj8ej2tpaJScnKysrq89PhmJGX5RixVVEipbPkmwLz5IEAABom/85xxkZGUGrd2RkZCg/P19utzvcIQIAAABAxHC73UpLS1NOTo5mzZqlnJwcpaWl9flrJwb6Ipzb7da5556rzZs3B23fvHmzzj333D5fgBF+WVlZSk1NVVFRkZqbm4P2NTc3q7i4WHa7XVlZWWGKEAAAIPL4fD4VFBQoNzdX5eXlyszM1JAhQ5SZmany8nLl5uZq7v9n797jojrv/IF/hkEuXqBeAI03CNTadrBJNFscJQFNmxjHMhkm6U+jbRN3vUGaKJAtaNe4m0A3gZAmqNHUXA2kCUyxjsFsojPJCNhUkjZMtioaMcYLEDVAFFSG8/uDPadzZIQZGJgZ5vN+vXiVOecJeXJ65jzPeS7fb2Ymw3gSERERERGBCyV70ueJvkuXLuGVV17B+vXrUVRUhPPnz7uzXoSul/9Vq1YBAObPny+7eefPnw8AWL16NV/+yaOYS5KIyHXsRxER8xwTEbmOfSgiIiL/xIWSPXN6ou8HP/gBLly4AAA4deoUVCoV1q5di/fffx8bN27ED37wA5w4cWLAKuqPzGYzmpqaMHfuXOzatUt28+7atQtz585FY2MjzGazp6tKfk7MJfnZZ59BrVYjLCwMarUatbW1zCVJRAT2o4ioO+Y5JiLqHftQREREBHChZG+cnug7fPgwOjo6AADZ2dm46aabcPLkSXz88cc4efIkZsyYgfXr1w9YRf2ROIG3adMmhzfvxo0bZeWIPE2hUHi6CkREXon9KCK6HvMcExH1jn0oIiIiArhQsjd9Ct1ZXV2NJ554AuHh4QCAkSNHYtOmTThw4IBbK0dEvoHxkYmInMd+FBEBzHNMROQq9qGIiIj8FxdK9syliT5xt057e3u3CzZx4kQ0NTW5r2aEpKQkAMDGjRsdvvw/8cQTsnLkGpvNBrPZjJKSEpjNZr+N39tfjI9MROQc9qOIyB7zHA8s9vWJhg72oYiIiIgLJXvm0kTf/Pnzcdttt6GlpQVHjhyRnTt58iTGjh3r1sr5u6SkJERGRuLAgQNISUmRvfynpKSgsrISkZGRnOjrA4PBgLi4OCQnJ2PJkiVITk5GXFwcd571AeMjExE5h/0oIrqemOe4trZWlufYarUyz3E/sK9PNLSwD0VERERcKNmzQGcLivngRCNHjpR93r17t9/Olg4UpVKJrVu3Qq/XY9++fTAajdK54cOHQ6FQYOvWrX578/aVGGZSo9GgpKQEKpUKVqsVubm50Ov1HFRxEeMjExH1jv0oIroRnU6HlJQUWCwWnD17FhMmTEBiYiL7+H3Evj7R0MI+FBEREYnEhZIZGRlQq9XS8ZiYGL/v5ysEQRA8XQlPa2lpQXh4OJqbmxEWFubp6nRjMBiQkZGB+vp66VhMTAzy8/P9+ubtC5vNhri4OMTHx6O8vFy2A62zsxNarRZWqxV1dXUcXHGS2WxGcnIyqqurkZCQ0O18dXU11Go1TCYTd58SkUd5e3vvi3hNicibsK9PNHDY5rsfrykREVHf2Gw2n1koOVjtPSf64BudK1+6eb0ZJ6XcjwMqROQrfKG99zW8pkTkTdjXJxo4bPPdj9eUiIho6Bus9t6lHH1///vf8Ytf/AI333wzQkNDMWLECMTHx+O3v/0tWlpaBqqOhK4wnklJSVi8eDGSkpI4YdJHDDPpfoyPTETkHPajiIgGFvv6REMT+1BEREREPXN6ou+9997D7NmzcfnyZcyZMwcBAQF4+OGHsXDhQrz11lu47bbbcO7cuYGsK1G/TZgwAQBgtVodnhePi+XIOWJ85NraWqjVaoSFhUGtVsNqtfp9fGQiIoD9KCKiwcC+PtHQwz4UERERUe+cDt156623YuXKlVi1ahUA4P3338evf/1r/OMf/8C1a9ewYMECTJ48Ga+88sqAVngg+EK4BIbudA+GmRxYvE/J2/Ee9W+ebO+Haj/KF/pQROQ/2NcnGjieavOHah8KYD+KiIjIH3hd6M7Dhw/jnnvukT7fddddOH78OM6ePYthw4Zh48aN2LNnz4BU0t8ZDAbExcUhOTkZS5YsQXJyMuLi4mAwGDxdNZ/DMJMDiyFmyZvxWUqexH4UEdHAY1+faOhhH4qIiIiod05P9E2cOBFHjhyRPh8/fhydnZ0YO3YsAGDSpEn49ttv3V9DP2cwGKDX6xEfHy97UY2Pj4der+cAdR8wzCSR/+GzlDyN/SgiosHBvj7R0MI+FBEREVHvAp0t+Itf/AL/+q//ivXr1yM4OBjPPvssfvaznyEoKAgA8Le//Q0xMTEDVlF/ZLPZkJGRAY1GIws9k5CQgPLycmi1WmRmZiIlJYWrUl2k0+mQkpLCEH5EfsD+WVpWVobKykrs3r0bEyZMQFlZGVJTU/kspQHHfhQR0eBhX59o6GAfioiIiKh3Tk/05eTk4NKlS/iv//ovXLlyBXfffTd+//vfS+cnTpyIrVu3Dkgl/ZXFYkF9fT1KSkpk+SUAICAgANnZ2VCr1bBYLEhKSvJMJX2YGGaSiIY28Vm6cuVKTJs2DfX19dK56OhorFixArt37+azlAYU+1FERIOLfX2ioYF9KCIiIqLeKQRBEDxdCU/z1gTIJSUlWLJkCVpbWzFy5Mhu51tbWxEWFobi4mIsXrzYAzUkIvJ+4rNUoVBAo9EgJycHKpUKVqsVubm5MBqNEASBz1I/4K3tvS/jNSUiIvIPbPPdj9eUiIho6Bus9t7pHX32vv76a9TX10OhUCA6OlqKjU7uNWHCBACA1WpFQkJCt/NWq1VWjoiIuouMjAQAzJkzx2EY5DvvvBMHDhyQyhENNPajiIiIiFzHPhQRERGRYwG9F/mnzz//HHfccQeioqLw4x//GP/yL/+CyMhIzJs3T5YcmdwjMTER0dHRyM3NRWdnp+xcZ2cn8vLyEBMTg8TERA/VkIjI93FjOw0W9qOIiIiIXMc+lOfYbDaYzWaUlJTAbDbDZrN5ukpERETkgNMTfefOncOdd96JpqYmPPvss3j33XexZ88ePPPMMzh79iwSExPR2Ng4kHX1O0qlEgUFBTAajdBqtaiurkZrayuqq6uh1WphNBqRn5/PpPJERD0Q26YDBw44fJZWVlbKyhENBPajiIiIiFzHPpTnGAwGxMXFITk5GUuWLEFycjLi4uJgMBg8XTUiIiK6jtMTfYWFhZg6dSo+/fRTPProo7j77rtxzz33YN26dfjkk08wefJkFBYWDmRd/ZJOp0NpaSlqa2uhVqsRFhYGtVoNq9WK0tJS6HQ6T1eRiMirieGN8/LyHD5Lc3NzZeWIBgL7UURERESuYx/KMwwGA/R6PeLj42ULJePj46HX6znZR0RE5GUUgpMxy2677Tb85je/wQMPPODw/FtvvYWnn34an3zyiVsrOBh8IQGyzWaDxWLB2bNnMWHCBCQmJnInHxGRE2w2G+Li4hAfH4+ysjJUVlZKz9I5c+YgNTUVVqsVdXV1fK4OcZ5s74dqP8oX+lBERETUf55q84dqHwrw3n6U/fuTfY5zoCuNjFar5fsTERGRkwarvQ90tuAXX3yB22677YbnZ82ahS+++MItlaLulEolkpKSPF0NIiKfI4ZB1uv10Ol0uOeeexAaGorPP/8cBQUF2LNnD0pLS/mSSgOK/Sgi6gkX9REROcY+1OCzWCyor69HSUmJbJIPAAICApCdnQ21Wg2LxcJxKiIiIi/h9ERfa2trjzOOo0aNwrfffuuWShEREbmTTqdDZmYmCgsLYTQapeOBgYHIzMxkGGQacOxHEdGNGAwGZGRkoL6+XjoWHR2NgoICtk9E5PfYhxp8Z8+eBQCoVCqH58XjYjkiGnq4CI3I9zg90Qd0dbBCQkIcnmtpaYGTUUCJiIgGlcFgQH5+PhYuXIgFCxYgNDQUbW1tqKioQH5+PhISEjiYSgOO/Sgiup6YA0mj0aCkpAQqlUrKH6vX65mTm4gI7EMNNjF3udVqRUJCQrfzVqtVVo6IhhYuQiPyTU7n6AsICIBCobjheUEQoFAoYLPZ3Fa5weKtcdGJiKj/mGOCRJ5s74dqP4p9KKK+Y/tERL7EU23+UO1DAd7bj2L7ROS/7Beh5eTkyBahGY1GLkIj6gOvy9FnMpkGrBLUO26ZJiLqG+aYIG/AfhQRXY/tExFR79iHGnz2Oc61Wi2ys7Olwf68vDxpsJ9jUkRDi81mQ0ZGBjQajWySPyEhAeXl5dBqtcjMzERKSgq//0ReyOmJvjvvvHMg60E94JZpIqK+Y44J8gbsR9FQwgVo7sH2iYiod+xDeYZOp0NpaSkyMjKgVqul4zExMdzRQzREcREakW9zeqKvpaXFqXLeFG5gKGDeDiKi/mGOCfIG7EfRUMEFaO7D9omIqHfsQ3mOTqdDSkoKF/cQ+QkuQiPybczRB8ZFJyIayvgsJRFz9Lmft/ahaGAwZ4d7sX0iIl/CHH3ux34UEXkTs9mM5ORkVFdXO1yEVl1dDbVaDZPJxB19RC7w6hx9giDg3nvvxR/+8AdMnDhxQCpG3DJNROQOzDFB3oD9KPJ1zNnhfmyfiIh6xz4UEdHgSExMRHR0NHJzcx0uQsvLy0NMTAwSExM9WEsiupE+5+hTKpVISEjAzTff7PZKURdumSYicg/mmCBPYz+KfB0XoA0Mtk9ERD1jH4qIaHBwERqRb3N6oo8GH/N2EBG5D3NMEBH1HRegDRy2T0RERETkDbgIjch3caLPi3HLNBGReymVSu40ISLqAy5AG1hsn4iIiIjIG3ARGpFvCui9yI31lBCZ+k/cMm00GqHValFdXY3W1lZUV1dDq9XCaDQiPz+fD1oiIiIfxH4U+RL7BWidnZ2yc1yARkREg4l9KCKigSUuQlu8eDGSkpI49kzkA5ze0Xf91tz29nasWrUKI0aMkB03GAzuqRkB4JZpIiKioYD9KPJ1zNlBRESewD4UERERUe+cnugLDw+XfV66dKnbK0OOccs0kX+y2Wz83hMNEexH0VDABWhERDTY2IciIiIi6p1CEATB05XwtJaWFoSHh6O5uRlhYWGerg4REQwGAzIyMlBfXy8di46ORkFBAQdSifqI7b378Zr6Jy5EISLyP2zz3Y/XlIiIaOgbrPa+Xzn6iIjI/QwGA/R6PeLj42W5OePj46HX6xmWhoiIPIo5O4iIiIiIiMhTbDYbzGYzSkpKYDabYbPZPF0lj+NEHxGRF7HZbMjIyIBGo0F5eTkSEhIwcuRIJCQkoLy8HBqNBpmZmWzAiIiIiIiIiIiIyK8YDAbExcUhOTkZS5YsQXJyMuLi4vx+YwQn+oiIvIjFYkF9fT1ycnIQECB/RAcEBCA7OxsnTpyAxWLxUA2JiIiIiIiIiIiIBhejoN1YoKcrQERE/3T27FkAgEqlcpgDSaVSycoRERERERERERERDWXXR0ETN0iIUdC0Wi0yMzORkpLil+klONFHRG7jaGLKHx+s/TFhwgQAQFFREbZt24b6+nrpXHR0NFasWCErR0RERERERERERDSUiVHQSkpKIAgCzGazbAw6OzsbarUaFosFSUlJnq7uoGPoTiJyC8ZHdo/ExEREREQgOzsbKpVKtg1dpVIhJycHkZGRSExM9HRViYiIiIiIiIiIiAacGN3s+PHjDsegv/jiC1k5f8OJPiLqN8ZHdi+FQiH9LgiC9ENE1B+/+93voFAo8Nhjj0nH2tvbkZaWhrFjx2LkyJFITU1FQ0OD5ypJRERERERERHQdMbrZ0qVLHY5BL126VFbO3ygEjh6jpaUF4eHhaG5uRlhYmKerQ+RTbDYb4uLiEB8fL4uPDACdnZ3QarWwWq2oq6tjGE8nmM1mJCcnIy8vDy+++CJOnjwpnRNDd+bk5MBkMvnlNnSi/vDn9v6vf/0rHnjgAYSFhSE5ORnPPfccAGD16tXYs2cPXn31VYSHhyM9PR0BAQGorKx06u/68zUlIiLyJ2zz3Y/XlIiIyHlXr17FiBEjMHbsWHz11VcIDPxnVrqOjg5MmjQJ58+fx6VLlxAUFOTBmsoNVnvPHX1E1C9ifOScnBzZJB8ABAQEIDs7GydOnIDFYvFQDX2LuL188uTJsp19oilTpsjKERH15ttvv8WDDz6Il156CaNHj5aONzc3Y8eOHXj22Wcxb948zJw5E6+88gqqqqpw8OBBD9aYiIiIiIiIiOifqqqq0NHRgcbGRuh0OtmOPp1Oh8bGRnR0dKCqqsrTVfUITvQRUb+IE04qlcrhefE4J6acI24vX7ZsmcNt6MuWLZOVIyLqTVpaGhYuXIi77rpLdrympgbXrl2THZ8+fTqmTJmC6urqwa4mEREREREREZFD4tjyG2+8gdraWqjVaoSFhUGtVsNqteKNN96QlfM3gb0XISK6MXHCyWq1IiEhodt5q9UqK0c9U6vVCAwMxNixY2EwGKRt6AkJCTAYDNI2dLVa7eGaEpEveOutt/DJJ5/gr3/9a7dz586dQ1BQEL7zne/IjkdFReHcuXMO/96VK1dw5coV6XNLS4tb60vkr2w2GywWC86ePYsJEyYgMTGRIc+JiIiGILb5RER9I44tx8bG4tixY92epR9//LGsnL/hjj7yWzabDWazGSUlJTCbzbDZbJ6ukk9KTExEdHQ0cnNz0dnZKTvX2dmJvLw8xMTEIDEx0UM19C3iNvSGhgaH29AbGhr8ehs6ETnv1KlTePTRR/Hmm28iJCTELX8zLy8P4eHh0s/kyZPd8neJ/JnBYEBcXBySk5OxZMkSJCcnIy4uDgaDwdNVIyIiIjdim09E1Hf2Y9AKhQJJSUlYvHgxkpKSoFAo/H4MmhN95JfYuXIfpVKJgoICGI1GaLVa2cSUVquF0WhEfn4+V6g5SdxevnPnTofb0Hfu3CkrR0R0IzU1NWhsbMRtt92GwMBABAYG4sMPP8Tzzz+PwMBAREVF4erVq/jmm29k/1xDQwPGjx/v8G9mZ2ejublZ+jl16tQg/JcQDV0GgwF6vd5huG69Xs++KRER0RDBNp+IqH84Bt0zn5ro+93vfgeFQoHHHntMOtbe3o60tDSMHTsWI0eORGpqKhoaGjxXyQHC3Wfuw86V++l0OpSWljqcmCotLYVOp/N0FX2G/Tb0I0eOoLCwEOnp6SgsLMThw4dx8803y8oREd3I/PnzUVtbi7/97W/Sz6xZs/Dggw9Kvw8bNgz79u2T/pkjR47gyy+/xOzZsx3+zeDgYISFhcl+fAH7Ue7F6+keNpsNGRkZ0Gg0KC8vR0JCAkaOHImEhASUl5dDo9EgMzOT15eIiMjHsc0nInIPjkHfmM/k6PvrX/+Kbdu2YcaMGbLja9euxZ49e/DOO+8gPDwc6enp0Ol0qKys9FBN3c9gMCAjIwP19fXSsejoaBQUFPj1zdsX13euAgK65rrFzpVWq0VmZiZSUlL8dva/r3Q6HVJSUhhrvp/EbeiPPPIImpqacPLkSencc889h4iICL/ehk5Ezhs1ahRUKpXs2IgRIzB27Fjp+PLly7Fu3TqMGTMGYWFheOSRRzB79myHOVd9FftR7sXr6T4WiwX19fUoKSmR+qSigIAAZGdnQ61Ww2KxICkpyTOVJCIion5jm09E5F6CIMg+X59Oyh/5xI6+b7/9Fg8++CBeeukljB49Wjre3NyMHTt24Nlnn8W8efMwc+ZMvPLKK6iqqsLBgwc9WGP34e4z9xI7Vzk5OTfsXJ04cQIWi8VDNfRtSqVSFh+Zk3yuUyqVuP/++3Ho0CG0t7dj+/btOHPmDLZv34729nYcOnQIer2e15aI3KKwsBAajQapqam44447MH78+CHVt2A/yr3E66lSqbB582a8/PLL2Lx5M1QqFa9nH4hhuK+fkBeJxxmum4iIyLexzScicg/xnXTGjBmyd/wZM2b4/TupQrh++tML/fKXv8SYMWNQWFiIpKQk3HLLLXjuueewf/9+zJ8/HxcvXsR3vvMdqfzUqVPx2GOPYe3atQ7/3pUrV3DlyhXpc0tLCyZPnozm5mavCkFls9kQFxeH+Ph42e4zoGuWWqvVwmq1oq6ujoP+TiopKcGSJUvQ2tqKkSNHdjvf2tqKsLAwFBcXY/HixR6oIfk78Xs/btw4fP3117IdEzExMRg7dizOnz/P7z1RH7S0tCA8PNzr2ntf5s3XlP0o9+qpfYqOjsa4cePYPrnIbDYjOTkZ1dXVDnfRVldXQ61Ww2QycXU/EXmcN7f5vorX1H+wzSci6j9ffccfrPbe63f0vfXWW/jkk0+Ql5fX7dy5c+cQFBQkm+QDgKioKJw7d+6GfzMvLw/h4eHSz+TJk91dbbfg7jP3E/OaWa1Wh+fF48x/Rp4ifu9feOEFHDt2DCaTCcXFxTCZTKirq8Pzzz/P7z0RkRPYj3Iv8XrW1NQ43CFZU1PD6+kiMVx3bm5ut1AznZ2dyMvLY7huIiKiIYBtPhFR//Edv2denaPv1KlTePTRR/H+++8jJCTEbX83Ozsb69atkz6LO/q8Dbf2u59958rRzD87V+Rp9t97MRSqPX7v+8dmszGPJJGfYD/KvU6fPg0AuOeeexzmOdZoNKioqJDKUe+USiUKCgqg1+uh1WqRnZ0NlUoFq9WKvLw8GI1GlJaWsp0iIiLycfZtfkpKCu655x6Ehoaira0Ne/fuxZ49e9jmExH1wv4d39H4nr+/43v1RF9NTQ0aGxtx2223ScdsNhs++ugjFBUV4b333sPVq1fxzTffyHb1NTQ0YPz48Tf8u8HBwQgODh7IqruF/e6z22+/vdvNy91nruOACnk7+++9o5Ae/N73ncFgQEZGRrdwcwUFBdDpdJ6rGBENCD5P3aupqQkAoNPpHK6e1Gq1qKiokMqRc3Q6HUpLS5GRkQG1Wi0dj4mJQWlpKdsnIiKiIUKn0yEzMxOFhYUwGo3S8cDAQGRmZrLNJyLqhfjuXlRUhG3btnUb31uxYoWsnL/x6hx9ra2tOHnypOzYQw89hOnTp+Pf//3fMXnyZERERKCkpASpqakAgCNHjmD69Ok3jHvtiLfGRWculIHjaMA/JiYG+fn57FyRR/lqvGlvJybr1Wg0yMnJkSb4c3NzpQl+fveHPm9t732ZN19TPk/d680338TSpUuxYMECGI3GbtdT3NG3c+dOPPjggx6sqW/ijnMi8nbe3Ob7Kl5T/yK+ky5cuBALFiyQdvRVVFRIO/r4TkpEdGM2mw0TJkxAU1MTNBoN1q9fL43vPfXUUzAajYiMjMSZM2e86l2KOfoAjBo1CiqVSvYzYsQIjB07FiqVCuHh4Vi+fDnWrVsHk8mEmpoaPPTQQ5g9e7bTk3zeTKlU4v7778ehQ4fQ1taG7du348yZM9i+fTva2tpw6NAh6PV6r7pxfYVOp3OY/4ydKvI0cdep0WiEVquV5UDSarUwGo3Iz8/n994FNpsNGRkZ0Gg0KC8vR0JCAkaOHCkLN5eZmQmbzebpqhKRG/F56l4TJ04EAFRUVDi8nhUVFbJy5BoxXPfixYuRlJTE+5KIyIv87ne/g0KhwGOPPSYda29vR1paGsaOHYuRI0ciNTUVDQ0NnqskeTX7d9Jdu3ZhzZo1eOihh7BmzRrs2rWL76RERE5SKBQAAEEQUFNTg7fffhs1NTXw4r1sg8ard/Q5kpSUhFtuuQXPPfccgK7OVUZGBkpKSnDlyhXcfffd2LJlS4+hO6/nrauo7Hf0NTU1yXY3ckcf0dDGXafuYzabkZycfMOd3tXV1VCr1TCZTN1yItLQ4q3tvS/zhWvK56l7sF9KROTffKHNHwh//etf8cADDyAsLAzJycnSWNTq1auxZ88evPrqqwgPD0d6ejoCAgJQWVnp9N/212vqj/hOSkTUf+Kz9MEHH8Qf//hHdHR0SOcCAwPxwAMPSBt6vOlZOljtvVfn6HPEbDbLPoeEhGDz5s3YvHmzZyo0gCwWC+rr61FSUuIwR9/HH38MtVoNi8XiVTcvEfWfTqdDSkoKw3i5gX2yXkf8PVkvkT+4fl1bZ2enh2riu+zzHC9cuBBZWVlSyKm9e/dKIafYThER0VDx7bff4sEHH8RLL72EJ598Ujre3NyMHTt2oLi4GPPmzQMAvPLKK/j+97+PgwcPDokIU+RefCclIuo/8Rn55ptvQqPRdAuDXFxcLCvnb3xuos+f2HcExHA+9tgRIBraHH3vyXViEl6r1erwpdtqtcrKEdHQYZ+f86233pLl59Tr9cyF4iKdTofS0lJkZGTAaDRKx2NiYngtiYhoyElLS8PChQtx1113ySb6ampqcO3aNdx1113SsenTp2PKlCk33LEFAFeuXMGVK1ekzy0tLQNXefIqfCclIuq/yMhIAMDcuXOxa9cuWd74VatW4Y477kBlZaVUzt94dY4+f2ffEXCEHQEiot4lJiYiOjoaubm53XbxdHZ2Ii8vDzExMUhMTPRQDYloINjnQikrK0N7ezt2796N9vZ2lJWVMRdKH+l0Ohw5cgSFhYVIT09HYWEhDh8+zEk+IiIaUt566y188sknyMvL63bu3LlzCAoKwne+8x3Z8aioKJw7d+6GfzMvLw/h4eHSz+TJk91dbfJSfCclIhp4Yv4+f8WJPi/GjgARUf+J4eaMRiO0Wi2qq6vR2tqK6upqaLVaGI1G5OfnM9xcH9lsNpjNZpSUlMBsNnPShLyGGAJdrVZj2rRpSE5OxpIlS5CcnIxp06Zh9uzZOHHiBCwWi6er6lMMBgO+973vYe3atSgqKsLatWvxve99DwaDwdNVIyIicotTp07h0UcfxZtvvomQkBC3/d3s7Gw0NzdLP6dOnXLb3ybvxndSIqL+a2xsBABUVlY6fJaKeXLFcv6GE31ejB0BIiL3EMPN1dbWQq1WIywsDGq1GlarleHm+sFgMCAuLk42gRIXF8cBf/IKYmjznJwcxMfHy/pR8fHxWL9+vawc9U4Mheroeur1en73yatwIQoR9VVNTQ0aGxtx2223ITAwEIGBgfjwww/x/PPPIzAwEFFRUbh69Sq++eYb2T/X0NCA8ePH3/DvBgcHIywsTPbj7fgsdR++kxL5Lz5L3UOMapibm+vwWfrUU0/JyvkbTvR5OXYEiIjcRxAE2efrd0uT8zjgT95OjMs/Z84ch6E758yZIytHPbMPhVpeXo6EhASMHDkSCQkJKC8vZyhU8ipciEJE/TF//nzU1tbib3/7m/Qza9YsPPjgg9Lvw4YNw759+6R/5siRI/jyyy8xe/ZsD9bcvfgsdT+dTodjx47BZDKhuLgYJpMJdXV1HNsjGsL4LHUfMfphVVUVjh49KnuWHjlyBNXV1X4d/ZATfT6AHQEiov4RJ6VmzJghm5SaMWMGJ6X6gAP+5Eu+/vprfPe735W9WH33u9/F119/7emq+RQxFGpOTo4s6TkABAQEIDs7m6FQyStwIQoR9deoUaOgUqlkPyNGjMDYsWOhUqkQHh6O5cuXY926dTCZTKipqcFDDz2E2bNnIyEhwdPVdws+SweOUqlEUlISFi9ejKSkJEbpIhrC+Cx1L/voh6mpqQgODoZGo0FwcDBSU1P9PvqhQrh+e4MfamlpQXh4OJqbm30idAIRETnPZrMhLi4O8fHxKC8vlw1Qd3Z2QqvVwmq1oq6uzm87A64ym81ITk5GdXW1w8GM6upqqNVqmEwmJCUlDX4Fb4Dtvft58zUtKSnBkiVLAABRUVH4r//6L2g0GhiNRvz2t79FQ0MDAKC4uBiLFy/2ZFV9gng9W1tbMXLkyG7nW1tbERYWxutJHsU2n2jgeHObPxiSkpJwyy234LnnngMAtLe3IyMjAyUlJbhy5QruvvtubNmypcfQndfz1mvKZykRUf/xWTpwDAYDMjIyUF9fLx2LiYlBfn6+V26MGqz2PnDA/jIREZEXEHehlJSU3HAXilqthsVi8apJKW8m5jRTqVQOz4vHmfuMPEkMyTl9+nS0t7djxYoV0rmYmBhMnz4dhw8fZuhOJ4l5DqxWq8MJfqvVKitH5Als84looJjNZtnnkJAQbN68GZs3b/ZMhQYQn6VERP3HZ+nA0el0SElJgcViwdmzZzFhwgQkJib6/YQpJ/qIiGhI46SU+3HAn3zJuHHjsH//flRWVkovAXPmzMG8efM8XTWfIuZDyM3NdbgiNS8vz6/zIZB3YJtPRNR/fJYSEfUfn6UDSwyDTP/EHH1ERDSk2U9KOcJJKdfZD/h3dnbKznHAn7xFY2MjAKCystJh/P7KykpZOeqZfT4ErVYryzGh1Wr9Ph8CeQe2+URE/cdnKRFR//FZSoONE31ERDSkcVLK/TjgT75AfGHKzc1FbW0t1Go1wsLCoFarYbVa8dRTT8nKUe90Oh1KS0sdXs/S0lKvzIdA/oVtPhFR//FZSkTUf3yW0mDjRB8REQ1pnJQaGBzwJ28nvlhVVVXh6NGjMJlMKC4uhslkwpEjR1BdXc0Xqz7Q6XQ4duyY7HrW1dXxO09egW0+EVH/8VlKRNR/fJbSYFMIgiB4uhKe1tLSgvDwcDQ3NyMsLMzT1SEiogFgMBiQkZGB+vp66VhMTAzy8/M5QN0PNpvNZxIgs713P2+/pgaDAXq9HhqNBtnZ2VCpVLBarcjLy4PRaOSkNNEQxTafyP28vc33Rd5+TfksJSLqPz5LabDae070wfs7V0RE5B6+NClF7sf23v184ZryxYrIP7HNJ3IvX2jzfY0vXFM+S4mI+o/PUv/Gib5B5AudKyIiIuoftvfu5yvXlC9WRERE/eMrbb4v4TUlIiIa+garvQ8csL9MbsUBKiIiIqK+USqVSEpK8nQ1iIiIiIiIiIjcjhN9PsBgMGDdunU4efKkdGzq1Kl49tlnGXKKiIiIiMjHcVEf+QLep0RERERE3inA0xWgnhkMBqSmpqKxsVF2vLGxEampqTAYDB6qGRERERER9ZfBYEBcXBySk5OxZMkSJCcnIy4ujv188iq8T4mIiIiIvBcn+ryYzWbDqlWrAADz589HdXU1WltbUV1djfnz5wMAVq9eDZvN5slqEhERERFRHxgMBuj1ejQ0NMiONzQ0QK/XcxKlH2w2G8xmM0pKSmA2m/nO1A/ifRofHy97J42Pj+d9SjTE8VlKRETkGxSCIAieroSneWsC5H379uGuu+7C3Llz8eGHHyIg4J/zsp2dnbjzzjtx4MABfPDBB9LEHxERETnmre29L+M1Jeo7m82Gm266CY2NjVi4cCHuvfdehIaGoq2tDe+++y727NmDyMhInDlzhuERXWQwGJCRkYH6+nrpWHR0NAoKCpj6wEU2mw1xcXGIj49HeXl5t3dSrVYLq9WKuro63qdDHNt89/P2a8pnKRERUf8NVnvPHX1ezGw2AwA2bdoke6ECgICAAGzcuFFWjoiIiIiIfIPZbEZjYyOmT5+Ozz//HGlpaXj44YeRlpaGzz//HNOnT0djYyP7+i7i7jP3slgsqK+vR05OjsN30uzsbJw4cQIWi8VDNSSigcBnKRGR+3B3NA0GTvT5CD4QiIiIiIiGDnEC7/Dhww4HUg8fPiwrR72z2WzIyMiARqNBeXk5EhISMHLkSCQkJKC8vBwajQaZmZl8l3LB2bNnAQAqlcrhefG4WI6IfB+fpURE7sM8xzRYONHnxZKSkgAAaWlpDh8IjzzyiKwcERERERH5hs7OTgCQBk6vH0hNSEiQlaPecfeZ+02YMAEAYLVaHZ4Xj4vliMj38VlKROQe3B1Ng4kTfV4sKSkJ4eHhOHz4MC5fvozt27fjzJkz2L59Oy5fvozDhw8jPDycE31ERERERD5m7NixAIC2tjaH5y9fviwrR73j7jP3S0xMRHR0NHJzc7tNOnd2diIvLw8xMTFITEz0UA2JyN3sn6WOokvxWUpE1DvujqbBxok+LxcUFAQAaG1txYoVK3DTTTdhxYoVaG1tBQAEBwd7snpERERERNQHUVFRAIC///3vSElJka3yTUlJwWeffSYrR73j7jP3UyqVKCgogNFohFarld2nWq0WRqMR+fn5UCqVnq4qEbmJ+IwsKipyGF2qqKhIVo6IiLrj7mgabJzo82IWiwVNTU3Iy8vr9oI/fvx45ObmorGxkQ8EIiIiIiIfM3HiROn3ffv2Qa1WIywsDGq1Gvv373dYjnrG3WcDQ6fTobS0FLW1tbL71Gq1orS0FDqdztNVJCI3SkxMREREBLKzs6FSqWQT/CqVCjk5OYiMjOSzlIioB4w0QYONE31eTPyip6en49ixYzCZTCguLobJZEJdXR3S09Nl5YiIiIiIyDeIk1KzZs1CRESE7FxERARmzZrFSSkXcffZwNHpdA7fSTnJRzQ0KRQK6XdBEKQfIiJyDiNN0GAL9HQF6MbsHwgJCQndcvHxgUBERERE5JvESSm9Xo+FCxfi8ccfR2hoKNra2rB3717s2bMHpaWlnJRykbj7LCMjA2q1WjoeExPD3Wf9pFQqmR+eyA9YLBY0NjYiLy8P27Zt6/Yszc3NRU5ODiwWC58JREQ3YB9pory8XBa+k5EmaCAoBC7JQUtLC8LDw9Hc3IywsDBPV0dis9kQFxeH+Ph4hw8ErVYLq9WKuro6DgAQERH1wlvbe1/Ga0rUfwaDARkZGaivr5eOxcTEID8/n5NS/WCz2WCxWHD27FlMmDABiYmJfGci6ge2+e7nrde0pKQES5YsQWtrK4KCgrBlyxYcP34csbGxWLNmDa5cuYKwsDAUFxdj8eLFnq6uz2H7ROQ/DAYD9Ho9NBqNFA7ZarUiLy8PRqORi9D8xGC199zR58XsV/lqtdobPhDYISAiIiIi8k06nQ4pKSkc9HMz7j4jIuobMWpUUVERtm3bJluI8vvf/x4rVqyQlSPnGQwGrFu3DidPnpSOTZ06Fc8++ywH+4mGIEaaoMHEHX3w3lVUIq7yJSIi6j9vb+99Ea8pERGRf2Cb737eek1tNhtuuukmNDY2QqPRYP369dKi86eeegpGoxGRkZE4c+YMF6W4wGAwIDU1FSEhIWhvb5eOi5/Lyso4xkc0RHEnr3/jjj6ScJUvEREREREReRIHqYj8h7gnQBAE1NTU4H//93/R1tYG7hXoG5vNhlWrVgEAFAqF7Jz4efXq1UhJSeFzlWgIYqQJGgyc6PMRfCAQERERERGRJziKMhMdHY2CggLuQCEaYiwWC5qamvDggw/ij3/8I/bs2SOdCwwMxJIlS1BcXAyLxcJxKieZzWY0NTUBAObPn+9wl2RjYyPMZjPmz5/v4doSEZEvCvB0BYiIiIiIiIjIOxkMBuj1esTHx6O6uhqtra2orq5GfHw89Ho9DAaDp6tIRG509uxZAEBxcTECA+X7AwIDA1FSUiIrR73bv38/AGD27NnYtWsXEhISMHLkSCQkJEif7cuRa2w2G8xmM0pKSmA2m2Gz2TxdJSKiQceJPiIiIiIichoHU4j8h81mQ0ZGBjQaDcrLy2WD0+Xl5dBoNMjMzORzgGgIiYyMBNAVtvOuu+6STfDfddddUvhOsRz17ssvvwQALFmyBAEB8qHYgIAALF68WFaOnGcwGBAXF4fk5GQsWbIEycnJiIuL4yIUIvI7DN3pI5gPgXwB71PydrxHiYj6h+H7iPyLxWJBfX09SkpKHA5OZ2dnQ61WM4Qf0RDS2dkJABg9ejT+9Kc/Sbv6EhIS8Kc//QmRkZG4ePGiVI56N2XKFABduyTXrFkje552dnaiuLhYVo6cI+44X7hwIbKyshAaGoq2tjZUVFRAr9ejtLSU/VMi8hvc0ecDuDqFfAHvU/fjjgn34j1KRNQ/DN9H5H/E0HwqlcrhefE4Q/gRDR0fffQRAODixYvQ6XSyNl+n0+HixYuyctS7efPmAQCqq6uRkpIiu6YpKSn4y1/+IitHvRN3nM+cORO1tbVIS0vDww8/jLS0NNTW1mLmzJnccU5EfoUTfV6OAyrkC3ifuh8npdyL9ygRUf8wfB+Rf5owYQIAwGq1OjwvHhfLEdHQ8cQTT6C2thZqtRphYWFQq9WwWq3YuHGjp6vmc5KSkqRQpx988IHsmu7btw9AVyhU7ox2nrjj/NChQ5gxY4bsPX/GjBk4dOgQTpw4AYvF4umqEhENCk70eTEOqJAv4H3qfpyUci/eo0RE/ScOpuTk5NwwfB8HU4iGnsTERERHRyM3N7dbmL7Ozk7k5eUhJiYGiYmJHqohEbmbONn0wQcf4OjRozCZTCguLobJZMKRI0ekiSlOSjlPqVRi69atUCgUUCgUsnPisa1btzKthAtOnz4NAFiwYIHD9/wFCxbIyhERDXWc6PNiHFAhX8D71L04KeV+9veoIAiycKiCIPAeJSJyAsP3EfknpVKJgoICGI1GaLVa2SI0rVYLo9GI/Px8Dk4TDSFJSUmIiIjAgQMHoNPpEBwcDI1Gg+DgYOh0Ohw4cIC7z/pAp9OhtLQUUVFRsuNRUVHMJdcHTU1NALquq6OxKK1WKytHRDTUcaLPi3FAhXwB71P34sSp+4n33vHjxx2GQ/3iiy9k5YiIqDuG7yPyX+LgtKMQfhycJhp6lEolXnzxRQDAvn37ZN/7/fv3AwB3n/WRTqfDsWPHZLsk6+rq+Bztg4iICABdEZEc7TgvLy+XlSMiGuo40efFOKBCvoD3qXtx4tT9xHtv2bJlDsOhLlu2TFaOiIi6Y/g+Iv/GwWki/6LT6VBWVibllRNFRkairKyM3/1+UCqVSEpKwuLFi5GUlMQJ0z6aOHEiAGDv3r0Od5zv3btXVo5cY7PZZNGQGFWKyPspBEEQPF0JT2tpaUF4eDiam5sRFhbm6epIbDYb4uLiEB8fj/Lyctnuns7OTmi1WlitVtTV1bFjQB7D+9S9zGYzkpOTUV1djYSEhG7nq6uroVarYTKZGCrFSVevXsWIESMwduxYfPXVVwgMDJTOdXR0YNKkSTh//jwuXbqEoKAgD9aUBpq3tve+jNfUv4g5ZDUaDbKzs6FSqWC1WpGXlwej0cidPeRVbDYbLBYLzp49iwkTJiAxMZF9UaJ+YJvvfr5wTfksJW8ljkWNGzcOX3/9Nerr66VzMTExGDt2LM6fP8+xqD4wGAzIyMiQXdPo6GgUFBSwr99HfJb6t8Fq7wN7L0KeIuZD0Ov1+NnPfobY2Fi0t7cjJCQEx48fx7vvvovS0lI+GMij7O9TrVZ7w4E/3qfOsd8x4WjilDsmXFdVVYWOjg40NDRAp9N1u0cbGhqkcpw8JSK6MTF8X0ZGBtRqtXQ8JiaGk3zkVThARUTkHuLuMyJvYz8Wde+99+JnP/sZx0zdwH5hX0lJiTR2kpubC71ezz5/H7BfSoOFE31eTqfT4Wc/+xl27drV7VxKSgofCOQVOPDnPpw4dT8xzOnOnTuxYcOGbvfozp07sXTpUoZDJSJygk6nQ0pKClekktfiABUREZF/0Ol0yMzMRGFhITo6OqTjgYGByMzMZHvvIpvNhoyMDGg0GtnC84SEBJSXl0Or1SIzMxMpKSns+zuJ/dKBw12S3TF0J7w7XMLjjz+OZ555BgEBAbJ8KOLnrKwsPP300x6sIdE/8SHrPo5W/MTExCA/P5+dABfZh0O97bbbsGXLFhw/fhyxsbFYs2YNampqGA7VT3hze++reE2JyJswpDzRwGGb7368pkT9I06iLFy4EAsWLEBoaCja2tpQUVGBPXv2cBLFRUwl417slw4cX9slOVjtPSf64L2dq6tXryI0NBSdnZ1YuHAhNmzYIM38P/nkk9izZw8CAgLQ1tbGvFJEQxAnTt3DPnZ/U1MTTp48KZ2bOnUqIiIiGLvfT3hre+/LeE39E9sn8lb2A1S33357t/v0448/5gAVUR+xzXc/XlOivuMkivuVlJRgyZIlaG1txciRI7udb21tRVhYGIqLi7F48WIP1NC3cOJ0YPjiBP9gtfcBvRchT3nhhRfQ2dmJGTNm4M9//jMSEhIwcuRIJCQk4M9//jPi4+PR2dmJF154wdNVJaIBIOZDWLx4MZKSktg57SOlUon7778fhw4dQnt7O7Zv344zZ85g+/btaG9vx6FDh6DX63l9iYicYDAYEBcXh+TkZCxZsgTJycmIi4uDwWDwdNWIpDDcx48fd3iffvHFF7JyRERE5JssFgvq6+uRk5Mjm+QDuqKgZWdn48SJE7BYLB6qoe+ZMGECAMBqtTo8Lx4Xy1HPxP6mSqVyeF48zn6p88TwsjNnzkRtbS3S0tLw8MMPIy0tDbW1tZg5cyYyMzNhs9k8XVWP4ESfFztw4AAAIDc312Gj9eSTT8rKERFRdzabDe+88w5mzZqF0NBQrFixAjfddBNWrFiB4cOHY9asWSgtLfXbjgARkbPE1ZPx8fGorq5Ga2srqqurER8fD71ez8k+8jhx4Gnp0qUO79OlS5fKyhEREZFv4iSK+yUmJiI6Ohq5ubmy9FFA1y7JvLw8xMTEIDEx0UM19C2cOHU/cYL/0KFDmDFjhqyvP2PGDBw6dMivJ/g50efFxG3SJ06ccHhejEPraDs1ERF1ETsCL7zwAo4dOwaTyYTi4mKYTCbU1dXh+eef9+uOABGRM8TVkxqNBuXl5bJIE+Xl5dBoNH69epK8g1qtRmBgIKKiomAwGGT3qcFgQFRUFAIDA6FWqz1dVSIiIuoHTqK4n1KpREFBAYxGI7RarWwSRavVwmg0Ij8/n9GQnMSJU/c7ffo0AGDBggUO30kXLFggK+dvONHnxZYtWwYA2LhxIy5fvoznnnsOjzzyCJ577jlcvnwZmzZtkpUjIqLu7Ff6OQqHypV+RES9Y3gk8gVVVVXo6OhAY2MjdDqdbIBKp9OhsbERHR0dqKqq8nRViQB0LaIwm80oKSmB2WzmYgkiIidxEmVg6HQ6lJaWora2Fmq1GmFhYVCr1bBarV6Z+8ybceLU/ZqamgB03aeCIMj6UIIgQKvVysr5m0BPV4BubP78+QgLC8OFCxcwYsQI2bm1a9cCAMLCwjB//nxPVI+IBpjNZoPFYsHZs2cxYcIEJCYmsgPQB/Yr/RwlQOZKPyKi3jE80sBim+8e4v33xhtvYMOGDbKdezExMXjjjTewdOlS3qfkFQwGAzIyMqRIPQAQHR2NgoICDqQSEfVCnETR6/XQarXIzs6GSqWC1WpFXl4ejEYjSktL2Z/qA51Oh5SUFPZN3UCcOM3IyOjWL+XEqesiIiIAAFu2bMGTTz6JkydPSuemTp2KMWPGyMr5G070eTGlUonk5GTs2rXrhmWSk5P5oCWvwUEq9+GLv/vYr/QrLy+X7UThSj8iIudw0cTAYZvvPuL9Fxsbi2PHjnXrl3788ceycuQa9vXdR8x5unDhQmRlZSE0NBRtbW2oqKiAXq/n4B8RkRM4iTJwxGhI1H+cOHWfiRMnAgA+/fRTREVFYfv27dBoNDAajfjtb3+LTz/9VFbO3ygEQRA8XQlPa2lpQXh4OJqbmxEWFubp6kiuXr2KESNGYMSIERg1ahS++uor6dzkyZPR0tKCS5cu4dKlSwgKCvJgTYk4SOVO4ou/RqNBTk6OtCotNzdXWpXGa+oa+2t6o5V+vKZDn7e2976M19R/2Gw2xMXFIT4+3uGiCa1WC6vVirq6Or60uoBtvnvxPh047Ou7j3ifjhs3Dl9//XW3azpu3DicP3/e6+5Ttvnux2tK5B5ciOJ+vKbkjeznSkaPHi3rQ8XExODChQteOVcyaO29QEJzc7MAQGhubvZ0VWQKCwsFAMJLL70kdHR0CCaTSSguLhZMJpPQ0dEhbNu2TQAgFBYWerqq5OfKysoEhUIhLFq0SKiurhZaW1uF6upqYdGiRYJCoRDKyso8XUWf0dHRIURHRwuLFi0SbDab7JzNZhMWLVokxMTECB0dHR6qoe8qKysToqOjBQDST0xMDO9PP+Kt7b0v4zX1L/btfVVVldDS0iJUVVWxve8jtvkDg/ep+7Gv714mk0kAICgUCiE0NFTWNw0NDRUUCoUAQDCZTJ6uqgzbfPfzhWvqaCyKiIY2R2Mn0dHRbO/J4+z7UBqNRigqKhJ27NghFBUVCRqNxu/7UAEgr3X8+HEAgEajkbZML168GElJSVAqldBoNLJyRJ5gs9mQkZEBjUaDsrIytLe3Y/fu3Whvb0dZWRk0Gg0yMzOZWN5JFosF9fX1yMnJka1CB4CAgABkZ2fjxIkTsFgsHqqh79LpdDh27BhMJhOKi4thMplQV1fHVehEfsBms8kSdbNN6hsxPFJtbS3UajXCwsKgVqthtVq586wP2OYPDN6n7mXf1y8vL0dCQgJGjhyJhIQElJeXs6/fB6dPnwYACIKA+fPno7q6Gq2traiursb8+fMh/F/QJbEckacYDAbExcUhOTkZS5YsQXJyMuLi4mAwGDxdNSIaIGK0ifj4eFn7FB8fD71ez+8/eZR9Pm6r1Yr09HQsX74c6enp+Pzzz/HGG2/Iyvkb5ujzYrGxsQAAo9GIf/3Xf+123mg0ysoReYI4SLVy5UpMmzatW+iZFStWYPfu3bBYLIzv7QSxMVKpVA7Pi8f9tdHqL8aZJ/I/DDfnXswx4T5s8wcO71P3Efv6JSUlN5yQVqvV7Ou74Ny5cwCAGTNmYNeuXdJ1TUhIwK5du3DLLbegtrZWKkfkCfahpUtKSmShpZlHkmhoun5xj337VF5eDq1Wi8zMTKSkpLBPRR7BfNw9444+L7ZmzRoEBgZiw4YN6OjokJ3r6OjAf/zHfyAwMBBr1qzxUA2J/jn4lJ2d7XDFT05Ojqwc9UxsjKxWq8Pz4nF/bbSIiFzBFakDw1GkCXId2/yBxfvUPTgh7X4XLlwAAIwYMcLhjvMRI0bIyhENNu7kJfJPjDZB3i4xMRHR0dHIzc2FQqGQ9fUVCgXy8vIQExODxMRET1fVIzjR58WCgoKwdu1aNDQ0YNKkSdi+fTvOnDmD7du3Y9KkSWhoaMDatWu9KrmkL2EYL/eIjIwEAMydO9fhS8CcOXNk5ahn9o1WZ2en7FxnZ6ffN1r9xe89kf/gIBV5O7b55As4Ie1+4uBpdXU1wsPDZWERw8PDcfDgQVk5osHGwX4i/8TFPeTtlEolCgoKYDQaodVqZYt5tVotjEYj8vPz/XaBH3uOXu7pp59GVlYWzp8/j5UrV2LixIlYuXIlzp8/j6ysLDz99NOerqJPYqz5waNQKDxdBZ/CRmvg8HtP5F84SEXejm0++QL7Celr167JFkxdu3aNE9J9YB/iVMzH11s5osHEwX4i/8TFPeQLmI/7xjjR5wOefvppXLp0CYWFhUhPT0dhYSEuXbrESb4+Yhgv92psbAQAVFZWOhykqqyslJWj3rHRcj9+7wcOd0mSt+IgFfkCtvnk7ewnpB3tPuOEtOsSExOlBSjz589HUVERduzYgaKiIsybNw9A14IUTp6Sp3Cwn8g/MdoE+QqdTocjR47I5koOHz7s9+9OCqGnJWR+oqWlBeHh4WhubkZYWJinq0MDyGazIS4uDvHx8SgrK0NlZaWUtHPOnDlITU2F1WpFXV0dX1adZDabkZycjLy8PGzbtg319fXSuZiYGPzbv/0bcnJyYDKZuCrVRTabrVtiWd6XrrP/3tsnlAa6OqtarZbf+z4yGAzIyMiQfe+jo6NRUFDglR0stvfu583XVGyfqqurkZCQ0O18dXU11Go12yfyCmzzyZsZDAakpqYiNDQUbW1t0vHhw4fj8uXLKCsr88p231uJ7ROAG15TAF7XPnlzm++rvPWa8v1pYLHNJ28mLpLWaDTIzs6GSqWC1WpFXl4ejEYjF6KRV+BYlGPc0Ud+RQzjpVarMW3aNNmK1GnTpmH27NkM4+UiccVPVVUVjh49CpPJhOLiYphMJhw5cgTV1dVc8dNHSqVSlliWnf++Yfi+gSG+AKhUKmzevBkvv/wyNm/eDJVKxV2S5BW4IpV8Cdt88lZivtNFixahublZ1tf/5ptvsGjRIuY7dZG4k3znzp3d8phHRkZi586dsnJEg42hpQcO00mQt2O0CfJ2HIu6sUBPV4BoMIkvS9nZ2Vi0aBFKSkqk1Sm5ubnIycmRlaPeiS8Ber0eqampyM7OhkajgdVqRWpqqrTihy8B5CkM3+d+4qDfzJkzYbVaYTQapXPR0dGYOXMmMjMzkZKSwu8+eYx9+6TVam+4IpX3KBHRjYkLpkpKSjBs2LBuO8yys7OhVqthsVi8aveZNxPDHcbGxuL48ePddvZ8/PHHsnJEniAO9mdkZECtVkvHY2JiONjfR/Y7pa4fi9Lr9byu5DV0Oh1SUlK485S8jv1YVG1trWwsaurUqX4/FsWJPvIr4orJuXPnykJQJCQkoLy8HHfccQcqKyu7rayknvElgLyZfY4JR+H7mGPCdeKg38mTJ7Fw4UJkZWVJoacqKiqwZ88eCILAQT/yOLZPRET9Y79gylG4OS6Ycp39jvPy8nJZX4k7zsmbcLDffcTBaY1G43AsSqvV+vXgNHkfMdoEkTcRx6Lq6+uxaNEivPXWW7JFE7t375bK+eP9y4k+8ltXr17Fiy++iOPHjyM2NharVq2CQqHwdLV8Fl8CyFtdP5hyfY4JDqa47vTp0wCAW265xeGOvltuuQWffvqpVI7Ik9g+ERH1nbgQqqioqFs+7ujoaKxYsUJWjnrHHefkSzjY7x72u6NvlE6Cu6OJiHomjjEtWLAAZWVlqKysxO7duzFhwgSUlZUhJSUFFRUVfjsWxYk+8iuNjY0AgAMHDmD48OEQBEE6t27dOumzWI5cw5cA8kYcTHG/pqYmAMDf/vY3h6FnxIk/sRyRp7F9IiLqm8TERERERCA7O7vbLv53330XOTk5iIyM5IIpF4k7ztetWyfbcR4dHc0d50RDEHdHExH1nzjGFB0djWnTpnVbgHbPPffIyvkbTvSRX7FfaapQKGQTfQEBAVISea5IJRpaGL7PvcaOHQsAiIiIgMFgQGBgV3ciISEBBoMBEydORGNjo1SOiIiIfJcY9WTfvn3Ys2ePdDwkJMRTVRoyGFGGyD9wdzQRUf9FREQAALZu3dpt0flTTz2FF198UVbO3wT0XoRo6Pjxj38MAAgKCsK3334Lk8mE4uJimEwmtLa2IigoSFaOiIYOnU6HY8eOyb73dXV1nOTrg/PnzwPo2v2s0+lQXV2N1tZWVFdXQ6fTSbuixXJERNQzm80Gs9mMkpISmM1mafEZkadZLBapXb9+UkoMP9fY2AiLxTLodfNlBoMBer0e8fHxsn5UfHw89Ho9DAaDp6tIRG5kvztapVLJvvcqlYq7o4mInDB+/Hjpd0EQUFNTg7fffhs1NTWyzTz25fwJd/SRX9m2bRuArvx8P//5z5GdnQ2NRgOr1Yqf//znuHr1qlTuscce82BNiWggMHyfe4iro2699VZ89tln3UJO3Xrrrfj000/9dhUVEZErDAYDMjIyuq3uLygo4GIU8jj7XCi7du1CZWWlFG5uzpw5fp8LpS9sNhsyMjKg0Wgc5pdJTU1FZmYmUlJSGFqeaAgRF0uIg9P/+7//i7a2NtngNBER9W7ixIl47733ZJEmAgMDMXHiRL/uk3Kij/zK8ePHAQB/+MMf8OSTT3YL4ffSSy/h3/7t36RyRETU3cSJEwEAn376KRYuXAitVou2tjaEhobi2LFjUmdLLEfkaY5yoXDwlLyBuKvn+txnFRUV0Ov1DC/dD/zeu4eY40Sn02HYsGHdFkxptVpUVFT4bS6UvrBYLKivr8fKlSsd5pdZsWIFdu/eDYvFwgVqREOEuDv6wQcfxB//+Mdug9NLlixBcXExv/dERD0Qo0ycPn0akZGRWLZsGW6++WZ88cUXeOONN6RJPrGcv+FEH/mV2NhYAF0rqI4dO9bt5X/Hjh2yckRE1F1iYiKio6OhVCqxd+9eWYg5pVKJ2NhYdHZ2MvQMeQXuliJvJe7qmTlzJqxWK4xGo3QuOjoaM2fO5K6ePuL33n3E3fkGgwEPP/ywFK4TADo7O1FeXi4rR707e/YsACAnJ6dbnsOGhgasX79eVo6IfJ/4fX7zzTexcOFCxMXFyRZKFhcXy8oREVF3kZGRAIDp06ejra0NBQUF0rno6GhMnz4dhw8flsr5G+boI7+yZs0aBAYGYsOGDRAEAUlJSVi8eDGSkpIgCAL+4z/+A4GBgVizZo2nq0pE5LWUSiXuv/9+HD9+HGPHjsUDDzyAX/3qV3jggQcwduxYHD9+HHq9ngPT5HHMgUTeTNzVU1NT4/AerampwYkTJ5j7zEX83ruXuDt/79690Gq1smuq1Wqxd+9eWTnqnTj4JAgCkpOT8eijj2LFihV49NFHkZycLIXx89dBKqKhyH5w+vPPP8fvf/97bN++Hb///e/x+eefY/r06bJyRDS0MB+3e40bNw51dXUwmUwoLi6GyWTC0aNHMW7cOE9XzaM40Ud+JSgoCGvXrkVDQwMmTZqE7du348yZM9i+fTsmTZqEhoYGrF27FkFBQZ6uqk9iw0XkH2w2G9555x3ExsbiwoULePvtt/Hqq6/i7bffxoULFxAbG4vS0lI+A8ij7HMglZeXIyEhASNHjkRCQgLKy8uh0WiQmZnJ+5Q8Rgwtc8899zi8R++55x5ZOeodv/fuJ+7inzlzJmpra6FWqxEWFga1Wg2r1YqZM2ciJiaGu/hd0NnZCQAIDg7Ge++9Jxvwf++99xAcHCwrR0RDx+HDh6FSqWSLJlQqFQ4fPuzpqhHRADEYDIiLi0NycjKWLFmC5ORkxMXFcfFZH4ghOSsrK5Gamorg4GBoNBoEBwcjNTUVlZWVsnL+hhN95HeefvppZGVl4fz581i5ciUmTpyIlStX4vz588jKysLTTz/t6Sr6JDZcRP5D3IXyxRdf4O6775atRL/77rvxxRdfcBcKeZx4n+bk5MhCzQFAQEAAsrOzeZ/2ERf2uId97jNH96hWq5WVo97xe+9+SqUSBQUFqKmpgUqlQlFREXbs2IGioiL88Ic/RE1NDfLz87mL3wUfffQRAODKlStQKpX4zW9+g7q6OvzmN7+BUqnElStXZOWIPIltvnucO3dO9lkQBOmnp3LkHN6n5K0YacK9JkyYAADIzc11uADtqaeekpXzN8zRR37p6aefxpNPPoktW7bg+PHjiI2NxZo1a7iTr4/Ehkuj0aCkpAQqlQpWqxW5ubnQ6/UoLS1lPhSiIUTcXXLLLbfg888/lyWTj46Oxi233IJPP/2Uu1DIo8QcJyqVyuF58ThzobiGuc/ch7nP3I/f+4Gh0+lQWlqKjIwMWS7JmJgY9vP7QByAHj58OCIjI/G73/0Ov/vd7wB0XdOGhgZcvnyZA9XkcWzz3UdctLN69WpUVFRArVZL52JiYrBq1Sq8+OKLXNzTB7xPyVtdH2lC7OuLkSa0Wi3zcbtIjDRRVVWFo0ePorKyEmfPnsWECRMwZ84cpKam+nWkCe7oI78VFBSExx57DC+88AIee+wxTvL1EUMkEfkf8QX0b3/7m8OVaX/7299k5Yg8QVzFZ7VaHZ4Xj/vrar++4IpU9xJzmlVUVCAlJQWbN2/Gyy+/jM2bNyMlJQUVFRWyctQ7fu8Hjk6nw7Fjx2S5UOrq6jiI2gfffPMNAOCmm27C0aNHZdf0yJEj0v0pliPyBLHNV6lUsvZJpVKxze8DcdFOfX29w+/9yZMnZeXIOeybkjdjpAn3EyNNGI1Gh6E7jUajX0eaUAjX7xP3Qy0tLQgPD0dzczPCwsI8XR0aJDabDRaLRZr5T0xM9NsHQX+YzWYkJyejuroaCQkJ3c5XV1dDrVbDZDIhKSlp8CtIRG73xhtv4Be/+AUiIyNx+vRpBAb+M0BAR0cHJk6ciMbGRrz++utYtmyZB2sqx/be/bz5mtpsNsTFxSE+Pl62ghLo2i2l1WphtVpRV1fH9t8JvJ7uJ15TpVKJ+vp62aIopVKJ6OhodHZ28pq6gPfpwOL7k3ukpaVhy5YtAACNRoOcnBxZRBRx1+SaNWuwefNmT1ZVxpvbfF/lrddUfJaOGzcOTU1N0iQUAEydOhURERE4f/48n6UuEMdNgK7v/T333IPQ0FC0tbVh79690vee4ybOY5tP3q6kpARLlixBa2srRo4c2e18a2srwsLCUFxcjMWLF3ughr7L0U7emJgY5Ofne+UitMFq77mjz0cw3rR7GQwGxMbGyvLJxcbGcrVPH9iHSHJ0nzJEEnkTPkvd4/z58wC6EhzrdDrZ6kmdTiclPhbLkWfk5eXh9ttvx6hRoxAZGQmtVosjR47IyrS3tyMtLQ1jx47FyJEjkZqaioaGBg/V2L3sV/tptVrZfarVav1+tZ+ruCLV/ZRKJe6//34cP34c48aNQ0ZGBjZv3oyMjAyMGzcOx48fh16v5z3qAn7vBw7zcbvPd7/7Xen3ffv2yfLL7N+/32E5osEktvmHDh3CjBkzZM/SGTNm4NChQ2zzXSSGm4uNjUVFRQXS09OxfPlypKeno6KiArGxsX4dbq4v2Dclb8dIEwNHp9PhyJEjKCwsRHp6OgoLC3H48GGvnOQbVAIJzc3NAgChubnZ01VxqKysTIiOjhYASD/R0dFCWVmZp6vmk8rKygQAQmhoqOyaip95XV1jMpkEAEJeXp7D+zQ3N1cAIJhMJk9Xlfwcn6Xus3PnTgGAcOuttwpTp07tdk1vvfVWAYCwc+dOT1dVxtvbe3e7++67hVdeeUWwWq3C3/72N+Hee+8VpkyZInz77bdSmVWrVgmTJ08W9u3bJxw6dEhISEgQ1Gq10/8OX7imjr77MTEx/O67qLi4WAAgtLa2Ch0dHYLJZBKKi4sFk8kkdHR0CC0tLQIAobi42NNV9RkdHR1CdHS0MGvWLIf36KxZs4SYmBiho6PD01X1Ofzeu1dZWZmgUCiERYsWCdXV1UJra6tQXV0tLFq0SFAoFLyuLrpy5YoQGBgohIeHC5MnT5bdp1OmTBHCw8OFwMBA4cqVK56uqowvtPm+xluvqdjXX7BggWCz2WTnbDabsGDBAq/s63u7rKwsAYAQFRUlZGRkCJs3bxYyMjKEqKgoAYCQlZXl6Sr6FPZNyduJff1FixY5fJYuWrSIff0+8rXxvcFq7znRJ3hv50oQ+FLlbh0dHUJERIQAQNBoNLJrqtFoBABCZGQkH7Iu6OjoECIjI3lNyavxWepe4gQ/AGHhwoXCr3/9a2HFihXCr3/9a2HhwoXSOW+b4Pfm9n4wNDY2CgCEDz/8UBAEQfjmm2+EYcOGCe+8845U5h//+IcAQKiurnbqb/rKNXX08k+u4cIe9xOvaXV1tcN7tKqqite0H/i9dw8OUg0MccA/MjJSuP/++4Vf/epXwv333y+9V3njgL+vtPm+xFuvaWFhoQBAeOmllxye37ZtmwBAKCwsHNyK+TAu7nE/9k3JF9iPRVVVVQktLS1CVVUVx6L6QbymGo1G2Lx5s/Dyyy8LmzdvFjQajddeU070CYKQm5srzJo1Sxg5cqQQEREhpKSkCIcPH5aVaWtrE9asWSOMGTNGGDFihKDT6YRz58659O/x1s4VX6rc74MPPhAACHPnznV4TefOnSsAED744AMP1dD3XD95at9wcaKvfzhA5R72z9KrV6/KrunVq1f5LO0D8ZrGxsYKSqVS9lKlVCqF2NhYr7ym3treD5a6ujoBgFBbWysIgiDs27dPACBcvHhRVm7KlCnCs88+69Tf9Pdr6k+4sMf97FeiO8KV6OQN7CekHeGEdN+lpKTI+lDiT0pKiqer5hDbfPfz1mvKHX3ux8U97se+KfkKRppwH/tFE9dHl5o6darXLpoYrPbeq3P0ffjhh0hLS8PBgwfx/vvv49q1a/jpT3+KS5cuSWXWrl2L3bt345133sGHH36IM2fODJl4rIw37X5msxkAsGnTJofXdOPGjbJy1DuLxYKmpibk5eXBarXKckx8/vnnyM3NRWNjI+9TFzEPivuIz1K1Wo1p06bJrum0adMwe/ZsPktdxLxSvqezsxOPPfYY5syZI+VOPXfuHIKCgvCd73xHVjYqKgrnzp1z+HeuXLmClpYW2Y8vuHr1Kp577jk88sgjeO6553D16lVPV8knCYIg+138ob6xz9vhKIcs83aQN7DPx+0I83H3jcFgwJ///GeEhITIjoeEhODPf/4z+/zkURMnTgQA7N2712G+071798rKUe/sn6VKpRJJSUlYvHgxkpKSoFQq+SztI/ZNyRfodDocO3YMJpMJxcXFMJlMqKurGzLzF4PJPodsY2Oj7FxjY6Pf55D16om+vXv34le/+hV++MMf4kc/+hFeffVVfPnll6ipqQEANDc3Y8eOHXj22Wcxb948zJw5E6+88gqqqqpw8OBBD9e+//hSRb5AvP/S09MdNlzp6emyctQ7g8EAvV6P+Ph42UtVfHw89Ho9X/xdJN572dnZDq9pTk6OrBz1zmaz4Z133sGsWbMQGhqKgoICpKWloaCgAMOHD8esWbNQWloKm83m6arS/0lLS4PVasVbb73Vr7+Tl5eH8PBw6Wfy5MluquHAefzxxzFixAisXbsWRUVFWLt2LUaMGIHHH3/c01XzKVzY436JiYmIjo7GI488gtjYWNlClNjYWPz6179GTEwMEhMTPV1Vn+Ro8pRcZz8h7QgnpF1ns9mwevVqCIKAu+66S9Y3veuuuyAIAlavXs17ljxGbJ9mzpyJ2tpaWZtvtVoxc+ZMtk8u4uIe92PflHyJowl+ct3p06el3+fPny/rQ82fP99hOb8yoPsF3WwgQk4JgveGS+DWfvfrLXTnnDlzGLrTRQzn414M2et+/N67n6+2T97a3g+0tLQ0YdKkScIXX3whO96XflR7e7vQ3Nws/Zw6dcqrr6mYAykqKkp46aWXhLNnzwovvfSSEBUV5bU5kLyVfZhJR997hpnsG/t7dPv27cKZM2eE7du38x7tJ0chkqKjoxkiqQ/YN3U/X+2b+ms/aiB58zW1z4FUVFQk7NixQygqKvLqHEjezFfDzXkz9k2J/E9+fr4AQJgxY4bD1Dzx8fECACE/P9/TVZVhjr7r2Gw2YeHChcKcOXOkY2+++aYQFBTUreztt98uPP744zf8W74ySNVTsl7xODsCrrk+hjfzyfUfX/7dixOn7mc/mOKoI8DcnK7z1bxS3jyYMhA6OzuFtLQ04aabbhKOHj3a7fw333wjDBs2TCgtLZWOHT58uMdn0PW8+ZpeuXJFCAwMFKKiooRr167Jzl27dk2IiooSAgMDhStXrniohr6F7ZP79dTXj4mJYV+/j8TB6UWLFsny9SxatIiD031kf03t3594Tftmw4YNAgBh3759Ds+///77AgBhw4YNg1yznnlzmz8QcnNzhVmzZgkjR44UIiIihJSUFOHw4cOyMm1tbcKaNWuEMWPGCCNGjBB0Op1w7tw5p/8d3n5Ny8rKuk1KcdFE33Fxj3uxb0rkf3JycgQAwrRp04QpU6bI2qcpU6YI06ZNEwAIOTk5nq6qDHP0XcddIacA3wk7JeZAOnToEL788kvZuS+//BKHDh1iDiQXKZVKbN26FQqFAvv27ZNt7d+/fz8UCgW2bt3Ka+oCpVKJgoICGI1Gh/H7jUYj8vPzeU2dxJC97ifG7T5w4ADCw8NlodHCw8Nx4MABWTnqHcN4+Ya0tDTs3LkTxcXFGDVqFM6dO4dz586hra0NABAeHo7ly5dj3bp1MJlMqKmpwUMPPYTZs2cjISHBw7Xvvy1btqCjowNPPvkkFAqFLESSQqHAf/7nf6KjowNbtmzxdFV9ghjGKzc3F52dnbJznZ2dyMvLYxgvF4k5Jl544QWH4c+ff/55v84x0Rc2mw0ZGRnQaDR4++23cfDgQWRnZ+PgwYN4++23odFokJmZyZCILtLpdCgtLXUYwq+0tJQ5ZmhI+vDDD5GWloaDBw/i/fffx7Vr1/DTn/4Uly5dksqsXbsWu3fvxjvvvIMPP/wQZ86cGXLfB4VC4ekqDAnXpz5YsWIFbrrpJqxYsYKpD/qIfVMi/xMQ0DWVdfToUYdzJUePHpWV8zc+8V+dnp4Oo9EIk8mESZMmScfHjx+Pq1ev4ptvvpGVb2howPjx42/497Kzs9Hc3Cz9nDp1aqCq3i82mw2vvfYaACAoKEh2Ljg4GADw2muvsSPgIvFFNSoqSnY8KiqKL6p9JF7Tzz77TPbyX1tby2vqIsbudz/xWjl6SVUoFNJxXlPn8aXKN2zduhXNzc1ISkrChAkTpJ8//vGPUpnCwkJoNBqkpqbijjvuwPjx44dMHtDjx48D6Pqex8XFySb54+LipM6/WI56xoU97sfFPe4nTp6GhYVh1KhRstyco0aNwqhRozh52g/Xt/l8D+2bpKQkAMDGjRtx7do1WX//2rVr2LRpk6wcecbevXvxq1/9Cj/84Q/xox/9CK+++iq+/PJL1NTUAACam5uxY8cOPPvss5g3bx5mzpyJV155BVVVVTh48KCHa99/zBvvXlzc437smxL5H/sxpusn8+w/++1Y1IDuF+ynwQg5JQjeGy7BPtxcW1ubUFhYKKSnpwuFhYVCW1ub18bu9xWOYnhT3zEXinswdr/72Yfva29vl33v29vbGb6vj3wxjJe3tve+zJuvaWFhofT8dBTCTzxXWFjo6ar6FEftfUxMjFd+572dGHIqLy/PYR8qNzeXIadcJIaWRg+5OeGFoaW9XVlZmQBAUCgUsvtU/Mzvv2s6OjqEiIgIAYAQGhoqu6biZ29MJ+HNbf5gqKurEwAItbW1giD0Ldfx9bz1mtqn53CU+oDpOVznq6kPfAH7pkT+o6KiQvqeBwUFyb739p8rKio8XVUZ5ugTBGH16tVCeHi4YDabhbNnz0o/ly9flsqsWrVKmDJlirB//37h0KFDwuzZs4XZs2e79O/x1s6VGLv/iSeecPjyv3HjRq+M3U/+h7lQ3Iux+91LHEgVB/uvn5QSz3Eg1XW+9lLlre29L/Pma3r58mWpw3/9RP6VK1ekFwH7fiU5h4ul3OP63NH2fSjmju6b9957TwAgjBkzxmFuzjFjxggAhPfee89DNfQ9HR0dQlhYmABACAgIkLX54uewsDDepy4S+/vXX1OlUum1/X1vbvMHms1mExYuXCjMmTNHOvbmm28KQUFB3crefvvtwuOPP+7w77S3twvNzc3Sz6lTp7zymnIhivvZ55Nz1I9iPrn+Yd+UyD8sXbpU1ibd6Gfp0qWerqoMc/SBIadETzzxhMNwCWJIDyJPss+FUlZWhvb2duzevRvt7e0oKytjLhQXMXa/+4khz3bu3Okwt8zOnTtl5cg1giDIPl8f1ovIU/7yl78AAK5evYopU6YgIyMDmzdvRkZGBqZMmYKrV6/KypHzlEolkpKSsHjxYiQlJTEkUj/YP0OFrkWY3Z6r5Lza2loAwKRJkyAIgiwkoiAImDhxoqwc9W7fvn1oaWkBACxYsED2TrpgwQIAQEtLC/bt2+fJavoUsb8fGxvr8HxsbCz7+14mLS0NVqsVb731Vr/+Tl5eHsLDw6WfyZMnu6mG7iW+F+Xk5Dgci1q/fr2sHPVOTH3wyCOPIDY2VhZSPjY2Fr/+9a+Z+qAf2Dcl8g+tra3S7yEhIbJzoaGhDsv5k0BPV6AnzrzkhoSEYPPmzdi8efMg1GhwiQ38mDFjYDAYEBjY9X9XQkICDAYDoqKicOHCBXYEyKPEWPMrV67EtGnTUF9fL52Ljo7GihUrsHv3blgsFuaZcIJ4PUtKSnD77bfDYrHg7NmzmDBhAhITE/Hxxx9DrVbzerpAzL0XGxuLY8eOObym9uXIOWLeDo1Gg7feegsqlQpWqxW5ubnQ6/XMz0keJw4+LVy4EHv27MGzzz4rOy8e5yAVeYrFYkFTUxPy8vKwbds2qNVq6VxMTAxyc3ORk5PDNt8FYj/0s88+Q3h4ONra2qRzoaGh0mf7/ir17PXXXwfQlTPyT3/6EyorK7F7925MmDABf/rTn3Drrbfi888/x+uvv46f/vSnHq6tbxD7+wCg0WiwYMEC6f6sqKiA0WiUyvG773np6ekwGo346KOPMGnSJOn4+PHjcfXqVXzzzTf4zne+Ix1vaGjA+PHjHf6t7OxsrFu3Tvrc0tLilZN9kZGRAIA5c+agvLxcynuUkJCA8vJy3HnnnThw4IBUjnqnVCpx//3345lnnumWV+rUqVM4efIksrKyOEFFRNQDsX0dPnw4Lly4gOrqaml8b/bs2RgzZgwuX758w3Z4qPPqHX3+TmzgL1y4gPvuu0+2iuq+++7DhQsXZOWIPEEcIM3Ozna42i8nJ0dWjnomXieVSuVwVZpKpZKVo96Jqydzc3OhUChk11ShUCAvL4+rJ11kv5O3vLwcCQkJGDlypPTyz5285A3Eyft3332322q/kJAQvPvuu7JyRINNbMvT09Nx7NgxmEwmFBcXw2Qyoa6uDunp6bJy1Lsb7ZACAIVC4VQ5khMnpG677TZMmzZNtgtl2rRpuPXWW2XlqHenT58G0LVDcteuXVizZg0eeughrFmzBrt27ZJ2SorlyDMEQUB6ejr+9Kc/Yf/+/YiJiZGdnzlzJoYNGybbzXrkyBF8+eWXmD17tsO/GRwcjLCwMNmPL+LOc9fZbDa8+uqrALruA3vi59dee43vT0REPRg9ejQA4PLly0hNTUVwcDA0Gg2Cg4ORmpqKy5cvy8r5G070ebHGxkbp93379snCze3fv99hOaLBJq7imzt3rsMB/zlz5sjKUc/EAWer1erwvHicA9POUyqVKCgogNFohFarlU1Ga7VaGI1G5Ofnc9GEC8SV6Dk5Od1WpAYEBCA7OxsnTpyAxWLxUA2JALVajYCAAAiCgHnz5mHz5s14+eWXsXnzZsybNw+CICAgIEC2i4poMNm3+Y4W97DNd93KlSsBAEFBQTh//rxs8vTrr79GUFCQrBz1Ljo6GkDXzj6VSiXrR6lUKikEuliOetfU1AQA0Ol0DkPMarVaWTnyjLS0NOzcuRPFxcUYNWoUzp07h3Pnzkk7g8PDw7F8+XKsW7cOJpMJNTU1eOihhzB79mwkJCR4uPb9I44xHThwwOH7U2Vlpawc9c5sNqOpqQlz585Fc3OzrH1qbm7G3Llz0djYCLPZ7OmqEgHompy2b584Cd0/V69exXPPPYdHHnkEzz33nJRGglwjRjsEgIqKCtlcyd69ex2W8yec6PNi4kt9Xl5et0mSyMhI5ObmysoReSP71dPUO/vdZ9fnOuvs7OTusz7S6XQoLS11mKOPISZdZ7/z1NELAHeekjewWCzSc3T//v1IS0vDww8/jLS0NGnBVGdnJyekyWPY5ruffW7OmJgYHD16FHfeeSeOHj2KmJgY5ubsg6VLl0q/22w21NTU4O2330ZNTY1s0M++HPUsIiICALBlyxbExcXJdknGxcXhxRdflJUjz9i6dSuam5uRlJSECRMmSD9//OMfpTKFhYXQaDRITU3FHXfcgfHjx8NgMHiw1u5hPxbl6P2JY1GuEyfwNm3ahGHDhskW9wwbNgwbN26UlSPyJIPB4LB9GgrPN094/PHHMWLECKxduxZFRUVYu3YtRowYgccff9zTVfM5YkjzMWPGdHt/stlsGDNmjKycv+FEnxcTX/6rqqpQV1cnW/Fz9OhRVFdX8+WfPE5cxVdZWcnVfm7A3WcDR6fTOQyNxkk+14kv9UVFRQ6TyRcVFcnKEXmC/UDJ9YtO7HeickCFPIVtvvuJC0weffRRnD9/HitXrsTEiROxcuVKnD9/Ho8++qisHPVu2LBh0u8VFRVIT0/H8uXLkZ6ejoqKCoflqGcTJ04EAHz66adoa2vD9u3bcebMGWzfvh1tbW349NNPZeXIMwRBcPjzq1/9SioTEhKCzZs348KFC7h06RIMBsOQyAtkPxZ19OhR2fvTkSNHOBZFNIQZDAbo9XqHqXn0ej0n+1z0+OOP45lnnsHYsWPx0ksv4ezZs3jppZcwduxYPPPMM5zsc1FSUhLCw8Nx4cIFjBs3Dg888AAeeughPPDAAxg3bhwuXLiA8PBwv53og0BCc3OzAEBobm72dFW6KSsrExQKhbBo0SKhqqpKaGlpEaqqqoRFixYJCoVCKCsr83QVyc+ZTCYBgJCXlydER0cLAKSfmJgYITc3VwAgmEwmT1fVp5SVlTm8nvzOkzfo6OgQIiMjBQBCcHCw7D4VP0dGRgodHR2erqqMN7f3vsqbr2lOTo4AQEhISBDa2tqEwsJCIT09XSgsLBTa2tqEhIQEAYCQk5Pj6aqSnysrKxOmTp0qe5ZGR0ezze8DsV9aXV0tXLlyRfa9v3LlilBVVcV+qYuKi4tl9+aNfoqLiz1dVZ9x5coVITAwUAgPD3f43Q8PDxcCAwOFK1eueLqqMt7c5vsqb76mHItyrw8++EAAIMydO1ew2WyyczabTZgzZ44AQPjggw88VEOirvf86OhoYdGiRQ7v00WLFgkxMTFe957vrcT2PioqSrh27Zrs3LVr14SoqCivbO+9WUdHhxARESEAEEJDQ2V9KPGzP49FcUefl2O4OfJ2XO03MLj7bGAwzrz7tLe3AwCuXbsmOy5+Fs8TecrYsWMBdO3c+d73vicLlfK9731P2tEjliPyJIY6dw/7cKiBgYF47LHH8MILL+Cxxx5DYGAgw6H2gZhCYuLEid3y8iqVSmnXGfNxO6+qqgodHR1obm5GfHw8ioqKsGPHDhQVFUGlUqG5uRkdHR2oqqrydFXJj3Esyr2SkpIQGRmJAwcOICUlRbZTKiUlBZWVlYiMjPTfXSjkFSwWC+rr65GTk9OtzQ8ICEB2djZOnDjB1AdO2rJlCzo6OvDkk092yxkXGBiI//zP/0RHRwe2bNnioRr6HovFgqamJuTl5SEqKkp2bvz48cjNzUVjY6Pf3qP+mZnQBwmCIPt8fRxacp3NZoPFYsHZs2cxYcIEJCYmMjRSH4hhp/R6PVJTU5GdnQ2NRgOr1YrU1FQYjUaUlpby2vaBUqlkR9+NDAYDMjIyUF9fLx2Ljo5GQUEBX1RdZDab0dLSAgAIDg5GW1ubdC4kJASXL19GS0sLzGYz5s+f76lqkp8TO/4nT57s9qL61VdfSX2p618QiAaTGB5Jo9GgpKQEKpVKyn+k1+s5mOoi+36pVqtFdna2dE3z8vLYL+2H06dPY+HChbj33nsRGhqKtrY2vPvuu9izZ4+nq+ZzxIUmO3fuxIYNG2A0GqVzMTEx2LlzJ5YuXcoQs+RxOp0OKSkpHDdxA6VSia1bt0Kv12Pfvn2y7/3w4cOhUCiwdetWXts+4viee4jtjkqlcnhePM72yTnHjx8HAGg0GofnxeNiOeqdeO+lp6cjKyur2/f+8uXLyMnJ8dt7lDv6vJz48j9jxgzZip8ZM2YwNnI/MLGse3G1H3k7xpl3r/379wMApk2bhoiICNm5cePGYdq0abJyRJ7QU44c+91TQyGXDvkmm82GjIwMaDQalJeXIyEhASNHjkRCQgLKy8uh0WiQmZnJ3ecuYr/Uvc6dOyf9rlAocOutt0Kv1+PWW2+VPUvty1HPxBzGsbGxOHLkCAoLC5Geno7CwkIcPnwYN998s6wcEQ0NYvt0/Q7oyMhItk/9wPE99xHbHavV6jAaktVqlZWjnsXGxgKAbGLfnnhcLEe9s79Hxc0RixcvRlJSEpRKJe/RAQ0M6iO8NS46YyMPDPtY89XV1UJra6tQXV3NWPNu4CgXCpGn2T9Lr169KphMJqG4uFgwmUzC1atX+Sztg6VLl0px0DUajbB582bh5ZdfFjZv3ixoNBrp3NKlSz1dVRlvbe99mTdf0/fee08AIIwcOdJhDqSRI0cKAIT33nvP01X1OWzv3cM+n5wjzCfXP7xP3aOwsFAAIKxevdph/uhVq1YJAITCwkJPV9VniH3TWbNmdWufpk6dKsyaNcsr+6be3Ob7Km+/po7yxjOHbP84yss7depUXtM+4viee9m3T46++97aPnkr5uhzP1+dKxms9p4TfYL3dq7sX/47Ojpkg9MdHR18+e8DX30g+AJ2WMlbic/SvLw8h53V3NxcPktdlJ2dLQAQhg8fLkyePFl2TSdPniwMHz5cACBkZ2d7uqoy3tre+zJvvqYbNmwQAAgKhULQaDRCUVGRsGPHDqGoqEjQaDSCQqEQAAgbNmzwdFV9SlZWlhAYGCj73gcGBgpZWVmerprPKS4uFgAIra2tDvv6LS0tAgChuLjY01X1ORycdp+dO3cKAIQFCxY4XDC1YMECAYCwc+dOT1fVp2RlZQkAhKioKGH79u3CmTNnhO3btwtRUVECAK98pnpzm++rvPmaihMooaGhsmdpaGgoJ1D6iJNS7sXxvYHhi+2TN7O/ntu2bRNOnz4tbNu2jdezH+yfpVVVVUJLS4tQVVXl1c9STvQNIm/tXIkv/zt37nT4oiq+dPHl33lcOT0wysrKZPfn9T/e+JD1BY4G/ch14rO0pxdVPktdk5+f3+N3XvzJz8/3dFVlvLW992XefE3Fib4nnnjC4S6U//iP/+BEn4vsX1QzMzOFLVu2CJmZmXxR7SMuRBkYHEh1L/E+7WlAhfepa3raMRETE+O1Oya8uc33Vd56TTs6OoTIyEgpeof9s1SM3hEZGel196g3Y5QZ9+P4nvv1tOOcO/r6jgsl3c/Ror6YmBiv7edzom8QeWvnyv6lqqfBaTZazuPKaffr6OgQwsLCBABCQECA7D4VP4eFhbEj4CKuRHefDz74QLqGN3pRBSB88MEHnq6qz3j99dedmuh7/fXXPV1VGW9t732ZN19T8bs/d+5chwMqc+bM4XffBWLomfDwcGHKlCmy7/qUKVOE8PBwhp5xUUdHhxAREcGBVDfi6n7389VJKW9mPzjtKMSstw5Oe3Ob76u89Zra96EcPUvZh3IdF/e4n/34niMc33MdI8sNHIaUdz9f2hwxWO19AMhrqdVqBAQEQBAEzJs3D9XV1WhtbUV1dTXmzZsHQRAQEBAAtVrt6ar6DDEZZ1FRkcNkvUVFRbJy1Lt9+/ahpaUFAHDvvffK7tN7770XANDS0oJ9+/Z5spo+xWAwQK/Xo6GhQXa8oaEBer2eSaVd1NnZCQAYPXo0/vSnPyEhIQEjR45EQkIC/vSnP2H06NGyctS76+/N/pYjGghJSUmIjIzEgQMHoNPpEBwcDI1Gg+DgYOh0OlRWViIyMhJJSUmerqpP2LJlCzo6OtDc3Iwf/ehHsvb+Rz/6EZqbm9HR0YEtW7Z4uqo+RaFQSL8LXYswIQiCB2vk2ywWC+rr65GTk4OAAPmrbkBAALKzs3HixAlYLBYP1dD3KJVKFBQUoKamBiqVCkVFRdixYweKiorwwx/+EDU1NcjPz4dSqfR0VX3G2bNnAQDHjx/H9773PaxduxZFRUVYu3Ytvve97+GLL76QlSMabGazGQCwadMmCIIAs9mMkpISmM1mCIKAJ554QlaOeid+n3NychAfHy/rR8XHx2P9+vWyctQ7cdzOarU6PC8e5/ie88T7T6VSQalUIikpCYsXL0ZSUhKUSiVUKpWsHDlPqVTilltugVqtxi233MJ+kxs4ukf9HSf6vJjFYpEGnhUKhezlXxwU6Ozs5IuqCxITExEZGYns7GyoVCpZ50qlUiEnJweRkZFITEz0dFV9xmuvvQagqyNgMBjQ3t6O3bt3o729HQaDQeoIiOWoZzabDatXr4YgCJg/f77sHp0/fz4EQcDq1aths9k8XVWf8dFHHwEALl68CJ1OJ7umOp0OFy9elJWj3n3yySduLUc0EJRKJbZu3QqFQoF9+/ZBrVYjLCwMarUa+/fvh0KhwNatW/lC4KS6ujoAwE9+8hOUl5fLFk2Ul5fjJz/5iawc9c5isaCxsRF5eXmora2V3aNWqxW5ublobGxkX98F9gNUNptNNjhts9k4QNVHOp0OpaWlsFqtSE9Px/Lly5Geno7PP/8cpaWl0Ol0nq6iTxEHnZctWwaVSoXNmzfj5ZdfxubNm6FSqbBs2TJZOSJPsVgsDhdIHzhwwNNV8zmRkZEAgDlz5jjsR82ZM0dWjnqXmJiI6Oho5Obmdlu029nZiby8PMTExHB8zwX2k6eO+lGcPO0bg8Hg8FnKRfzkboGergDdmLg66oknnsCrr74q27kXExODjRs3YtOmTTCbzZg/f76Haul77FdJc+V0/508eRIAcNttt2HatGmor6+XzkVHRyMxMRFWq1UqRz0zm81obGzE3LlzsWvXLmk1ekJCAnbt2oU77rgDlZWV/N73wRNPPIFXXnlF9iyNjo6WnqXkvC+//FL6PTIyEj/4wQ/Q2dmJgIAA/O///i8aGxu7lSPyBHFwOiMjQ9Y+RUVFIT8/n4PTLhAXmc2cOVNa3X/27FlMmDABiYmJuPXWW/H+++/LdqhRz8TJpsmTJzu8blOmTJGVo97ZR+/Ytm1bt37pihUrZOXIeTqdDhqNBlu2bMHx48cRGxuLNWvWICgoyNNV8zlqtRqBgYEYMWIEPvvsMxiNRunclClTEBYWhkuXLjFyD3lMUlISnnzySTzxxBPQaDQoKSmBSqWC1WrFU089Jb07MSqC+3BMynXijnO9Xg+tVist6LdarcjLy4PRaERpaSkX9blAnDx95JFH8PXXX3frR40bN46Tpy4SI3YtXLgQWVlZCA0NRVtbGyoqKqDX67lgityKO/p8QGJiIo4dOwaTyYTi4mKYTCbU1dVh7ty5nq6az7FYLGhqakJeXh6sVqts5fTnn3/OldN9EB0dDQB4/fXXHe6SfOONN2TlqGf2YVIchZximBTXiS+gf/zjH7udEwRBOs4XVecFBwcD6LonGxsbYTab8dFHH0kT1eK9K5Yj8iSdTuewH8UXKtf8+Mc/BtAVwvPmm2+WrUi9+eab8eKLL8rKUe/sd/U4CuPFXT2uY/SOgWMwGByGmeRqdNdVVVVJoZCvXLmC7du348yZM9i+fTuuXLkihUKuqqrydFXJTyUmJsreRR0tkA4ICOCz1AXiQsgDBw4gJSVFtpM3JSUFlZWVsnLkHHFRn6PICJxAcZ1SqcT999+PQ4cOoa2tTdY+tbW14dChQ9Dr9Zw8dZLNZkNGRgZmzpyJzz77DGlpaXj44YeRlpaGzz77DDNnzkRmZiYjdvWRo12n/o47+ryYuIpq48aN+PDDD2WD0J2dnVxF1Qf2K6evXzHV2dnJldN9sGzZMrz55psA5C8A178IiINVRIMtKSkJYWFh+Mc//oGoqChs374dGo0GRqMRv/3tb3Hy5EmEhYXxWeqC8ePHA7hxXkPxuFiOiHzf5MmTAXTl3W1vb8e///u/Y/ny5dixYwcKCwtx9epVWTnqnbirZ+zYsTAYDAgM7Ho1S0hIgMFgwKRJk3D+/Hnu6nERo3e4n7ga/foFPOfOneNq9D44ffo0AODWW2/FxYsXpZ2mQFfknltvvRWffvqpVI5osFVVVUn9+X379sl2nQ4fPhxAV3+/qqqK71BOEhftPPjgg/jjH/8ou6aBgYFYsmQJiouLubinD3Q6HVJSUmCxWGTRJjgZ5TqbzYZ33nkHs2bNQlNTk6x9io6OxqxZs1BaWoq8vDxeXyeIuaPr6+sRGhoqO9fU1CRFQLJYLHyWushgMGDdunWy6HFTp07Fs88+69d9Uu7o82JJSUmIiIiQVvzYr0hNSUnBgQMHEBkZyYeBC8RO09KlSzFjxgzZNZ0xYwaWLl0qK0e9EwelAGDPnj2yVVTvvvuuw3J0Y+L3eePGjQ7jzHOCv2/Eganm5masWLECN910E1asWIGWlhYAQEhIiCer53MmTZrk1nJEA4k5EdxDnJQaPnw4bDYb/vu//xvTpk3Df//3f6OzsxPDhw9HYGAgJ6VcIO7qaWhocJhDtqGhgbt6XMToHe5nnz/6+hCzYh555o92TVNTEwBgzZo1OHLkCAoLC5Geno7CwkIcPnwYq1atkpUjGmziwuedO3d2yxkXGRmJnTt3yspR78Qd52+++Sbuvvtu2Y6+u+++G8XFxdxx3g9KpRJJSUlYvHgxkpKSOAnVR+LE1AsvvICjR4/K2qcjR47g+eefx4kTJ9iPcpL9gp358+fL+vr2qXi4sMc1BoMBqamp3XZANzY2IjU11a/f8znR58WUSqUUBmnfvn2yF9X9+/cDALZu3coGzAXiIFVUVBQMBoMsAbLBYEBUVBQHqVzUU2gJ+9XTDEHhHPsJ/p/97Gd47LHHsHLlSjz22GP42c9+xgn+PhAH/R588EFcu3ZNdu7atWtYsmQJB/1cJE6Quqsc0UARd6E4Couo1+v9+iXAVeKk1OXLl7sN6IvHOSnlGvuBVEchpziQ6jrxWqWnpzsM2Zueni4rR70Tw3IDNx6kEsN4k3MiIiIAdIVCdhQOVRwDEMsRDTZx4XNsbCyOHz8ue5YeO3YMN998s6wcOUccH1EoFLj11luh1+tx6623Mr8xeQ2xf3T8+HGH7dMXX3whK0c9a2hoAAD86Ec/gsFgQHt7O3bv3o329nYYDAbMmDFDVo56Z7PZpAVR8+bNky2amDdvHgD49QI0brHxcjqdDllZWSgoKJAdb29vR1ZWll9vR+0LcZCqsbEROp2uW7LexsZGCILAEBQusO/ch4SEoL293eFnvgQ4R5zgT01NxZ49exyW4QS/a8ROaHFxMRYuXIgFCxbIEiCXlJTIylHvzpw5I/0uXktHn+3LEQ02MSeCRqNBeXm5lGsmISEB5eXl0Gq1yMzMREpKCp+pTnD2GclnqfPsB1KPHTvWLeTUxx9/LCtHvROvldVqRUJCQrf+vNVqlZWj3okLTGfPno1du3bJnqW7du3CnDlzcPDgQezfv1+2Op1ubOLEiQCATz/91GFY+fr6elk5osGWmJiI6Oho5Obmory8vFsamby8PMTExHD3mQvsd5y/+OKLssXl4rXOyclhCD/yKPsoaIsWLUJJSYk0Zpqbm8soaC46f/48AKCtrQ3Tpk2T2neg63sfFBQkK0e9M5vNaGpqwve//31YrVbZuOnUqVMxffp0HD58GGaz2S/7pdzR5+UMBgOeeeaZbvkQgoOD8cwzz3AluovEwac33njD4crpN954Q1aOeifukgwPD0dUVJTsXFRUFMLDw7lL0kUHDx4EgG4r+8SBFfE8OUcMNzNnzhzs2rULa9aswUMPPYQ1a9ZIA1T25ah3ly9fln7vKe+RfTmiwSaGnsnJyZGen6KAgABkZ2cz9IwLxo4dK/1+fbhj+8/25ahn9gOpCoVCFnJKoVBwILUP7K/ptWvXYDabUVJSArPZjGvXrvGa9oGYP2bJkiUQBEF2TQVBwOLFi2XlqHf2708hISGysPKhoaF8fyKPUyqVKCgogNFohFarle3k1Wq1MBqNyM/P50IpF4hjTJMnT3a4g2/KlCmyckSewCho7iW+gx49ehRtbW3Yvn07zpw5g+3bt6OtrQ1Hjx6VlaPeiREk/vGPfzhMyXX48GFZOX/DHX1ezH476vz587F+/XppJcVTTz0Fo9GI1atXcyW6C7hy2v3EXZLNzc0IDg7GAw88gBEjRuDSpUswm81obm6WynFlWu+uXr2KwsJCREVF4YsvvsD27dtx/PhxxMbGYsWKFbj55ptRWFiIJ598Ulr9Q/3T00SVP7p8+bLUObqRyZMnA+ga3B89erTshXT06NEQBAHt7e2YPHkyPvnkkx7/1vTp0zF8+PD+V5zoOuJ9qVKpYLPZurX5KpVKVo569ve//136/a677nLYLxXL/fSnP/VUNX2KOJCq1+uh1Wq7RZowGo0oLS1lP98F9tc0PDy8247z9vZ2XlMXiYPPRUVFeOaZZ2QTelOmTJEWpIrlqHf270+JiYnIysqSIiLs3btXep7y/Yk8SafTobS0FBkZGbJB/ZiYGJSWljK6lIvEMaZly5ZBo9F02ym1bNkyWTkiT2AUNPe64447AAAjRoxAcHAwVqxYIZ2bOnWqNHYqlqPedXZ2AuiKNOEoao9arcZf/vIXqZy/4USfFxO3o86dOxcGgwGVlZXYvXs3JkyYAIPBgHnz5uHAgQN+ux21L+xX+ZaVlcnOMQRF34hJYyMjI9HY2Ii3335bdl48zuSyztmyZQs6Ojqg0+nwwx/+ULa1//e//z3uu+8+vPjii9iyZQsee+wxj9XTl4h5ZQ4cOOBwILWyslJWzt8dPnwYM2fOdKpse3t7t0kS+3Cdr7zyCl555ZUe/0ZNTQ1uu+021ytK1AtxoKSoqAjbtm3rFipFfNHigIpzxGelSBAE6ef6cllZWYNZNZ/GgdSB4WgRj0Kh4OKePpg3bx5yc3Nx5MiRbivOv/rqK2kgRcyLQr2zz8+5YcMGP4lgRAAATftJREFUaWIP6Pru79y5E0uXLuVCFPI4nU6HlJSUbouluFjCdeJOqbFjx8JgMCAwsGs4VtwpNWnSJJw/f547pfrI0aI+3qeus4+CtmHDhm590zfeeIPtkwvEftOlS5e6TTw1NjZKC9K4o895Y8aMAdB1TR0Rr6lYzt9wos+LidtM77rrLoexfH/xi19wos9F4irf1NRUhIWFOcwnV1ZWxg6BC5qamgB0NVJRUVFYtmwZbr75ZnzxxRd44403pKSyYjnq2fHjxwF05eFzFBP9xRdflJXzd87sPmttbQUApKenw2AwyDqrEydORFpaGjZv3ozW1lbuPkPXf2NNTU2PZWw2G+6++25cvHjxhmXGjBmDvXv39vo8nT59ep/qSdSbxMREREZGIjs7GwsXLpTtmHj33XeRk5ODyMhILu5xkvgytWjRIin8uSgmJgYLFy7Enj17bvjSRTfGgVT3EXNzLlq0CG+//TZefPFFKTLCqlWr8MADDzA3p4sSExN7nSRVKBR8lrqAUWaI/I+4U6qhocHhTilx3IQ7pVxnMBiQkZHRbcy0oKCAC6ZcxPbJvewXk1+5ckV2zv4zF507b/z48QCAzz77DD/72c+wYMEC6R2/oqICn332maycv+FEnw/YtGmTw639//Vf/+Xpqvm0nh6y5LzRo0cDAIKCgvDll1/Kwkk+9dRTGDVqFK5evSqVo55FR0cDAGbMmCENUL355puIjY3F22+/jR//+Mf47LPPpHL+zpXdZ0VFRd2OnT59Gps3bwYArFy5ste/4Q+7z4YPH+7Uf+Mf/vAH6PV6hISEOAyN9tJLL+H2228fyKoS9UocmN6/f78sUXdoaKinquSzZs2ahQ8++AAHDhzAV1991S209MSJE6VyRJ4i5uZcuXIlvv/973eLjLBixQrs3r0bFouFA6lOslgs0rM0KChItlBS/CwIAiwWCxefwrlFaCNGjMBNN92Ef//3f0dBQQGGDRuGzs5ODBs2DJ9++il+85vfYOLEiRgxYkSPi9D8YQEaeRYnUNzn+p281y+Y4k7evjEYDNDr9Q7HTPV6PaMjuMg+Clp5ebmsr8QoaK6LjIwE0LUI+sKFC7JznZ2d0nGxHPVOfOcEgHfffVf2jm+f/9S+nD/hRJ8XEx+co0ePdri1PzIyEhcvXuQD1gX2eQ/vvfdexMXFoa2tDaGhoTh27Bj27NnDvIcu+utf/wqgK7ecXq/vtjLt6tWrUrlf/vKXnqyqT4iPjwfQlax35MiRsNls0rnMzEwMGzZMVs7fObP7DOga5H/88ceRmJiI5ORkbNq0CRs3boTJZILFYsHTTz/tVMgp7j77J/twc/Yv/+PHj0d+fj5fqMjjLBaLtJvcfmDa/nNjYyMH/J1011134Xe/+x0uXryIUaNGycLPZGRkSJ/vuusuT1XRZ3Eg1X3EAdKcnByEhITIzjU0NGD9+vWyctQ7McrMAw880C31QUdHBx544AG8/fbbjDLzf1xZhHbmzJkeF0X9y7/8S4//vD8sQCPP4QSKe9nvlDpy5Ai2bNkiLZhas2aN9E7LnVLOE3fxazQah7m6tFotd/G7yD7XcUpKCu655x5ZDtk9e/Yw13EfiJN5v/jFL6QIaK+//jp38vVBYmIiIiIi0NTU5HDR+eXLl/06ag8n+ryY+OC8cOEC7rvvPuTk5Mg6V2LIND5gnSfmPfz+978Pq9Uqm/mfOnUqpk+fjsOHD/NF1QXiCt8ZM2bgs88+k61Mi46Olo4zJ4pzzp8/D6BrEFqhUGDp0qXIyMhAQUEB3nzzTWlwWizn75zdfXbbbbfh5ptvRkZGBjZt2gSga7c0cyD1jxhubseOHVi5ciW2bduG5cuXs10ir2CfGzY4OFg22Wf/mTlknZOUlISwsDC0tLR0yzEhfg4LC+OkqYs4kOpe4opoQRAwf/58rF+/XrqmTz31lJQLjSunXff2229Do9F0C5F0fX5uf+fsIjSgayFaYWGhLL/xxIkT8dhjj3EBGnkUJ1DcT9wp9cgjj6CpqQknT56Uzj333HOIiIjgTikXibv4S0pKuuU4CwgIQHZ2NtRqNRf1uUin0yEzMxPPPvusLIdsYGAgMjMz2S91gX37PmvWLERHRyM4OBjR0dGYNWsW3n333W7lqHfizr158+Z165faj/P7JYGE5uZmAYDQ3Nzs6arIFBcXCwAEAEJoaKj0OwBh+PDh0u/FxcWerqrP2LBhg3TdNBqNsHnzZuHll18WNm/eLGg0Gunchg0bPF1Vn1FYWCi7pkVFRcKOHTuEoqIi2TUtLCz0dFV9wnvvvScAEEJCQgSlUin73iuVSiEkJEQAILz33nuerqpP6ujoELZt2yYAELZt2yZ0dHR4ukpDQk1NjQBAqKmp8XRVeuSt7b0v8+ZrWlBQIGufqqurhdbWVqG6ulrWPhUUFHi6qj6ho6NDCA8Pl7VL1/+Eh4fzueqCjo4OITo6Wli0aJFgs9lk52w2m7Bo0SIhJiaG19QF//M//yMAEEaPHi20t7cLJpNJKC4uFkwmk9De3i6MHj1aACD8z//8j6er6jPsr+m1a9dk565du8Zr2k++1Df15jbfV3nrNTWZTAIAobq62uH5qqoqAYBgMpkGt2I+LisrSwAgREVFCdu3bxfOnDkjbN++XYiKihIACFlZWZ6uok8Rx0xbW1sdnm9paeGYaR+UlZUJCoWi2zh0aGiooFAohLKyMk9X0WekpaUJAIQf//jH3cb3AgMDhdtvv10AIKSlpXm6qj5DbJ/y8vKE6Oho2TWNiYkRcnNzvbJ9Gqz2Xr7kgbyKuGU/Ly8PUVFRsnNRUVHIzc2VlaPeiSvOp02bhtraWqSlpeHhhx9GWloaamtr8d3vfldWjnq3Zs0aBAYGIjw8HLW1tUhPT8fy5cuRnp4Oq9WK8PBwBAYGYs2aNZ6uqk+ora0F0HWPNjU1Yc6cOZg8eTLmzJmDpqYmTJs2TVaOXKNUKqX8UbNmzeIKVKIhTAzbOXr0aJSWlqK9vR27d+9Ge3s7SktLpdyxYjnqmdlsRnNzM4DuOQ7FHFHNzc1SmD/qnbgSPScnB4IgwGw2o6SkBGazGYIgIDs7GydOnIDFYvF0VX3GRx99BAC4ePEivvOd7yA5ORlLlixBcnIyvvOd70gRUcRy1Dtxl8TFixdx3333obq6Gq2traiursZ9990nXdPrd1OQc9g3JW8khjdWqVQOz4vHGQbZeTabDe+88w5mzZqF0NBQrFixAjfddBNWrFiB4cOHY9asWSgtLZWl7qCeiWOhVqvV4XnxOMdMnWez2bB69WopMoJ9mz9//nwIgoDVq1fzPnWS8H+Rzf7yl79I6bhESqVSSoUkMAKa08R2Jz09HceOHYPJZEJxcTFMJhPq6uqQnp4uK+dvGLrTQ1xJ0l1RUYG33noLH3/8MQ4fPozp06fjX/7lX/D44487laQbYKJu0ZgxYwB05T+7nn3oBLEc9S4oKAhr167FM888g5CQEKxbt06KOf3mm2+iubkZWVlZCAoK8nRVfYKYn+ezzz6T3YenTp2SfbbP40NERN199dVXALoGp0ePHt0tfr/4WSxHPdu/fz8AYPbs2fjwww9RWVmJs2fPYsKECZgzZw7uuOMOHDx4EPv372f4cyeJL6DHjx/H4sWLu+Xoe/LJJ2XlyDU3ys1JrhHzxygUCuzbt08Wxmv48OFQKBQQBIF5ZoiGEPsJlNtvvx0Wi0Vq8xMTEzmB0gf2YSYdXdOPP/6YYSZdJIZDzc3NlYWYBboW7+fl5TEcqovMZjMaGxsxd+5c7Nq1Sxa2d9euXbjjjjtQWVnJdEdOuvnmm6Xfw8PDkZSUhOHDh+Py5cvStb6+HPXs+vbpev7ePnGiz0NcTdKdkJBww/O9JekGmKhb5Gw+DubtcM3TTz8NACgsLMSzzz4rHQ8MDERWVpZ0nnoXGxvr1nJERP5qypQp0u/Xr5K0/2xfjm7syy+/BAAsWbIEw4YN6zYItXjxYhw8eFAqR70TX0CXLl2KRYsWdcvRt3TpUlk56p39YF5ISIhscs/+Mwf9nCfef7m5udi2bZtsQjoqKgr/9m//hpycHN6nRENIT/nkpk6dynxyfWC/S1KpVHbrR3GXpOuUSiUKCgqg1+uh1WqRnZ0t9aPy8vJgNBpRWlrKndIuECNzbNq0SYo2YT8h/cQTT+AnP/kJJ/qc9IMf/ABA12KpxsbGbnmNxcVSYjnqHdunnnGiz0MGM0m3+O8joKGhwa3l6J8SEhIwYcIEnDp1Sjo2YcKEHiepqbuHHnoIa9euBQC0tLSgpqZG6ljNnDkTYWFhUjkiIrqxO++8Uwpz3tPOnjvvvHNQ6+WrxAnR4uJirFy5stuOvpKSElk56p1arUZgYCDGjh0Lg8EghfRJSEiAwWDApEmTcP78eajVag/X1DeFhYXh+eefh0ajgdFoxIYNG7irrw/EAZWqqiocPXq023c/NTXVrwdUiIYipVKJ+++/H8888wwiIyPxwAMPyHahHDp0CFlZWZxAcQF3SQ4MnU6H0tJSZGRkyPpLMTExKC0thU6n82DtfJfFYsHy5cu7RZv45S9/6blK+aCqqioANw7NKR6vqqrCggULBq1evsy+fbo+bPypU6dw8uRJv26fONHnIcOHD3d6h91tt92GtWvXYseOHVi5ciW2bduG5cuX++1N2x/2IU7tw3Zd/7m3UKgkZzAYkJqa2i1nz9dff43U1FSUlZWxg+Wk9evXS79/97vfxX/+539KA1T/7//9P1m5oqIiT1SRiMgnONtPYn/KOfPmzUNubi6qq6sRHh5+wz6Us4vQqOulvqOjA42Njbjvvvtwzz33SNdy7969aGxshCAIqKqqYhgvJ3344YfS762trVixYoX02T6NwYcffoif/vSng1o3b+RMOgkASEtLw+OPP4677roLS5YsgVKpxJEjR7Bx40ZYLBY8/fTT+Pvf/97j32AqCSLfIeaTi4qKQkNDQ7ddKFFRUSgtLUVeXh77UU7iLpSB1dnZKfvMHHJ9k5SUhCeffBJPPPEENBqNLNrEU089hU2bNknlqHcdHR3S7+LuPUef7ctRz2w2G1577TUAQHBwsOydNCQkBJcvX8Zrr73mt+0TJ/p8BJN0u4d9OKk777wTly5dwvnz5zF27FiMGDECe/fu7VaOemaz2bBq1aoey6xevRopKSm8b9H7gIq401er1WL37t1YuXKldE6pVCIlJQW7du1CTU0Nc3MSEfXAPhKCO8r5u6SkJISHh6O5uRlXrlyRnRM/i7knyDlieK5f//rXKCoqkuU+CwwMxK9//Wv8/ve/ZxgvF4h9+OXLl+P999+X9ekjIiIwf/58vPzyy+zr/x9X0kkAwEcffYSPPvqo2/GsrKxe/1mmkiDyHWI+OaArrckvfvEL3Hzzzfjiiy/w+uuvSxGQmE/Oefa7UKKiopCRkSFd0507d3KXZB/daNF5U1MTF533QWJiIgICAqSJU0EQpB9RQEAAJ6SddPHiRQBdi82ampqwfft2HD9+HLGxsVixYgUiIiJw+fJlqRz1zj6P5P79+7tFmkhOTvbrPJKc6CO/Ijb+QUFB0qSevaCgIFy9erVbJ4FuzGw2o6mpCQAwf/58rF+/Xrbix2g0orGx0W8fstdzdkClvLy82zGbzYZdu3YBAA4ePNjr3+GAChH5MzFUijPlli1bNsC1GRqCgoIAdF81LX4ODg4e9Dr5MjE81/PPP4+QkBDZitRhw4bh+eefl5Wj3omhYy0WCxQKRbfzFotFVs7fuZJOAujqi5aXlyM3Nxc5OTnQarVOD0ozlQSR7xDTcUREROD06dNSaGkAyMvLw0033YSmpiZZ2g7qmbhLMjY2FidPnkRBQYF0LjAwELGxsdwl6SL7Rec3GovionPXVFVVSf36ffv2yRahiYvIOzs7GW3CSefOnQPQteD/5z//OXJycrB8+XJYrVb8/Oc/x+XLl2XlqHf2eSSvD90ZEBDg93kkOdFHfuX222/HBx98gKtXrzo8Lx6//fbbB7NaPm3//v0AuvLJ7Nq1S3rQip/nzJmDgwcPYv/+/X75kL1ebwMqbW1tmDt3LoYNG4aPPvoIx48fx9KlS7Fz507ExsbijjvuwLVr13DgwIFeJ6Q5oEJE/sx+p969996LhQsXSmER9+zZg3fffbdbOboxi8UiLey5Pvy5mLensbGRq/tdoFarpVXTycnJDu/RgIAA5uhzgRhi9ujRo90m+r788ktpRTpDzHZxJZ2ESKlUIjc3F6mpqVxQRjRE/eUvfwHQtTvafpIP6JqUeuihh/D000/jL3/5CxdLOUncJalQKLBw4UIsWLBAavMrKiqwZ88eCILAfpQLxEXnc+fOxTvvvIMXX3wRb775JmJjY/HOO+/gJz/5CQ4cOOC3A/59IUaR2LlzJ9avXy8LMRsZGYknn3wSS5cuZbQJJ40aNUr6/UYTp9eXI+cwj6RjnOgjv+LsQAkHVJwnruJ78MEHHa6mWLx4MQ4ePMjVfv/HmQEVMTznnXfeicWLFwMA3n//fTz88MO4du0aUlJSMGfOnMGoLhGRzxJXSAL/zIEg/thPANiXoxs7ffo0AGDBggXYtWtXtzApKSkpqKiokMpR7ywWi7Rqev/+/dLkM9CVYwLoWjVtsVg4QOWkxMRE2ffdnvhZoVAw5BQRUQ/E5+Unn3yCzs5O2Xt+Z2cnPv30U1k56p3YP7rnnntkC6QBYNWqVdBoNOxHuUjc2XPTTTdh1KhRsjxnWVlZUshOTvQ5T4wicerUqW7f787OTin0OaNNOGfZsmXYuXMnRo4ciTFjxnQLKX/+/Hl8++23XDDhAvs8kvfeey9SUlLQ1taG0NBQ1NXV+X0eSU70kV+xXz3RWzmNRjPAtfF+veWTA7pCSwHASy+9hISEBFy5cgX19fWIjo5GcHAw/vCHP0jlesopx3xy/1ReXg6tVotdu3ZJSWbF/01JSXEY1pOIiOQiIiIAdLU/e/fuxZ49e6RzSqUSw4YNw7Vr16Ry1DNxN59Op3O4sEer1aKiokIqR70TB6gA3DDvoViOA1TOsVgsvQ48izsmeE2JiBz77ne/C6BrsalWq0V2drYUEjEvLw8ffPCBrBz1jv2ogfP2228jKioKTz75JDQaDYxGIzZs2IC3337b01XzOYmJiYiMjER2dra06EzU2NiInJwcREZGcsGUk+bPn4+wsDC0tLRg+PDhWLdunSw357fffouwsDD2SV1gn0fSfpGkPX/OI8mJPvIrf/3rX6XfQ0JC0N7e7vCzfTl/5mw+OQD47LPPegx5+vLLL+Pll1++4Xnmk5MrLy9HW1sbfvnLX+Kdd97B/fffj9dee435I4mInBQTEwMAuHbtWrdzNpsNNptNVo56Jk6IbtmyBZs2bcJXX30lnZs0aZJ0nhOnzrPPdXjvvfciLi5OWpF67NgxaXL6+pyIdGNiSHmgK2ek/YSpfV+fIeWJiG5szZo1yMrKwogRI1BbWyuLeBQTE4OwsDBcunQJa9as8WAtfYvYPzIYDHj44Ye77ZIUF/OyH+W8uXPnAugKJ/vll19KuaT/9V//Fb/4xS8wYsQIdHR0SOXIOWJf6fqUR+Jn+3FU6plSqcQrr7yC1NRUNDU14dlnn5XOiRFmXnnlFeaQdIF9Hskb8ec8kpzoI7/y7bffAgBmzJiB5uZmWbzp8ePHY9SoUaitrZXK+bve8skBXYOld999Ny5evNhtQEX8PGbMGOzdu7fHxov55LoLDQ3Fb37zG7zzzjv4zW9+w0k+IiIXJCUlITc316ly1LuJEycCgBSuy95XX30lTfyJ5ah3o0ePBtDV3tfW1sp2nU6ZMkXK3SOWo96JffvJkydDqVTK8naMHz8eNpsNp06dkr0DEBGRXFBQENauXYtnnnkGwcHBuP/++zFixAhcunQJH374IZqbm5GVlSVNrFDvxP5RRUWFw12SFRUVsnLUu88//xwA0NHRgfvuuw9xcXFob29HSEgIjh07JoXy/Pzzz3H33Xd7sqo+w2w2o6WlBUDXeJ59Tu6QkBBcvnwZLS0tjDbhAp1Oh7KyMqxbt07W/5w6dSoKCgqkELPkHGfTQvlr+ihO9NGQ4UyYyaioKBw9ehRWqxUfffQRPvnkExw+fBjTp0/H/2/vzqOjqtK1gT9VlTmVhCGEDAZDsCFBRIYAIdwItLSNCIYO9KUlcbrBi6AuJaCtdisLtLWZgnwqwoVoi4RBIBeUBhwYcwmDDagQw2CYISEIgUpC5jrfH+k6pshUFXbVOafq+a3FMkntpHa91vCe85797n79+uGBBx6Qx7XUZhJwj1aTtuwnBwDLly/HuHHjGrWgsBT2li1b1uJqPyIiItFs3TeG+8vYhvsci1dSUgIAqKioQGVlJaZPny638/nss8/kkyuWcWS7CxcuYPTo0Vi9erV8IvVvf/ubzW38iYjc3dy5c3Hy5Els2rQJ69ats7otKSkJc+fOVWhm2pSYmIioqCgEBwfjxx9/tMqXoqKiEBcXh2vXrrltu7m2OHPmjPx1cy38bh9HLbN0RoiPj8eePXsa7cn9wAMPYP/+/eyMYKfk5GSMHj0aixcvRkFBAbp164apU6fyYok22Lt3L4D689XXr1/Hvn375Ofo4MGD0aFDB9y6dQt79+51y70PWegjl2FPm0mz2dzi8v2cnJxW/xZbTf6quStUQkJCeIUKEREpYvfu3c3eptPp5ALf7t278dBDDzlrWpr1zTff2DzukUcecfBsXM/Vq1exYMEC+XtLOx+yT2RkpPy1JEmN/jU1joiIGsvOzsamTZvg5eVl1cLPy8sLmzZtQnZ2No/z7WAwGLBgwQKMHz8eo0aNQlJSkrz6rKCgAFu2bMH69evZws8O3bp1k7+27NnV1PcNx1HLLKugUlJS4Onp2ajzyWOPPYb9+/e77WqptsrOzsb06dOtOk0sWrSI50vb4NixYwDqV0Te/hw1m83o0qULjh8/Lo9zNyz0kcuwpc1kdXU1hgwZ0mI/X71ej71797Z6ZQVbTVpLTk5GUlISMjMzMXnyZCxduhRpaWlMVImISBEttfCLiopCbW0tW/jZYebMmfLXt59MMRgM8p6HM2fOZKHPRh06dJC/9vT0bHQi1dIOveE4allwcLD89fbt263aoTZsgd5wHBERWaurq8Ozzz4LAI2O5y3fT5kyBUlJSTzet0NycjJmzJiBhQsXym0lgfo95mbMmMET/naaNGkSpk2bBp1Oh/DwcKv9o8PDw3Hp0iVIkoRJkyYpOEttsVwIlZWVhalTpzbaS3L16tVW46h12dnZGD9+PB555BG8/PLLcmv+rVu3Yvz48Vi/fj1f+/9mS6c+y1Zb+fn5GD58OCZOnCgfi65atUr+/bKyMrfs1MdCn4bkleXhnr/dg7yyPPQDV5LdztY2k9OnT8e8efOaveJn+vTpiI+Pd+RUXZbBYEBcXBwAIC4ujkk/EREpLigoCIcPH27UeqZv375KT01Trl27BgAIDAxEcXFxozYpISEhMJlM8jhqXUhIiPz17TlTwxMrDcdRy0JDQ5u9reGKvpbGERG5u127duHq1asAgAcffBB/+ctfGrVBLi4u5j5ddsrOzsb8+fPx8MMPw9fXFyUlJWjfvj0qKiowf/58xMfH84S/HZYvXw6g/vO9uLgYf/7zn5GWlobMzEwsXLhQ/txfvnw5XnrpJQVnqh2//e1v8c4772D//v1ISkrC66+/Lr/2LT+3jKPW1dXVYfr06ejfvz+OHTtm1UI+KioK/fv3x4wZM3jRxL/Z06kPAPbs2YM9e/Y0edsPP/zglp36WOjTCEmSsK5oHXwifLCuaB1SpVS29GkjSy/5jIwMq5/r9XpMnz6dveaJiIhcwN133w2gvr1HcnIyXn/9dYwePVr+Pi8vz2octczf3x9Afb7UVJsUS15qGUeta1gUtezH19T3LJ7aLiIiQv66srLS6raG3zccR0RE1iz7dA0ePBjZ2dnYu3cvvvzyS4SFhSE7O5v7dLWB5YR/dHQ0vvrqK7kTAlB/sU90dDRP+Nvp1KlTAOpz+YsXL2LOnDmYM2cOgPpVknfffTfOnTsnj6PWDRs2DJ06dcLVq1exfft2q8KUpTNCSEhIo5ae1LScnBycPXsW586da7R39DvvvIPNmzdDkiTk5OQwprC9U19CQgIkSUL79u0RHx+PrVu34uGHH8b+/ftRUlICnU6H3Nxct+zUp299CKlB7uVcnKmo30D2TMUZ5F7OVXhG2jZ37lzcunUL6enpAID09HSUl5ezyEdEROQiGl5p+u233yIhIQGBgYFISEjA9u3bmxxHzUtISAAA3LhxA2PGjMG+fftQWlqKffv2YcyYMbh586bVOGpdx44d5a99fHysbmv4fcNx1LLExEQEBgYCsF4V2fD7wMBAJCYmOn1uRERacf78eQBAz5490b17d7k92vDhw9G9e3fExsZajaPWWU74FxQUIDg4GMuWLUNhYSGWLVuG4OBgFBQU4MyZM8jJyVF6qpphucjsscceQ2lpKZ577jk89NBDeO6552AymTBhwgSrcdQ6g8GAJUuWNHmbJY4fffQRi9E2unTpEgBg5MiR2LhxI+Lj42E0GhEfH4+NGzdi5MiRVuPcnaVTX0v/4uPjMWPGDABASUkJtm7dCgDYunUrSkpKAAAzZsxAfHx8q3/L1dp2AlzRpwmSJOH9I+9DDz3MMEMPPd4/8j4SwhP4gXUHvLy8kJKSgoyMDKSkpLRa6Sci18A2yETaZ0v//sDAQLRv3x4lJSVWLfsAyK27O3TogMDAwBb797ti7/6mtBbTgIAA+estW7Zgy5YtzY5zx/0Q2qK4uFj++vbnaMPvG46jltXV1cl7d4wcORKjRo2S90KxPG/LyspQV1fHk1RERM3o0qULACAzMxOjRo1CUlISKioq4Ovri1OnTuGTTz6xGketu3DhAgCgU6dOuHjxIjw86k/HTpo0CU899RTCw8Nx9epVeRy1btCgQfjwww+xePFirFq1Si48f/311/jyyy9x48YNeRzZLjk5GRs2bMC0adOsivmdOnVCRkYG28vawdICOTk5uckL0MaOHYutW7fK48g2lkU6CxYsaLRvfHp6ulsv4mGhTwNyL+ci71qe/L0ZZuRdy0Pu5VwMiRii4MyIiLSFbZCJXIO9/furqqqa/P769esYOHBgi7/rir37m2JvTJuTkZHRqD367dwlpq25fv26/PXtn0UNTwY0HEctW7x4McxmM6ZMmYKtW7daFaS7du2KZ599FkuWLMHixYu5Xw8RUTOGDRuGd955BwCavbDHMo5sc+DAAQBAWlqaXOSz8PDwwNNPP425c+fiwIEDePzxx5WYouZERkYCAEwmEyorK/HYY48hLi4O//rXv7BhwwZUV1dbjSPb7d+/v9Eqs4sXL2L//v0s9NmhU6dOAOr353zyyScb7Rm/ceNGq3Fku7lz5+Ltt9/Ga6+9hoyMDKSnp+Pdd991+0U8LPSpnLyaT6eHWfq1Sq3XcVUfEZG9mmqDzAsmiLTHlv79Fjt27EBGRgYKCwvln4WHh2PatGk2te10xd79TbElposWLcKKFSvQrl07BAQE4MKFC4iMjERpaSlu3LiBJ554Ai+++KJN90XWbt9P7vY9+8g2BQUFAIA333wT77//PnJycuQTKomJibhy5QqWLFkijyMiosZuX3lyp+Po15X6hw8fRk1NTaMT/keOHLEaR61LSEiAh4cH9Ho9qqursXr1aqxevVq+3cvLC2azmW3l7fTKK69g3rx5CAkJwRNPPIHo6GicPn0aK1aswLx58wDArVdM2cOyJ/TWrVsRFBRkld9bOk40HEf2Yae+xljoU7nbV/NZmCWu6iMisoc7t0E+deoUSktLHfb38/Pzrf7rKAEBAfjNb37j0PsgbbD077dFv379MG3aNGRmZmLy5MlYunQp0tLS2LbvNrbE9NNPP0Xnzp2xcOFCuR3ShQsX4OHhgZdffpkH/XZqae89nU4nn+zjHn2269atGwBg8+bNmDRpUqPVJps3b7YaR0REjV2+fFn+Wq/XW7VGa/h9w3HUMssxzNdff93iCX8e69guNzcXtbW1AOpXRA0fPhz+/v4oLy/Hzp075XaIubm5XH1qo+rqaixcuBBBQUHw8/PD/Pnz5duioqIQFBSEhQsX4u2332ZRxQaJiYkICQlBcXExKioq4N/TH2EpYSjMKkT5T+UAgJCQEO4dTcKw0KdilpPSOuggofFVPTro3OYkNRHRnXLXNsinTp1C9+7dnXJfqampDr+PkydP8gCY7GYwGBAXFwcAiIuLY5HvDrBNijgtFfAaXtHPQl89W/bmjI+Ph8FgwJ///Gf07t0bNTU1OHv2LKKiouDp6YnXXnsNBoMB8fHx3EuSiKgZ+/btAwCEhobC29sb586dk2/r0qULKioqcOXKFezbt49tJm00depUTJ8+3apoeju9Xo+pU6c6cVbaZmkt2bdvX5SUlODzzz+Xb+vatSvuuusuHDlypFELSmre4sWLUVtbC5PJhMTERLz88styIXrr1q345z//CUmS2ALdDpauHTqdDp3Hd4ZPhA86j++MM2+dgSRJjbp6EN0JFvpUrMZcg6LyoiaLfAAgQUJReRFqzDXwMvDkChFRc9y5DbJlJd/KlSsRGxvrkPs4fO0wVl5eidTwVPTr6Jh9t/Lz85GamurQlYlEZBu2SRHju+++s3nck08+6eDZqJ89+0hev34dgwYNavb2wYMHt/o3uJckEbkry0o9f39/1NbWWq1CkcolGI1GXLlyhSv67GAwGGA0GmEymRAQEIApU6agW7duKCgowMqVK1FRUQGj0ciL0exgWbE3depUPP30043adVu6eVjGUetOnToFAOjduzeOHj0qd0IAgLvvvhu9e/fGDz/8II+jlu3atQsmkwkA4H+vP/yi6y8g84v2g/+9/ig7VgaTyYRdu3bhwQcfVHKq5CJY6FMxL4MX1oxeg+uV1wHUH9ympKQgKytL3tukg08HFvmIiFrBNshAbGysQ05YSpKEuf+ci2u4hm+rvkVa3zSXLZqStuWV5eGev92DvLI89ANP3pPyampq5K99fHysruht+H3Dce7Mnr05Fy1ahKysLNTV1ck/MxgMSElJsWkfScv9ERG5o4CAAAD1+57q9XpE/TVKXoVy9u2z8qo0yzhqXU5ODkwmE1JSUrB27Vos3ba0vni6rRBV16swceJErFq1Cjk5OWwzaaNOnToBALKzs/Ff//VfVnEzm83YuHGj1ThqneU4/ocffsCYMWOwZs0a9OrVC8eOHcM777yDL7/80moctWzHjh3y1xETIgAzAD0Ac/33J46dkMex0EcisNCncqH+oQj1DwUAVPpWovJcJaJ8o9CzY0+FZ0ZE5Bii95OTJAlzCua02AZ5zt45mNltpvCE1R32lGtYRHWXoilpjyRJWFe0Dj4RPlhXtA6pUioPUElxRUVF8tcPPvggHn744UbtkW4f587s2Zvz008/xbJly9hiloioDSZOnIiVK1cCAILuD7JahdKuTztcP3xdHke2KSwsBAAsWbIEH330EYZnDkdV+yrEPBODnWk7AQCrVq2Sx1HrIiIiAADbtm3Do48+im7duqGyshI+Pj4oKCjAtm3brMZR6wYMGAAA8PT0xPr16+W8KT4+HuvXr4fRaERNTY08jlp25swZAEBg70B4Rnr+eoMe8Iz0RGDvQJh+NMnjiO4UC30CiT45fbv8/Hyr/zqKO5yYJtIKd1uB4oj95HQeOnRf0B2eQZ5N3i5BwsnCkxiQMgBSbdOtku+EK+8pd3tLVHdohUralHs5F2cq6g+gzlScYUGaVMXX1xd5eXlyYQ+o31vGUvSjtmGLWcdwx9zU0W3DeZxPauPh8eupwvZj2kOqk6Az6CDVSWg3+tdCX8Nx1LKwsDAAwLPPPovNxzYjclokAKCqfRUiEyPxyL2PWI2j1iUmJiIqKgoVFRVWOZRF586d4efnh8TERAVmp00lJSUA6rtJdOnSBSkpKYiOjsbp06eRlZUld5mwjKOWWdrGBo8Nlt9HLaQ6CcFjg2H60cT2siQMP5UFccTJ6eakpqY6/D5c+cQ0OZa7Hfw7kjuuQHHUfnLXqq+htK7+b1dVVuFy4WWEh4XD28cbABDoEYgOBzoIuz/APfaUu70lqju1QiXtkAvS0MMMM/RgQZoc79atWzh+/HiLY2prawEAFRUVuHnzJv70pz/B29sbVVVV+Oqrr+QiX21tLQ4fPtzi34qJiYGfn5+YyRM1w91yU2ce4wM8zif12LNnDwDA2Msor+YDAJ1BB79oPxh7GVF2rAx79uzB7373O6WmqSmJiYkICgpCVlYWerzVAzpJB0knQSfpEDo+FKveWIWgoCAWpexgMBhw//33Y9OmTU3efuXKFSQlJXHfQztY2pyGhITgypUryMjIsLo9JCQExcXFbIdqI29v70bvoxYN30+9vb0VmJ1r4Dloayz0CeKok9MNVVRU4OzZs4iKioKvr69D7sMdTkyT47jbwb+jufMKFEftJ0fi3L6az4Kr+khtGhWkwYI0Od7x48fRv39/m8eXlJRgzZo1Td62ZcsWbNmypcXfP3ToED83yeHcLTd1xjE+wON8Uh/LHnxdJnaBZJag0zdYhWKW0GViF/z0+k/yOGpdXV0dSktLYexlhGekp7ylhKST4BnpCWMvI0p/KkVdXR0LUzaqrq7GF198AaB+zzhJ+rU7j+X7L774AtXV1VzVbyNLm9Pi4mKEhIRg2LBh8PPzw61bt7Br1y4UFxdbjaOWFV8tRkhySKP3UQvJLCEkOQTF24oVmJ328Rx0Yyz0Cebok9NDhrjugRRpn7sd/DsSV6CQ2t1ePLHgqj5SExakSSkxMTE4dOhQi2Pq6uowfPhwlJeXo3379ujevTsOHDiAQYMG4eTJkygpKYHRaMSOHTtaPekXExMjcvpEjbhzburoY/x9l/fh86uf49Wur6JfOAv2pLyOHTvC2MsIfbi+0W06vQ66cB2MvYzo2LGjArPTpsWLF8NsNqPXs71Qai5tVDzt9Wwv7H9+PxYvXoyXXnpJuYlqyKJFiyBJEnx9fdGxY0dcvHhRvi0iIgLXrl1DRUUFFi1ahJdfflnBmWpHQkICPDw84O/vD19fX/zz2D8RlhKGwqxCdPLrhKCgIJSXlyMhIUHpqWrCmXNn0LFjxyaLfED9+6lnB0+cOcc9+tqC56AbY6GP3BqX+Irjzgf/jsAVKKRmlte7Djr5atSGdNDx9U+qwIK04zCHapmfn59NhYEVK1Zg3LhxuHHjBg4cOAAAOHDggPze+emnn2LAgAEOnSuRLZibOoYkSVh0eBFO3zyNRYcXIT4snrkTKS4kJMSmVSghISEKzE6bCgoK6lueGsugg3VMdXodyoxlMPYyoqCgQKEZao+lZWdFRQV++eUXq9t++eUXVFZWyuNY6LNNbm4uamtrYTKZUFVVhfBXwuET4YPO4zvj8tzLqKqqgiRJyM3NxbBhw5SeruqZa8w4Pes0DAGGJi88NUtm1JpqEagLVHCW2sRz0E1joY/cFpf4iuWuB/+nTp0S3gJHkiTMKZgjf2BZ6KHHnL1zMLPbTIc8VwMCArhnB9mkxlyDovKiJot8ACBBQlF5EWrMNfAysE0KKYMFacdhDiVOcnIyNmzYgPT0dPzi/8uvV03f6oQFCxYgOTlZ6SkScXW0AzU8hnKHYyfShk6hneB53bPVVSidQrlPF2DbvryeXp4ISQ4BJABNhVUCQpJD4FnmyX1528BS1Gvue7JNYWEhgPrPfY/feMh7y/lF+8HjNx6oPFppNc7dtfbav/fee5GTk4Oa6zUwGAyoq6uTb2v4/b2J9/J1byd3PQfdGhb6yG254xJfRxSlAGUKU2ooSp06dQrdu3cX/neNvYyImhHV6OdmmHGm4gyGPTUMZcfKhN8vAJw8eVLxuJL6eRm8sGb0GlyvvN7smA4+HVjkI0WxIO047phDOVJycjIeffRR/P6z36NYX4xBMwbhq8e/gocHD9XIPo7K9Y+WHm1xdfSKPStwX8B9Qu9TDbm+o91eQGXhlNTCQ+eB07NOo9t93VBVVWV1Uj88PBxeXl4o+LEAHuv4OQXYti+vzkOH7gu6N13kAwAd4NnBE++98x4WZixs8W9xX956PXv2xN69ewEAXl5eSE9Px6RJk7B8+XJkZGSgurpaHke2abhKN3RcKKQ6CTqDDlKdhNBxofj56M+Nxrkze/bkbljku/37nJycVv8OX/e/4gVozeOnMqmWow5UgV8LU5ar/HXQufxKKUcVpQDlClNKF6Usz8+VK1ciNjZWyN+UJAmzCmbhbMXZZlegDH9juPDnan5+PlJTUx32miPXE+ofilD/UKWnQdSs2wvSx48fR0pKCrKysuT9zFiQth/bpDjGgSsHUKwvBgAU64tx4MoBFk/JLo7M9aPfjIZvlG+zLfxm75iN07NPC79fpXN9R2t0NTrbSpNKFBcXo+Z6DY7vPo7Ro0djeup0+Pr6oqKiAtu2bcPmzZvlcWTbvrwAMG/JPGz8eiOCAoMwbPgwbNq0CUlJSdi1cxdumm5i7ENjserAKpvuj4DAwECrr6urq7Ft2zZUV1cjMDBQbufZcBy1zFJ8MvYywifKR/65zqCDT5RPffvZY2WNilbuypbX/uOPP46ffvqp2dt79uyJzz77zKb7onrcnqN5LPSRKjnyQBVoXJiSILn8SilHFKUAZQpTaitKxcbGCruyprquGqafTS2uQDFJJtzX5z6enCYiakXDgnSlbyUqz1UiyjcKPTvyyt62YpsU8Vg8dRx32kvSUbl+jbkG6SfSYao1NXm7Tq9DWPcwrPpuFTz1nkLuU225viPwanRSii1tJi2vveeffx7Z2dlyYQ8AIiIi8Nxzz+HDDz9EaWkp283B9n15V//ParzyyitYuHAh1h5dCwBY+//WwsPDA9OmTcPcuXMdPVWXsmvXLvnrX375BRkZGa2Oo5bt3r0bQH0bWZgB6BvcaK7/edmxMuzevRsPPfSQInNUE1te+3l5eRg4cCC+++67RrcNGDAABw8edNT0XBK352gZC32kSo46UAV+LUydqzjXqM2kO6yUElmUAliYEo0tEUk0XW0l+obq4XvjJHBZ3/ovqJTvjZPoG6qHrpb7LRAphSemHYPFU8dw170kRef6ALAhdkOruSlX+duHV6OTUuxpNffBBx80+tmlS5fw4YcfAgAmT57c6t9guzlrc+fOxdtvv43XXnsNGRkZSE9Px7vvvgsvLx7fk/LOnz8PYy+jvDefFX39Xn3GXkacP3/e+ZPTsIMHD6KsrAyjR4/G7t27MXToUGzevBlGo1HpqTmc6G59NeYaXLh5ocVz0BdvXsTBQweFXYBmoYZufa1hoY9UzREHqnsv7cWZY2ca/dzSZrIytJIHVXZgYUo8tkQkkXzKzuPwZCOwZzKwR+nZtF0sgMOTjcgvOw8gQenpELklnpgWj8VTx+FekuIwNxWLV6OTkmxtM7ljxw688sorSExMxPDhwzFr1izMnDkTO3fuRE5ODubOnYvf/va3Nt0fWfPy8kJKSgoyMjKQkpLCIl8bxcXFWT2X/Xv6IywlDIVZhSj/qdxqHNkmskskQqJDAAlN7ycp1a/qi6yNdPbUNM9oNCIjIwP9+/dHRkaG2xT5HNGtz7ODJwwBhmZvrzXVIr4kXvj9Asp362sNC33kVnhQ5Rg8+CdSr0pjF/RbWoasrCzEavhAO//f+6tljuqi9FSI3BJzKMdg8dQx2A6V1KzGXIOi8qIWr0YvKi9CjbmGF0uScLa2mezXrx+io6Mxffp0zJo1CwAwa9YsdO3aFevXr0dycrKjp0rUoocffhhLly6Vv+88vjN8InzQeXxnq31jH374YSWmp0lDhw9F9k/ZTRf5AEBXX2QZeu9Qp86LtMmR3fosKioqcPbsWURFRcHX19ch9wGor1tfc1joI7fCgyoicjeShw+OFJlR0a47EN5H6em0WUWRGUeKzJA8fFofTJojuqVHU/Lz863+6yhqaenBNinqx+Kp47AdKqkZO6KQViQnJyMpKQmZmZmYPHkyli5dirS0NBgMza+kIHKW1atXy183bDdpaS9ZdqxMHpeUlKTIHNXElv05OwR1wLX3rqHMXAZPT0/U1NTIt3l5eqG6phpGvREd1nZocX9Od9ibk2zniG59DQ0ZwtzegoU+cis8qCIiIlIXR7X0aE5qaqrD70Pplh5sk6INvADNMdgOlbSAHVFIKwwGg9z6MC4ujkU+Uo0DBw4AAMLCwuCb7AupToLOoINUJyEkOQQB1wJQWFgoj3N39uzPCQCVqGzy+0pUYuDAgS3+LvfmJFIGC32C6Gor0TdUD98bJ4HLeqWn02a+N06ib6geutrK1gdrFA+qiNTLVd5LAfW8n966dQsAWrzi7k45o12Co1dhkXKc0dIDcN7zVA0tPdgmRRtuvwDt+L9bFGdlZcl7Grn6BWiOWM17tPRoi+1QV+xZgfsC7hN6n6666pSIiEgJtqw+M5vrL+bxjfWVV/MBgM6gg1+0H6RYCSisH8fVZ7bvzwnU79GZkZGBwsJC+Wfh4eGYNm0a9+YkUjEW+gTxKTuPw5ONwJ7JwB6lZ9N2sQAOTzYiv+w8gASlp0NEbsZV3ksB9byfWg6QnnnmGcXmIFJAQIDSUyAHcXRLD8D92nqwTYr6NbwArdK3EpXnKhHlG4WeHXsqPDPHc9TK0+g3o+Eb5QudvvGqPcksYfaO2VZ794jiiqtOiYiIlGDX6rP/gLyaz0Kqk6D7Dx2wAzh//nyLf8tdVp/Zuj8nUL9H57Rp09i2l0hjWOgTpNLYBf2WliErKwuxGr5yIf/fVxJnjuqi9FSIyA25ynspoJ7307FjxwJw7JWKlhU3jl6RxRUTRKQkR+8l6ax9JAF1vJ86YuVpjbkG6SfSYao1NXm7Tq9DWPcwrPpulbC9JF151SkREZESbFl9VlFRgZHPjrRazWdhWdVn7GXEtiXbWuw+wdVnTXPHtr3M9UnrWOgTRPLwwZEiMyradQfC+yg9nTarKDLjSJEZkoePovNg+z7xGFPxGFPxXOW9FFDP+2lwcDAmTZrklPtyxoosIiIlOHMvSWfsIwmoZwWa6M+ODbEbWt2Pm238yRbM9YmIlGHL6jNJktAjrQcqzZXNruLvkdYDCQncm5dax1yfXAELfYJwDySx2L5PPMZUPMaUiIjIPXDfQ+3gftwkCnN90gquQhHL0fEEnBdTNcTTUWrMNTCGGVFVWdXk7Tq9DsYwI2rMNS693zGJwVyfXAELfYJwDySx2L5PPMZUPMaUiIjIvXDfQyL3wVyftICrUMRyZjwB58TUVVf1eBm8sGb0GlyvvI6qqiq8+eab+PbbbzFixAjMnj0b3t7e6ODTgUU+sgtzfdIyFvoE4R5IYrF9n3iMqXiMqXiusjoaUM8KaSIiIiKyH3N90gKuQhHLGfEEnNexS+l4OlrDVfyzps7C5szNmDV1FvqG91V4Zo7HladEdDsW+gThHkhERHfO1VZHA8qvkCZSO+6BRER05/heKp4zLkAD3GuLDnIcrkIRyxnn3dwppixKicWVp6QFzE2dj4U+UiUeVInHmIrH1WfiudLqaEAdBwFEasc9kEgLeKBKasf3UvF4ARoR0Z1jUUo8rjwVj7m+eMxNnY+FPlIlHlSJx5iKx5iKx9XRyrt165b83LbVnVw96ciiLmkD90ASjweq4vFAVTxXeZ6q5TnK91LxnHEBGuBeW3SQeK7yXgqo5/2UxGJRynG48lQc5vriMTd1Phb6SJV4UCUeYyoeV5+RKzp+/Dj69+/fpt9ty9WThw4dYsHVzZVX1+8btPd0GSramR12P045+C+sU8UeSDxQFY8HquK5yvNULc9RvpeK58wL0ABehEZt4yrvpYB63k/JMViUIjVjri+eM3JTp3VBU0lu2hoW+hTCFRMt40GVeIypeFx9pjy+l4oXExODQ4cO2fU7d5JcxWg8iaY7x9XR4vFAVTzJwwdHisyoaNcdCO+j6FzuVEWRWRUHqq7yPFXLc5TvpUTuqcQQjH5Ly/DGG284LK+uqqrC5cuXER4eDm9vb4fcBwCcOXMGf/3rXxV9P+UKSfEYU/EYU/FYlBKPuanzsdCnEK6YEI8n/MVzZkzdIZ6A/THlc7RlfC8Vz8/Pr02PkVdPUlu1ZXW05SDJHmfOnMEbb7yBt956C127drX59+w9EFPD6ui2HKi2JaZtZU9M1XKgau++vM6MJ2BnTFWyL6+9z1O1xlQtz1G+l6oDj5/I2X46dRZHisxIfm6W0lMRxti+k3J3/stJrpAUjTEVjzEVjkUp8ezNTZ2ZlwKumZvqJEmSlJ6E0kwmE4KCgnDz5k0EBgY65T7bcgBwpysmXP0g4PDhw20+4d8W7nDC35kxdYd4AoypaHwvJXso8Xnv6pSKKd9LW7Z8+XKXOkgFgJMnTyp6YMWYiudqMVU6nm3B91LxXDmmzKOa9uGHH2LevHkoKirC/fffj/fffx8DBw606XdFxPSXX37Bxo0bbT5OsWwN4Sz2bkGh9InUT/5nMd6f+YJi9+8In+/4HvfE3qfY/TOm4jGm4tn7Xgo49/1Ua++lbeHK5/WdlUO5TKFP6eSKlMcT/uI5M6buEE/A/pjyOUokDj/vm9fWPEqpmPLzqWVtOVBV64o+QB0HqvbGVK2rzywY09bZE1M1xLMt+F4qnivHlHlUY2vXrsUTTzyBJUuWYNCgQXjvvfewbt06nDhxAiEhIa3+Pi86Vx+151CA9j6f1B5Td8ihAMbUEXh+TyxX/nxioc8OWkyuiIiIyLn4ed+0O8mjGFMiIiL3wM/8xgYNGoQBAwbggw8+AACYzWZERkbihRdewKuvvtrq7zOmRERErs9Zn/fa3rHz3zIyMvDMM8/g6aefRs+ePbFkyRL4+fnh448/VnpqRERERKrGPIqIiIjIPtXV1Th06BBGjBgh/0yv12PEiBHYt2+fgjMjIiIid6T5Ql9bkquqqiqYTCarf0RERETuxt48ijkUERERUX3rvLq6OnTu3Nnq5507d0ZRUVGTv8M8ioiIiBxF84W+tiRX7777LoKCguR/kZGRzpgqERERkarYm0cxhyIiIiJqG+ZRRERE5CiaL/S1xWuvvYabN2/K/y5cuKD0lIiIiIhUjzkUERERERAcHAyDwYArV65Y/fzKlSsIDQ1t8neYRxEREZGjeCg9gTvVluTK29sb3t7ezpgeERERkWrZm0cxhyIiIiICvLy80L9/f2zfvh1jx44FAJjNZmzfvh3PP/98k7/DPIqIiIgcRfMr+homVxaW5Grw4MEKzoyIiIhI3ZhHEREREbVNeno6li1bhk8//RT5+fmYMmUKysvL8fTTTys9NSIiInIzml/RB9QnV08++STi4uIwcOBAvPfee0yuiIiIiGzAPIqIiIjIfhMmTMDVq1fx5ptvoqioCH369MG2bdsa7X1MRERE5GguUehjckVERETUNsyjiIiIiNrm+eefb7ZVJxEREZGz6CRJkpSehNJMJhOCgoJw8+ZNBAYGKj0dIiIicgB+3ovHmBIREbkHfuaLx5gSERG5Pmd93mt+jz4iIiIiIiIiIiIiIiIid8RCHxEREREREREREREREZEGsdBHREREREREREREREREpEEs9BERERERERERERERERFpEAt9RERERERERERERERERBrEQh8RERERERERERERERGRBrHQR0RERERERERERERERKRBLPQRERERERERERERERERaRALfUREREREREREREREREQaxEIfERERERERERERERERkQax0EdERERERERERERERESkQR5KT0ANJEkCAJhMJoVnQkRERI5i+Zy3fO7TnWMORURE5B6YR4nHPIqIiMj1OSuHYqEPQGlpKQAgMjJS4ZkQERGRo5WWliIoKEjpabgE5lBERETuhXmUOMyjiIiI3IejcyidxMuxYDabcfnyZQQEBECn0yk9nWaZTCZERkbiwoULCAwMVHo6LoExFY8xFYvxFI8xFU8rMZUkCaWlpQgPD4dez+7lImglhwK08zzVCsZTPMZUPMZUPMZUPK3ElHmUeFrJo7TyHNUSxlQ8xlQ8xlQsxlM8rcTUWTkUV/QB0Ov1uOuuu5Sehs0CAwNV/eTVIsZUPMZULMZTPMZUPC3ElFegi6W1HArQxvNUSxhP8RhT8RhT8RhT8bQQU+ZRYmktj9LCc1RrGFPxGFPxGFOxGE/xtBBTZ+RQvAyLiIiIiIiIiIiIiIiISINY6CMiIiIiIiIiIiIiIiLSIBb6NMTb2xszZ86Et7e30lNxGYypeIypWIyneIypeIwpaQGfp2IxnuIxpuIxpuIxpuIxpqR2fI6Kx5iKx5iKx5iKxXiKx5ha00mSJCk9CSIiIiIiIiIiIiIiIiKyD1f0EREREREREREREREREWkQC31EREREREREREREREREGsRCHxEREREREREREREREZEGsdCncsOGDcNLL73U4pioqCi89957TpmPK2BMxWNMxWI8xWNMidwTX/tiMZ7iMabiMabiMaZE7oeve/EYU/EYU/EYU7EYT3ImFvoc7KmnnoJOp2v07+eff3baHPLy8jBu3DhERUVBp9Np/s1DDTFdtmwZEhMT0b59e7Rv3x4jRozAwYMHnXb/oqkhptnZ2YiLi0O7du3g7++PPn364LPPPnPa/Yukhng2tGbNGuh0OowdO1aR+xdBDTH9xz/+0ej+fXx8nHb/Stm3bx8MBgMeeeQRpadCbkgNr31XyqPUEE/mUOK5Ug4FqCOmDTGPEoN5FPMoci41vO5dKYcC1BFT5lHiMY9yLK3nUWqIJ3Mo5lC28lB6Au5g5MiR+OSTT6x+1qlTJ6fd/61btxAdHY0//vGPmDZtmtPu15GUjumuXbvw2GOPISEhAT4+PpgzZw4eeugh5OXlISIiwmnzEEnpmHbo0AF/+ctfEBMTAy8vL2zevBlPP/00QkJC8Pvf/95p8xBF6XhanD17FjNmzEBiYqLT71s0NcQ0MDAQJ06ckL/X6XROvX8lZGZm4oUXXkBmZiYuX76M8PBwpadEbkbp176r5VFKx5M5lHiulkMBysfUgnmUWMyjmEeRcyn9une1HApQPqbMo8RjHuU4rpJHqSGezKGYQ9mCK/qcwNvbG6GhoVb/DAYDAGD37t0YOHAgvL29ERYWhldffRW1tbXN/q3i4mKMGTMGvr6+6Nq1K7Kyslq9/wEDBmDevHn405/+BG9vb2GPS0lKxzQrKwtTp05Fnz59EBMTg+XLl8NsNmP79u3CHqOzKR3TYcOG4Q9/+ANiY2PRrVs3vPjii+jduzf+7//+T9hjdCal4wkAdXV1SElJwaxZsxAdHS3kcSlJDTHV6XRW99+5c2chj02tysrKsHbtWkyZMgWPPPII/vGPfwAAJk6ciAkTJliNrampQXBwMFasWAEAKC0tRUpKCvz9/REWFoaFCxfa1LaC6HZKv/ZdLY9SOp7MoZhD2ULpmALMo5hH3TnmUaQ0pV/3rpZDAcrHlHkU8yhbKB1TwLXyKDXEkznUPwAwh2oNC30KunTpEkaNGoUBAwbghx9+wEcffYTMzEy8/fbbzf7OU089hQsXLmDnzp1Yv349Fi9ejOLiYifOWt2UiumtW7dQU1ODDh063OlDUB0lYipJErZv344TJ07ggQceEPEwVMOZ8Zw9ezZCQkKQlpYm8iGojjNjWlZWhrvvvhuRkZFISkpCXl6eyIeiOp9//jliYmLQo0cPpKam4uOPP4YkSUhJScGXX36JsrIyeexXX32FW7du4Q9/+AMAID09HXv37sUXX3yBb775Bjk5OTh8+LBSD4VcEPMosZhDicccSjzmUeIxj3Ic5lGkVsyhxGMeJR7zKPGYR4nFHMpxmEO1kUQO9eSTT0oGg0Hy9/eX/40fP16SJEl6/fXXpR49ekhms1ke/+GHH0pGo1Gqq6uTJEmShg4dKr344ouSJEnSiRMnJADSwYMH5fH5+fkSAGnhwoU2zefuu++2eaxaqS2mkiRJU6ZMkaKjo6WKioo7f4AKUEtMb9y4Ifn7+0seHh6St7e3lJmZKfaBOoka4pmTkyNFRERIV69eleeUlJQk9oE6kRpimpubK3366afSkSNHpF27dkmjR4+WAgMDpQsXLoh/wCqRkJAgvffee5IkSVJNTY0UHBws7dy5U/56xYoV8tjHHntMmjBhgiRJkmQymSRPT09p3bp18u03btyQ/Pz85P8PRLZQw2u/Ia3nUWqLpyQxh2IO1ZgaYso8inmUCMyjSElqeN03pPUcSpLUF1NJYh7FPKoxNcTUlfIoNcSTORRzKFtxjz4nGD58OD766CP5e39/fwBAfn4+Bg8ebNVXd8iQISgrK8PFixfRpUsXq7+Tn58PDw8P9O/fX/5ZTEwM2rVr59gHoEJqiunf//53rFmzBrt27dL0ZqhqiGlAQAC+//57lJWVYfv27UhPT0d0dDSGDRt2Zw9OAUrGs7S0FI8//jiWLVuG4OBgQY9IeUo/RwcPHozBgwfL3yckJCA2NhZLly7FW2+9dScPTZVOnDiBgwcP4n//938BAB4eHpgwYQIyMzMxbNgw/Od//ieysrLw+OOPo7y8HJs2bcKaNWsAAKdPn0ZNTQ0GDhwo/72goCD06NFDkcdC2qb0a9/VqCmezKGYQzWHeZR4Sj9PmUcxjyLnU/p174rUFFPmUcyjmsM8Siyln6PMoZhD2YqFPifw9/fHPffco/Q0XIpaYjp//nz8/e9/x7fffovevXsrPZ07ooaY6vV6eQ59+vRBfn4+3n33XU0mV0rGs6CgAGfPnsWYMWPkn5nNZgD1H5AnTpxAt27dFJnbnVDDc7QhT09P9O3bFz///LPSU3GIzMxM1NbWWm14LEkSvL298cEHHyAlJQVDhw5FcXExvvnmG/j6+mLkyJEKzphcldpe+1qnlngyhxLLlXIogHmUI6jhedoQ8yjmUeR4anvduwK1xJR5lFjMo8RxxTxKDc/RhphDMYdqDvfoU1BsbCz27dsHSZLkn+3duxcBAQG46667Go2PiYlBbW0tDh06JP/sxIkTuHHjhjOmqwnOjOncuXPx1ltvYdu2bYiLixMyfzVS8nlqNptRVVXVpnmrlTPiGRMTg6NHj+L777+X/z366KMYPnw4vv/+e0RGRgp9TEpT6jlaV1eHo0ePIiwsrM1zV6va2lqsWLECCxYssHoe/fDDDwgPD8fq1auRkJCAyMhIrF27FllZWfjjH/8IT09PAEB0dDQ8PT3x3XffyX/z5s2bOHnypFIPiVwQ8yixmEOJxxxKPOZR4jGPEo95FKkdcyjxmEeJxzxKPOZRYjGHEo851J1hoU9BU6dOxYULF/DCCy/g+PHj2LRpE2bOnIn09HTo9Y3/1/To0QMjR47E5MmTceDAARw6dAiTJk2Cr69vi/dTXV0tvzCqq6tx6dIlfP/99y5Z+XdWTOfMmYM33ngDH3/8MaKiolBUVISioiKrzUBdhbNi+u677+Kbb77B6dOnkZ+fjwULFuCzzz5Damqqox6aIpwRTx8fH/Tq1cvqX7t27RAQEIBevXrBy8vLkQ/R6Zz1HJ09eza+/vprnD59GocPH0ZqairOnTuHSZMmOeqhKWbz5s0oKSlBWlpao+fSuHHjkJmZCQCYOHEilixZgm+++QYpKSny7wcEBODJJ5/Eyy+/jJ07dyIvLw9paWnQ6/VWbS2I7gTzKLGYQ4nHHEo85lHiMY8Sj3kUqR1zKPGYR4nHPEo85lFiMYcSjznUnWGhT0ERERHYsmULDh48iPvvvx/PPvss0tLS8Ne//rXZ3/nkk08QHh6OoUOHIjk5Gf/93/+NkJCQFu/n8uXL6Nu3L/r27YvCwkLMnz8fffv2dck3BGfF9KOPPkJ1dTXGjx+PsLAw+d/8+fNFPyTFOSum5eXlmDp1Ku69914MGTIEGzZswMqVK13ueeqseLoTZ8W0pKQEzzzzDGJjYzFq1CiYTCbk5uaiZ8+eoh+S4jIzMzFixAgEBQU1um3cuHH417/+hR9//BEpKSn46aefEBERgSFDhliNy8jIwODBgzF69GiMGDECQ4YMQWxsrKb3jyB1YR4lFnMo8ZhDicc8SjzmUeIxjyK1Yw4lHvMo8ZhHicc8SizmUOIxh7ozOqnh+lIiIiIiBygvL0dERAQWLFiAtLQ0padDREREpBnMo4iIiIjs5045lIfSEyAiIiLXc+TIERw/fhwDBw7EzZs3MXv2bABAUlKSwjMjIiIiUjfmUURERET2c+ccioU+IiIicoj58+fjxIkT8PLyQv/+/ZGTk4Pg4GClp0VERESkesyjiIiIiOznrjkUW3cSERERERERERERERERaZBe6QkQERERERERERERERERkf1Y6CMiIiIiIiIiIiIiIiLSIBb6iIiIiIiIiIiIiIiIiDSIhT4iIiIiIiIiIiIiIiIiDWKhj4iIiIiIiIiIiIiIiEiDWOgjIiIiIiIiIiIiIiIi0iAW+oiIiIiIiIiIiIiIiIg0iIU+IiIiIiIiIiIiIiIiIg1ioY+IiIiIiIiIiIiIiIhIg/4/5c/UNTIBn7kAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x1200 with 6 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def make_gradcam_heatmap(img_array, model, layer_name, class_idx):\n",
+        "    grad_model = tf.keras.models.Model(\n",
+        "        [model.inputs], [model.get_layer(layer_name).output, model.output]\n",
+        "    )\n",
+        "\n",
+        "    with tf.GradientTape() as tape:\n",
+        "        conv_outputs, predictions = grad_model(img_array)\n",
+        "        loss = predictions[0, :, :, :, class_idx]\n",
+        "\n",
+        "    grads = tape.gradient(loss, conv_outputs)\n",
+        "    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))\n",
+        "\n",
+        "    conv_outputs = conv_outputs[0]\n",
+        "    heatmap = conv_outputs @ pooled_grads[..., tf.newaxis]\n",
+        "    heatmap = tf.squeeze(heatmap)\n",
+        "\n",
+        "    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)\n",
+        "    return heatmap.numpy()"
+      ],
+      "metadata": {
+        "id": "uAiV6T6e2nTS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import cv2\n",
+        "import numpy as np\n",
+        "def make_gradcam_heatmapmine(img_array, model, layer_name, class_idx):\n",
+        "    grad_model = tf.keras.models.Model(\n",
+        "        [model.inputs], [model.get_layer(layer_name).output, model.output]\n",
+        "    )\n",
+        "\n",
+        "    with tf.GradientTape() as tape:\n",
+        "        conv_outputs, predictions = grad_model(img_array)\n",
+        "        loss = predictions[0, :, :, :, class_idx]\n",
+        "\n",
+        "   # grads = tape.gradient(loss, conv_outputs)\n",
+        "    output = conv_outputs[0]\n",
+        "   # print(output.shape,len(output))\n",
+        "    grads = tape.gradient(loss, conv_outputs)[0]\n",
+        "    gate_f = tf.cast(output > 0, 'float32')\n",
+        "    gate_r = tf.cast(grads > 0, 'float32')\n",
+        "    guided_grads = tf.cast(output > 0, 'float32') * tf.cast(grads > 0, 'float32') * grads\n",
+        "\n",
+        "    weights = tf.reduce_mean(guided_grads, axis=(0, 1,2))\n",
+        "   # print(len(weights))\n",
+        "    cam = np.ones(output.shape[0: 3], dtype = np.float32)\n",
+        "   # print(cam.shape)\n",
+        "    for i, w in enumerate(weights):\n",
+        "        #print(w)\n",
+        "        cam += w * output[:, :, :, i]\n",
+        "    heatmap=cam\n",
+        "    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)\n",
+        "    return heatmap#heatmap.numpy()"
+      ],
+      "metadata": {
+        "id": "bEIAt8rv2nW3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def visualise_attention_per_model(image,mask,prediction,attention_heatmaps,layer_index):\n",
+        "    fig, axs = plt.subplots(3, len(attention_heatmaps[0])+3, figsize=(14,10))\n",
+        "    model_names=['Attention', 'No Attention']\n",
+        "    class_names=['Background','(NCR/NET)','(ED)','(ET)']\n",
+        "    for k in range(0,2):\n",
+        "      heatmap_3clsses=np.zeros(attention_heatmaps[k][0][layer_index].shape[0:2])\n",
+        "      for class_idx in range(len(attention_heatmaps[0])):\n",
+        "         # selected_slice = int(attention_heatmaps[k][class_idx][layer_index].shape[2]/2)\n",
+        "          selected_slice = 80\n",
+        "          # First display the image slice\n",
+        "          axs[k,class_idx].imshow(image[0, :, :, selected_slice, 0], cmap='gray')\n",
+        "          # Then overlay the heatmap, using a suitable alpha\n",
+        "          axs[k,class_idx].imshow(attention_heatmaps[k][class_idx][layer_index][:, :, selected_slice], cmap='hot', alpha=0.5)\n",
+        "          axs[k,class_idx].set_title(f'{class_names[class_idx]}')\n",
+        "          heatmap_3clsses=heatmap_3clsses+attention_heatmaps[k][class_idx][layer_index][:, :, selected_slice]\n",
+        "      axs[k,class_idx+1].imshow(mask[:, :, selected_slice])\n",
+        "      axs[k,class_idx+1].set_title('Ground Truth')\n",
+        "      axs[k,class_idx+2].imshow(prediction[k][:, :, selected_slice])\n",
+        "      axs[k,class_idx+2].set_title('Prediction')\n",
+        "      axs[k,class_idx+3].imshow(heatmap_3clsses)\n",
+        "      axs[k,class_idx+3].set_title(model_names[k])\n",
+        "    plt.show()\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "D-d8DrM13oDv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_heatmap_layers(image,my_model,list_layers,class_idx):\n",
+        "  features_layer=[]\n",
+        "  for i, layer_name in enumerate(list_layers):\n",
+        "      heatmap = make_gradcam_heatmapmine(image, my_model, layer_name, class_idx)\n",
+        "      features_layer.append(heatmap)\n",
+        "  return features_layer\n"
+      ],
+      "metadata": {
+        "id": "KfIyT-dTCvPI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from matplotlib import colors\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def visualise_attention_per_model(image, mask, prediction, attention_heatmaps, layer_index):\n",
+        "    # Change here: 2 rows instead of 3\n",
+        "    fig, axs = plt.subplots(2, len(attention_heatmaps[0])+3, figsize=(14, 10))\n",
+        "\n",
+        "    model_names = ['Attention', 'No Attention']\n",
+        "    class_names = ['Background', '(NCR/NET)', '(ED)', '(ET)']\n",
+        "\n",
+        "    # Custom colormap\n",
+        "    cmap = colors.ListedColormap(['blue', 'green', 'red'])\n",
+        "    norm = colors.BoundaryNorm([0.5, 1.5, 2.5, 4.5], cmap.N)\n",
+        "\n",
+        "    for k in range(2):\n",
+        "        heatmap_3classes = np.zeros(attention_heatmaps[k][0][layer_index].shape[:2])\n",
+        "        for class_idx in range(len(attention_heatmaps[0])):\n",
+        "            selected_slice = 80\n",
+        "            # First display the image slice\n",
+        "            axs[k, class_idx].imshow(image[0, :, :, selected_slice, 0], cmap='gray')\n",
+        "            # Then overlay the heatmap\n",
+        "            axs[k, class_idx].imshow(attention_heatmaps[k][class_idx][layer_index][:, :, selected_slice], cmap='hot', alpha=0.5)\n",
+        "            axs[k, class_idx].set_title(f'{class_names[class_idx]}')\n",
+        "\n",
+        "            heatmap_3classes += attention_heatmaps[k][class_idx][layer_index][:, :, selected_slice]\n",
+        "\n",
+        "        # Adjustments for the indexing of Ground Truth and Prediction plots\n",
+        "        axs[k, len(attention_heatmaps[0])].imshow(image[0, :, :, selected_slice, 0], cmap='gray')\n",
+        "        masked_mask = np.ma.masked_where(mask[:, :, selected_slice] == 0, mask[:, :, selected_slice])\n",
+        "        axs[k, len(attention_heatmaps[0])].imshow(masked_mask, cmap='jet', alpha=0.5)\n",
+        "        axs[k, len(attention_heatmaps[0])].set_title('Ground Truth')\n",
+        "\n",
+        "        axs[k, len(attention_heatmaps[0])+1].imshow(image[0, :, :, selected_slice, 0], cmap='gray')\n",
+        "        masked_prediction = np.ma.masked_where(prediction[k][:, :, selected_slice] == 0, prediction[k][:, :, selected_slice])\n",
+        "        axs[k, len(attention_heatmaps[0])+1].imshow(masked_prediction, cmap='jet', alpha=0.5)\n",
+        "        axs[k, len(attention_heatmaps[0])+1].set_title('Prediction')\n",
+        "\n",
+        "        # Plotting the combined heatmap\n",
+        "        axs[k, len(attention_heatmaps[0])+2].imshow(heatmap_3classes, cmap='hot', alpha=0.5)\n",
+        "        axs[k, len(attention_heatmaps[0])+2].set_title(model_names[k])\n",
+        "\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "D_8_WVSxIFI6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "images_path=\"/content/drive/MyDrive/Bratsdataset/BraTs2020/fold_0/val/\"\n",
+        "val_list=os.listdir(images_path)\n",
+        "image_names=os.listdir(images_path)"
+      ],
+      "metadata": {
+        "id": "bWLDumK33h8E"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras.models import load_model\n",
+        "\n",
+        "my_model = load_model('/content/drive/MyDrive/AttentionDice.hdf5', compile=False)\n",
+        "my_modelno = load_model('/content/drive/MyDrive/NoAttentionDice.hdf5', compile=False)\n",
+        "my_modelwdl = load_model('/content/drive/MyDrive/AttentionWDL.hdf5', compile=False)\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MYjMSU8g3tSn",
+        "outputId": "60a5028c-8b6a-4701-ef5d-5acbbf96bab3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for i, dir_name in enumerate(image_names[:1]):\n",
+        "#173\n",
+        "    image = np.load(images_path +'train173/image_173.npy')#[0]\n",
+        "    mask  = np.load(images_path +'train173/mask_173.npy')#[0]\n",
+        "    print(mask.shape)\n",
+        "    print(np.unique(mask))\n",
+        "    image = np.expand_dims(image,0)#\n",
+        "\n",
+        "    prediction = my_model.predict(image)\n",
+        "    prediction_mask = np.argmax(prediction, axis=-1)[0,:,:,:]\n",
+        "\n",
+        "    layers=['layer_1','upsample_layer_3_conv2','layer_8_concatenate','layer_9','layer_18_maxpool','upsample_layer_1_conv']#,'upsample_layer_2_conv1','upsample_layer_2_dropout','upsample_layer_3_conv1','upsample_layer_2_dropout']\n",
+        "    attention_heatmap_per_class=[]\n",
+        "    for class_idx in range(prediction.shape[-1]):\n",
+        "        H=get_heatmap_layers(image,my_model,layers,class_idx)\n",
+        "        attention_heatmap_per_class.append(H)\n",
+        "\n",
+        "    predictionold = my_modelno.predict(image)\n",
+        "    print('shape', predictionold.shape)\n",
+        "    predictionold_mask = np.argmax(predictionold, axis=-1)[0,:,:,:]\n",
+        "    print('shape', predictionold_mask.shape)\n",
+        "    layers=['layer_1','upsample_layer_3_conv2','layer_8_concatenate','layer_9','layer_18_maxpool','upsample_layer_1_conv']\n",
+        "    noattention_heatmap_per_class=[]\n",
+        "    for class_idx in range(predictionold.shape[-1]):\n",
+        "        H=get_heatmap_layers(image,modelno,layers,class_idx)\n",
+        "        noattention_heatmap_per_class.append(H)\n",
+        "    visualise_attention_per_model(image,mask,[prediction_mask,predictionold_mask],[attention_heatmap_per_class,noattention_heatmap_per_class],1)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 691
+        },
+        "id": "4uZ9OKMAINIc",
+        "outputId": "5dc704f0-c4a3-43f0-f856-6f2d245e7f31"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7ea9bb1a3e20> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1/1 [==============================] - 3s 3s/step\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7ea9bb1cd090> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1/1 [==============================] - 3s 3s/step\n",
+            "shape (1, 128, 128, 128, 4)\n",
+            "shape (128, 128, 128)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x1000 with 14 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for i, dir_name in enumerate(image_names[:1]):\n",
+        "\n",
+        "    image = np.load(images_path +'train356/image_356.npy')#[0]\n",
+        "    mask  = np.load(images_path +'train356/mask_356.npy')#[0]\n",
+        "    print(mask.shape)\n",
+        "    print(np.unique(mask))\n",
+        "    image = np.expand_dims(image,0)#\n",
+        "\n",
+        "    prediction = my_model.predict(image)\n",
+        "    prediction_mask = np.argmax(prediction, axis=-1)[0,:,:,:]\n",
+        "\n",
+        "    layers=['layer_1','upsample_layer_3_conv2','layer_8_concatenate','layer_9','layer_18_maxpool','upsample_layer_1_conv']#,'upsample_layer_2_conv1','upsample_layer_2_dropout','upsample_layer_3_conv1','upsample_layer_2_dropout']\n",
+        "    attention_heatmap_per_class=[]\n",
+        "    for class_idx in range(prediction.shape[-1]):\n",
+        "        H=get_heatmap_layers(image,my_model,layers,class_idx)\n",
+        "        attention_heatmap_per_class.append(H)\n",
+        "\n",
+        "    predictionold = my_modelno.predict(image)\n",
+        "    print('shape', predictionold.shape)\n",
+        "    predictionold_mask = np.argmax(predictionold, axis=-1)[0,:,:,:]\n",
+        "    print('shape', predictionold_mask.shape)\n",
+        "    layers=['layer_1','upsample_layer_3_conv2','layer_8_concatenate','layer_9','layer_18_maxpool','upsample_layer_1_conv']\n",
+        "    noattention_heatmap_per_class=[]\n",
+        "    for class_idx in range(predictionold.shape[-1]):\n",
+        "        H=get_heatmap_layers(image,modelno,layers,class_idx)\n",
+        "        noattention_heatmap_per_class.append(H)\n",
+        "    visualise_attention_per_model(image,mask,[prediction_mask,predictionold_mask],[attention_heatmap_per_class,noattention_heatmap_per_class],1)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 636
+        },
+        "id": "U01yoZpN335M",
+        "outputId": "c56332b8-ca96-4382-e87f-dc70d6279360"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 3s 3s/step\n",
+            "1/1 [==============================] - 3s 3s/step\n",
+            "shape (1, 128, 128, 128, 4)\n",
+            "shape (128, 128, 128)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x1000 with 14 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for i, dir_name in enumerate(image_names[:1]):\n",
+        "\n",
+        "    image = np.load(images_path +'train235/image_235.npy')#[0]\n",
+        "    mask  = np.load(images_path +'train235/mask_235.npy')#[0]\n",
+        "    print(mask.shape)\n",
+        "    print(np.unique(mask))\n",
+        "    image = np.expand_dims(image,0)#\n",
+        "\n",
+        "    prediction = my_model.predict(image)\n",
+        "    prediction_mask = np.argmax(prediction, axis=-1)[0,:,:,:]\n",
+        "\n",
+        "    layers=['layer_1','upsample_layer_3_conv2','layer_8_concatenate','layer_9','layer_18_maxpool','upsample_layer_1_conv']#,'upsample_layer_2_conv1','upsample_layer_2_dropout','upsample_layer_3_conv1','upsample_layer_2_dropout']\n",
+        "    attention_heatmap_per_class=[]\n",
+        "    for class_idx in range(prediction.shape[-1]):\n",
+        "        H=get_heatmap_layers(image,my_model,layers,class_idx)\n",
+        "        attention_heatmap_per_class.append(H)\n",
+        "\n",
+        "    predictionold = my_modelno.predict(image)\n",
+        "    print('shape', predictionold.shape)\n",
+        "    predictionold_mask = np.argmax(predictionold, axis=-1)[0,:,:,:]\n",
+        "    print('shape', predictionold_mask.shape)\n",
+        "    layers=['layer_1','upsample_layer_3_conv2','layer_8_concatenate','layer_9','layer_18_maxpool','upsample_layer_1_conv']\n",
+        "    noattention_heatmap_per_class=[]\n",
+        "    for class_idx in range(predictionold.shape[-1]):\n",
+        "        H=get_heatmap_layers(image,modelno,layers,class_idx)\n",
+        "        noattention_heatmap_per_class.append(H)\n",
+        "    visualise_attention_per_model(image,mask,[prediction_mask,predictionold_mask],[attention_heatmap_per_class,noattention_heatmap_per_class],1)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 636
+        },
+        "id": "E5AJsIRS3-iZ",
+        "outputId": "22780084-1635-4c00-e849-01bbc5edca97"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(128, 128, 128)\n",
+            "[0. 1. 2. 3.]\n",
+            "1/1 [==============================] - 3s 3s/step\n",
+            "1/1 [==============================] - 2s 2s/step\n",
+            "shape (1, 128, 128, 128, 4)\n",
+            "shape (128, 128, 128)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1400x1000 with 14 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import os\n",
+        "import glob\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from keras.models import load_model\n",
+        "\n",
+        "def plot_confusion_matrix(y_true, y_pred):\n",
+        "    y_true = np.reshape(y_true, -1)\n",
+        "    y_pred = np.reshape(y_pred, -1)\n",
+        "    cm = confusion_matrix(y_true, y_pred)\n",
+        "    return cm\n",
+        "\n",
+        "def get_con_matrix_all_sklrn(best_model, images_dir_path, class_names=['Background', '(NCR/NET)', '(ED)', '(ET)']):\n",
+        "    et_class_index = class_names.index('(ET)')\n",
+        "    aggregated_cm = np.zeros([len(class_names), len(class_names)])\n",
+        "    conf_matrices_all = []\n",
+        "    image_names_list = os.listdir(images_dir_path)\n",
+        "\n",
+        "    for dir_name in image_names_list:\n",
+        "        image_files = glob.glob(os.path.join(images_dir_path, dir_name, 'image_*.npy'))\n",
+        "        mask_files = glob.glob(os.path.join(images_dir_path, dir_name, 'mask_*.npy'))\n",
+        "\n",
+        "        if not image_files or not mask_files:\n",
+        "            continue\n",
+        "\n",
+        "        image = np.load(image_files[0])\n",
+        "        mask = np.load(mask_files[0])\n",
+        "        image = np.expand_dims(image, 0)\n",
+        "        prediction = best_model.predict(image)\n",
+        "        prediction_mask = np.argmax(prediction, axis=-1)[0, :, :, :]\n",
+        "\n",
+        "        cm = plot_confusion_matrix(mask, prediction_mask)\n",
+        "\n",
+        "        # Include this matrix only if 'ET' class is present in ground truth or predictions\n",
+        "        if et_class_index in np.unique(mask):\n",
+        "            conf_matrices_all.append(cm)\n",
+        "            aggregated_cm += cm\n",
+        "\n",
+        "    return aggregated_cm, conf_matrices_all\n",
+        "\n",
+        "def calculate_mean_std(matrices):\n",
+        "    normalized_matrices = []\n",
+        "\n",
+        "    for cm in matrices:\n",
+        "        row_sums = cm.sum(axis=1)[:, np.newaxis]\n",
+        "        normalized_cm = cm.astype('float') / (row_sums + 1e-10)\n",
+        "        normalized_matrices.append(normalized_cm)\n",
+        "\n",
+        "    normalized_matrices_array = np.array(normalized_matrices)\n",
+        "    mean_matrix = np.mean(normalized_matrices_array, axis=0)\n",
+        "    std_matrix = np.std(normalized_matrices_array, axis=0)\n",
+        "\n",
+        "    return mean_matrix, std_matrix"
+      ],
+      "metadata": {
+        "id": "9h_mcVVU1ZdZ"
+      },
+      "execution_count": 42,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "images_path = \"/content/drive/MyDrive/Bratsdataset/fold_0/val/\"\n",
+        "cf_matrix_sklrn, all_conf_mat = get_con_matrix_all_sklrn(my_modelwdl, images_path)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zIUVmvlXEGi9",
+        "outputId": "6a0c7fb2-0469-4ed3-ab25-78197be9eade"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n",
+            "WARNING:root:The given value for groups will be overwritten.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1/1 [==============================] - 1s 504ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "1/1 [==============================] - 0s 36ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 31ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 35ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 33ms/step\n",
+            "1/1 [==============================] - 0s 32ms/step\n",
+            "1/1 [==============================] - 0s 34ms/step\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Compute the mean and std from individual normalized matrices\n",
+        "mn, std = calculate_mean_std(all_conf_mat)\n",
+        "\n",
+        "# Normalize the mean and std matrices\n",
+        "row_sums_mean = mn.sum(axis=1)[:, np.newaxis]\n",
+        "mn_normalized = mn.astype('float') / (row_sums_mean + 1e-10)\n",
+        "\n",
+        "row_sums_std = std.sum(axis=1)[:, np.newaxis]\n",
+        "std_normalized = std.astype('float') / (row_sums_std + 1e-10)\n",
+        "\n",
+        "# Plotting\n",
+        "class_names = ['Background', '(NCR/NET)', '(ED)', '(ET)']\n",
+        "fig, ax = plt.subplots(figsize=(len(class_names) * 1.3, len(class_names) * 1.3))\n",
+        "sns.heatmap(mn_normalized, annot=True, fmt=\".2f\", cmap=\"RdBu_r\", xticklabels=class_names, yticklabels=class_names)\n",
+        "plt.ylabel('Actual')\n",
+        "plt.xlabel('Predicted')\n",
+        "plt.show()\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(len(class_names) * 1.3, len(class_names) * 1.3))\n",
+        "sns.heatmap(std_normalized, annot=True, fmt=\".2f\", cmap=\"RdBu_r\", xticklabels=class_names, yticklabels=class_names)\n",
+        "plt.ylabel('Actual')\n",
+        "plt.xlabel('Predicted')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 943
+        },
+        "id": "-Ie6uDVqEGpl",
+        "outputId": "189d8e7f-fa97-4628-fdae-31c93d2d4b0f"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 520x520 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 520x520 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "machine_shape": "hm",
+      "provenance": [],
+      "gpuType": "A100"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.9.13"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "4e6b91564c519e785cb8f19922fce277bd8a3f0cc51b281d20f180a6b8d1a3df"
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/LATUP-Net/preprocess.py b/LATUP-Net/preprocess.py
new file mode 100644
index 0000000..6a7a6f6
--- /dev/null
+++ b/LATUP-Net/preprocess.py
@@ -0,0 +1,120 @@
+#get data ready
+#1-Combine 
+#2-Changing mask pixel values (labels) from 4 to 3 (as the original labels are 0, 1, 2, 4)
+#3-Visualize
+
+import numpy as np
+import nibabel as nib
+import os
+import glob
+from sklearn.preprocessing import MinMaxScaler
+
+# Set the base directory (you can modify this to point to your dataset's location)
+base_directory = os.getcwd()
+
+# Define the dataset path dynamically
+TRAIN_DATASET_PATH = os.path.join(base_directory, 'Results', 'data2020')
+
+# Initialize a MinMaxScaler for normalization
+scaler = MinMaxScaler()
+
+# Use glob to find files using relative paths
+t2_list = sorted(glob.glob(os.path.join(TRAIN_DATASET_PATH, '*/*t2.nii')))
+t1ce_list = sorted(glob.glob(os.path.join(TRAIN_DATASET_PATH, '*/*t1ce.nii')))
+flair_list = sorted(glob.glob(os.path.join(TRAIN_DATASET_PATH, '*/*flair.nii')))
+mask_list = sorted(glob.glob(os.path.join(TRAIN_DATASET_PATH, '*/*seg.nii')))
+
+# Process each image in the dataset
+for img in range(len(t2_list)):
+    print("Now preparing image and masks number: ", img)
+
+    # Load and scale T2, T1ce, Flair images
+    temp_image_t2 = nib.load(t2_list[img]).get_fdata()
+    temp_image_t2 = scaler.fit_transform(temp_image_t2.reshape(-1, temp_image_t2.shape[-1])).reshape(temp_image_t2.shape)
+
+    temp_image_t1ce = nib.load(t1ce_list[img]).get_fdata()
+    temp_image_t1ce = scaler.fit_transform(temp_image_t1ce.reshape(-1, temp_image_t1ce.shape[-1])).reshape(temp_image_t1ce.shape)
+
+    temp_image_flair = nib.load(flair_list[img]).get_fdata()
+    temp_image_flair = scaler.fit_transform(temp_image_flair.reshape(-1, temp_image_flair.shape[-1])).reshape(temp_image_flair.shape)
+
+    # Load the mask and change pixel values from 4 to 3
+    temp_mask = nib.load(mask_list[img]).get_fdata()
+    temp_mask[temp_mask == 4] = 3
+
+    # Combine the processed images into a single array
+    temp_combined_images = np.stack([temp_image_flair, temp_image_t1ce, temp_image_t2], axis=3)
+
+    # Crop the images and masks to (128, 128, 128)
+    temp_combined_images = temp_combined_images[56:184, 56:184, 13:141]
+    temp_mask = temp_mask[56:184, 56:184, 13:141]
+
+    # Check if the mask contains enough non-background information
+    val, counts = np.unique(temp_mask, return_counts=True)
+    if (1 - (counts[0] / counts.sum())) > 0.01:  # Check if non-background is more than 1%
+        print("Save Me")
+
+        # Create directories and save the processed images and masks
+        train_dir = os.path.join(TRAIN_DATASET_PATH, f'train{img}')
+        os.makedirs(train_dir, exist_ok=True)
+        np.save(os.path.join(train_dir, f'image_{img}.npy'), temp_combined_images)
+        np.save(os.path.join(train_dir, f'mask_{img}.npy'), temp_mask)
+    else:
+        print("I am useless")
+
+#  KFold split as saved pickle file for data split 
+
+import os
+import pickle
+from sklearn.model_selection import KFold
+
+# Set the base directory (you can modify this to point to your dataset's location)
+base_directory = os.getcwd()
+
+# Define the dataset path dynamically
+DATA_PATH = os.path.join(base_directory,'Results','data2020')  # Modify  as needed
+
+# Initialize KFold
+kf = KFold(n_splits=5, random_state=1, shuffle=True)
+
+# Get the list of images from the dynamic path
+train_img_list = os.listdir(DATA_PATH)
+
+# Perform KFold splitting
+Kfolds = kf.split(train_img_list, train_img_list)
+train_5fold = []
+valid_5fold = []
+nb_fold = 0
+
+for train_idx, val_idx in Kfolds:
+    print('Training for fold ' + str(nb_fold) + ' started...')
+
+    train_img_fold = [train_img_list[k] for k in list(train_idx)]
+    valid_img_fold = [train_img_list[k] for k in list(val_idx)]
+
+    # Save the lists to files
+    with open('valid_list' + str(nb_fold) + '.pkl', "wb") as list_valid:
+        pickle.dump(valid_img_fold, list_valid)
+
+    with open('train_list' + str(nb_fold) + '.pkl', "wb") as list_train:
+        pickle.dump(train_img_fold, list_train)
+
+    print(len(train_img_fold), len(valid_img_fold))
+    train_5fold.append(train_img_fold)
+    valid_5fold.append(valid_img_fold)
+    nb_fold += 1
+
+# Save the 5-fold data dictionary
+five_fold_dic = dict(train=train_5fold, validation=valid_5fold)
+with open('folds_dic.pkl', "wb") as list_train:
+    pickle.dump(five_fold_dic, list_train)
+
+# Load the split
+with open('folds_dic.pkl', 'rb') as m:
+    folds_dict = pickle.load(m)
+
+valid_img_fold = folds_dict['train'][0]
+train_img_fold = folds_dict['validation'][0]
+
+print(valid_img_fold)
+print(train_img_fold)
diff --git a/LATUP-Net/requirements.txt b/LATUP-Net/requirements.txt
new file mode 100644
index 0000000..a00b724
--- /dev/null
+++ b/LATUP-Net/requirements.txt
@@ -0,0 +1,19 @@
+# requirements.txt
+#
+# SPDX-FileCopyrightText: Copyright (C) 2022 Frank C Langbein <frank@langbein.org>, Cardiff University
+# SPDX-License-Identifier: AGPL-3.0-or-later
+numpy>=1.23
+nibabel>=5.0.1
+tensorflow>=2.10
+tensorflow-addons>=0.20
+matplotlib>=3.5
+scikit-learn>=1.1
+ipywidgets>=7.7.1
+ipympl>=0.9.2
+screeninfo>=0.8
+segmentation-models-3D>=1.0.4
+pydot>=1.4.2
+pandas>=1.3.5
+seaborn>=0.12.2
+SimpleITK>=2.2.1
+medpy>=0.2
\ No newline at end of file
diff --git a/LICENSES/AGPL-3.0-or-later.txt b/LICENSES/AGPL-3.0-or-later.txt
new file mode 100644
index 0000000..0c97efd
--- /dev/null
+++ b/LICENSES/AGPL-3.0-or-later.txt
@@ -0,0 +1,235 @@
+GNU AFFERO GENERAL PUBLIC LICENSE
+Version 3, 19 November 2007
+
+Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+
+Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.
+
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+A patent license is "discriminatory" if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License.  You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007.
+
+Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law.
+
+12. No Surrender of Others' Freedom.
+
+If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License.  If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may
+not convey it at all.  For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program.
+
+13. Remote Network Interaction; Use with the GNU General Public License.
+
+Notwithstanding any other provision of this License, if you modify the Program, your modified version must prominently offer all users interacting with it remotely through a computer network (if your version supports such interaction) an opportunity to receive the Corresponding Source of your version by providing access to the Corresponding Source from a network server at no charge, through some standard or customary means of facilitating copying of software.  This Corresponding Source shall include the Corresponding Source for any work covered by version 3 of the GNU General Public License that is incorporated pursuant to the following paragraph.
+
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+
+14. Revised Versions of this License.
+
+The Free Software Foundation may publish revised and/or new versions of the GNU Affero General Public License from time to time.  Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns.
+
+Each version is given a distinguishing version number.  If the Program specifies that a certain numbered version of the GNU Affero General Public License "or any later version" applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation.  If the Program does not specify a version number of the GNU Affero General Public License, you may choose any version ever published by the Free Software Foundation.
+
+If the Program specifies that a proxy can decide which future versions of the GNU Affero General Public License can be used, that proxy's public statement of acceptance of a version permanently authorizes you to choose that version for the Program.
+
+Later license versions may give you additional or different permissions.  However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version.
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+15. Disclaimer of Warranty.
+
+THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+16. Limitation of Liability.
+
+IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
+
+17. Interpretation of Sections 15 and 16.
+
+If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee.
+
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+
+            How to Apply These Terms to Your New Programs
+
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+
+To do so, attach the following notices to the program.  It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found.
+
+     <one line to give the program's name and a brief idea of what it does.>
+     Copyright (C) <year>  <name of author>
+
+     This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
+
+     This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU Affero General Public License for more details.
+
+     You should have received a copy of the GNU Affero General Public License along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+If your software can interact with users remotely through a computer network, you should also make sure that it provides a way for users to get its source.  For example, if your program is a web application, its interface could display a "Source" link that leads users to an archive of the code.  There are many ways you could offer source, and different solutions will be better for different programs; see section 13 for the specific requirements.
+
+You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU AGPL, see <http://www.gnu.org/licenses/>.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..336d94f
--- /dev/null
+++ b/README.md
@@ -0,0 +1,36 @@
+# BCa - Brain Cancer Segmentation Python Module
+
+> SPDX-FileCopyrightText: Copyright (C) 20222023 Ebtihal Alwadee <AlwadeeEJ@cardiff.ac.uk>, PhD student at Cardiff University\
+> SPDX-FileCopyrightText: Copyright (C) 2022-2023 Frank C Langbein <frank@langbein.org>, Cardiff University
+>
+> SPDX-License-Identifier: AGPL-3.0-or-later
+
+This contains python code for brain cancer segmentation with deep learning, including a range of network
+architectures.
+
+The architectures consist of basic 3D UNets and the 3D LATUP-Net segmentation model. The LATUP-Net code
+is a prototype, separate in the LATUP-Net folder for now.
+
+Results of using these modules are available at https://qyber.black/ca/bca/results-bca-unet.
+
+## Installation
+
+Clone it with `git clone URL` where URL is the clone URL for the repository you wish to clone. The development
+repo is at https://qyber.black/ca/bca/code-bca, but you may find it at mirrors as well.
+
+The `requirements.txt` file contains the dependencies, to be installed with `pip3 install -r requirements.txt`.
+
+## API Documentation
+
+The API documentation is accessible at https://ca.qyber.dev/bca/code-bca.
+
+The folder `bca` contains the python module for the actual functionalities. It has been documented using pdoc. Run
+```pdoc -h localhost -p 8888 -n bca```
+in the project's root folder and then view the documentation with your browser via the URL
+```http://localhost:8888```
+Of course you can use pdoc to generate the documentation differently (see the pdoc documentation at https://pdoc.dev/).
+
+## Citation
+
+E Alwadee, FC Langbein. **BCa - Brain Cancer Segmentation using Deep Learning**. V0.1. Software. 2023. 
+https://qyber.black/ca/bca/code-bca
diff --git a/bca/__init__.py b/bca/__init__.py
new file mode 100644
index 0000000..dd906bc
--- /dev/null
+++ b/bca/__init__.py
@@ -0,0 +1,34 @@
+# bca/__init__.py - BCa module
+#
+# SPDX-FileCopyrightText: Copyright (C) 2022-2023 Frank C Langbein <frank@langbein.org>, Cardiff University
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+"""
+# BCa - Brain Cancer Segmentation with Deep Learning
+
+This module provides functions and classes in the following sub-modules to handle 
+brain cancer datasets and train deep tensorflow models.
+
+## General Modules
+
+Modules with general functionality for datasets and models:
+  * **bca.dataset**: Represent brain cancer datasets on disk and create keras sequences for deep learning.
+  * **bca.trainer**: Train models using the dataset sequences with tensorflow.
+  * **bca.model**: Base class for model generator classes (used by models below)
+  * **bca.scheduler**: Scheduler to train models remotely from jupyter notebooks or from the command line
+    (mostly limited to Linux as `rsync` and `ssh` are needed). Tasks for the scheduler are created by trainer.
+
+## Models
+
+The following deep learning models are available to use:
+
+  * **bca.unet**: 3D Unet architecture.
+
+## Misc
+
+Other sub-modules are:
+
+  * **bca.cfg**: Provides a configuration class to hold the BCa configuration. This is mostly for internal use.
+"""
+
+__all__ = ["dataset", "model", "trainer", "scheduler", "unet", "cfg"]
\ No newline at end of file
diff --git a/bca/cfg.py b/bca/cfg.py
new file mode 100644
index 0000000..18462d0
--- /dev/null
+++ b/bca/cfg.py
@@ -0,0 +1,212 @@
+# bca/cfg.py - Configuration class
+#
+# SPDX-FileCopyrightText: Copyright (C) 2022-2023 Frank C Langbein <frank@langbein.org>, Cardiff University
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+import os
+import matplotlib.pyplot as plt
+import json
+
+class Cfg:
+  """Class holding BCa's configuration options.
+
+  This class holds and generated BCa's configuration options, via json files and environment variables.
+  Any new configuration options will be added to the json files, while unused ones are removed, when
+  it's called. To use it in a file, import it as
+
+  ```from bca.cfg import Cfg```
+
+  This will setup the configuration and it can be accessed via `Cfg`. Note that all values are in the class,
+  (static), not in any object.
+
+  It uses the json files `cfg.json` in the bca root directory and `~/.config/bca.json`; `cfg.json` overwrites
+  `~/.config/bca.json` and default configuration from the sources are only used if they are not in the json file
+  (and then they are added to it).
+
+  For the configuration options in the json values and their defaults see `Cfg.val` in the sources.
+
+  In addition the environment variable `BCA_DEV` can be used to set specific behaviour of the code, often only
+  useful for development. See `Cfg.dev` in the sources for what they are.
+  """
+
+  def __init__(self):
+    """Constructor - do not call.
+    
+    The constructor of the `Cfg` class should never be called as it hold all values in the class. Use the
+    static methods of the class only.
+    """
+    raise Exception("Tried to create a Cfg objects; values should only be held in the class")
+
+  # Log levels
+  Error = 1
+  Warn = 2
+  Info = 3
+
+  @staticmethod
+  def log(level):
+    """Return `True` if the configured verbose level is at least `level`
+
+    Args:
+      * `level`: Minimum log-level (`Cfg.Error`, `Cfg.Warn`, or `Cfg.Info`)
+
+    Return:
+      * `True` if log-level indicates message should be printed; `False` otherwise.
+
+    Example:
+      ```python
+      if Cfg.log(Cfg.Warn):
+        print("Warning: something went wrong")
+      ```
+    """
+    return level <= Cfg.val["log"]
+
+  # Default configuration - do not overwrite here but set alternatives in file
+  # These are static variables for the class, accessed via the class. No object
+  # of this class should be used; all methods are static.
+  #
+  # Change these values in ROOT_PATH/cfg.json (generated after first run; overwrites
+  # defaults here) or ~/.config/bca.json (not generated; overwrites cfg.json and
+  # defaults here). Do not edit defaults here; will not work once cfg file is generated.
+  val = {
+    'path_root': None,
+    'parallel': 2,              # Number of parallel threads for some tasks (via joblib)
+    'multiprocessing': True,    # Use multiprocessing (way not work on windows, so disable here)
+    'low_mem_percentage': 90,   # Percentage of memory indicating low CPU memory
+    'brain_cmap': 'gray',       # Default color map for brain images
+    'gt_cmap': 'gray',          # Ground truth colormap for output masks
+    'pr_cmap': 'viridis',       # Prediction colormap for output masks
+    'col_label0': [0,0,0,1],    # Label colors for masks
+    'col_label1': [1,0,0,1],
+    'col_label2': [0,1,0,1],
+    'col_label3': [1,0,1,1],
+    'col_label4': [0,0,1,1],
+    'figsize': (5.0,5.0),       # Sub-figure size
+    'default_screen_dpi': 96,   # Image resolution for display (default, used if estimation fails)
+    'screen_dpi': None,
+    'image_dpi': [300],         # Image resolution for saving 
+    'log': 1,                   # Level of log messages printed (Cfg.{Error,Warn,Info} = 1,2,3)
+    'tf_log': 'ERROR',          # Tensorflow logging level
+    'executors': {              # List of executors for scheduler (configure in cfg.json)
+      #'scw': {                   # Example slurm cluster
+      #  'type': 'slurm',
+      #  'max_tasks': 10,
+      #  'host': 'hawklogin.cf.ac.uk',
+      #  'user': 'USERNAME',
+      #  'account': 'ACCOUNT',
+      #  'remote_folder': 'code-bca',
+      #  'partitions': "gpu_v100,gpu",
+      #  'nodes': 1,
+      #  'ntasks': 1,
+      #  'ntasks_per_node': 1,
+      #  'cpus_per_task': 4,
+      #  'mem': '96G',
+      #  'gres': 'gpu:2',
+      #  'time': '2-00:00:00',
+      #  'modules': [ "system/auto", "hpcw", "python/3.10.4", "load CUDA/11.5"]
+      #},
+      #'localhost': {             # Example host node (don't use localhost; see local type)
+      #  'type': 'host',
+      #  'host': 'localhost',
+      #  'user': 'USERNAME',
+      #  'remote_folder': 'exec-bca',
+      #},
+      'local': {                # Local node (made this the default, but probably needs editing)
+        'type': 'local'
+      }
+    },
+    "disabled_executors": []  # Short-cut to disable executors if they should not be used
+  }
+  # Development flags for extra functionalities and test (not relevant for use).
+  # These are set via the environment variable BCA_DEV (colon separated list),
+  # but all in use should be in the comments here for reference:
+  #  FIXED_BATCH_SIZE: used in dataset.py by SeqGen to fix the batch_size (cuts off "modulo" samples)
+  dev_flags = set()
+  file = os.path.expanduser(os.path.join('~','.config','bca.json'))
+
+  @staticmethod
+  def init(bin_path):
+    """Initialize the configuration values as static values of the class.
+
+    This is used mostly internally to initialise the class (will be called upon import).
+
+    Args:
+      * `bin_path`: path to the foler containing the `cfg.py` file
+    """
+    # Root path of bca
+    Cfg.val["path_root"] = os.path.dirname(bin_path)
+    # Load cfg file - data folders and other Cfg values can be overwritten by config file
+    # We first load ROOT/cfg.json, if it exists, then the user config file
+    root_cfg_file = os.path.join(Cfg.val["path_root"],'cfg.json')
+    root_cfg_vals = {}
+    for fc in [root_cfg_file, Cfg.file]:
+      if os.path.isfile(fc):
+        with open(fc, "r") as fp:
+          js = json.load(fp)
+          if fc == root_cfg_file:
+            root_cfg_vals = js
+          for k in js.keys():
+            if k in Cfg.val:
+              Cfg.val[k] = js[k]
+            else:
+              if fc != root_cfg_file: # We fix this here later
+                raise Exception(f"Unknown config file entry {k} in {fc}")
+    # Setup plot defaults
+    if Cfg.val["screen_dpi"] == None:
+      Cfg.val["screen_dpi"] = Cfg._screen_dpi()
+    plt.rcParams["figure.figsize"] = Cfg.val['figsize']
+    # Store configs in ROOT/cfg.json if it does not exist
+    changed = False
+    del_keys = []
+    for k in root_cfg_vals.keys(): # Do not store paths and remove old values
+      if k[0:5] == 'path_' or k not in Cfg.val:
+        del_keys.append(k)
+        changed = True
+    for k in del_keys:
+      del root_cfg_vals[k]
+    for k in Cfg.val: # Add any new values (except paths)
+      if k[0:5] != 'path_' and k not in root_cfg_vals:
+        root_cfg_vals[k] = Cfg.val[k]
+        changed = True
+    if changed:
+      with open(root_cfg_file, "w") as fp:
+        print(json.dumps(root_cfg_vals, indent=2, sort_keys=True), file=fp)
+    # Dev flags
+    if 'BCA_DEV' in os.environ:
+      for f in os.environ['BCA_DEV'].split(":"):
+        Cfg.dev_flags.add(f)
+
+  @staticmethod
+  def dev(flag):
+    """Check if the developmnet flag `flag` has been set.
+
+    See the sources for available flags. If you add any, make sure to add them to the sources!
+
+    Args:
+      * `flag`: string containing the development flag.
+    """
+    # Development flags for custom code behaviour; set via BCA_DEV environment variable
+    return flag in Cfg.dev_flags
+
+  @staticmethod
+  def _screen_dpi():
+    # DPI for plots on screen
+    try:
+      from screeninfo import get_monitors
+    except ModuleNotFoundError:
+      return Cfg.val['default_screen_dpi']
+    try:
+      m = get_monitors()[0]
+    except:
+      return Cfg.val['default_screen_dpi']
+    from math import hypot
+    try:
+      dpi = hypot(m.width, m.height) / hypot(m.width_mm, m.height_mm) * 25.4
+      return dpi # set in cfg.json if this is not working
+    except:
+      return Cfg.val['default_screen_dpi']
+
+# TF log-level default
+if 'TF_CPP_MIN_LOG_LEVEL' not in os.environ:
+  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '4'
+# Find base folder
+Cfg.init(os.path.dirname(os.path.realpath(__file__)))
\ No newline at end of file
diff --git a/bca/dataset.py b/bca/dataset.py
new file mode 100644
index 0000000..ce271c8
--- /dev/null
+++ b/bca/dataset.py
@@ -0,0 +1,737 @@
+# bca/dataset.py - Brain cancer dataset
+#
+# SPDX-FileCopyrightText: Copyright (C) 2022 Frank C Langbein <frank@langbein.org>, Cardiff University
+# SPDX-FileCopyrightText: Copyright (C) 2022-2023 Ebtihal Alwadee <AlwadeeEJ@cardiff.ac.uk>, PhD student at Cardiff University
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+from .cfg import Cfg
+
+import os
+import sys
+import psutil
+import joblib
+import csv
+import random
+import numpy as np
+import nibabel as nib
+import nibabel.processing as nibp
+
+from tensorflow.keras.utils import Sequence
+
+class Dataset:
+  """Represents original dataset to genreate training sequences from, with preprocessing.
+
+  This is an iterator of the dataset and also implements `len` and `[idx]` operators and
+  can be printed and converted to a string.
+  """
+
+  def __init__(self, folder, cache, seg_mask="seg"):
+    """Create a dataset from folder following BraTS structure.
+
+    Args:
+      * `folder`: Folder containing original dataset;
+      * `cache`: Cache folder for pre-processed data;
+      * `seg_mask`: Name of segmentation mask channel.
+    """
+    self.folder = folder
+    self.cache = cache
+    self.seg_mask = seg_mask
+
+    # Find all patient folders and channels available for all of them
+    if Cfg.log(Cfg.Info):
+      print(f"# Initialising dataset {self.folder}")
+    self.patients = []
+    self.channels = []
+    for f in sorted(os.listdir(self.folder)):
+      p_fldr = os.path.join(self.folder,f)
+      p_chs = []
+      if os.path.isdir(p_fldr) and f[0] != '.':
+        try:
+          self.patients.append(f)
+          id = int(f.split("_")[-1])
+          for ff in os.listdir(p_fldr):
+            if ff[-4:] == ".nii" or ff[-7:] == ".nii.gz":
+              p_chs.append(ff.split(".")[0].split("_")[-1].lower())
+          if len(self.channels) == 0:
+            self.channels = p_chs
+          else:
+            for c in self.channels:
+              if c not in p_chs:
+                self.channels.remove(c)
+        except:
+          pass
+    self.channels.sort()
+    if Cfg.log(Cfg.Info):
+      print(f"  Patients: {len(self)}")
+      print(f"  Channels: "+", ".join(self.channels))
+
+    # Crop
+    self.crops = None
+    self.crops_type = "orig"
+
+  def filter_low_labels(self, c, min_label_per):
+    """Remove samples with small number of labels in channel `c` from dataset.
+
+    Args:
+      * `c`: Channel name used to make decision on (must be a segmentation mask);
+      * `min_label_per`: minimum percentage of data in sample.
+    """
+    remove_idx = []
+    for k in reversed(range(0,len(self))): # Reversed to delete from end to start, so indices remain valid
+      fn = os.path.join(self.folder, self.patients[k], self.patients[k]+'_'+c+'.nii')
+      if not os.path.isfile(fn):
+        fn += ".gz"
+      data = nib.load(fn).get_fdata()
+      labels, label_counts = np.unique(data, return_counts=True)
+      labels = [int(l) for l in labels]
+      cnt_a = np.sum(label_counts)
+      cnt_l = np.sum([label_counts[l] for l in range(0,len(labels)) if labels[l] != 0])
+      if  cnt_l/cnt_a < min_label_per:
+        remove_idx.append(k)
+    # Delete indices
+    for l in remove_idx:
+      del self.patients[l]
+      if self.crops_type == "bb":
+        del self.crops[l]
+
+  def __repr__(self):
+    return f"bca.dataset.Dataset({self.folder})"
+
+  def __str__(self):
+    str = f"# Dataset: {self.folder} [{len(self)} patients]\n" + \
+          f"Cache: {self.cache}\n" + \
+          f"Channels (patient {self.patients[0]}):\n"
+    data = self[0]
+    for c in self.channels:
+      str += f"  {c}: {data[c].shape} ({data[c].get_data_dtype()})\n"
+    if self.crops_type != "orig":
+      if self.crops_type == "f":
+        str += f"Crop: {self.crops}\n"
+      elif self.crops_type == "bb":
+        str += "Crop: bounding-box\n"
+      else:
+        str += f"Crop: UNKNOWN {self.crops_type}\n"
+    return str
+
+  def __iter__(self):
+    # Iterator via patients dictionary: init
+    self._iter = iter(self.patients)
+    return self
+
+  def __next__(self):
+    # Iterator via patients dictionary: next
+    return next(self._iter)
+
+  def __len__(self):
+    # Number of patients for len(.)
+    return len(self.patients)
+
+  def __getitem__(self, idx):
+    # Load nii common-modalities from patient array index
+    data = {}
+    for c in self.channels:
+      fn = os.path.join(self.folder, self.patients[idx], self.patients[idx]+'_'+c+'.nii')
+      if not os.path.isfile(fn):
+        fn += ".gz"
+      data[c] = nib.load(fn)
+    return data
+
+  def patient_name(self,idx):
+    """Map patient index to (folder) name.
+
+    Args:
+      * `idx`: patient/sample index in dataset.
+    
+    Return:
+      * Patient folder name.
+    """
+    return self.patients[idx]
+
+  def patient_idx(self,pid):
+    """Find index for patient pid.
+
+    Args:
+      * `pid`: patient/sample name.
+
+    Return:
+      * Patient index in dataset.
+    """
+    return self.patients.index(pid)
+
+  def browse(self):
+    """Interactive widget to browse data in notebooks.
+    """
+    from IPython.display import display, clear_output
+    import matplotlib.pyplot as plt
+    from matplotlib.colors import ListedColormap, BoundaryNorm
+    import matplotlib.patches as patches
+    from ipywidgets import interact
+    # Custom color map for BraTS labels
+    seg_cmap = ListedColormap([Cfg.val["col_label0"],
+                               Cfg.val["col_label1"],
+                               Cfg.val["col_label2"],
+                               Cfg.val["col_label3"],
+                               Cfg.val["col_label4"]])
+    seg_norm = BoundaryNorm([-.5,.5,1.5,2.5,3.5,4.5], seg_cmap.N)
+    cid = 1
+    data = self[cid-1]
+    def view(idx, slice, overlay):
+      # Display set of slices for patient id
+      nonlocal cid, data
+      if cid != idx:
+        # Caching data
+        cid = idx
+        data = self[cid-1]
+      fig, ax = plt.subplots(1,len(self.channels),sharex=True,sharey=True,dpi=Cfg.val["screen_dpi"],figsize=(Cfg.val["figsize"][0]*len(self.channels),Cfg.val["figsize"][1]))
+      for k, c in enumerate(self.channels):
+        stack = data[c].get_fdata()
+        if c == self.seg_mask:
+          ax[k].imshow(stack[:,:,slice-1], cmap=seg_cmap, norm=seg_norm, interpolation='nearest')
+        else:
+          ax[k].imshow(stack[:,:,slice-1], cmap=Cfg.val["brain_cmap"], interpolation='nearest')
+        if overlay == self.seg_mask and c != self.seg_mask:
+          # Overlay segmentation
+          stack = data[self.seg_mask].get_fdata()
+          ax[k].imshow(stack[:,:,slice-1], cmap=seg_cmap, norm=seg_norm, interpolation='nearest', alpha=0.5)
+        if c == self.seg_mask: 
+          # Labels for segmentation mask
+          vals = [int(v) for v in np.unique(stack[:,:,slice-1]) if v > 0.0]
+          if len(vals) > 0:
+            pats = [patches.Patch(color=seg_cmap(v), label=str(v)) for v in vals]
+            ax[k].legend(handles=pats, loc=0, borderaxespad=0.1)
+        if self.crops is not None:
+          # Indicate crop
+          if self.crops_type == "f":
+            if slice-1 >= self.crops[2][0] and slice-1 <= self.crops[2][1]:
+              rect = patches.Rectangle((self.crops[0][0]-1,self.crops[1][0]-1),
+                                       self.crops[0][1] - self.crops[0][0]+1,
+                                       self.crops[1][1] - self.crops[1][0]+1, 
+                                       linewidth=1, edgecolor='r', facecolor='none')
+              ax[k].add_patch(rect)
+          elif self.crops_type == "bb":
+            if slice-1 >= self.crops[idx-1][2][0] and slice-1 <= self.crops[idx-1][2][1]:
+              rect = patches.Rectangle((self.crops[idx-1][0][0]-1,self.crops[idx-1][1][0]-1),
+                                       self.crops[idx-1][0][1] - self.crops[idx-1][0][0]+1,
+                                       self.crops[idx-1][1][1] - self.crops[idx-1][1][0]+1, 
+                                       linewidth=1, edgecolor='r', facecolor='none')
+              ax[k].add_patch(rect)
+          else:
+            raise Exception(f"Illegal crops {self.crops_type}")
+        ax[k].set_title(self.patients[idx-1]+"-"+c)
+      plt.tight_layout()
+      plt.show()
+    # Start interactive widget
+    interact(view, idx=(1,len(self)), slice=(1,data[self.channels[0]].shape[-1]), overlay=["None", self.seg_mask])
+
+  def crop(self, xr, yr, zr):
+    """Set single crop region.
+
+    Args:
+      * `xr`, `yr`, `zr`: tuples of index ranges indicating crop.
+    """
+    self.crops_type = "f"
+    self.crops = [xr,yr,zr]
+
+  def crop_to_bb(self):
+    """Crop to bounding box per patient across all channels.
+
+    Computes a custom crop based on the empty region across all channels per sample/patient.
+    """
+    # Load from cache, if valid
+    self.crops_type = "bb"
+    self.crops = [None] * len(self)
+    cache_data = []
+    cache_updated = False
+    cache = os.path.join(self.cache, "crop_bb.csv")
+    if os.path.isfile(cache):
+      with open(cache, "r") as f:
+        rows = csv.reader(f)
+        for row in rows:
+          try:
+            idx = self.patients.index(row[0])
+            self.crops[idx] = [[int(row[1]),int(row[2])],[int(row[3]),int(row[4])],[int(row[5]),int(row[6])]]
+          except ValueError:
+            pass
+          cache_data.append(row)
+    # Find missing bounding boxes
+    indices = [k for k in range(0,len(self)) if self.crops[k] is None]
+    if len(indices) > 0:
+      new_crops = joblib.Parallel(n_jobs=Cfg.val['parallel'], prefer="threads")(joblib.delayed(Dataset._bb_stack)(self[k]) for k in indices)
+      for k in range(0,len(indices)):
+        self.crops[indices[k]] = new_crops[k]
+        cache_data.append([self.patient_name(indices[k]), *np.array(new_crops[k]).flatten()])
+        cache_updated = True
+    # Cache results
+    if cache_updated:
+      os.makedirs(self.cache, exist_ok=True)
+      if os.path.exists(cache):
+        os.remove(cache)
+      cache_data.sort(key=lambda x : x[0])
+      with open(cache, "w") as f:
+        out = csv.writer(f)
+        for k in range(0,len(cache_data)):
+          out.writerow(cache_data[k])
+
+  @staticmethod
+  def _bb_stack(data):
+    # Find bouding box of 3D stack with index k
+    indices = np.where(np.sum([np.abs(data[c].get_fdata()) for c in data], axis=0) > 0)
+    return  [[np.min(indices[1]),np.max(indices[1])], # Note, row/column vs width/height!
+             [np.min(indices[0]),np.max(indices[0])],
+             [np.min(indices[2]),np.max(indices[2])]]
+
+  def sequences(self, k, dim, inp, out, batch_size, pre_proc=None, seed=None, fixed_batch_size=False):
+    """Create sequence generators for train,test pairs of all sets.
+
+    Args:
+      * `k`: split indicator:
+        * `]0,1[`: percentage of data in training set;
+        * `1`: all data goes to training set;
+        * `>1`: k-fold cross-validation split
+      * `dim`: Spatial dimension of data to rescale it to;
+      * `inp`: List or list of lists of input channel names (for single or multiple inputs);
+      * `out`: List or list of lists of output channel names (for single or multiple outputs);
+        * For both a special notation can be used for the segmentation mask:
+          * "seg+1+2+3" combines labels 1, 2, 3 to a single 0,1 mask;
+          * "seg=1=2" keeps labels 1,2 as is in the channel and removes any other labels;
+      * `batch_size`: Batch size for processing;
+      * `pre_proc`: Function to use for pre-processing data (see `norm_minmax` and `norm_histeq_mask` below),
+        `None` for no pre-processing;
+      * `seed`: Random number generator seed for split;
+      * `fixed_batch_size`: Boolean indicating whether sequence generators should produce a fixed sized batches.
+    """
+    # Init seed/rng for split reproducibility
+    if seed is None:
+      seed = random.randrange(sys.maxsize)
+
+    # Split
+    if k >= 1:
+      k = int(k)
+    self._split(k, seed)
+
+    # Cache name
+    if self.crops_type == "f":
+      data_cache = "f"+"x".join([str(c[0])+"_"+str(c[1]) for c in self.crops])
+    elif self.crops_type == "bb" or self.crops_type == "orig":
+      data_cache = self.crops_type
+    else:
+      raise Exception(f"Illegal crops {self.crops_type}")
+    data_cache = os.path.join(self.cache, data_cache+"-"+"_".join([str(d) for d in dim])+"-"+("none" if pre_proc is None  else pre_proc.__name__))
+    os.makedirs(data_cache, exist_ok=True)
+
+    # Create samples in cache
+    joblib.Parallel(n_jobs=Cfg.val['parallel'], prefer="threads")(joblib.delayed(self._create_sample)(p, dim, pre_proc, data_cache, self.seg_mask) for p in range(0,len(self)))
+
+    # Memory cache across sequences
+    cache = Cache(data_cache, self.channels, dim, inp, out, self.seg_mask)
+
+    # Create train/test pair sequences
+    if k == 1:
+      # Sequence for single set, no train/test split
+      ns = [self.patients[m] for m in range(0,len(self))]
+      seqs = [(SeqGen(ns, cache, dim, batch_size, k=1, k_n=0, seed=0, shuffle=True, fixed_batch_size=fixed_batch_size), None)]
+    elif k <= 1:
+      # Train set is labelled 0, as only one split
+      ns_train = [self.patients[m] for m in range(0,len(self)) if self.set[m] == 0]
+      ns_test = [self.patients[m] for m in range(0,len(self)) if self.set[m] != 0]
+      seqs = [(SeqGen(ns_train, cache, dim, batch_size, k=k, k_n=0, seed=seed, shuffle=True, fixed_batch_size=fixed_batch_size),
+               SeqGen(ns_test,  cache, dim, batch_size, k=k, k_n=0, seed=seed, shuffle=False, fixed_batch_size=fixed_batch_size))]
+    else:
+      seqs = []
+      # Train set is labelled != l, as multiple folds
+      for l in range(0,k):
+        # Sequence for fold l
+        ns_train = [self.patients[m] for m in range(0,len(self)) if self.set[m] != l]
+        ns_test = [self.patients[m] for m in range(0,len(self)) if self.set[m] == l]
+        seqs.append((SeqGen(ns_train, cache, dim, batch_size, k=k, k_n=l, seed=seed, shuffle=True, fixed_batch_size=fixed_batch_size),
+                     SeqGen(ns_test,  cache, dim, batch_size, k=k, k_n=l, seed=seed, shuffle=False, fixed_batch_size=fixed_batch_size)))
+    return seqs
+
+  def _split(self, k, seed):
+    # Split dataset for training
+    #   k = 0: one set
+    #   k \in (0,1): k% split
+    #   k in 1,2,3...: k-fold split
+    if k == 1:
+      # Single dataset
+      if  Cfg.log(Cfg.Info):
+        print(f"# Single dataset (no split): {len(self)} set")
+      self.set = np.zeros(len(self),dtype=np.uint8)
+    elif k > 0 and k < 1:
+      # two-fold split
+      if Cfg.log(Cfg.Info):
+        print(f"#  {k*100} : {100 - k*100} % split: ({int(k*len(self))},{len(self)-int(k*len(self))}) sets")
+      # Simple two-fold split, with shuffle
+      idx = np.arange(0,len(self))
+      np.random.default_rng(seed=seed).shuffle(idx)
+      split = np.round(len(self) * k).astype(np.uint64)
+      self.set = np.ndarray(len(self),dtype=np.uint8)
+      self.set[idx[0:split]] = 0
+      self.set[idx[split:]] = 1
+    elif int(k) > 1:
+      # k-fold split
+      k = int(k)
+      if Cfg.log(Cfg.Info):
+        print(f"# {k}-fold split of {len(self)}: {len(self) // k} per set; {k-len(self) % k} set(s) with one more")
+      # Venetian blinds k-fold split with shuffle
+      idx = np.arange(0,len(self))
+      np.random.default_rng(seed=seed).shuffle(idx)
+      self.set = np.floor(idx % k).astype(np.uint8)
+    else:
+      raise Exception(f"Illegal k: {k}")
+
+  def _create_sample(self, pidx, dim, pre_proc, data_cache, seg_mask):
+    # Create single input/output sample in cache
+    if os.path.isfile(os.path.join(data_cache,self.patient_name(pidx)+".npy")):
+      return
+    # Setup data array
+    data = self[pidx]
+    label_counts = None
+    unique_labels = None
+    X = np.empty((*dim, len(self.channels)), dtype=np.float32)
+    for s,c in enumerate(self.channels):
+      # Crop and determine scale
+      if self.crops_type == "orig":
+        stack = data[c]
+        vs = stack.header.get_zooms()
+        sx = vs[0]
+        sy = vs[1]
+        sz = vs[2]
+      else:
+        if self.crops_type == "f":
+          crp = self.crops
+        elif self.crops_type == "bb":
+          crp = self.crops[pidx]
+        else:
+          raise Exception(f"Illegal crops {self.crops_type}")
+        stack = data[c].slicer[crp[1][0]:crp[1][1],crp[0][0]:crp[0][1],crp[2][0]:crp[2][1]]
+        vs = stack.header.get_zooms()
+        sx = (crp[1][1] - crp[1][0]) * vs[0] / dim[0]
+        sy = (crp[0][1] - crp[0][0]) * vs[1] / dim[1]
+        sz = (crp[2][1] - crp[2][0]) * vs[2] / dim[2]
+      # Resample 
+      if c == seg_mask:
+        # Transform mask
+        org_data = stack.get_fdata()
+        labels = np.unique(org_data)
+        masks = np.zeros((len(labels),*dim))
+        # Split masks into labels, transform, and combine again
+        for l, ul in enumerate(labels):
+          # Transform label mask
+          new_data = np.array(org_data==ul, dtype=np.float32)
+          new_stack = nib.Nifti1Image(new_data, stack.affine, stack.header)
+          new_stack = nibp.conform(new_stack, out_shape=dim, voxel_size=(sx,sy,sz), orientation="LPS")
+          masks[l,...] = new_stack.get_fdata()
+        # Vote for maximum value per voxel
+        mask = np.argmax(masks,axis=0)
+        # Map indices to labels
+        for l in reversed(range(0,len(labels))):
+          X[(mask==l),s] = labels[l]
+      else:
+        # Transform image
+        stack = nibp.conform(stack, out_shape=dim, voxel_size=(sx,sy,sz), orientation="LPS")
+        X[...,s] = stack.get_fdata().astype(np.float32)
+      if pre_proc is not None:
+        pre_proc(X[...,s], c, seg_mask)
+    np.save(os.path.join(data_cache,self.patient_name(pidx)+".npy"), X)
+
+  @staticmethod
+  def norm_minmax(x, ch, seg_mask):
+    """Function to normalise input and output samples mapping `[min,max]` to `[0,1] in place.
+
+    This can be a `pre_proc` function for `sequences`.
+
+    Args:
+      * `x`: data sample;
+      * `ch`: channel name;
+      * `seg_mask`: segmentation mask name.
+    """
+    if ch != seg_mask:
+      x -= np.min(x) # Mask background
+      x /= np.max(x)
+
+  @staticmethod
+  def norm_histeq_mask(x, ch, seg_mask):
+    """Function to normalise input and output samples applying histogram normalisation and then mapping the result to `[0,1]` in place.
+
+    This can be a `pre_proc` function for `sequences`.
+
+    Args:
+      * `x`: data sample;
+      * `ch`: channel name;
+      * `seg_mask`: segmentation mask name.
+    """
+    if ch != seg_mask:
+      xf = x.flatten()
+      # Mask background
+      mask = xf > 0.0
+      xf[np.logical_not(mask)] = 0.0 # Background to mask
+      # Normalise to have the bins right
+      xf[mask] /= np.max(xf[mask])
+      # from http://www.janeriksolem.net/histogram-equalization-with-python-and.html
+      hg, bins = np.histogram(xf[mask], np.linspace(0,1,2**12), density=True)
+      # Equalise
+      cdf = hg.cumsum()
+      cdf /= cdf[-1]
+      # Linear interpolation of cdf to find new values
+      xf[mask] = np.interp(xf[mask], bins[:-1], cdf)
+      np.copyto(x, xf.reshape(x.shape))
+
+class Cache():
+  """Cache to store samples for a pre-processed dataset in memory for faster access.
+
+  It stores the samples in memory until we run out (indicating by a config. parameter).
+  Then it keeps loading them from disk instead. This is used across a set of sequences
+  generated for the same dataset.
+  """
+
+  def __init__(self, data_folder, channels, dim, inp_chs, out_chs, seg_mask):
+    """Cache of npy loaded from data_folder.
+
+    Args:
+      * `data_folder`: Folder to load data from (pre-processed data from Dataset);
+      * `channels`: Channel names in sequence of channel index for samples in `data_folder`;
+      * `dim`: Spatial dimension of samples in `data_folder`;
+      * `inp_chs`: list of lists of input channel names for each input;
+      * `out_chs`: list of lists of output channel names for each output;
+      * `seg_mask`: segmentation mask channel name.
+    """
+    self.data_folder = data_folder
+    self.channels = channels
+    self.dim = dim
+    self.seg_mask = seg_mask
+    if isinstance(inp_chs[0], list):
+      self.inp_chs = inp_chs
+    else:
+      self.inp_chs = [inp_chs]
+    self.inp_chs_idx = [[self._channel_idx(c) for c in ic] for ic in self.inp_chs]
+    self.inp_chs_mask = [[self._channel_mask(c) for c in ic] for ic in self.inp_chs]
+    if isinstance(out_chs[0], list):
+      self.out_chs = out_chs
+    else:
+      self.out_chs = [out_chs]
+    self.out_chs_idx = [[self._channel_idx(c) for c in oc] for oc in self.out_chs]
+    self.out_chs_mask = [[self._channel_mask(c) for c in oc] for oc in self.out_chs]
+    self.clear()
+
+  def _channel_idx(self, c):
+    # Get index in data array of channel to copy
+    try:
+      return self.channels.index(c)
+    except:
+      return self.channels.index(self.seg_mask)
+
+  def _channel_mask(self, c):
+    # Get processing for mask channel
+    if c[0:len(self.seg_mask)] == self.seg_mask:
+      if c[len(self.seg_mask)] == "=":
+        # Preserve labels listed (use negative index to indicate this for later processing)
+        return [-int(l) for l in c[(len(self.seg_mask)+1):].split("=")]
+      elif c[3] == "+":
+        # Combine listed labels to binary mask
+        return [int(l) for l in c[(len(self.seg_mask)+1):].split("+")]
+    return []
+
+  def clear(self):
+    """Clear cache.
+    """
+    self.cacheX = {}
+    self.cacheY = {}
+    self.warn = False
+
+  def get(self, id):
+    """Get sample.
+
+    Args:
+      * `id`: Name of sample to return.
+    
+    Return:
+      * `X`, `Y`: Numpy array for input and output.
+    """
+    X = [np.ndarray((*self.dim,len(ic)), dtype=np.float32) for ic in self.inp_chs]
+    Y = [np.ndarray((*self.dim,len(oc)), dtype=np.float32) for oc in self.out_chs]
+    self.copy_to(id,X,Y)
+    return X, Y
+
+  def copy_to(self, id, X, Y):
+    """Copy sample to memory.
+
+    Args:
+      * `id`: Name of sample;
+      * `X`: Numpy array to store input;
+      * `Y`: Numpy array to store output.
+    """
+    if id not in self.cacheX:
+      # Load
+      data = np.load(os.path.join(self.data_folder,id+".npy"))
+      # Create X,Y input/output pair
+      for V in [(X,self.inp_chs_idx,self.inp_chs_mask), (Y,self.out_chs_idx,self.out_chs_mask)]:
+        for k, ci in enumerate(V[1]):
+          for l, p in enumerate(ci):
+            np.copyto(V[0][k][...,l], data[...,p], casting="no")
+            if len(V[2][k][l]) > 0:
+              # Process mask
+              if V[2][k][l][0] > 0:
+                # Combine labels
+                midx = (V[0][k][...,l] == V[2][k][l][0])
+                for kk in range (1,len(V[2][k][l])):
+                  midx |= (V[0][k][...,l] == V[2][k][l][kk])
+                V[0][k][...,l] = midx
+              else:
+                # Preserve labels
+                midx = (V[0][k][...,l] != -V[2][k][l][0])
+                for kk in range (1,len(V[2][k][l])):
+                  midx &= (V[0][k][...,l] != -V[2][k][l][kk])
+                V[0][k][midx,l] = 0
+      # Cache
+      if psutil.virtual_memory().percent < Cfg.val["low_mem_percentage"]:
+        if self.warn:
+          print(f"***Warning: memory {psutil.virtual_memory().percent}% full; restarting cache***")
+          self.warn = False
+        self.cacheX[id] = [np.copy(X[k]) for k in range(0,len(self.inp_chs_idx))]
+        self.cacheY[id] = [np.copy(Y[k]) for k in range(0,len(self.out_chs_idx))]
+      elif not self.warn:
+        print(f"***Warning: memory {psutil.virtual_memory().percent}% full; stopping cache***")
+        self.warn = True
+    else:
+      # Get from cache
+      for k in range(0,len(self.cacheX[id])):
+        np.copyto(X[k], self.cacheX[id][k])
+      for k in range(0,len(self.cacheY[id])):
+        np.copyto(Y[k], self.cacheY[id][k])
+
+class SeqGen(Sequence):
+  """Keras a sequence of data generator.
+
+  This is used to generate a data sequence for training and testing. It only stores
+  the names of the samples and uses `bca.dataset.Cache` to load and cache, as much as
+  possible, the samples. It is generates by `bca.dataset.Dataset.sequences`.
+
+  This implements `len` and `[idx]` operators.
+  """
+
+  def __init__(self, names, cache, dim, batch_size, k, k_n, seed, shuffle=True, fixed_batch_size=False):
+    """Construct a keras data Sequence.
+
+    Args:
+      * `names`: List of sample names in the sequence;
+      * `cache`: Cache to use to load and cache the data;
+      * `dim`: Dimensions of each simple;
+      * `batch_size`: Training batch size;
+      * `k`: Parameter indicating split of dataset used to generate this sequence;
+      * `k_n`: Parameter indicating which fold this sequence belongs to;
+      * `seed`: Seed used to generate the split
+      * `shuffle`: Boolean indicating whether to shuffle data sequence after each epoch;
+      * `fixed_batch_size`: Boolean indicating whether to use fixed batch size; this means batches are
+        truncated if they are smaller than the batch size specified.
+    """
+    super(SeqGen, self).__init__()
+    self.names = names
+    self.cache = cache
+    self.dim = dim
+    self.batch_size = batch_size
+    self.k = k
+    self.k_n = k_n
+    self.seed = seed
+    self.shuffle = shuffle
+    self.idx = np.arange(0,len(self.names))
+    self.rng = np.random.default_rng()
+    self.fixed_batch_size = fixed_batch_size
+    self.on_epoch_end() # Run shuffle on first, too
+
+  def __len__(self):
+    # Number of batches per epoch
+    batches = len(self.names) // self.batch_size
+    # We fix the batch size on request. E.g. if we setup the module with a fixed batch_size for all inputs.
+    # This is especially necessary on some systems to avoid errors in some cases, so we also allow to overwrite this with a dev flag.
+    # The particular issue we ran into was a count=0 tensor at the bottle neck of a standard UNet3D on a V100-16GB with CUDA 11.5; cudnn 8.{3,6}.
+    # A cudnn error was triggered on a count=0 BatchDescriptor. Could not reproduce this on more recent hardware / software.
+    if not (self.fixed_batch_size or Cfg.dev("FIXED_BATCH_SIZE")) and batches * self.batch_size < len(self.names):
+      batches += 1
+    return batches
+
+  def __getitem__(self, index):
+    # Generate one batch
+    batch_idx = self.idx[index*self.batch_size:min((index+1)*self.batch_size,len(self.names))]
+    # Data
+    X = [np.empty((len(batch_idx), *self.dim, len(ic)), dtype=np.float32) for ic in self.cache.inp_chs]
+    Y = [np.empty((len(batch_idx), *self.dim, len(oc)), dtype=np.float32) for oc in self.cache.out_chs]
+    # Load
+    for l in range(0,len(batch_idx)):
+      self.cache.copy_to(self.names[batch_idx[l]],
+                         [X[k][l,:] for k in range(0,len(X))],
+                         [Y[k][l,:] for k in range(0,len(Y))])
+    if len(X) == 1:
+      X = X[0]
+    if len(Y) == 1:
+      Y = Y[0]
+    return X, Y
+
+  def on_epoch_end(self):
+    """Method called at the end of every epoch.
+
+    Shuffle indices, if requested.
+    """
+    if self.shuffle == True:
+      self.rng.shuffle(self.idx)
+
+  def disable_shuffle(self):
+    """Disable shuffling of data sequence.
+    """
+    self.shuffle = False
+    self.idx = np.arange(0,len(self.names))
+  
+  def enable_shuffle(self):
+    """Enable shuffling of data sequence.
+    """
+    self.shuffle = True
+    self.idx = np.arange(0,len(self.names))
+    self.on_epoch_end()
+
+  def clear_cache(self):
+    """Clear Cache.
+    """
+    self.cache.clear()
+
+  def browse(self):
+    """Interactive widget to browse data in notebooks.
+    """
+    import matplotlib.pyplot as plt
+    from matplotlib.colors import ListedColormap, BoundaryNorm
+    import matplotlib.patches as patches
+    from ipywidgets import interact
+    cid = 1
+    X, Y = self.cache.get(self.names[cid-1])
+    seg_cmap = ListedColormap([Cfg.val["col_label0"],
+                               Cfg.val["col_label1"],
+                               Cfg.val["col_label2"],
+                               Cfg.val["col_label3"],
+                               Cfg.val["col_label4"]])
+    seg_norm = BoundaryNorm([-.5,.5,1.5,2.5,3.5,4.5], seg_cmap.N)
+    def view(idx, slice):
+      # Display set of slices for patient id
+      nonlocal cid, X, Y
+      if cid != idx:
+        cid = idx
+        X, Y = self.cache.get(self.names[cid-1])
+      x_size = np.sum([x.shape[-1] for x in X])
+      y_size = np.sum([y.shape[-1] for y in Y])
+      fig, ax = plt.subplots(1,x_size+y_size,sharex=True,sharey=True,dpi=Cfg.val["screen_dpi"],figsize=(Cfg.val["figsize"][0]*(x_size+y_size),Cfg.val["figsize"][1]))
+      ax_idx = 0
+      for V in [(X,self.cache.inp_chs_mask),(Y,self.cache.out_chs_mask)]:
+        for l in range(0,len(V[0])):
+          for k in range(0,V[0][l].shape[-1]):
+            if len(V[1][l][k]) > 0:
+              ax[ax_idx].imshow(V[0][l][:,:,slice-1,k], cmap=seg_cmap, norm=seg_norm, interpolation='nearest')
+              vals = [int(v) for v in np.unique(V[0][l][:,:,slice-1,k]) if v > 0.0]
+              if len(vals) > 0:
+                pats = [patches.Patch(color=seg_cmap(v), label=str(v)) for v in vals]
+                ax[ax_idx].legend(handles=pats, loc=0, borderaxespad=0.1)
+            else:
+              ax[ax_idx].imshow(V[0][l][:,:,slice-1,k], cmap=Cfg.val["brain_cmap"], interpolation='nearest')
+            ax[ax_idx].set_title(self.names[cid-1]+"-"+str(V[1][l][k]))
+            ax_idx += 1
+      plt.tight_layout()
+      plt.show()
+    # Start interactive widget
+    interact(view, idx=(1,len(self.names)), slice=(1,X[0].shape[2]))
\ No newline at end of file
diff --git a/bca/model.py b/bca/model.py
new file mode 100644
index 0000000..f977e21
--- /dev/null
+++ b/bca/model.py
@@ -0,0 +1,90 @@
+# bca/model.py - Abstract model class
+#
+# SPDX-FileCopyrightText: Copyright (C) 2022-2023 Frank C Langbein <frank@langbein.org>, Cardiff University
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+from .cfg import Cfg
+
+import tensorflow as tf
+
+from abc import ABC, abstractmethod
+
+class ModelGen(ABC):
+  """Abstract base class for model generators.
+
+  This is a class is the base class for classes to construct a model. It must, for any such class, contain
+  a name field, which must uniquely identified the architecture specified with the parameters. It must provide
+  a `construct` method which returns the tensorflow `Model`. The plot method should be universal. The
+  constructor should set he parameters for the `construct` method.
+  """
+
+  @abstractmethod
+  def __init__(self, name):
+    """Abstract base class method to create an architecture class which constructs a model as specified.
+
+    For an example of how to use it, see the `Unet3D` class in `bca/unet.py`.
+    
+    Args:
+      * `name`: name of the architecture, must be unique for the parameters;
+    """
+    self.name = name
+
+  @abstractmethod
+  def construct(self, seq, batch_size, jit_compile=True):
+    """Construct a tensorflow functional architecture.
+
+    Args:
+      * `seq`: keras data Sequence (see `bca.dataset.SeqGen`) for the data to be used with the model';
+               this  can also be a tuple where the first part is the input size and the last tuple entry the
+               number of output classes - this is to be able to generate the model independent of the data,
+               e.g., for plotting.
+      * `batch_size`: batch size for training; in particular this is used to adjust the learning rate for the optimiser
+      * `jit_compile`: `jit_compile` argument for `Model.fit`
+
+    Retrun:
+      * Constructed and compiled model with optimiser
+    """
+    pass
+
+  def plot(self, dim, text=False):
+    """Plot model architecture.
+    
+    It runs in a separate process such that GPU resources used are cleared after the run. If the summary text/plot
+    files already exist, it does not recreate them (recall that model names must be unique). Of course you can
+    delete the cached files.
+
+    Args:
+      * `dim`: Model input shape - spatial dimensions and number of channels (last)
+      * `text`: If True, show text summary instead.
+    """
+    # Run this in separate process so we clear resources afterwards
+    import os
+    file = self.__class__.__name__+"plot.png" # temporary file
+    if Cfg.val['multiprocessing']:
+      import multiprocessing
+      p = multiprocessing.Process(target=self._plot,kwargs={"file": file, "dim": dim, "text": text})
+      p.start()
+      p.join()
+      if p.exitcode != 0:
+        raise Exception("Process failed")
+    else:
+      self._plot(file, dim, text)
+    if text:
+      with open(file,"r") as f:
+        print(f.read())
+    else:
+      from IPython import display
+      display.display(display.Image(filename=os.path.join(file)))
+    os.remove(file)
+
+  def _plot(self, file, dim, text):
+    # Get model
+    model = self.construct(seq=dim, batch_size=4)
+    # Plot
+    if text:
+      with open(file,"w") as f:
+        model.summary(print_fn=lambda l : print(l, file=f), line_length=128, expand_nested=True, show_trainable=True)
+    else:
+      tf.keras.utils.plot_model(model, to_file=file, show_shapes=True, 
+                                show_dtype=True, show_layer_names=True, show_layer_activations=True,
+                                rankdir='TB', expand_nested=True, dpi=Cfg.val['screen_dpi'])
\ No newline at end of file
diff --git a/bca/scheduler.py b/bca/scheduler.py
new file mode 100755
index 0000000..7418ff5
--- /dev/null
+++ b/bca/scheduler.py
@@ -0,0 +1,547 @@
+#!/usr/bin/env python3
+#
+# scheduler.py - scheduler for training tasks
+#
+# SPDX-FileCopyrightText: Copyright (C) 2022-2023 Frank C Langbein <frank@langbein.org>, Cardiff University
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+from .cfg import Cfg
+
+import os
+import glob
+import subprocess
+import time
+import multiprocessing
+
+class SlurmScheduler:
+  """Provides run and check method to schedule and check completion of task on a slurm cluster.
+  It assumes `rsync`, `ssh`, etc. are available on local and remote system.
+  """
+
+  def __init__(self, config, local_folder, id="bca", scw=True):
+    """Setup scheduler.
+
+    Args:
+      * `config`: Configuration values, taken from `bca.cfg` using the json config files. See 
+        arguments needed there (val variable in `bca.cfg.Cfg`);
+      * `local_folder`: Local root folder containing the bca repository;
+      * `id`: id to use for SCW jobs (extended by the task folder name)
+      * `scw`: use work-around for SCW cluster to force fixed batch-size to avoid crash. See
+         HACK comments in source.
+    """
+    # Setup config
+    self.max_tasks = config["max_tasks"]
+    self.host = config["host"]
+    self.user = config["user"]
+    self.account = config["account"]
+    self.remote_folder = config["remote_folder"]
+    self.partitions = config["partitions"]
+    self.nodes = config["nodes"]
+    self.ntasks = config["ntasks"]
+    self.ntasks_per_node = config["ntasks_per_node"]
+    self.cpus_per_task = config["cpus_per_task"]
+    self.mem = config["mem"]
+    self.gres = config["gres"]
+    self.time = config["time"]
+    self.modules = config["modules"]
+    self.local_folder = local_folder
+    self.id = id
+    self.scw = scw # See HACK below to fix crash on specific hardware
+
+  @staticmethod
+  def _proc_res(res, out_start=0):
+    # Helper to process output
+    for line in res.stdout.decode("utf-8").split("\n")[out_start:]:
+      for lline in reversed(line.split("\r")):
+        if len(lline) > 0:
+          line = lline
+          break
+      if len(line) > 0:
+        print("    "+line)
+    for line in res.stderr.decode("utf-8").split("\n"):
+      for lline in reversed(line.split("\r")):
+        if len(lline) > 0:
+          line = lline
+          break
+      if len(line) > 0:
+        print("    E:"+line)
+    return res.returncode != 0
+
+  def run(self, task):
+    """Setup and schedule task.
+
+    This synchronises the code and data and initiates the remote job. It stores information about
+    the job locally, so it can find it again, in the model folder. That means if the scheduler
+    is interrupted, it can pick it up from the interruption and check where the job is at.
+
+    Args:
+      * `task`: `task.py` file generated by `bca.train.Trainer` to execute the training task
+    """
+    print("  ## Sync'ing files")
+    for src in ["bca", "requirements.txt", "cfg.json"]:
+      res = subprocess.run(["rsync","-am","--delete",
+                            "--exclude=__pycache__",
+                            os.path.join(Cfg.val["path_root"],src),
+                            self.user+"@"+self.host+":"+self.remote_folder],
+                           capture_output=True)
+      if SlurmScheduler._proc_res(res):
+        raise Exception(f"Sync'ing {src} failed")
+
+    task_path = os.path.dirname(task)
+    data_path = os.path.dirname(os.path.dirname(os.path.dirname(task_path)))
+    res = subprocess.run(["ssh",self.user+"@"+self.host,"mkdir -p "+os.path.join(self.remote_folder,data_path)],capture_output=True)
+    if SlurmScheduler._proc_res(res):
+      raise Exception(f"Mkdir {data_path} failed")
+    res = subprocess.run(["rsync","-am","--delete",
+                          "--include=*.npy","--exclude=*",
+                          os.path.join(self.local_folder,data_path)+"/",
+                          self.user+"@"+self.host+":"+os.path.join(self.remote_folder,data_path)],
+                         capture_output=True)
+    if SlurmScheduler._proc_res(res):
+      raise Exception(f"Sync'ing {data_path} failed")
+
+    print(f"  ## Schedule job for {task}")
+    with open(os.path.join(self.local_folder,task_path,"job.sh"), "w") as f:
+      f.write( "#!/bin/sh -l\n")
+      f.write(f"#SBATCH --job-name={self.id}/{task_path}\n")
+      f.write(f"#SBATCH --output={self.remote_folder}/{task_path}/out.log\n")
+      f.write(f"#SBATCH --error={self.remote_folder}/{task_path}/err.log\n")
+      f.write(f"#SBATCH -p {self.partitions}\n")
+      f.write(f"#SBATCH --nodes={self.nodes}\n")
+      f.write(f"#SBATCH --ntasks={self.ntasks}\n")
+      f.write(f"#SBATCH --ntasks-per-node={self.ntasks_per_node}\n")
+      f.write(f"#SBATCH --cpus-per-task={self.cpus_per_task}\n")
+      f.write(f"#SBATCH --mem={self.mem}\n")
+      f.write(f"#SBATCH --gres={self.gres}\n")
+      f.write(f"#SBATCH --time={self.time}\n")
+      if len(self.modules) > 0:
+        f.write("\nmodule purge\n")
+        for m in self.modules:
+          f.write(f"module load {m}\n")
+      f.write( "\n")
+      # HACK: this fixes an issue with non-constant batch sizes on SCW V100-16GB hardware with CUDA 11.5; cudnn 8.{3,6} (not tested elsewhere).abs(x)
+      if self.scw:
+        f.write('export BCA_DEV=FIXED_BATCH_SIZE\n\n')
+      # END HACK
+      f.write(f"cd ~/{self.remote_folder}\n")
+      f.write(f'echo "Job: $SLURM_JOB_NAME - $SLURM_JOB_ID on $SLURM_JOB_NODELIST"\n')
+      f.write(f"/usr/bin/env python3 {task}\n")
+      f.write(f'test "$?" = 0 && date >~/{self.remote_folder}/{task_path}/done\n')
+    res = subprocess.run(["ssh",self.user+"@"+self.host,"mkdir -p "+os.path.join(self.remote_folder,task_path)],capture_output=True)
+    if SlurmScheduler._proc_res(res):
+      raise Exception(f"Mkdir {task_path} failed")
+    res = subprocess.run(["rsync","-am","--delete",
+                          "--exclude=__pycache__",
+                          os.path.join(self.local_folder,task_path)+"/",
+                          self.user+"@"+self.host+":"+os.path.join(self.remote_folder,task_path)],
+                         capture_output=True)
+    if SlurmScheduler._proc_res(res):
+      raise Exception(f"Sync'ing {task_path} failed")
+
+    res = subprocess.run(["ssh",self.user+"@"+self.host,f"sbatch -A {self.account} {self.remote_folder}/{task_path}/job.sh"],
+                         capture_output=True)
+    if SlurmScheduler._proc_res(res):
+      raise Exception(f"Scheduling {task} failed")
+
+  def check(self, task):
+    """Check status of the task.
+
+    Checks the status of the task and returns it for processing by "bca.scheduler.schedule()".
+
+    Args:
+      * `task`: `task.py` file generated by `bca.train.Trainer` to execute the training task
+    """
+    task_path = os.path.dirname(task)
+    res = subprocess.run(["ssh",self.user+"@"+self.host,f"ls {self.remote_folder}/{task_path} 2>/dev/null 1>&2"], capture_output=True)
+    if SlurmScheduler._proc_res(res):
+      return "OFFLINE"
+    result = "DONE"
+    res = subprocess.run(["ssh",self.user+"@"+self.host,f"ls {self.remote_folder}/{task_path}/done 2>/dev/null 1>&2"], capture_output=True)
+    if SlurmScheduler._proc_res(res):
+      res = subprocess.run(["ssh",self.user+"@"+self.host,f"squeue -n {self.id}/{task_path}"], capture_output=True)
+      SlurmScheduler._proc_res(res, out_start=1)
+      output = res.stdout.decode("utf-8").split("\n")[:-1]
+      if len(output) > 1: # Need at least two lines of output to have the process still running
+        res = subprocess.run(["ssh",self.user+"@"+self.host,f"tail -4 {self.remote_folder}/{task_path}/out.log 2>/dev/null"], capture_output=True)
+        SlurmScheduler._proc_res(res)
+        return "WAIT"
+      result = "FAILED"
+    res = subprocess.run(["rsync","-am","--delete",
+                          "--exclude=__pycache__",
+                          self.user+"@"+self.host+":"+os.path.join(self.remote_folder,task_path)+"/",
+                          task_path],
+                         capture_output=True)
+    if SlurmScheduler._proc_res(res):
+      raise Exception(f"Copying {task} results failed")
+    res = subprocess.run(["ssh",self.user+"@"+self.host,f"rm -rf {self.remote_folder}/{task_path}"], capture_output=True)
+    if SlurmScheduler._proc_res(res):
+      raise Exception(f"Removing remote result folder failed")
+    return result
+
+class HostScheduler:
+  """Provides run and check method to schedule and check completion of task on a single host (assumed to be able to handle one task at a time).
+  It assumes rsync, ssh, etc. are available on local and remote system.
+  """
+
+  def __init__(self, config, local_folder):
+    """Setup scheduler.
+
+    Args:
+      * `config`: Configuration values, taken from `bca.cfg` using the json config files. See 
+        arguments needed there (val variable in `bca.cfg.Cfg`);
+      * `local_folder`: Local root folder containing the bca repository;
+      * `id`: id to use for jobs (extended by the task folder name)
+    """
+    # Config
+    self.max_tasks = 1
+    self.host = config["host"]
+    self.user = config["user"]
+    self.remote_folder = config["remote_folder"]
+    self.local_folder = local_folder
+
+  @staticmethod
+  def _proc_res(res, out_start=0):
+    # Helper to process output
+    for line in res.stdout.decode("utf-8").split("\n")[out_start:]:
+      for lline in reversed(line.split("\r")):
+        if len(lline) > 0:
+          line = lline
+          break
+      if len(line) > 0:
+        print("    "+line)
+    for line in res.stderr.decode("utf-8").split("\n"):
+      for lline in reversed(line.split("\r")):
+        if len(lline) > 0:
+          line = lline
+          break
+      if len(line) > 0:
+        print("    E:"+line)
+    return res.returncode != 0
+
+  def run(self, task):
+    """Setup and schedule task.
+
+    This synchronises the code and data and initiates the remote job. It stores information about
+    the job locally, so it can find it again, in the model folder. That means if the scheduler
+    is interrupted, it can pick it up from the interruption and check where the job is at.
+
+    Args:
+      * `task`: `task.py` file generated by `bca.train.Trainer` to execute the training task
+    """
+    print("  ## Sync'ing files")
+    for src in ["bca", "requirements.txt", "cfg.json"]:
+      res = subprocess.run(["rsync","-am","--delete",
+                            "--exclude=__pycache__",
+                            os.path.join(Cfg.val["path_root"],src),
+                            self.user+"@"+self.host+":"+self.remote_folder],
+                           capture_output=True)
+      if HostScheduler._proc_res(res):
+        raise Exception(f"Sync'ing {src} failed")
+
+    task_path = os.path.dirname(task)
+    data_path = os.path.dirname(os.path.dirname(os.path.dirname(task_path)))
+    res = subprocess.run(["ssh",self.user+"@"+self.host,f"mkdir -p {os.path.join(self.remote_folder,data_path)}"],capture_output=True)
+    if HostScheduler._proc_res(res):
+      raise Exception(f"Mkdir {data_path} failed")
+    res = subprocess.run(["rsync","-am","--delete",
+                          "--include=*.npy","--exclude=*",
+                          os.path.join(self.local_folder,data_path)+"/",
+                          self.user+"@"+self.host+":"+os.path.join(self.remote_folder,data_path)],
+                         capture_output=True)
+    if HostScheduler._proc_res(res):
+      raise Exception(f"Sync'ing {data_path} failed")
+
+    print(f"  ## Schedule job for {task}")
+    with open(os.path.join(self.local_folder,task_path,"job.sh"), "w") as f:
+      f.write( "#!/bin/sh -l\n")
+      f.write(f'cd ~/{self.remote_folder}\n')
+      f.write(f'echo -n $$ >{task_path}/pid\n')
+      f.write(f'/usr/bin/env python3 {task} >{task_path}/out.log 2>{task_path}/err.log\n')
+      f.write(f'test "$?" = 0 && date >~/{self.remote_folder}/{task_path}/done\n')
+    res = subprocess.run(["ssh",self.user+"@"+self.host,f"mkdir -p {os.path.join(self.remote_folder,task_path)}"],capture_output=True)
+    if HostScheduler._proc_res(res):
+      raise Exception(f"Mkdir {task_path} failed")
+    res = subprocess.run(["rsync","-am","--delete",
+                          "--exclude=__pycache__",
+                          os.path.join(self.local_folder,task_path)+"/",
+                          self.user+"@"+self.host+":"+os.path.join(self.remote_folder,task_path)],
+                         capture_output=True)
+    if HostScheduler._proc_res(res):
+      raise Exception(f"Sync'ing {task_path} failed")
+
+    res = subprocess.run(["ssh",self.user+"@"+self.host,f"nohup sh {self.remote_folder}/{task_path}/job.sh >/dev/null 2>&1 &"],
+                         capture_output=True)
+    if HostScheduler._proc_res(res):
+      raise Exception(f"Scheduling {task} failed")
+
+  def check(self, task):
+    """Check status of the task.
+
+    Checks the status of the task and returns it for processing by "bca.scheduler.schedule()".
+
+    Args:
+      * `task`: `task.py` file generated by `bca.train.Trainer` to execute the training task
+    """
+    task_path = os.path.dirname(task)
+    res = subprocess.run(["ssh",self.user+"@"+self.host,f"ls {self.remote_folder}/{task_path} 2>/dev/null 1>&2"], capture_output=True)
+    if HostScheduler._proc_res(res):
+      return "OFFLINE"
+    result = "DONE"
+    res = subprocess.run(["ssh",self.user+"@"+self.host,f"ls {self.remote_folder}/{task_path}/done 2>/dev/null 1>&2"], capture_output=True)
+    if HostScheduler._proc_res(res):
+      res = subprocess.run(["ssh",self.user+"@"+self.host,f"test -f {self.remote_folder}/{task_path}/pid && ps -p `cat {self.remote_folder}/{task_path}/pid`"], capture_output=True)
+      HostScheduler._proc_res(res, out_start=1)
+      output = res.stdout.decode("utf-8").split("\n")[:-1]
+      if len(output) > 1: # Need at least two lines of output to have the process still running
+        res = subprocess.run(["ssh",self.user+"@"+self.host,f"tail -4 {self.remote_folder}/{task_path}/out.log 2>/dev/null"], capture_output=True)
+        HostScheduler._proc_res(res)
+        return "WAIT"
+      result = "FAILED"
+    res = subprocess.run(["rsync","-am","--delete",
+                          "--exclude=__pycache__",
+                          self.user+"@"+self.host+":"+os.path.join(self.remote_folder,task_path)+"/",
+                          task_path],
+                         capture_output=True)
+    if HostScheduler._proc_res(res):
+      raise Exception(f"Copying {task} results failed")
+    res = subprocess.run(["ssh",self.user+"@"+self.host,f"rm -rf {self.remote_folder}/{task_path}"], capture_output=True)
+    if HostScheduler._proc_res(res):
+      raise Exception("Removing remote result folder failed")
+    return result
+
+class LocalScheduler:
+  """Provides run and check method to schedule and check completion of task locally (assumed to be able to handle one task at a time).
+  """
+
+  def __init__(self):
+    """Setup scheduler.
+    """
+    # Config
+    self.max_tasks = 1
+    self.process = None
+
+  def _execute(self, task):
+    # Helper to execute task locally in separate process
+    from contextlib import redirect_stdout, redirect_stderr
+    task_path = os.path.dirname(task)
+    with open(os.path.join(task_path,"err.log"), "a" if os.path.isfile(os.path.join(task_path,"err.log")) else "w") as ferr:
+      with open(os.path.join(task_path,"out.log"), "a" if os.path.isfile(os.path.join(task_path,"out.log")) else "w") as fout:
+        with redirect_stderr(ferr):
+          with redirect_stdout(fout):
+            with open(task) as f:
+              exec(compile(f.read(), task, "exec"))
+
+  def run(self, task):
+    """Setup and schedule task.
+
+    This simply runs the job locally in a separate process, using the data stored locally directly.
+    If the scheduler is interrupted this local job will also be interrupted (but can be continued from
+    the last save model for the task).
+
+    Args:
+      * `task`: `task.py` file generated by `bca.train.Trainer` to execute the training task
+    """
+    if Cfg.val["multiprocessing"]:
+      self.process = multiprocessing.Process(target=self._execute,kwargs={"task": task})
+      self.process.start()
+    else:
+      self._execute(task)
+
+  def check(self, task):
+    """Check status of the task.
+
+    Checks the status of the task and returns it for processing by "bca.scheduler.schedule()".
+
+    Args:
+      * `task`: `task.py` file generated by `bca.train.Trainer` to execute the training task
+    """
+    task_path = os.path.dirname(task)
+    if Cfg.val["multipropcessing"]:
+      if self.process is None:
+        if os.path.isfile(os.path.join(task_path,"status")):
+          with open(os.path.join(task_path,"status"), "r") as f:
+            status = f.read()
+          if len(status) >= 8 and status[0:8] == "training":
+            if os.path.exists(os.path.join(task_path,"last")):
+              return "DONE"
+          os.remove(os.path.join(task_path,"status"))
+        return "OFFLINE"
+      if self.process.is_alive():
+        with open(os.path.join(task_path,"out.log"),"r") as f:
+          for line in (f.readlines()[-4:]):
+            print("  "+line.strip(),)
+        return "WAIT"
+      if os.path.isdir(os.path.join(task_path,"last")):
+        return "DONE"
+      return "FAILED"
+    if os.path.isfile(os.path.join(task_path,"status")):
+      with open(os.path.join(task_path,"status"), "r") as f:
+        status = f.read()
+      if len(status) >= 8 and status[0:8] == "training":
+        if os.path.exists(os.path.join(task_path,"last")):
+          return "DONE"
+      os.remove(os.path.join(task_path,"status"))
+    return "FAILED"
+
+def schedule(task_folder="results", wait=60, notebook=True):
+  """Run scheduler.
+
+  This checks the `task_folder` for any `task.py` jobs to execute and runs these on the
+  executors which are specified in the configuration (see `bca.cfg.Cfg`). Depending on
+  how many jobs can be started on the executor, it checks if a new job can be started.
+  It also checks the status of existing jobs and updates information accordingly. It
+  uses the various schedulers to execute them on different remote (or local) platforms.
+  It can be interrupted and restarted (but a job run by the local scheduler will be
+  interrupted). It stops when all jobs are completed (or when it is interrupted). There
+  should only be one scheduler running for a task folder.
+
+  Note, `bca/scheduler.py` can also be run from the command line to execute the scheduler.
+
+  The scheduler is likely only to work on Linux or UNIX-like systems and requires `ssh`
+  and `rsync` to run, among other standard UNIX tools. Under other operating systems,
+  it may be best to run it in a VM (e.g. running a notebook/lab server to execute this
+  from via a browser).
+  
+  To run this from the command line write a script such as
+  ```python
+  if __name__ == '__main__':
+    from bca.bca.scheduler import schedule, schedule_clean
+    task_folder="results"
+    schedule_clean(task_folder=task_folder)
+    schedule(task_folder=task_folder, wait=60, notebook=False)
+  ```
+
+  Args:
+    * `task_folder`: Root of folder hierarchy containing tasks;
+    * `wait`: seconds to wait between checks/scheduling new jobs;
+    * `notebook`: format output for notebook (only shows latest results for clarify).
+  """
+  if notebook:
+    from IPython import display
+
+  # Sanity checks
+  if not os.name == 'posix':                                                                         
+    print("**WARNING - ths scheduler only runs reliably and is only supported on Linux/POSIX**")  
+  local_folder = os.path.dirname(os.path.abspath(task_folder))
+  if not os.path.isdir(local_folder): 
+    raise Exception("Cannot find location of data-root folder (containing {local_folder})")
+ 
+  # Collect executors from config
+  execs = {}
+  for k in Cfg.val["executors"].keys():
+    t = Cfg.val["executors"][k]["type"]
+    if t == "slurm":
+      execs[k] = SlurmScheduler(Cfg.val["executors"][k], local_folder, scw=True if k == "scw" else False) # See scw HACK above.
+    elif t == "local":
+      execs[k] = LocalScheduler()
+    elif t == "host":
+      execs[k] = HostScheduler(Cfg.val["executors"][k], local_folder)
+    else:
+      raise Exception(f"Unknown executor type {t}")
+  first_run = True
+
+  # Keep scheduling (until nothing more found; can be interrupted)
+  while True:
+    if not notebook:
+      print("========================================================================")
+    not_scheduled = 0
+    running_total = 0
+    running = {}
+    for k in execs.keys():
+      running[k] = 0
+    # Check running
+    tasks = glob.glob(os.path.join(task_folder,"**","task.py"), recursive=True)
+    for task in tasks:
+      task_path = os.path.dirname(task)
+      if os.path.isfile(os.path.join(task_path,"status")):
+        with open(os.path.join(task_path,"status"), "r") as f:
+          status = f.read()
+        if len(status) >= 8 and status[0:8] == "training":
+          print(f"# Status {task_path}:")
+          key = status.split(":")[1].strip()
+          stat = execs[key].check(task)
+          print(f"  {stat}")
+          if stat == "DONE" or stat == "FAILED":
+            with open(os.path.join(task_path,"status"), "w") as f:
+              f.write("end")
+          else:
+            running[k] += 1
+            running_total += 1
+    # Schedule
+    tasks = glob.glob(os.path.join(task_folder,"**","task.py"), recursive=True)
+    for task in tasks:
+      task_path = os.path.dirname(task)
+      if not os.path.isfile(os.path.join(task_path,"status")):
+        scheduled = False
+        for k in execs.keys():
+          if k not in Cfg.val["disabled_executors"]: # Only disable executors for new jobs
+            if running[k] < execs[k].max_tasks:
+              with open(os.path.join(task_path,"status"), "w") as f:
+                f.write(f"training:{k}")
+              print(f"# New {task_path}")
+              try:
+                execs[k].run(task)
+              except Exception as e:
+                print(e)
+                if os.path.isdir(os.path.join(task_path,"last")):
+                  shutil.rmtree(os.path.join(task_path,"last"))
+                with open(os.path.join(task_path,"status"), "w") as f:
+                  f.write(f"end")
+              running[k] += 1
+              running_total += 1
+              scheduled = True
+              break
+        if scheduled == False:
+          not_scheduled += 1
+    if notebook:
+      display.clear_output(wait=True)
+    # Wait...
+    if first_run:
+      first_run = False # Makes sure local processes get initialised faster, if it has to be restarted
+    else:
+      if running_total == 0 and not_scheduled == 0:
+        print("All tasks complete.")
+        return
+      time.sleep(wait)
+
+def schedule_clean(task_folder="results"):
+  """Cleanup failed tasks.
+
+  Cleanup tasks that are failed to enable scheduling them again (assuming the issues have been fixed).
+
+  Note, it also cleans up tasks that are not running and not complete.
+
+  Args:
+    * `task_folder`: Root of folder hierarchy containing tasks;
+  """
+
+  # Sanity checks
+  if not os.name == 'posix':                                                                         
+    print("**WARNING - ths scheduler only runs reliably and is only supported on Linux/POSIX**")  
+  local_folder = os.path.dirname(os.path.abspath(task_folder))
+  if not os.path.isdir(local_folder): 
+    raise Exception(f"Cannot find location of data-root folder (containing {local_folder})")
+ 
+  # Keep scheduling (until nothing more found; can be interrupted)
+  for task in glob.glob(task_folder+"/**/task.py", recursive=True):
+    folder = os.path.dirname(task)
+    if not os.path.exists(os.path.join(folder, "last")):
+      clean=False
+      if os.path.isfile(os.path.join(folder,"status")):
+        with open(os.path.join(folder,"status"), "r") as f:
+          status = f.read()
+        if status[0:8] != "training":
+          clean = True
+        else:
+          print(f"Running:  {folder}")
+      else:
+        clean=True
+      if clean:
+        disp=True
+        for f in ["done", "err.log", "out.log", "status", "job.sh"]:
+          p = os.path.join(folder, f)
+          if os.path.exists(p):
+            if disp:
+              print(f"Cleaning: {folder}")
+              disp = False
+            os.remove(p)
\ No newline at end of file
diff --git a/bca/trainer.py b/bca/trainer.py
new file mode 100644
index 0000000..9167c9d
--- /dev/null
+++ b/bca/trainer.py
@@ -0,0 +1,967 @@
+# bca/trainer.py - Train models
+#
+# SPDX-FileCopyrightText: Copyright (C) 2022-2023 Frank C Langbein <frank@langbein.org>, Cardiff University
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+from .cfg import Cfg
+
+import os
+import sys
+import shutil
+import json
+import numpy as np 
+import collections
+import csv
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import segmentation_models_3D as sm
+import multiprocessing
+
+import tensorflow as tf
+tf.get_logger().setLevel(Cfg.val['tf_log'])
+
+from IPython import display
+
+dsc = sm.metrics.FScore(name="DSC")
+"""F1/Dice score metric."""
+dsc_loss = sm.losses.DiceLoss()
+"""F1/Dice loss."""
+iou = sm.metrics.IOUScore(name="IoU")
+"""IoU(Intersection of Union)/Jaccard index metric."""
+
+class ChkptLogger(tf.keras.callbacks.Callback):
+  """Log history and store best/last model Callback.
+
+  This callback is used by the training to record the model history and save the best and last model.
+  """  
+
+  def __init__(self, path, best_monitor=("","DSC"), best_cmp=np.greater, save_traces=False, save_last=True):
+    """Create a ChkptLogger.
+
+    Args:
+      * `path`: Path where to store data (folder containing the models);
+      * `best_monitor`: Selects metric to decide upon the best model. This is a pair of strings where the metric name
+        must start with the first entry and finish with the second (for multiple outputs, etc. and to decide between
+        test or training metric);
+      * `best_cmp`: Function to compare metric values for selecting best model (whether greater or smaller values are better);
+      * `save_traces`: Boolean indicating whether to save traces with the model;
+      * `save_last`: Boolean indicating whether to save last model
+    """
+    super().__init__()
+    self.path = path
+    # History Log
+    self.history_file = "history.json"
+    self.history = {}
+    # Model checkpoint
+    self.best_model_path = os.path.join(path,"best")
+    self.best_model_path_old = os.path.join(path,"best-old")
+    self.best_model_path_new = os.path.join(path,"best-new")
+    self.best_monitor = best_monitor # pair - first part matches start and last part matches end, to account for multiple outputs
+    self.best_cmp = best_cmp
+    self.last_model_path = os.path.join(path,"last")
+    self.last_model_path_new = os.path.join(path,"last-new")
+    self.save_traces = save_traces
+    self.save_last = save_last
+
+  def on_train_begin(self, logs=None):
+    """Called at the beginning of training to process history and setup model checkpoints.
+
+    Args:
+      * `logs`: Dict. Currently no data is passed to this argument for this method but that may change in the future.
+    """
+    # History log
+    hf = os.path.join(self.best_model_path, self.history_file)
+    if os.path.exists(hf):
+      with open(hf, "r") as f:
+        self.history = json.load(f)
+    else:
+      self.history["time"] = []
+      self.history["epochs"] = 0
+      self.history["best"] = None
+      self.history["best_epoch"] = None
+      self.history["logs"] = {}
+    # Model checkpoint
+    for p in [self.best_model_path_old, self.best_model_path_new, self.last_model_path_new]:
+      if os.path.isdir(p):
+        shutil.rmtree(p)
+
+  def on_epoch_begin(self, batch, logs=None):
+    """Called at the start of an epoch to initiate timing history.
+
+    Args:
+      * `epoch`: Integer, index of epoch';
+      * `logs`: Dict. Currently no data is passed to this argument for this method but that may change in the future.
+    """
+    self.last_time = tf.timestamp().numpy()
+
+  def on_epoch_end(self, epoch, logs=None):
+    """Called at the end of an epoch to record logs and update best saved model.
+
+    Args: 
+      * `epoch`: Integer, index of epoch;
+      * `logs`: Dict, metric results for this training epoch, and for the validation epoch if validation is performed.
+         Validation result keys are prefixed with val_. For training epoch, the values of the Model's metrics are returned.\
+         Example: `{'loss': 0.2, 'accuracy': 0.7}`.
+    """
+    # History log
+    self.history["time"].append(tf.timestamp().numpy()-self.last_time)
+    logs = logs or {}
+    for k in logs.keys():
+      if k not in self.history["logs"]:
+        self.history["logs"][k] = [np.nan]*self.history["epochs"]
+      self.history["logs"][k].append(logs[k])
+    for k in self.history["logs"].keys():
+      if k not in logs.keys():
+        self.history["logs"][k].append(np.nan)
+    self.history["epochs"] += 1
+    # Save best model
+    cur_val = 0
+    for k in logs.keys():
+      # Combine values to find best, if there are multiple outputs
+      if k[:len(self.best_monitor[0])] == self.best_monitor[0] and k[-len(self.best_monitor[1]):] == self.best_monitor[1]:
+        cur_val += logs[k]
+    if self.history["best"] is None or self.best_cmp(cur_val, self.history["best"]):
+      self.history["best"] = cur_val
+      self.history["best_epoch"] = self.history["epochs"]
+      self.model.save(self.best_model_path_new, overwrite=False, save_traces=self.save_traces)
+      with open(os.path.join(self.best_model_path_new,self.history_file), "w") as fp:
+        print(json.dumps(self.history, indent=2, sort_keys=True), file=fp)
+      if os.path.isdir(self.best_model_path):
+        os.rename(self.best_model_path, self.best_model_path_old)
+      os.rename(self.best_model_path_new, self.best_model_path)
+      if os.path.isdir(self.best_model_path_old):
+        shutil.rmtree(self.best_model_path_old)
+
+  def on_train_end(self, logs=None):
+    """Called at the end of training to save last model.
+
+    Args:
+      * `logs`: Dict. Currently the output of the last call to `on_epoch_end()` is passed to this argument for this method but that may change in the future.
+    """
+    if self.save_last:
+      # Save last model
+      self.model.save(self.last_model_path_new, overwrite=False, save_traces=self.save_traces)
+      with open(os.path.join(self.last_model_path_new,self.history_file), "w") as fp:
+        print(json.dumps(self.history, indent=2, sort_keys=True), file=fp)
+      if os.path.isdir(self.last_model_path):
+        shutil.rmtree(self.last_model_path)
+      os.rename(self.last_model_path_new, self.last_model_path)
+
+class Trainer:
+  """Train and evaluate networks.
+
+  This class handles training of a network, either locally by running the training directly, or
+  remotely, but creating tasks to be executed by `bca.scheduler.schedule`. The training can be
+  interrupted and continued, locally or remotely (it uses the ChkptLogger to store the best model
+  so far and continues from it).
+  """
+
+  def __init__(self, model, epochs):
+    """Args:
+      * `model`: model object providing a `model.name` name variable and a `model.construct(seq, batch_size, jit_compile)`
+         method to create the model. See, for instance, `bca.unet.Unet3D`.
+      * `epochs`: number of training epochs.
+    """
+    self.model = model
+    self.epochs = epochs
+
+  def _model_path(self, seq):
+    # Return path where to store the model
+    return os.path.join(seq.cache.data_folder,
+                        self.model.name
+                        +"-"+"_".join([l for ic in seq.cache.inp_chs for l in ic])
+                        +"-"+"_".join([l for oc in seq.cache.out_chs for l in oc]),
+                        str(self.epochs)+"-"+str(seq.batch_size),
+                        str(seq.seed)+"-"+str(seq.k)+"-"+str(seq.k_n))
+
+  def _status(self, path):
+    # Report status: start -> training:EXECUTOR -> end[done|failed]
+    s_fn = os.path.join(path,"status")
+    if os.path.isfile(s_fn):
+      with open(s_fn, "r") as f:
+        status = f.read()
+      if status == "end":
+        if os.path.isdir(os.path.join(path,"last")):
+          return "done"
+        return "failed"
+      else:
+        return "training"
+    return "start"
+
+  def _set_status(self, path, status):
+    # Set status: start -> training:EXECUTOR -> end[done|failed]
+    s_fn = os.path.join(path,"status")
+    if status == "start":
+      if os.path.isfile(s_fn):
+        os.remove(s_fn)
+      if os.path.isdir(os.path.join(path,"last")):
+        os.rmtree(os.path.join(path,"last"))
+    else:
+      os.makedirs(path,exist_ok=True)
+      with open(s_fn, "w") as f:
+        f.write(status)
+    return status
+
+  def train(self, seqs, jit_compile=True, remote=False):
+    """Train the model.
+
+    Trains the model on `seqs` Sequences (see `bca.dataset.SeqGen`, created from `bca.dataset.Dataset`),
+    either directly on the local system or remotely by only generating the tasks to be executed by
+    `bca.scheduler.schedule`. Training can be interrupted and continued from the last saved best model.
+    We run this in a separate process such that after it is done the process quits and returns the
+    GPU/tensorflow resources, not to occupy those while the notebook is running, etc., which can cause
+    resource issues (e.g. if some other GPU process is run while the notebook is active).
+
+    Args:
+      * `seqs`: A list of keras Sequences as data generator (`bca.dataset.SeqGen`); each element of the list
+        is assumed to be a pair of a training and test sequence (set the 2nd to None for no test sequence).
+      * `jit_compile`: `jit_compile` argument for `Model.fit()`
+      * `remote`: run jobs remotely; this means only the `task.py` files will be created for execution by the
+         scheduler.
+    """
+    # Run this in separate process so we clear resources afterwards
+    if Cfg.val["multiprocessing"]:
+      p = multiprocessing.Process(target=self._train, kwargs={"seqs": seqs, "jit_compile": jit_compile,
+                                                              "remote": remote})
+      p.start()
+      p.join()
+      if p.exitcode != 0:
+        raise Exception("Process failed")
+    else:
+      self._train(seqs, jit_compile, remote)
+
+  def _train(self, seqs, jit_compile, remote):
+    # Train model on sequences
+    if remote:
+      s_start = 0
+      s_training = 0
+      s_done = 0
+      s_failed = 0
+    for k,seq in enumerate(seqs):
+      print(f"* Fold {k+1}: ", end="")
+      path = self._model_path(seq[0])
+      train_run = False
+      s = self._status(path)
+      if s == "start":
+        if remote:
+          # Remote train model
+          print(f"start remote - {path}")
+          self._remote_train_model(path, seq[0], seq[1], jit_compile)
+          s_start += 1
+        else:
+          print(f"start local - {path}")
+          self._train_model(path, seq[0], seq[1], jit_compile)
+          s = self._status(path)
+      elif s == "training":
+        if remote:
+          print(f"training remote - {path}")
+          s_training += 1
+        else:
+          # If we train locally and this is in training, it failed and we need to restart
+          s_fn = os.path.join(path,"status")
+          with open(s_fn, "r") as f:
+            status = f.read()
+          if len(status) == 8:
+            # Local training (no executor specified)
+            print(f"restart local - {path}")
+            self._train_model(path, seq[0], seq[1], jit_compile)
+            s = self._status(path)
+            train_run = True
+      elif s == "done":
+        print(f"done - {path}")
+        if remote:
+          s_done += 1
+      elif s == "failed":
+        print(f"failed - {path}")
+        if remote:
+          s_failed += 1
+      else:
+        raise Exception(f"Unknonw status {s}")
+      if not remote and train_run:
+        display.clear_output(wait=True)
+    if remote:
+      print(f"=> Done: {s_done}; Failed: {s_failed}; Training: {s_training}; Start: {s_start}")
+
+  def _train_model(self, path, train_seq, test_seq, jit_compile):
+    # Train model locally, not using any executors
+    if os.path.exists(os.path.join(path, "task.py")):
+      os.remove(os.path.join(path, "task.py"))
+    self._set_status(path,"training")
+    # In case of custom training loops or different distribution strategies, this part of the code needs to be adapted
+    # We suggest to add flags to the model and adjust the code here depending on the flags; also needs to be done in _remote_train_model to match
+    with tf.distribute.MirroredStrategy().scope():
+      if os.path.isdir(os.path.join(path,"best")):
+        # Load model checkpoint
+        model = tf.keras.models.load_model(os.path.join(path,"best"), custom_objects=Trainer.custom_objects)
+        with open(os.path.join(path,"best","history.json"), "r") as f:
+          history = json.load(f)
+        red_epochs = history["epochs"]
+      else:
+        # Setup model
+        model = self.model.construct(seq=train_seq, batch_size=train_seq.batch_size, jit_compile=jit_compile)
+        red_epochs = 0
+    # Fit model
+    model.fit(train_seq, validation_data=test_seq, epochs=self.epochs-red_epochs,
+              callbacks=[ChkptLogger(path)], verbose=1)
+    #
+    self._set_status(path,"end")
+
+  def _remote_train_model(self, path, train_seq, test_seq, jit_compile):
+    # Training file is not fully secure and must be created by task scheduler for remote tasks (depends on platform)
+    # Create task script
+    os.makedirs(path,exist_ok=True)
+    best_model_path = os.path.join(path,'best')
+    if not os.path.isdir(best_model_path):
+      # Save initial model so it can be loaded by task
+      model = self.model.construct(seq=train_seq, batch_size=train_seq.batch_size, jit_compile=jit_compile)
+      # We need to call this to initialise the optimizer such that the script can load the model for training
+      # Setting the gradient to 0 should not change the trainable variables ?
+      model.optimizer.apply_gradients(zip([0.0]*len(model.trainable_weights), model.trainable_weights))
+      #
+      model.save(best_model_path, overwrite=True, save_traces=False)
+      history = {
+          "time": [],
+          "epochs": 0,
+          "best": None,
+          "best_epoch": None,
+          "logs": {}
+        }
+      with open(os.path.join(best_model_path,"history.json"), "w") as fp:
+        print(json.dumps(history, indent=2, sort_keys=True), file=fp)
+      tf.keras.backend.clear_session()
+    task_file = os.path.join(path, "task.py")
+    with open(task_file, "w") as f:
+      f.write(f"# Training task: {path}\n")
+      f.write( "import os\n")
+      f.write( "import json\n")
+      f.write( "import tensorflow as tf\n")
+      f.write(f"tf.get_logger().setLevel('{Cfg.val['tf_log']}')\n")
+      f.write( "from bca.trainer import ChkptLogger, Trainer\n")
+      f.write( "from bca.dataset import Cache, SeqGen\n")
+      f.write(f"cache = Cache('{train_seq.cache.data_folder}', {train_seq.cache.channels}, {train_seq.cache.dim}, {train_seq.cache.inp_chs}, {train_seq.cache.out_chs}, '{train_seq.cache.seg_mask}')\n")
+      f.write(f"ns_train={train_seq.names}\n")
+      f.write(f"train_seq = SeqGen(ns_train, cache, {train_seq.dim}, {train_seq.batch_size}, k={train_seq.k}, k_n={train_seq.k_n}, seed={train_seq.seed}, shuffle={train_seq.shuffle})\n")
+      f.write(f"ns_test={test_seq.names}\n")
+      f.write(f"test_seq = SeqGen(ns_test, cache, {test_seq.dim}, {test_seq.batch_size}, k={test_seq.k}, k_n={test_seq.k_n}, seed={test_seq.seed}, shuffle={test_seq.shuffle})\n")
+      f.write(f"best_path=os.path.join('{path}','best')\n")
+      f.write( "with open(os.path.join(best_path,'history.json'), 'r') as f:\n")
+      f.write( "  history = json.load(f)\n")
+      f.write( "red_epochs = history['epochs']\n")
+      # In case of custom training loops or different distribution strategies, this part of the code needs to be adapted
+      # We suggest to add flags to the model and adjust the code here depending on the flags; also needs to be done in _train_model to match
+      f.write( "with tf.distribute.MirroredStrategy().scope():\n")
+      f.write( "  model = tf.keras.models.load_model(best_path, custom_objects=Trainer.custom_objects)\n")
+      f.write(f"model.fit(train_seq, validation_data=test_seq, epochs={self.epochs}-red_epochs, callbacks=[ChkptLogger('{path}')], verbose=1)\n")
+
+  def eval(self, seqs, mode="best", fs=[dsc,iou], std_eval=None):
+    """Evaluate models for Sequences.
+
+    This creates the `evaluation.json` file in the model folder with the evaluation results for the
+    trained model. If the file already exists, it is assumed the evaluation is complete.
+
+    It runs in a separate process such that GPU resources used are cleared after the run.
+
+    Args:
+      * `seqs`: A list of keras Sequences as data generator (`bca.dataset.SeqGen`); each element of the list
+        is assumed to be a pair of a training and test sequence (set the 2nd to None for no test sequence).
+      * `mode`: "last" or "best" to decide which model to evaluate on (we generally assume "best")
+      * `fs`: Metrics to use (must be per sample, but called on single sample with sample index)
+      * `std_eval`: Function mapping single output P and expected output Y sample onto standardised data to make metrics comparable; metrics are also computed for this output; output should be a dictionary mapping NAMES to standardised lists [PP,YY]; generally we assume NAMES linked to labels for BraTS2020: "whole": 1,2,4, "necrotic": 1, "enhancing": 4, "edema": 2, "core": 1,4 but any can be used (for space reasons we often abbreviate to three letters). The function needs to know how to convert the output of the network to the standardised data. Note that P and Y are lists of tensors, for each output tensor, even if there is only one output, while PP and YY should only be one output tensor to be compared with the metrics. Generally we have P[OUTPUT-INDEX][SAMPLE=0,data-axes,OUTPUT-CHANNEL] and PP[SAMPLE=0,data-axes,STD_OUTPUT-CHANNEL] (often only one STD_OUTPUT-CHANNEL).
+    """
+    # Run this in separate process so we clear resources afterwards
+    if Cfg.val["multiprocessing"]:
+      p = multiprocessing.Process(target=self._eval, kwargs={"seqs": seqs, "mode": mode, "fs": fs,
+                                                             "std_eval": std_eval})
+      p.start()
+      p.join()
+      if p.exitcode != 0:
+        raise Exception("Process failed")
+    else:
+      self._eval(seqs, mode, fs, std_eval)
+
+  def _eval(self, seqs, mode, fs, std_eval):
+    # Evaluate the sequences; helper to run it in separate process
+    for k,seq in enumerate(seqs):
+      print(f"* Fold {k+1}")
+      self._eval_model(seq[0], seq[1], mode, fs, std_eval)
+    print("Evaluation complete.")
+    display.clear_output(wait=True)
+
+  def _eval_model(self, train_seq, test_seq, mode, fs, std_eval):
+    # Eval model for train/test sequence
+    path = self._model_path(train_seq)
+    # Return if model does not exist
+    if not os.path.isdir(os.path.join(path,"last")):
+      print("Model did not complete training")
+      return
+    eval_path = os.path.join(path,mode,"evaluation.json")
+    if os.path.isfile(eval_path):
+      # Check if evaluation is OK or needs updating/is broken
+      with open(eval_path, "r") as f:
+        eval_data = json.load(f)
+      broken = False
+      for f in ["train_total", "test_total", "train_per_sample", "test_per_sample", 
+                "train_std_per_sample", "test_std_per_sample"]:
+        if f not in eval_data:
+          broken = True
+          break
+      if not broken and std_eval is not None:
+        if  len(eval_data["train_std_per_sample"]) == 0 or len(eval_data["test_std_per_sample"]) == 0:
+          broken = True
+      if not broken:
+        return
+      os.remove(eval_path)
+
+    # Get model
+    model = tf.keras.models.load_model(os.path.join(path,mode), custom_objects=Trainer.custom_objects)
+
+    # Evaluate model on sequence with model metrics
+    tre = model.evaluate(x=train_seq, verbose=1)
+    if test_seq is None:
+      tee = {}
+    else:
+      tee = model.evaluate(x=test_seq, verbose=1)
+    # Map to dictionary, with fixing issue if there is only one metric
+    try:
+      tre = dict(zip(model.metrics_names, tre))
+      tee = dict(zip(model.metrics_names, tee))
+    except:
+      tre = dict(zip(model.metrics_names, [tre]))
+      tee = dict(zip(model.metrics_names, [tee]))
+
+    # Evaluate prediction of each train/test sample with metrics specified in fs
+    # Train predictions
+    print("Evaluating training samples...")
+    tr_res = {} # Direct/raw evaluation data for training
+    tr_std = {} # Standardised evaluation data for training
+    for X, Y in train_seq:
+      # Collect metrics per sample
+      if not isinstance(Y,list):
+        Y = [Y]
+      P = model.predict(X, verbose=0)
+      if not isinstance(P,list):
+        P = [P]
+      # Metrics on actual output
+      for k in range(0,P[0].shape[0]):
+        for kk in range(0,len(Y)):
+          for f in fs:
+            key = f.name
+            if len(Y) > 1:
+              key += "-"+str(kk) # Add output index, if multiple outputs
+            val = float(f(Y[kk][k:k+1,...], P[kk][k:k+1,...]).numpy())
+            if key in tr_res:
+              tr_res[key].append(val)
+            else:
+              tr_res[key] = [val]
+            # Last channel is index for output maps - if there is more than one
+            # map we apply the metrics to each individually as well to handle
+            # multi-segmentation, etc. results
+            if Y[kk].shape[-1] > 1:
+              for ch in range(0, Y[kk].shape[-1]):
+                ch_key = key + f"_c{ch}"
+                val = float(f(Y[kk][k:k+1,...,ch], P[kk][k:k+1,...,ch]).numpy())
+                if ch_key in tr_res:
+                  tr_res[ch_key].append(val)
+                else:
+                  tr_res[ch_key] = [val]
+      # Standardised metrics
+      if std_eval is not None:
+        for k in range(0,P[0].shape[0]):
+          # Standardise data
+          std_data = std_eval([P[kk][k:k+1,...] for kk in range(0,len(Y))], 
+                              [Y[kk][k:k+1,...] for kk in range(0,len(Y))])
+          for std_name in std_data:
+            # Compute metrics for standardised data
+            if std_name not in tr_std:
+              tr_std[std_name] = {}
+            for f in fs:
+              key = f.name
+              val_std = float(f(std_data[std_name][0], std_data[std_name][1]).numpy())
+              if key in tr_std[std_name]:
+                tr_std[std_name][key].append(val_std)
+              else:
+                tr_std[std_name][key] = [val_std]
+    # Test predictions
+    te_res = {} # Direct/raw evaluation data for testing
+    te_std = {} # Standardised  evaluation data for testing
+    if test_seq is not None:
+      print("Evaluating test samples...")
+      for X, Y in test_seq:
+        # Collect metrics per sample
+        if not isinstance(Y,list):
+          Y = [Y]
+        P = model.predict(X, verbose=0)
+        if not isinstance(P,list):
+          P = [P]
+        for k in range(0,P[0].shape[0]):
+          for kk in range(0,len(Y)):
+            # Metrics on actual output
+            for f in fs:
+              key = f.name
+              if len(Y) > 1:
+                key += "-"+str(kk) # Add output index, if multiple outputs
+              val = float(f(Y[kk][k:k+1,...],P[kk][k:k+1,...]).numpy())
+              if key in te_res:
+                te_res[key].append(val)
+              else:
+                te_res[key] = [val]
+              # Last channel is index for output maps - if there is more than one
+              # map we apply the metrics to each individually as well to handle
+              # multi-segemtnation, etc. results
+              if Y[kk].shape[-1] > 1:
+                for ch in range(0, Y[kk].shape[-1]):
+                  ch_key = key + f"_c{ch}"
+                  val = float(f(Y[kk][k:k+1,...,ch], P[kk][k:k+1,...,ch]).numpy())
+                  if ch_key in te_res:
+                    te_res[ch_key].append(val)
+                  else:
+                    te_res[ch_key] = [val]
+        # Standardised metrics
+        if std_eval is not None:
+          for k in range(0,P[0].shape[0]):
+            # Standardise data
+            std_data = std_eval([P[kk][k:k+1,...,] for kk in range(0,len(Y))], 
+                                [Y[kk][k:k+1,...] for kk in range(0,len(Y))])
+            for std_name in std_data:
+              # Compute metrics for standardised data
+              if std_name not in te_std:
+                te_std[std_name] = {}
+              for f in fs:
+                key = f.name
+                val_std = float(f(std_data[std_name][0], std_data[std_name][1]).numpy())
+                if key in te_std[std_name]:
+                  te_std[std_name][key].append(val_std)
+                else:
+                  te_std[std_name][key] = [val_std]
+
+    # Save evaluation data
+    eval = {
+        "train_total": tre, 
+        "test_total": tee,
+        "train_per_sample": tr_res,
+        "test_per_sample": te_res,
+        "train_std_per_sample": tr_std,
+        "test_std_per_sample": te_std
+      }
+    with open(eval_path, "w") as fp:
+      print(json.dumps(eval, indent=2, sort_keys=True), file=fp)
+
+    # Cleanup
+    tf.keras.backend.clear_session()
+
+  def plot_model(self, seq, mode="best", text=False, save_only=False):
+    """Plot model.
+    
+    The model must exist to have the shapes, so this is for analysis after training even if not strictly needed.
+
+    It runs in a separate process such that GPU resources used are cleared after the run. If the summary text/plot
+    files already exist, it does not recreate them (recall that model names must be unique). Of course you can
+    delete the cached files.
+
+    Args:
+      * `seqs`: A list of keras Sequences as data generator or a single Sequence from the generators. This is used
+        to determine which model to load and plot from all the options (in principle they should all be the same,
+        but sometimes they may not). If a list is provided the `seq[0][0]` model is used.
+      * `mode`: "last" or "best" to decide which model to evaluate on (we generally assume "best")
+      * `text`: If True, show text summary instead.
+      * `save_only`: If True, only save files, do not show anything
+    """
+    # Run this in separate process so we clear resources afterwards
+    if isinstance(seq, list):
+      seq = seq[0][0]
+    path = self._model_path(seq)
+    im_dpi=True
+    for dpi in Cfg.val['image_dpi']:
+      if not os.path.isfile(os.path.join(path,"..","architecture@"+str(dpi)+".png")):
+        im_dpi=False
+        break
+    if not (os.path.isfile(os.path.join(path,"..","summary.txt")) and im_dpi and            
+            os.path.isfile(os.path.join(path,"..","architecture@"+str(Cfg.val['screen_dpi'])+".png"))):
+      if Cfg.val["multiprocessing"]:
+        p = multiprocessing.Process(target=self._plot_model,kwargs={"path": path, "seq": seq, "mode": mode})
+        p.start()
+        p.join()
+        if p.exitcode != 0:
+          raise Exception("Process failed")
+      else:
+        self._plot_model(path, seq, mode)
+    if not save_only:
+      if text:
+        with open(os.path.join(path,"..","summary.txt"),"r") as f:
+          print(f.read())
+      else:
+        display.display(display.Image(filename=os.path.join(path,"..","architecture@"+str(Cfg.val['screen_dpi'])+".png")))
+
+  def _plot_model(self, path, seq, mode="best"):
+    # Get model
+    if os.path.isdir(os.path.join(path,mode)):
+      model = tf.keras.models.load_model(os.path.join(path,mode), custom_objects=Trainer.custom_objects)
+    else:
+      os.makedirs(path,exist_ok=True)
+      model = self.model.construct(seq=seq, batch_size=seq.batch_size)
+    # Plot
+    with open(os.path.join(path,"..","summary.txt"),"w") as f:
+      model.summary(print_fn=lambda l : print(l, file=f), line_length=128, expand_nested=True, show_trainable=True)
+    for dpi in [Cfg.val["screen_dpi"], *Cfg.val["image_dpi"]]:
+      tf.keras.utils.plot_model(model, to_file=os.path.join(path,"..","architecture@"+str(dpi)+".png"), show_shapes=True, 
+                                show_dtype=True, show_layer_names=True, show_layer_activations=True,
+                                rankdir='TB', expand_nested=True, dpi=dpi)
+
+  def plot_results(self, seqs, eval_mode="best"):
+    """Plot model histories and analysis results after training and evaluation is complete.
+
+    This collects the evaluation results for the sequences and plots them for a jupyter notebook.
+
+    The x' prime values are the averages of the per-sample metrics for each fold.
+
+    Args:
+      * `seqs`: A list of keras Sequences as data generator or a single Sequence from the generators. This is used
+        to determine which model to load and plot from all the options (in principle they should all be the same,
+        but sometimes they may not). If a list is provided the `seq[0][0]` model is used.
+      * `mode`: "last" or "best" to decide which model to evaluate on (we generally assume "best")
+
+    Return:
+      * pandas frame of training results over the folds
+    """
+    history = []
+    evals = []
+    for l in range(len(seqs)):
+      path = self._model_path(seqs[l][0])
+      if not os.path.isdir(os.path.join(path,"last")):
+        print(f"Stopping evaluation: model for fold {l} did not complete training")
+        return
+      if not os.path.isfile(os.path.join(path,eval_mode,"evaluation.json")):
+        print(f"{eval_mode} model for fold {l} not evaluated")
+        return
+      with open(os.path.join(path, "last", "history.json"), "r") as f:
+        history.append(json.load(f))
+      with open(os.path.join(path, eval_mode, "evaluation.json"), "r") as f:
+        evals.append(json.load(f))
+
+    # Plot histories
+    fig_cols = max(4,len(history))
+    fig = plt.figure(dpi=Cfg.val["screen_dpi"],figsize=(Cfg.val["figsize"][0]*fig_cols,Cfg.val["figsize"][1]*(3+len(evals[0]["train_per_sample"]))))
+    std_eval_len = len(evals[0]["train_std_per_sample"])
+    if std_eval_len > 0:
+      std_eval_len *= len(evals[0]["train_std_per_sample"][list(evals[0]["train_std_per_sample"].keys())[0]])
+    gs = fig.add_gridspec(3+len(evals[0]["train_per_sample"])+std_eval_len,fig_cols)
+    loss_n = len([k for k in history[0]["logs"] if "loss" in k])
+    cols = plt.cm.rainbow(np.linspace(0,1,len(history[0]["logs"])))
+    ax0 = None
+    for k in range(0,len(history)):
+      if k == 0:
+        ax = fig.add_subplot(gs[0,k])
+        ax.set_ylabel("Loss")
+        ax0 = ax
+      else:    
+        ax = fig.add_subplot(gs[0,k], sharex=ax0, sharey=ax0)
+      ax.set_xlabel(f"Epoch (Fold {k+1})")
+      ax.set_prop_cycle(color=cols[0:loss_n])
+      ax2 = None
+      for key in history[k]["logs"].keys():
+        if "loss" in key:
+          ax.plot(range(1,len(history[k]["logs"][key])+1), history[k]["logs"][key], label=key)
+        else:
+          if ax2 == None:
+            ax2 = ax.twinx()
+            ax2.set_prop_cycle(color=cols[loss_n:])
+            if k == len(history)-1:
+              ax2.set_ylabel("Metric")
+          ax2.plot(range(1,len(history[k]["logs"][key])+1), history[k]["logs"][key], label=key)
+      if ax2 is not None:
+        li1, la1 = ax.get_legend_handles_labels()
+        li2, la2 = ax2.get_legend_handles_labels()
+        ax.legend(li1+li2, la1+la2, loc="lower left", bbox_to_anchor=(-0.12,0.99), ncol=3)
+      else:
+        ax.legend(li, la)
+
+    # Plot times per epoch
+    ax0 = None
+    for k in range(0,len(history)):
+      ax = fig.add_subplot(gs[1,k])
+      sns.histplot(data=history[k]["time"],color='#1f77b4')
+      m = np.mean(history[k]["time"])
+      s = np.std(history[k]["time"])
+      plt.axvline(x=m,color='#1f77b4')
+      plt.errorbar(x=m,y=np.mean(ax.get_ylim()),xerr=s,color='#1f77b4')
+      ax.set_xlabel(f"Time (s) per Epoch: {m:.4f}σ{s:.4f}")
+
+    # Metric distributions over samples
+    palette = plt.cm.rainbow(np.linspace(0,1,2*len(evals[0]["train_per_sample"].keys()))).tolist()
+    palette = np.array(palette[0::2] + palette[1::2])
+    for k in range(0,len(evals)):
+      for f,fk in enumerate(evals[k]["train_per_sample"].keys()):
+        # Raw network metrics
+
+        ax = fig.add_subplot(gs[2+f,k])
+        # Train
+
+        mean = np.mean(evals[k]["train_per_sample"][fk])
+        std = np.std(evals[k]["train_per_sample"][fk])
+        sns.histplot(data=evals[k]["train_per_sample"][fk],
+                     label=f"Train {eval_mode} {fk}': {mean:.4f}σ{std:.4f}",
+                     ax=ax,color=palette[2*f])
+        plt.sca(ax)
+        plt.axvline(x=mean,color=palette[2*f])
+        r = ax.get_ylim()
+        ax.errorbar(x=mean,y=np.mean(r)+0.02*(r[1]-r[0]),xerr=std,color=palette[2*f])
+        # Test
+        if evals[k]["test_per_sample"] is not None:
+          mean = np.mean(evals[k]["test_per_sample"][fk])
+          std = np.std(evals[k]["test_per_sample"][fk])
+          sns.histplot(data=evals[k]["test_per_sample"][fk],
+                       label=f"Test {eval_mode} {fk}': {mean:.4f}σ{std:.4f}",
+                       ax=ax,color=palette[2*f+1])
+          plt.sca(ax)
+          plt.axvline(x=mean,color=palette[2*f+1])
+          r = ax.get_ylim()
+          ax.errorbar(x=mean,y=np.mean(r)-0.02*(r[1]-r[0]),xerr=std,color=palette[2*f+1])
+        ax.legend()
+
+    # Standardised metrics
+    for k in range(0,len(evals)):
+      for keyn, key in enumerate(evals[k]["train_std_per_sample"]):
+        for f,fk in enumerate(evals[0]["train_std_per_sample"][key].keys()):
+          ax = fig.add_subplot(gs[2+len(evals[k]["train_per_sample"].keys())
+                                  +keyn*len(evals[0]["train_std_per_sample"][key].keys())+f,k])
+          # Train
+          mean = np.mean(evals[k]["train_std_per_sample"][key][fk])
+          std = np.std(evals[k]["train_std_per_sample"][key][fk])
+          sns.histplot(data=evals[k]["train_std_per_sample"][key][fk],
+                      label=f"Train {eval_mode} STD {key.upper()} {fk}: {mean:.4f}σ{std:.4f}",
+                      ax=ax,color=palette[2*f])
+          plt.sca(ax)
+          plt.axvline(x=mean,color=palette[2*f])
+          r = ax.get_ylim()
+          ax.errorbar(x=mean,y=np.mean(r)+0.02*(r[1]-r[0]),xerr=std,color=palette[2*f])
+          # Test
+          if evals[k]["test_std_per_sample"] is not None:
+            mean = np.mean(evals[k]["test_std_per_sample"][key][fk])
+            std = np.std(evals[k]["test_std_per_sample"][key][fk])
+            sns.histplot(data=evals[k]["test_std_per_sample"][key][fk],
+                         label=f"Test {eval_mode} STD {key.upper()} {fk}: {mean:.4f}σ{std:.4f}",
+                         ax=ax,color=palette[2*f+1])
+            plt.sca(ax)
+            plt.axvline(x=mean,color=palette[2*f+1])
+            r = ax.get_ylim()
+            ax.errorbar(x=mean,y=np.mean(r)-0.02*(r[1]-r[0]),xerr=std,color=palette[2*f+1])
+          ax.legend()
+
+    # Results
+    data = np.zeros((len(seqs)+2, len(evals[0]["train_total"]) + len(evals[0]["test_total"]) +
+                                  len(evals[0]["train_per_sample"]) + len(evals[0]["test_per_sample"]) +
+                                  std_eval_len +
+                                  (std_eval_len if len(evals[0]["test_per_sample"]) > 0 else 0) ))
+    cs=[]
+    idx=[]
+    for k in range(0,len(seqs)):
+      idx.append(f"Fold {k+1}")
+      r = 0
+      # Overall metrics
+      for c,key in enumerate(evals[k]["train_total"].keys()):
+        data[k,r] = evals[k]["train_total"][key]
+        r += 1
+        if k == 0:
+          cs.append(f"{key}")
+        if key in evals[k]["test_total"]:
+          data[k,r] = evals[k]["test_total"][key]
+          r += 1
+          if k == 0:
+            cs.append(f"val_{key}")
+      # Per-sample metrics
+      for c,key in enumerate(evals[k]["train_per_sample"].keys()):
+        data[k,r] = np.mean(evals[k]["train_per_sample"][key])
+        r += 1
+        if k == 0:
+          cs.append(f"{key}'")
+        if key in evals[k]["test_per_sample"]:
+          data[k,r] = np.mean(evals[k]["test_per_sample"][key])
+          r += 1
+          if k == 0:
+            cs.append(f"val_{key}'")
+      # STD per-sample metrics
+      for c,key in enumerate(evals[k]["train_std_per_sample"].keys()):
+        for cc,metrickey in enumerate(evals[k]["train_std_per_sample"][key].keys()):
+          data[k,r] = np.mean(evals[k]["train_std_per_sample"][key][metrickey])
+          r += 1
+          if k == 0:
+            cs.append(f"STD-{key}-{metrickey}")
+          if key in evals[k]["test_std_per_sample"]:
+            if metrickey in evals[k]["test_std_per_sample"][key]:
+              data[k,r] = np.mean(evals[k]["test_std_per_sample"][key][metrickey])
+              r += 1
+              if k == 0:
+                cs.append(f"val_STD-{key}-{metrickey}")
+    idx.append("Mean")
+    idx.append("Std")
+    data[len(seqs),:] = np.mean(data[0:len(seqs),:], axis=0)
+    data[len(seqs)+1,:] = np.std(data[0:len(seqs),:], axis=0)
+    data = pd.DataFrame(data, columns=cs, index=idx)
+
+    # Plot across folds
+    d = data.iloc[0:data.shape[0]-2,:]
+    ax = fig.add_subplot(gs[-1,0])
+    d1 = d.loc[:,[col for col in d.columns if 'loss' in col]]
+    sns.boxplot(data=d1, ax=ax, palette=plt.cm.rainbow(np.linspace(0,1,len([c for c in d.columns if 'loss' in c]))))
+    sns.stripplot(data=d1, jitter=False, palette='dark:black', size=10, alpha=0.5, ax=ax)
+    ax.set_title(f"Final losses across folds for {eval_mode} model")
+
+    ax = fig.add_subplot(gs[-1,1:])
+    d1 = d.loc[:,[col for col in d.columns if 'loss' not in col]]
+    sns.boxplot(data=d1, ax=ax, palette=plt.cm.rainbow(np.linspace(0,1,len([c for c in d.columns if 'loss' not in c]))))
+    sns.stripplot(data=d1, jitter=False, palette='dark:black', size=10, alpha=0.5, ax=ax)
+    ax.set_title(f"Final metrics across folds for {eval_mode} model")
+    ax.tick_params(axis="x", labelrotation=90)
+
+    plt.tight_layout()
+    plt.show()
+
+    pd.set_option('display.max_columns', None)
+    display.display(data)
+
+    return data
+
+  def browse_predict(self, seqs, mode="best"):
+    """Interactive widget to browse predicted data in notebooks.
+
+    This runs tensorflow in a sub-process for the prediction, so GPU resources can easily be released.That
+    also means usually only one prediction browsing process can be active, unless there are sufficient GPU
+    resources. To restart a stopped browsing process, restart the jupyter notebook cell.
+
+    Args:
+      * `seqs`: A list of keras Sequences as data generator (`bca.dataset.SeqGen`); each element of the list
+        is assumed to be a pair of a training and test sequence. All sequences and their results can be viewed.
+      * `mode`: "last" or "best" to decide which model to evaluate on (we generally assume "best")
+    """
+    from ipywidgets import interact, IntSlider, Dropdown, Button
+
+    # Run prediction in separate process (to be able to easily kill tensorflow); communication via queues
+    if Cfg.val["multiprocessing"]:
+      inp_q = multiprocessing.Queue()
+      out_q = multiprocessing.Queue()
+      p = multiprocessing.Process(target=Trainer._browse_predict_proc,args=(inp_q,out_q))
+      p.start()
+    else:
+      inp_q = None
+      ouot_q = None
+      p = None
+
+    # First sample
+    cseq = 1
+    cset = 0
+    cid = int(len(seqs[cseq-1][cset].names)/2)
+    X, Y = seqs[cseq-1][cset].cache.get(seqs[cseq-1][cset].names[cid-1])
+    if Cfg.val["multiprocessing"]:
+      inp_q.put({"path": os.path.join(self._model_path(seqs[cseq-1][0]),mode), "X": X})
+      P = out_q.get()
+    else:
+      P = type(self)._browse_predict(os.path.join(self._model_path(seqs[cseq-1][cset]),mode), X)
+
+    def view(idx, slice, set, seq, overlay):
+      # Display set of slices
+      nonlocal cid, cset, cseq, inp_q, out_q, X, Y, P, p
+      if Cfg.val["multiprocessing"] and p == None:
+        return
+      set = 0 if set == "train" else 1
+      if cid != idx or cset != set or cseq != seq:
+        cid = idx
+        cset = set
+        cseq = seq
+        X, Y = seqs[cseq-1][cset].cache.get(seqs[cseq-1][cset].names[cid-1])
+        if Cfg.val["multiprocessing"]:
+          inp_q.put({"path": os.path.join(self._model_path(seqs[cseq-1][cset]),mode), "X": X})
+          P = out_q.get()
+        else:
+          P = type(self)._browse_predict(os.path.join(self._model_path(seqs[cseq-1][cset]),mode), X)
+      x_size = np.sum([x.shape[-1] for x in X])
+      y_size = np.sum([y.shape[-1] for y in Y])
+      fig, ax = plt.subplots(1,x_size+y_size,sharex=True,sharey=True,dpi=Cfg.val["screen_dpi"],figsize=(Cfg.val["figsize"][0]*(x_size+y_size),Cfg.val["figsize"][1]))
+      ax_idx = 0
+      for l in range(0,len(X)):
+        for k in range(0,X[l].shape[-1]):
+          ax[ax_idx].imshow(X[l][:,:,slice-1,k], cmap=Cfg.val["brain_cmap"], interpolation='nearest')
+          ax[ax_idx].set_title(seqs[cseq-1][cset].names[cid-1]+"-"+seqs[cseq-1][cset].cache.channels[seqs[cseq-1][cset].cache.inp_chs_idx[l][k]])
+          ax_idx += 1
+      for l in range(0,len(Y)):
+        for k in range(0,Y[l].shape[-1]):
+          if "GroundTruth" in overlay:
+            ax[ax_idx].imshow(Y[l][:,:,slice-1,k], cmap=Cfg.val["gt_cmap"], interpolation='nearest')
+          if "Prediction" in overlay:
+            ax[ax_idx].imshow(P[l][0,:,:,slice-1,k], cmap=Cfg.val["pr_cmap"], interpolation='nearest', alpha=0.5)
+          ax[ax_idx].set_title(seqs[cseq-1][cset].names[cid-1]+"-"+seqs[cseq-1][cset].cache.channels[seqs[cseq-1][cset].cache.out_chs_idx[l][k]])
+          ax_idx += 1
+      plt.tight_layout()
+      plt.show()
+
+    # Start/stop button
+    if Cfg.val["multiprocessing"]:
+      btn = Button(description="Stop")
+      def btn_clicked(b):
+        nonlocal inp_q, p
+        inp_q.put({"path": "quit"})
+        p.join()
+        if p.exitcode != 0:
+          raise Exception("Prediction process failed")
+        b.description="Prediction Halted"
+        p = None
+      btn.on_click(btn_clicked)
+      display.display(btn)
+
+    # Start interactive widget
+    idx_widget = IntSlider(min=1, max=len(seqs[cseq-1][cset].names))
+    set_widget = Dropdown(options=["test", "train"], value="train")
+    def update_idx_range(*args):
+      idx_widget.max = len(seqs[cseq-1][0 if set_widget.value == "train" else 1].names)
+    set_widget.observe(update_idx_range, 'value')
+    seq_widget = Dropdown(options=[l for l in range(1,len(seqs)+1)], value=cseq)
+    overlay_widget = Dropdown(options=["GroundTruth+Prediction", "GroundTruth", "Prediction"], value="GroundTruth+Prediction")
+    interact(view, idx=idx_widget, slice=(1,X[0].shape[2]), set=set_widget, seq=seq_widget, overlay=overlay_widget)
+
+  @staticmethod
+  def _browse_predict_proc(inp_q, out_q):
+    # Prediction in separate process, via queue's, taking in 
+    # task dictionary with path and X argument.
+    path = None
+    model = None
+    while True:
+      task = inp_q.get()
+      if task["path"] == "quit":
+        break
+      # Get model
+      if path != task["path"]:
+        model = tf.keras.models.load_model(task["path"], custom_objects=Trainer.custom_objects)
+        path = task["path"]
+      X = task["X"]
+      if len(X) > 1:
+        P = model.predict([tf.expand_dims(X[l], axis=0) for l in range(0,len(X))], verbose=0)
+      else:
+        P = model.predict(tf.expand_dims(X[0], axis=0), verbose=0)
+      if not isinstance(P,list):
+        P = [P]
+      out_q.put(P)
+
+  # Static variables for _browse_predict in current process
+  _predict_path = None
+  _predict_model = None
+
+  @staticmethod
+  def _browse_predict(path, X):
+    # Prediction in current process
+    if path != Trainer._predict_path:
+      Trainer._predict_model = tf.keras.models.load_model(path, custom_objects=Trainer.custom_objects)
+      Trainer._predict_path = path
+    if len(X) > 1:
+      P = Trainer._predict_model.predict([tf.expand_dims(X[l], axis=0) for l in range(0,len(X))], verbose=0)
+    else:
+      P = Trainer._predict_model.predict(tf.expand_dims(X[0], axis=0), verbose=0)
+    if not isinstance(P,list):
+      P = [P]
+    return P
+
+  # Custom objects for loading models
+  custom_objects = {
+    dsc.__name__: dsc,
+    dsc_loss.__name__: dsc_loss,
+    iou.__name__: iou
+  }
\ No newline at end of file
diff --git a/bca/unet.py b/bca/unet.py
new file mode 100644
index 0000000..84f70ca
--- /dev/null
+++ b/bca/unet.py
@@ -0,0 +1,166 @@
+# bca/unet.py - UNet models
+#
+# SPDX-FileCopyrightText: Copyright (C) 2022-2023 Frank C Langbein <frank@langbein.org>, Cardiff University
+# SPDX-FileCopyrightText: Copyright (C) 2022 Ebtihal Alwadee <AlwadeeEJ@cardiff.ac.uk>, PhD student at Cardiff University
+# SPDX-License-Identifier: AGPL-3.0-or-later
+
+from .cfg import Cfg
+
+import tensorflow as tf
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Input, Conv3D, Conv3DTranspose, Dropout, MaxPooling3D, LeakyReLU, concatenate
+from tensorflow.keras.regularizers import l2
+
+from .trainer import dsc_loss, dsc, iou
+from .model import ModelGen
+
+class UNet3D(ModelGen):
+  """Constructor to create 3D UNets based on the arguments provided.
+
+  This is a class to construct a network, not a Model class. It must, for any such class, contain a name field, which
+  must uniquely identified the architecture specified with the parameters. It must provide a `construct` method
+  which returns the tensorflow `Model`.
+
+  Note that this class takes tensorflow objects and functions as arguments where needed, to construct the network.
+  """
+
+  def __init__(self, name, enc, dec, loss, classify_kernel_regularizer=l2(0.02), metrics=None, optimiser=None, fixed_batch_size=False):
+    """Create an architecture class which constructs a 3D UNet as specified.
+    
+    Args:
+      * `name`: name of the architecture, must be unique for the parameters;
+      * `enc`: a list of dictionaries that are arguments for `_enc_block` method to construct the encoder;
+      * `dec`: a list of dictionaries that are arguments for `_dec_block` method to construct the decoder;
+      * `loss`: the loss function (e.g. as defined in `bca.trainer`)
+      * `classify_kernel_regularizer`: regularizer for the final classification layer
+      * `metrics`: tensorflow metrics to record during training
+      * `optimiser`: tensorflow optimiser to use for training: this must be a function (e.g. lambda expression) to generate the optimiser with a single argument indicating the batch size.
+      * `fixed_batch_size`: if true, use as input a fixed batch-size (may have performance advantages or sometimes needed to avoid crashes; see scw Hack in the `bca.scheduler` code)
+
+    **`_enc_block`** is used to create an encoder block and its arguments are provided in the `enc` dictionary list:
+      * `x` - input from the previous encoder block or from `Input`, using tensorflow's functional API
+      * `name` - name of the encoder block (used to name its layers)
+      * `filters` - number of filters
+      * `kernel` - 3D convolution kernel size
+      * `activation` - activation function (used as `activation` argument to `Conv3D`)
+      * `kernel_initializer` - initialiser for kernel (used as `kernel_initializer` argument to `Conv3D`)
+      * `dropout` - dropout rate (only if positive; otherwise layer is skipped)
+      * `max_pooling` - max pooling layer; if `None`, it is not used (for last layer)
+    Return:
+      * If `max_pooling` is `None`:
+        * Output of last layer ("before `max-pooling`"), None 
+      * Otherwise:
+        * max-pooling output, output before max pooling (for cross-link)
+
+    **`_dec_block`** is used to create a decoder block and its arguments are provided in the `dec` dictionary list:
+      * `x` - input from the previous decoder or the latent representation, using tensorflow's functional API
+      * `y` - input linking from the corresponding encoder block, using tensorflow's functional API
+      * `name` - name oft he decoder block (used to name its layers)
+      * `conv_trans_filters` - number of 3D transposed-convolution filters
+      * `conv_trans_strides` - 3D transposed-convolution strides
+      * `filters` - number of 3D convolution filters
+      * `kernel` - kernel of 3D convolutions
+      * `activation` - activation function (used as `activation` argument to `Conv3D`)
+      * `kernel_initializer` - initialiser for kernel (used as `kernel_initializer` argument to `Conv3D`)
+      * `dropout` - dropout rate (only if positive; otherwise layer is skipped)
+    Return:
+      * Output of last layer
+
+    Example:
+    ```python
+    from bca.trainer import dsc_loss, dsc, iou
+    model = UNet3D(name="UNet3D-UniqueNameForParameters",
+                   enc=[{"filters": 16},
+                        {"filters": 32},
+                        {"filters": 64},
+                        {"filters":128, "kernel_regularizer":keras.regularizers.l2(0.02)},
+                        {"filters":256, "kernel_regularizer":keras.regularizers.l2(0.02), "max_pooling":None}],
+                   dec=[{"filters":128},
+                        {"filters": 64},
+                        {"filters": 32, "kernel_regularizer":keras.regularizers.l2(0.02)},
+                        {"filters": 16, "kernel_regularizer":keras.regularizers.l2(0.02)}],
+                   loss=dsc_loss,
+                   metrics=[dsc,iou])
+    ```
+    """
+    super().__init__(name)
+    self.enc = enc
+    self.dec = dec
+    self.classify_kernel_regularizer = classify_kernel_regularizer
+    self.loss = loss
+    self.metrics = metrics
+    if optimiser is None: # Optimiser must be a function creating the optimiser using batch_size
+      self.optimiser = lambda batch_size : tf.keras.optimizers.Adam(learning_rate = 1e-4 * batch_size / 16.0)
+    else:
+      self.optimiser = optimiser
+    self.fixed_batch_size = fixed_batch_size
+
+  def construct(self, seq, batch_size, jit_compile=True):
+    """Construct a tensorflow functional architecture.
+
+    Args:
+      * `seq`: keras data Sequence (see `bca.dataset.SeqGen`) for the data to be used with the model';
+               this  can also be a tuple where the first part is the input size and the last tuple entry the
+               number of output classes - this is to be able to generate the model independent of the data,
+               e.g., for plotting.
+      * `batch_size`: batch size for training; in particular this is used to adjust the learning rate for the optimiser
+      * `jit_compile`: `jit_compile` argument for `Model.fit`
+
+    Retrun:
+      * Constructed, compiled, functional model with specified optimiser (Adam is default)
+    """
+    # Construct model (incl. compile)
+    if isinstance(seq,tuple):
+      # For plotting model, seq should be a tuple (used only by plot below)
+      inputs = Input(shape=seq[:-1],batch_size=None) 
+      classes = seq[-1] # Last element of tuple is number of classes
+    else:
+      if len(seq.cache.inp_chs) != 1 or len(seq.cache.out_chs) != 1:
+        raise Exception("Invalid input/output numbers")
+      inputs = Input(shape=(*seq.dim,len(seq.cache.inp_chs[0])),batch_size=batch_size if self.fixed_batch_size else None)
+      classes = len(seq.cache.out_chs[0]) # Determine classes from output shape
+    # Encoder
+    x = inputs
+    u = [None] * len(self.enc)
+    for c,e in enumerate(self.enc):
+      x, u[c] = UNet3D._enc_block(x, name=f"enc{c+1}", **e)
+    # Decoder
+    for c,d in enumerate(self.dec):
+      x = UNet3D._dec_block(x, u[-c-2], name=f"dec{c+1}", **d)
+    # Classifier
+    x = Conv3D(classes, (1, 1, 1), kernel_regularizer=self.classify_kernel_regularizer, activation='sigmoid', name="class")(x)
+    # Setup model
+    model = Model(inputs=inputs, outputs=x, name=self.name) 
+    model.compile(loss=self.loss, optimizer=self.optimiser(batch_size), metrics=self.metrics, jit_compile=jit_compile)
+    return model
+
+  @staticmethod
+  def _enc_block(x, name, filters, kernel=(3,3,3), activation=LeakyReLU(alpha=0.1),
+                 kernel_initializer='he_uniform', kernel_regularizer=None, dropout=0.2, 
+                 max_pooling=(2,2,2)):
+    # Encoder block (see __init__)
+    x = Conv3D(filters, kernel, activation=activation, kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,
+               padding='same', name=name+"-conv1")(x)
+    if dropout > 0.0:
+      x = Dropout(dropout, name=name+"-dropout")(x)
+    x = Conv3D(filters, kernel, activation=activation, kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,
+               padding='same', name=name+"-conv2")(x)
+    if max_pooling is None:
+      return x, None
+    y = MaxPooling3D(max_pooling, name=name+"-max_pooling")(x)
+    return y, x
+
+  @staticmethod
+  def _dec_block(x, y, name, filters, conv_trans_filters=(2,2,2), conv_trans_strides=(2,2,2), kernel=(3,3,3),
+                 activation=LeakyReLU(alpha=0.1), kernel_initializer='he_uniform', kernel_regularizer=None,
+                 dropout=0.2):
+    # Decoder block (see __init__)
+    x = Conv3DTranspose(filters, conv_trans_filters, strides=conv_trans_strides, padding='same', name=name+"-conv_trans")(x)
+    x = concatenate([x,y])
+    x = Conv3D(filters, kernel, activation=activation, kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,
+               padding='same', name=name+"-conv1")(x)
+    if dropout > 0.0:
+      x = Dropout(dropout, name=name+"-dropout")(x)
+    x = Conv3D(filters, kernel, activation=activation, kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,
+               padding='same', name=name+"-conv2")(x)
+    return x
\ No newline at end of file
diff --git a/leakycnn-split-same3unet-alltumor-wesstienloss-5-10-2022__1_.ipynb b/leakycnn-split-same3unet-alltumor-wesstienloss-5-10-2022__1_.ipynb
deleted file mode 100644
index 50a9dc0..0000000
--- a/leakycnn-split-same3unet-alltumor-wesstienloss-5-10-2022__1_.ipynb
+++ /dev/null
@@ -1,2789 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "5e5077b0",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "#Code can be divided into a few parts....\n",
-    "#1-Combine \n",
-    "#2-Changing mask pixel values (labels) from 4 to 3 (as the original labels are 0, 1, 2, 4)\n",
-    "#3-Visualize\n",
-    "import numpy as np\n",
-    "import nibabel as nib\n",
-    "import os \n",
-    "import glob\n",
-    "from tensorflow.keras.utils import to_categorical\n",
-    "import matplotlib.pyplot as plt\n",
-    "from tifffile import imsave\n",
-    "\n",
-    "from sklearn.preprocessing import MinMaxScaler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ddd548b8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1854.603271484375\n"
-     ]
-    }
-   ],
-   "source": [
-    "scaler = MinMaxScaler()\n",
-    "TRAIN_DATASET_PATH = 'C:/Users/c21097211/Desktop/Bratsdataset/BraTS2020_TrainingData/MICCAI_BraTS2020_TrainingData/'\n",
-    "# VALIDATION_DATASET_PATH = 'dataset/BraTS2020_ValidationData/MICCAI_BraTS2020_ValidationData/'\n",
-    "\n",
-    "test_image_flair=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_flair.nii').get_fdata()\n",
-    "print(test_image_flair.max())\n",
-    "#Scalers are applied to 1D so let us reshape and then reshape back to original shape. \n",
-    "test_image_flair=scaler.fit_transform(test_image_flair.reshape(-1, test_image_flair.shape[-1])).reshape(test_image_flair.shape)\n",
-    "\n",
-    "\n",
-    "test_image_t1=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_t1.nii').get_fdata()\n",
-    "test_image_t1=scaler.fit_transform(test_image_t1.reshape(-1, test_image_t1.shape[-1])).reshape(test_image_t1.shape)\n",
-    "\n",
-    "test_image_t1ce=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_t1ce.nii').get_fdata()\n",
-    "test_image_t1ce=scaler.fit_transform(test_image_t1ce.reshape(-1, test_image_t1ce.shape[-1])).reshape(test_image_t1ce.shape)\n",
-    "\n",
-    "test_image_t2=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_t2.nii').get_fdata()\n",
-    "test_image_t2=scaler.fit_transform(test_image_t2.reshape(-1, test_image_t2.shape[-1])).reshape(test_image_t2.shape)\n",
-    "\n",
-    "test_mask=nib.load(TRAIN_DATASET_PATH + 'BraTS20_Training_355/BraTS20_Training_355_seg.nii').get_fdata()\n",
-    "test_mask=test_mask.astype(np.uint8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "9fae2821",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0 1 2 4]\n",
-      "[0 1 2 3]\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(np.unique(test_mask))  #0, 1, 2, 4 (Need to reencode to 0, 1, 2, 3)\n",
-    "#Reassign mask values 2 to 1\n",
-    "test_mask[test_mask==4] = 3  #Reassign mask values 4 to 1\n",
-    "print(np.unique(test_mask)) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c64f9297",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 5 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import random\n",
-    "#n_slice=random.randint(0, test_mask.shape[2])\n",
-    "n_slice=90\n",
-    "plt.figure(figsize=(12, 8))\n",
-    "\n",
-    "plt.subplot(231)\n",
-    "plt.imshow(test_image_flair[:,:,n_slice], cmap='gray')\n",
-    "plt.title('Image flair')\n",
-    "plt.subplot(232)\n",
-    "plt.imshow(test_image_t1[:,:,n_slice], cmap='gray')\n",
-    "plt.title('Image t1')\n",
-    "plt.subplot(233)\n",
-    "plt.imshow(test_image_t1ce[:,:,n_slice], cmap='gray')\n",
-    "plt.title('Image t1ce')\n",
-    "plt.subplot(234)\n",
-    "plt.imshow(test_image_t2[:,:,n_slice], cmap='gray')\n",
-    "plt.title('Image t2')\n",
-    "plt.subplot(235)\n",
-    "plt.imshow(test_mask[:,:,n_slice])\n",
-    "plt.title('Mask')\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "aeebc220",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x648 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x648 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x648 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 864x648 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(128, 128, 128, 3)\n",
-      "(128, 128, 128, 3)\n",
-      "(128, 128, 128, 1)\n",
-      "(128, 128, 128)\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Try images:\n",
-    "from keras.models import load_model\n",
-    "import numpy as np\n",
-    "\n",
-    "\n",
-    "test_img = np.load(\"/Users/c21097211/Desktop/Bratsdataset/part23channel/val/train322/image_322.npy\")\n",
-    "\n",
-    "test_mask = np.load(\"/Users/c21097211/Desktop/Bratsdataset/part23channel/val/train322/mask_322.npy\")\n",
-    "#test_mask_argmax=np.argmax(test_mask, axis=3)\n",
-    "\n",
-    "test_pred = np.load(\"/Users/c21097211/Desktop/Bratsdataset/part23channel/val/train322/predicted_mask_322.npy\")\n",
-    "        \n",
-    "        \n",
-    "ptsx,ptsy,ptsz=np.where(test_pred >0)\n",
-    "xmin,xmax,ymin,ymax,zmin,zmax=ptsx.min(),ptsx.max(),ptsy.min(),ptsy.max(),ptsz.min(),ptsz.max()  \n",
-    "m2 = test_mask[xmin:xmax,ymin:ymax,zmin:zmax]\n",
-    "m1 = test_img[xmin:xmax,ymin:ymax,zmin:zmax]\n",
-    "\n",
-    "original= np.load(\"C:/Users/c21097211/Desktop/Bratsdataset/BraTS2020_TrainingData/input_data_3channels/masks/mask_322.npy\")\n",
-    "original=np.argmax(original, axis=3)\n",
-    "#test_pred_argmax=np.argmax(test_pred, axis=3)\n",
-    "\n",
-    "test_pred=np.resize(test_pred,(test_pred.shape[0],test_pred.shape[1],test_pred.shape[2],1))\n",
-    "try1= test_img * test_pred\n",
-    "# print(test_prediction_argmax.shape)\n",
-    "# print(test_mask_argmax.shape)\n",
-    "# print(np.unique(test_prediction_argmax))\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "#n_slice=random.randint(0, test_mask.shape[2])\n",
-    "n_slice=45\n",
-    "plt.figure(figsize=(12, 9))\n",
-    "plt.subplot(231)\n",
-    "plt.imshow(test_img[:,:,n_slice, 0], cmap='gray')\n",
-    "plt.title('Image flair')\n",
-    "plt.subplot(232)\n",
-    "plt.imshow(test_img[:,:,n_slice, 1], cmap='gray')\n",
-    "plt.title('Image t1ce')\n",
-    "plt.subplot(233)\n",
-    "plt.imshow(test_img[:,:,n_slice, 2], cmap='gray')\n",
-    "plt.title('Image t2')\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "plt.figure(figsize=(12, 9))\n",
-    "plt.subplot(231)\n",
-    "plt.imshow(try1[:,:,n_slice, 0], cmap='gray')\n",
-    "plt.title('Image flair')\n",
-    "plt.subplot(232)\n",
-    "plt.imshow(try1[:,:,n_slice, 1], cmap='gray')\n",
-    "plt.title('Image t1ce')\n",
-    "plt.subplot(233)\n",
-    "plt.imshow(try1[:,:,n_slice, 2], cmap='gray')\n",
-    "plt.title('Image t2')\n",
-    "plt.subplot(234)\n",
-    "plt.imshow(test_mask[:,:,n_slice])\n",
-    "plt.title('Mask')\n",
-    "plt.show()\n",
-    "plt.figure\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "plt.figure(figsize=(12, 9))\n",
-    "plt.subplot(231)\n",
-    "plt.imshow(m1[:,:,n_slice, 0], cmap='gray')\n",
-    "plt.title('Image flair')\n",
-    "plt.subplot(232)\n",
-    "plt.imshow(m1[:,:,n_slice, 1], cmap='gray')\n",
-    "plt.title('Image t1ce')\n",
-    "plt.subplot(233)\n",
-    "plt.imshow(m1[:,:,n_slice, 2], cmap='gray')\n",
-    "plt.title('Image t2')\n",
-    "plt.subplot(234)\n",
-    "plt.imshow(m2[:,:,n_slice])\n",
-    "plt.title('Mask')\n",
-    "plt.show()\n",
-    "plt.figure\n",
-    "\n",
-    "n_slice=45\n",
-    "plt.figure(figsize=(12, 9))\n",
-    "plt.subplot(235)\n",
-    "plt.imshow(test_pred[:,:,n_slice])\n",
-    "plt.title('pred')\n",
-    "plt.show() \n",
-    "n_slice=45\n",
-    "plt.figure(figsize=(12, 9))\n",
-    "plt.subplot(235)\n",
-    "plt.imshow(original[:,:,n_slice])\n",
-    "plt.title('original')\n",
-    "plt.show() \n",
-    "\n",
-    "\n",
-    "print(test_img.shape)\n",
-    "print(test_mask.shape)\n",
-    "print(test_pred.shape)\n",
-    "print(original.shape)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "bcdd3434",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from skimage.transform import resize\n",
-    "def load_img_mask(img_mask_list):\n",
-    " \n",
-    "\n",
-    "\n",
-    "    images=[]\n",
-    "    \n",
-    "    masks=[]\n",
-    "#     masks=np.zeros([80,80,80,len(img_mask_list)],dtype=np.float64)\n",
-    "    for i, dir_name in enumerate(img_mask_list):  \n",
-    "\n",
-    "        \n",
-    "       original_image = np.load(glob.glob(dir_name+'/'+'image_*.npy')[0])\n",
-    "\n",
-    "       original_mask  =  np.load(glob.glob(dir_name+'/'+'mask_*.npy')[0])\n",
-    "\n",
-    "\n",
-    "\n",
-    "       predicted_mask =  np.load(glob.glob(dir_name+'/'+'predicted_mask_*.npy')[0])\n",
-    "\n",
-    "\n",
-    "        \n",
-    "       ptsx,ptsy,ptsz=np.where(predicted_mask >0)\n",
-    "       xmin,xmax,ymin,ymax,zmin,zmax=ptsx.min(),ptsx.max(),ptsy.min(),ptsy.max(),ptsz.min(),ptsz.max()  \n",
-    "        \n",
-    "       #crop=  predicted_mask[xmin:xmax,ymin:ymax]\n",
-    "    \n",
-    "       #crop2 = np.resize(crop,(crop.shape[0],crop.shape[1],crop.shape[2],1))\n",
-    "    \n",
-    "       m2 = original_mask[xmin:xmax,ymin:ymax,zmin:zmax]\n",
-    "        \n",
-    "       mask_=resize(m2,(80,80,128))\n",
-    "\n",
-    "#        print(\"new\",m2.shape)\n",
-    "\n",
-    "\n",
-    "       \n",
-    "       image = original_image[xmin:xmax,ymin:ymax,zmin:zmax]\n",
-    "       image_=resize(image,(80,80,128),mode='constant',preserve_range=True)\n",
-    "    \n",
-    "    \n",
-    "       masks.append(mask_)\n",
-    "\n",
-    "       images.append(image_)\n",
-    "\n",
-    "    images = tf.convert_to_tensor(np.array(images))\n",
-    "    masks = tf.convert_to_tensor(np.array(masks))\n",
-    "    \n",
-    "    return(images,masks)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ee070d49",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def imageLoader(path, batch_size):\n",
-    "    \n",
-    "    img_mask_list=glob.glob(path+'/*')\n",
-    "\n",
-    "    L = len(img_mask_list)\n",
-    "\n",
-    "    #keras needs the generator infinite, so we will use while true  \n",
-    "    while True:\n",
-    "\n",
-    "        batch_start = 0\n",
-    "        batch_end = batch_size\n",
-    "\n",
-    "        while batch_start < L:\n",
-    "            limit = min(batch_end, L)\n",
-    "                       \n",
-    "            X,Y = load_img_mask( img_mask_list[batch_start:limit])\n",
-    "           \n",
-    "\n",
-    "            yield (X,Y) #a tuple with two numpy arrays with batch_size samples     \n",
-    "\n",
-    "            batch_start += batch_size   \n",
-    "            batch_end += batch_size"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7b8d450a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "import os \n",
-    "train_path=\"C:/Users/c21097211/Desktop/part23channel/train\"\n",
-    "val_path=\"C:/Users/c21097211/Desktop/part23channel/val\"\n",
-    "\n",
-    "batch_size=1\n",
-    "train_img_datagen1=imageLoader(train_path,batch_size)\n",
-    "val_img_datagen1=imageLoader(val_path,batch_size)\n",
-    "\n",
-    "train_img_list= os.listdir(train_path)\n",
-    "val_img_list= os.listdir(val_path)\n",
-    "#To verify generator \n",
-    "imgt, mskt = train_img_datagen1.__next__()\n",
-    "imgv, mskv = val_img_datagen1.__next__()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "94f7d4b5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# To Test Output of the Generator\n",
-    "import matplotlib.pyplot as plt\n",
-    "import random\n",
-    "img_num = random.randint(0,imgt.shape[0]-1)\n",
-    "test_img=imgt[img_num]\n",
-    "c=mskt[img_num]\n",
-    "test_mask=np.argmax(c, axis=3)\n",
-    "n_slice=60\n",
-    "\n",
-    "\n",
-    "plt.subplot(231)\n",
-    "plt.imshow(test_img[:,:,n_slice, 0], cmap='gray')\n",
-    "plt.title('Image flair')\n",
-    "plt.subplot(232)\n",
-    "plt.imshow(test_img[:,:,n_slice, 1], cmap='gray')\n",
-    "plt.title('Image t1ce')\n",
-    "plt.subplot(233)\n",
-    "plt.imshow(test_img[:,:,n_slice, 2], cmap='gray')\n",
-    "plt.title('Image t2')\n",
-    "plt.subplot(234)\n",
-    "\n",
-    "plt.imshow(test_mask[:,:,n_slice])\n",
-    "plt.title('Mask')\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "# To Test Output of the Generator\n",
-    "import matplotlib.pyplot as plt\n",
-    "import random\n",
-    "img_num = random.randint(0,imgv.shape[0]-1)\n",
-    "test_img=imgv[img_num]\n",
-    "c=mskv[img_num]\n",
-    "test_mask=np.argmax(c, axis=3)\n",
-    "n_slice=60\n",
-    "plt.subplot(231)\n",
-    "plt.imshow(test_img[:,:,n_slice, 0], cmap='gray')\n",
-    "plt.title('Image flair')\n",
-    "plt.subplot(232)\n",
-    "plt.imshow(test_img[:,:,n_slice, 1], cmap='gray')\n",
-    "plt.title('Image t1ce')\n",
-    "plt.subplot(233)\n",
-    "plt.imshow(test_img[:,:,n_slice, 2], cmap='gray')\n",
-    "plt.title('Image t2')\n",
-    "plt.subplot(234)\n",
-    "\n",
-    "plt.imshow(test_mask[:,:,n_slice])\n",
-    "plt.title('Mask')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "675fc178",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-----------------------------------------------------------\n",
-      "KerasTensor(type_spec=TensorSpec(shape=(None, 20, 20, 32, 64), dtype=tf.float32, name=None), name='tf.math.multiply/Mul:0', description=\"created by layer 'tf.math.multiply'\")\n",
-      "-----------------------------------------------------------\n",
-      "-----------------------------------------------------------\n",
-      "KerasTensor(type_spec=TensorSpec(shape=(None, 10, 10, 16, 64), dtype=tf.float32, name=None), name='average_pooling3d/AvgPool3D:0', description=\"created by layer 'average_pooling3d'\")\n",
-      "-----------------------------------------------------------\n",
-      "Model: \"model\"\n",
-      "__________________________________________________________________________________________________\n",
-      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
-      "==================================================================================================\n",
-      " input_1 (InputLayer)           [(None, 80, 80, 128  0           []                               \n",
-      "                                , 3)]                                                             \n",
-      "                                                                                                  \n",
-      " conv3d (Conv3D)                (None, 80, 80, 128,  2624        ['input_1[0][0]']                \n",
-      "                                 32)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_1 (Conv3D)              (None, 80, 80, 128,  27680       ['conv3d[0][0]']                 \n",
-      "                                 32)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_3 (Conv3D)              (None, 80, 80, 128,  27680       ['conv3d[0][0]']                 \n",
-      "                                 32)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_5 (Conv3D)              (None, 80, 80, 128,  128032      ['conv3d[0][0]']                 \n",
-      "                                 32)                                                              \n",
-      "                                                                                                  \n",
-      " instance_normalization (Instan  (None, 80, 80, 128,  64         ['conv3d_1[0][0]']               \n",
-      " ceNormalization)                32)                                                              \n",
-      "                                                                                                  \n",
-      " instance_normalization_1 (Inst  (None, 80, 80, 128,  64         ['conv3d_3[0][0]']               \n",
-      " anceNormalization)              32)                                                              \n",
-      "                                                                                                  \n",
-      " instance_normalization_2 (Inst  (None, 80, 80, 128,  64         ['conv3d_5[0][0]']               \n",
-      " anceNormalization)              32)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_2 (Conv3D)              (None, 80, 80, 128,  27680       ['instance_normalization[0][0]'] \n",
-      "                                 32)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_4 (Conv3D)              (None, 80, 80, 128,  27680       ['instance_normalization_1[0][0]'\n",
-      "                                 32)                             ]                                \n",
-      "                                                                                                  \n",
-      " conv3d_6 (Conv3D)              (None, 80, 80, 128,  128032      ['instance_normalization_2[0][0]'\n",
-      "                                 32)                             ]                                \n",
-      "                                                                                                  \n",
-      " concatenate (Concatenate)      (None, 80, 80, 128,  0           ['conv3d_2[0][0]',               \n",
-      "                                 96)                              'conv3d_4[0][0]',               \n",
-      "                                                                  'conv3d_6[0][0]']               \n",
-      "                                                                                                  \n",
-      " instance_normalization_3 (Inst  (None, 80, 80, 128,  192        ['concatenate[0][0]']            \n",
-      " anceNormalization)              96)                                                              \n",
-      "                                                                                                  \n",
-      " max_pooling3d_3 (MaxPooling3D)  (None, 40, 40, 64,   0          ['instance_normalization_3[0][0]'\n",
-      "                                96)                              ]                                \n",
-      "                                                                                                  \n",
-      " tf.identity_2 (TFOpLambda)     (None, 40, 40, 64,   0           ['max_pooling3d_3[0][0]']        \n",
-      "                                96)                                                               \n",
-      "                                                                                                  \n",
-      " conv3d_7 (Conv3D)              (None, 40, 40, 64,   165952      ['tf.identity_2[0][0]']          \n",
-      "                                64)                                                               \n",
-      "                                                                                                  \n",
-      " instance_normalization_4 (Inst  (None, 40, 40, 64,   128        ['conv3d_7[0][0]']               \n",
-      " anceNormalization)             64)                                                               \n",
-      "                                                                                                  \n",
-      " conv3d_8 (Conv3D)              (None, 40, 40, 64,   110656      ['instance_normalization_4[0][0]'\n",
-      "                                64)                              ]                                \n",
-      "                                                                                                  \n",
-      " instance_normalization_5 (Inst  (None, 40, 40, 64,   128        ['conv3d_8[0][0]']               \n",
-      " anceNormalization)             64)                                                               \n",
-      "                                                                                                  \n",
-      " max_pooling3d_4 (MaxPooling3D)  (None, 20, 20, 32,   0          ['instance_normalization_5[0][0]'\n",
-      "                                64)                              ]                                \n",
-      "                                                                                                  \n",
-      " tf.identity_3 (TFOpLambda)     (None, 20, 20, 32,   0           ['max_pooling3d_4[0][0]']        \n",
-      "                                64)                                                               \n",
-      "                                                                                                  \n",
-      " global_average_pooling3d (Glob  (None, 64)          0           ['tf.identity_3[0][0]']          \n",
-      " alAveragePooling3D)                                                                              \n",
-      "                                                                                                  \n",
-      " dense (Dense)                  (None, 8)            512         ['global_average_pooling3d[0][0]'\n",
-      "                                                                 ]                                \n",
-      "                                                                                                  \n",
-      " dense_1 (Dense)                (None, 64)           512         ['dense[0][0]']                  \n",
-      "                                                                                                  \n",
-      " tf.math.multiply (TFOpLambda)  (None, 20, 20, 32,   0           ['tf.identity_3[0][0]',          \n",
-      "                                64)                               'dense_1[0][0]']                \n",
-      "                                                                                                  \n",
-      " batch_normalization (BatchNorm  (None, 20, 20, 32,   256        ['tf.math.multiply[0][0]']       \n",
-      " alization)                     64)                                                               \n",
-      "                                                                                                  \n",
-      " re_lu (ReLU)                   (None, 20, 20, 32,   0           ['batch_normalization[0][0]']    \n",
-      "                                64)                                                               \n",
-      "                                                                                                  \n",
-      " conv3d_9 (Conv3D)              (None, 20, 20, 32,   4160        ['re_lu[0][0]']                  \n",
-      "                                64)                                                               \n",
-      "                                                                                                  \n",
-      " average_pooling3d (AveragePool  (None, 10, 10, 16,   0          ['conv3d_9[0][0]']               \n",
-      " ing3D)                         64)                                                               \n",
-      "                                                                                                  \n",
-      " conv3d_10 (Conv3D)             (None, 10, 10, 16,   221312      ['average_pooling3d[0][0]']      \n",
-      "                                128)                                                              \n",
-      "                                                                                                  \n",
-      " instance_normalization_6 (Inst  (None, 10, 10, 16,   256        ['conv3d_10[0][0]']              \n",
-      " anceNormalization)             128)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_11 (Conv3D)             (None, 10, 10, 16,   442496      ['instance_normalization_6[0][0]'\n",
-      "                                128)                             ]                                \n",
-      "                                                                                                  \n",
-      " instance_normalization_7 (Inst  (None, 10, 10, 16,   256        ['conv3d_11[0][0]']              \n",
-      " anceNormalization)             128)                                                              \n",
-      "                                                                                                  \n",
-      " max_pooling3d_5 (MaxPooling3D)  (None, 5, 5, 8, 128  0          ['instance_normalization_7[0][0]'\n",
-      "                                )                                ]                                \n",
-      "                                                                                                  \n",
-      " tf.identity_4 (TFOpLambda)     (None, 5, 5, 8, 128  0           ['max_pooling3d_5[0][0]']        \n",
-      "                                )                                                                 \n",
-      "                                                                                                  \n",
-      " up_sampling3d (UpSampling3D)   (None, 10, 10, 16,   0           ['tf.identity_4[0][0]']          \n",
-      "                                128)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_12 (Conv3D)             (None, 10, 10, 16,   262400      ['up_sampling3d[0][0]']          \n",
-      "                                256)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_13 (Conv3D)             (None, 10, 10, 16,   1769728     ['conv3d_12[0][0]']              \n",
-      "                                256)                                                              \n",
-      "                                                                                                  \n",
-      " tf.identity_5 (TFOpLambda)     (None, 10, 10, 16,   0           ['conv3d_13[0][0]']              \n",
-      "                                256)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_14 (Conv3D)             (None, 10, 10, 16,   1769728     ['tf.identity_5[0][0]']          \n",
-      "                                256)                                                              \n",
-      "                                                                                                  \n",
-      " up_sampling3d_1 (UpSampling3D)  (None, 20, 20, 32,   0          ['conv3d_14[0][0]']              \n",
-      "                                256)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_15 (Conv3D)             (None, 20, 20, 32,   262272      ['up_sampling3d_1[0][0]']        \n",
-      "                                128)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_16 (Conv3D)             (None, 20, 20, 32,   442496      ['conv3d_15[0][0]']              \n",
-      "                                128)                                                              \n",
-      "                                                                                                  \n",
-      " tf.identity_6 (TFOpLambda)     (None, 20, 20, 32,   0           ['conv3d_16[0][0]']              \n",
-      "                                128)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_17 (Conv3D)             (None, 20, 20, 32,   442496      ['tf.identity_6[0][0]']          \n",
-      "                                128)                                                              \n",
-      "                                                                                                  \n",
-      " up_sampling3d_2 (UpSampling3D)  (None, 40, 40, 64,   0          ['conv3d_17[0][0]']              \n",
-      "                                128)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_18 (Conv3D)             (None, 40, 40, 64,   65600       ['up_sampling3d_2[0][0]']        \n",
-      "                                64)                                                               \n",
-      "                                                                                                  \n",
-      " conv3d_19 (Conv3D)             (None, 40, 40, 64,   110656      ['conv3d_18[0][0]']              \n",
-      "                                64)                                                               \n",
-      "                                                                                                  \n",
-      " tf.identity_7 (TFOpLambda)     (None, 40, 40, 64,   0           ['conv3d_19[0][0]']              \n",
-      "                                64)                                                               \n",
-      "                                                                                                  \n",
-      " conv3d_20 (Conv3D)             (None, 40, 40, 64,   110656      ['tf.identity_7[0][0]']          \n",
-      "                                64)                                                               \n",
-      "                                                                                                  \n",
-      " up_sampling3d_3 (UpSampling3D)  (None, 80, 80, 128,  0          ['conv3d_20[0][0]']              \n",
-      "                                 64)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_21 (Conv3D)             (None, 80, 80, 128,  16416       ['up_sampling3d_3[0][0]']        \n",
-      "                                 32)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_22 (Conv3D)             (None, 80, 80, 128,  27680       ['conv3d_21[0][0]']              \n",
-      "                                 32)                                                              \n",
-      "                                                                                                  \n",
-      " tf.identity_8 (TFOpLambda)     (None, 80, 80, 128,  0           ['conv3d_22[0][0]']              \n",
-      "                                 32)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_23 (Conv3D)             (None, 80, 80, 128,  27680       ['tf.identity_8[0][0]']          \n",
-      "                                 32)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_24 (Conv3D)             (None, 80, 80, 128,  132         ['conv3d_23[0][0]']              \n",
-      "                                 4)                                                               \n",
-      "                                                                                                  \n",
-      "==================================================================================================\n",
-      "Total params: 6,624,356\n",
-      "Trainable params: 6,624,228\n",
-      "Non-trainable params: 128\n",
-      "__________________________________________________________________________________________________\n",
-      "(None, 80, 80, 128, 3)\n",
-      "(None, 80, 80, 128, 4)\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Build the model\n",
-    "import tensorflow as tf\n",
-    "from keras.models import Model\n",
-    "from keras.layers import Input, Reshape, Dense, Conv3D, BatchNormalization, UpSampling3D, AveragePooling3D, MaxPooling3D, concatenate, GlobalAveragePooling3D, Conv3DTranspose, BatchNormalization, Dropout, Lambda\n",
-    "from keras import regularizers\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "from keras.metrics import MeanIoU\n",
-    "Lrelu = tf.keras.layers.LeakyReLU(alpha=0.1)\n",
-    "from tensorflow_addons.layers import InstanceNormalization\n",
-    "from tensorflow.python.keras.layers import Dropout, SpatialDropout3D\n",
-    "'''\n",
-    "A transition layer is used to control the complexity of the model. \n",
-    "It reduces the number of channels by using an 1*1 convolution. \n",
-    "Moreover, it halves the height and width via average pooling with a stride of 2.\n",
-    "'''     \n",
-    "def SqueezeAndExcitation(inputs, ratio=8):\n",
-    "    b,_, _, _,c= inputs.shape\n",
-    "    x = GlobalAveragePooling3D()(inputs)\n",
-    "    x = Dense(c//ratio, activation=\"relu\", use_bias=False)(x)\n",
-    "    x = Dense(c, activation=\"sigmoid\", use_bias=False)(x)\n",
-    "    x = inputs * x\n",
-    "    return x\n",
-    "\n",
-    "def TransitionBlock(inputs):\n",
-    "        b,_, _, _,c= inputs.shape\n",
-    "        x = BatchNormalization()(inputs)\n",
-    "        x = tf.keras.layers.ReLU()(x)\n",
-    "        x = tf.keras.layers.Conv3D(c, kernel_size=1)(x)\n",
-    "        x = tf.keras.layers.AvgPool3D(pool_size=2, strides=2)(x)\n",
-    "        return x\n",
-    "     \n",
-    "def CNN_Model(IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS, num_classes):\n",
-    "    kernel_initializer =  'he_uniform'\n",
-    "    inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS))\n",
-    "    #s = Lambda(lambda x: x / 255)(inputs)   #No need for this if we normalize our inputs beforehand\n",
-    "    s = inputs\n",
-    "    \n",
-    "    conv = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(s)\n",
-    "    conv1 = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(conv)\n",
-    "    B1= InstanceNormalization(axis=-1)(conv1)\n",
-    "    conv1 = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(B1)\n",
-    "    pool1 = MaxPooling3D((2, 2, 2))(conv1)\n",
-    "   \n",
-    "    # 3x3 conv\n",
-    "    conv2 = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(conv)\n",
-    "    B2= InstanceNormalization(axis=-1)(conv2)\n",
-    "    conv2 = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(B2)\n",
-    "    s2= SpatialDropout3D(0.3)(conv2)\n",
-    "    pool2 = MaxPooling3D((2, 2, 2))(s2)\n",
-    "\n",
-    "    # 5x5 conv\n",
-    "    conv3 = Conv3D(32, (5, 5, 5), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(conv)\n",
-    "    B3= InstanceNormalization(axis=-1)(conv3)\n",
-    "    conv3 = Conv3D(32, (5, 5, 5), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(B3)\n",
-    "    s3= SpatialDropout3D(0.3)(conv3)\n",
-    "    # 3x3 max pooling\n",
-    "    pool3 = MaxPooling3D((3, 3, 3), strides=(1,1,1), padding='same')(s3)\n",
-    "\n",
-    "   \n",
-    "    # concatenate filters, assumes filters/channels last\n",
-    "    layer_out = concatenate([conv1  , conv2, conv3], axis=-1)\n",
-    "    Bo= InstanceNormalization(axis=-1)(layer_out)\n",
-    "    poolo = MaxPooling3D((2, 2, 2))(Bo)  \n",
-    "    drop1 = SpatialDropout3D(0.3)(poolo)\n",
-    "   \n",
-    "    conv4 = Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(drop1)\n",
-    "    B4= InstanceNormalization(axis=-1)(conv4)\n",
-    "    conv4 = Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(B4)\n",
-    "    B4= InstanceNormalization(axis=-1)(conv4)\n",
-    "    pool4= MaxPooling3D((2, 2, 2))(B4)  \n",
-    "    drop2 = SpatialDropout3D(0.3)(pool4) \n",
-    "   \n",
-    "    attention_layer1 = SqueezeAndExcitation( drop2)\n",
-    "    print(\"-----------------------------------------------------------\")\n",
-    "    print(attention_layer1)\n",
-    "    print(\"-----------------------------------------------------------\")\n",
-    "\n",
-    "    tranisition_layer1 = TransitionBlock(attention_layer1)\n",
-    "    print(\"-----------------------------------------------------------\")\n",
-    "    print(tranisition_layer1)\n",
-    "    print(\"-----------------------------------------------------------\")\n",
-    "\n",
-    "    \n",
-    "    conv6 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(tranisition_layer1)\n",
-    "    B6 = InstanceNormalization(axis=-1)(conv6)\n",
-    "    conv6 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(B6)\n",
-    "    B6= InstanceNormalization(axis=-1)(conv6)\n",
-    "    pool6= MaxPooling3D((2, 2, 2))(B6)  \n",
-    "    drop4 = SpatialDropout3D(0.3)(pool6) \n",
-    "\n",
-    "#Upsampling \n",
-    "                                                                                                  \n",
-    "\n",
-    "    u8 = Conv3D(256, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(UpSampling3D(size =(2,2,2))(drop4))                                                                                                  \n",
-    "    c8 = InstanceNormalization(axis=-1)(u8)\n",
-    "    c8 = Conv3D(256, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(u8)\n",
-    "    c8 = SpatialDropout3D(0.3)(c8)\n",
-    "    c8 = Conv3D(256, (3,3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(c8)\n",
-    "                                                                                                         \n",
-    "    u9 = Conv3D(128, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(UpSampling3D(size =(2,2,2))(c8))\n",
-    "    c9 = InstanceNormalization(axis=-1)(u9)\n",
-    "    c9 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, \n",
-    "                kernel_regularizer=regularizers.l2(0.02), padding='same')(u9)\n",
-    "    c9 = SpatialDropout3D(0.3)(c9)\n",
-    "    c9 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, \n",
-    "                 kernel_regularizer=regularizers.l2(0.02), padding='same')(c9)\n",
-    "\n",
-    "    u10= Conv3D(64, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(UpSampling3D(size =(2,2,2))(c9))\n",
-    "    c10 = InstanceNormalization(axis=-1)(u10)\n",
-    "    c10= Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
-    "              kernel_regularizer=regularizers.l2(0.02), padding='same')(u10)\n",
-    "    c10 = SpatialDropout3D(0.3)(c10)                                                                                               \n",
-    "    c10= Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
-    "                 kernel_regularizer=regularizers.l2(0.02), padding='same')(c10)\n",
-    "    \n",
-    "    u11= Conv3D(32, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(UpSampling3D(size =(2,2,2))(c10))\n",
-    "    c11 = InstanceNormalization(axis=-1)(u11)\n",
-    "    c11= Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
-    "              kernel_regularizer=regularizers.l2(0.02), padding='same')(u11)\n",
-    "    c11 = SpatialDropout3D(0.3)(c11)                                                                                               \n",
-    "    c11= Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer,\n",
-    "                 kernel_regularizer=regularizers.l2(0.02), padding='same')(c11)\n",
-    "                                                                               \n",
-    "                                                                                                       \n",
-    "                                                                            \n",
-    "                       \n",
-    "    outputs = Conv3D(num_classes, (1, 1, 1), kernel_regularizer=regularizers.l2(0.02),activation='softmax')(c11)\n",
-    "\n",
-    "    model = Model(inputs=[inputs], outputs=[outputs])\n",
-    "    #compile model outside of this function to make it flexible.\n",
-    "\n",
-    "    return model                \n",
-    "                     \n",
-    "\n",
-    "#Test if everything is working ok.\n",
-    "model = CNN_Model(80, 80, 128, 3, 4)\n",
-    "\n",
-    "\n",
-    "model.summary() \n",
-    "print(model.input_shape)\n",
-    "print(model.output_shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "e09d006a",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from tensorflow.keras.utils import plot_model\n",
-    "plot_model(model, \n",
-    "           show_shapes = True,\n",
-    "           show_dtype = False,\n",
-    "           show_layer_names = True, \n",
-    "           rankdir = 'TB', \n",
-    "           expand_nested = False, \n",
-    "           dpi = 70)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "d47c96ac",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Define loss, metrics and optimizer to be used for training\n",
-    "import keras\n",
-    "import keras.backend as K\n",
-    "def dice(y_true, y_pred):\n",
-    "    #computes the dice score on two tensors\n",
-    "\n",
-    "    sum_p=K.sum(y_pred,axis=0)\n",
-    "    sum_r=K.sum(y_true,axis=0)\n",
-    "    sum_pr=K.sum(y_true * y_pred,axis=0)\n",
-    "    dice_numerator =2*sum_pr\n",
-    "    dice_denominator =sum_r+sum_p\n",
-    "    dice_score =(dice_numerator+K.epsilon() )/(dice_denominator+K.epsilon())\n",
-    "    return dice_score\n",
-    "\n",
-    "\n",
-    "def dice_whole_metric(y_true, y_pred):\n",
-    "    #computes the dice for the whole tumor\n",
-    "\n",
-    "    y_true_f = K.reshape(y_true,shape=(-1,4))\n",
-    "    y_pred_f = K.reshape(y_pred,shape=(-1,4))\n",
-    "    y_whole=K.sum(y_true_f[:,1:],axis=1)\n",
-    "    p_whole=K.sum(y_pred_f[:,1:],axis=1)\n",
-    "    dice_whole=dice(y_whole,p_whole)\n",
-    "    return dice_whole\n",
-    "\n",
-    "def dice_en_metric(y_true, y_pred):\n",
-    "    #computes the dice for the enhancing region\n",
-    "\n",
-    "    y_true_f = K.reshape(y_true,shape=(-1,4))\n",
-    "    y_pred_f = K.reshape(y_pred,shape=(-1,4))\n",
-    "    y_enh=y_true_f[:,-1]\n",
-    "    p_enh=y_pred_f[:,-1]\n",
-    "    dice_en=dice(y_enh,p_enh)\n",
-    "    return dice_en\n",
-    "\n",
-    "def dice_core_metric(y_true, y_pred):\n",
-    "    ##computes the dice for the core region\n",
-    "\n",
-    "    y_true_f = K.reshape(y_true,shape=(-1,4))\n",
-    "    y_pred_f = K.reshape(y_pred,shape=(-1,4))\n",
-    "    \n",
-    "#     workaround for tf\n",
-    "    y_core=K.sum(tf.gather(y_true_f, [1,3],axis =1),axis=1)\n",
-    "    p_core=K.sum(tf.gather(y_pred_f, [1,3],axis =1),axis=1)\n",
-    "    \n",
-    "#     y_core=K.sum(y_true_f[:,[1,3]],axis=1)\n",
-    "#     p_core=K.sum(y_pred_f[:,[1,3]],axis=1)\n",
-    "    dice_core=dice(y_core,p_core)\n",
-    "    return dice_core\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "22c60968",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import keras.backend as K\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from keras.losses import mean_squared_error\n",
-    "\n",
-    "\n",
-    "M_tree_4 = np.array([[0., 1., 1., 1.,],\n",
-    "                     [1., 0., 0.6, 0.5],\n",
-    "                     [1., 0.6, 0., 0.7],\n",
-    "                     [1., 0.5, 0.7, 0.]], dtype=np.float64)\n",
-    "\n",
-    "def wasserstein_disagreement_map(prediction, ground_truth, M):\n",
-    "    \"\"\"\n",
-    "    Function to calculate the pixel-wise Wasserstein distance between the\n",
-    "    flattened pred_proba and the flattened labels (ground_truth) with respect\n",
-    "    to the distance matrix on the label space M.\n",
-    "    :param prediction: the logits after softmax\n",
-    "    :param ground_truth: segmentation ground_truth\n",
-    "    :param M: distance matrix on the label space\n",
-    "    :return: the pixelwise distance map (wass_dis_map)\n",
-    "    \"\"\"\n",
-    "    # pixel-wise Wassertein distance (W) between flat_pred_proba and flat_labels\n",
-    "    # wrt the distance matrix on the label space M\n",
-    "    n_classes = K.int_shape(prediction)[-1]\n",
-    "    # unstack_labels = tf.unstack(ground_truth, axis=-1)\n",
-    "    ground_truth = tf.cast(ground_truth, dtype=tf.float64)\n",
-    "    # unstack_pred = tf.unstack(prediction, axis=-1)\n",
-    "    prediction = tf.cast(prediction, dtype=tf.float64)\n",
-    "    # print(\"shape of M\", M.shape, \"unstacked labels\", unstack_labels,\n",
-    "    #       \"unstacked pred\" ,unstack_pred)\n",
-    "    # W is a weighting sum of all pairwise correlations (pred_ci x labels_cj)\n",
-    "    pairwise_correlations = []\n",
-    "    for i in range(n_classes):\n",
-    "        for j in range(n_classes):\n",
-    "            pairwise_correlations.append(\n",
-    "                M[i, j] * tf.multiply(prediction[:,i], ground_truth[:,j]))\n",
-    "    wass_dis_map = tf.add_n(pairwise_correlations)\n",
-    "    return wass_dis_map\n",
-    "\n",
-    "def generalised_wasserstein_dice_loss(y_true, y_predicted ):\n",
-    "    \"\"\"\n",
-    "    Function to calculate the Generalised Wasserstein Dice Loss defined in\n",
-    "    Fidon, L. et. al. (2017) Generalised Wasserstein Dice Score for Imbalanced\n",
-    "    Multi-class Segmentation using Holistic Convolutional Networks.\n",
-    "    MICCAI 2017 (BrainLes)\n",
-    "\n",
-    "    :param prediction: the logits (before softmax)\n",
-    "    :param ground_truth: the segmentation ground_truth\n",
-    "    :param weight_map:\n",
-    "    :return: the loss\n",
-    "    \"\"\"\n",
-    "    # apply softmax to pred scores\n",
-    "    n_classes = K.int_shape(y_predicted)[-1]\n",
-    "\n",
-    "\n",
-    "    ground_truth = tf.cast(tf.reshape(y_true,(-1,n_classes)), dtype=tf.int64)\n",
-    "    pred_proba = tf.cast(tf.reshape(y_predicted,(-1,n_classes)), dtype=tf.float64)\n",
-    "\n",
-    "    # M = tf.cast(M, dtype=tf.float64)\n",
-    "    # compute disagreement map (delta)\n",
-    "    M = M_tree_4\n",
-    "    # print(\"M shape is \", M.shape, pred_proba, one_hot)\n",
-    "    delta = wasserstein_disagreement_map(pred_proba, ground_truth, M)\n",
-    "    # compute generalisation of all error for multi-class seg\n",
-    "    all_error = tf.reduce_sum(delta)\n",
-    "    # compute generalisation of true positives for multi-class seg\n",
-    "    one_hot = tf.cast(ground_truth, dtype=tf.float64)\n",
-    "    true_pos = tf.reduce_sum(\n",
-    "        tf.multiply(tf.constant(M[0, :n_classes], dtype=tf.float64), one_hot),\n",
-    "        axis=1)\n",
-    "    true_pos = tf.reduce_sum(tf.multiply(true_pos, 1. - delta), axis=0)\n",
-    "    WGDL = 1. - (2. * true_pos) / (2. * true_pos + all_error)\n",
-    "\n",
-    "    return tf.cast(WGDL, dtype=tf.float32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "e1fbf9c2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "import segmentation_models_3D as sm\n",
-    "metrics = ['accuracy',dice_whole_metric, dice_en_metric,dice_core_metric]\n",
-    "\n",
-    "LR = 0.0001\n",
-    "optim = tf.keras.optimizers.Adam(LR)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "9c650bee",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-----------------------------------------------------------\n",
-      "KerasTensor(type_spec=TensorSpec(shape=(None, 20, 20, 32, 64), dtype=tf.float32, name=None), name='tf.math.multiply_3/Mul:0', description=\"created by layer 'tf.math.multiply_3'\")\n",
-      "-----------------------------------------------------------\n",
-      "Model: \"model_3\"\n",
-      "__________________________________________________________________________________________________\n",
-      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
-      "==================================================================================================\n",
-      " input_4 (InputLayer)           [(None, 80, 80, 128  0           []                               \n",
-      "                                , 3)]                                                             \n",
-      "                                                                                                  \n",
-      " conv3d_69 (Conv3D)             (None, 80, 80, 128,  2624        ['input_4[0][0]']                \n",
-      "                                 32)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_70 (Conv3D)             (None, 80, 80, 128,  27680       ['conv3d_69[0][0]']              \n",
-      "                                 32)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_72 (Conv3D)             (None, 80, 80, 128,  27680       ['conv3d_69[0][0]']              \n",
-      "                                 32)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_74 (Conv3D)             (None, 80, 80, 128,  256064      ['conv3d_69[0][0]']              \n",
-      "                                 64)                                                              \n",
-      "                                                                                                  \n",
-      " instance_normalization_54 (Ins  (None, 80, 80, 128,  64         ['conv3d_70[0][0]']              \n",
-      " tanceNormalization)             32)                                                              \n",
-      "                                                                                                  \n",
-      " instance_normalization_55 (Ins  (None, 80, 80, 128,  64         ['conv3d_72[0][0]']              \n",
-      " tanceNormalization)             32)                                                              \n",
-      "                                                                                                  \n",
-      " instance_normalization_57 (Ins  (None, 80, 80, 128,  128        ['conv3d_74[0][0]']              \n",
-      " tanceNormalization)             64)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_71 (Conv3D)             (None, 80, 80, 128,  27680       ['instance_normalization_54[0][0]\n",
-      "                                 32)                             ']                               \n",
-      "                                                                                                  \n",
-      " conv3d_73 (Conv3D)             (None, 80, 80, 128,  27680       ['instance_normalization_55[0][0]\n",
-      "                                 32)                             ']                               \n",
-      "                                                                                                  \n",
-      " conv3d_75 (Conv3D)             (None, 80, 80, 128,  512064      ['instance_normalization_57[0][0]\n",
-      "                                 64)                             ']                               \n",
-      "                                                                                                  \n",
-      " concatenate_3 (Concatenate)    (None, 80, 80, 128,  0           ['conv3d_71[0][0]',              \n",
-      "                                 128)                             'conv3d_73[0][0]',              \n",
-      "                                                                  'conv3d_75[0][0]']              \n",
-      "                                                                                                  \n",
-      " instance_normalization_59 (Ins  (None, 80, 80, 128,  256        ['concatenate_3[0][0]']          \n",
-      " tanceNormalization)             128)                                                             \n",
-      "                                                                                                  \n",
-      " max_pooling3d_24 (MaxPooling3D  (None, 40, 40, 64,   0          ['instance_normalization_59[0][0]\n",
-      " )                              128)                             ']                               \n",
-      "                                                                                                  \n",
-      " tf.identity_3 (TFOpLambda)     (None, 40, 40, 64,   0           ['max_pooling3d_24[0][0]']       \n",
-      "                                128)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_76 (Conv3D)             (None, 40, 40, 64,   221248      ['tf.identity_3[0][0]']          \n",
-      "                                64)                                                               \n",
-      "                                                                                                  \n",
-      " instance_normalization_60 (Ins  (None, 40, 40, 64,   128        ['conv3d_76[0][0]']              \n",
-      " tanceNormalization)            64)                                                               \n",
-      "                                                                                                  \n",
-      " conv3d_77 (Conv3D)             (None, 40, 40, 64,   110656      ['instance_normalization_60[0][0]\n",
-      "                                64)                              ']                               \n",
-      "                                                                                                  \n",
-      " instance_normalization_61 (Ins  (None, 40, 40, 64,   128        ['conv3d_77[0][0]']              \n",
-      " tanceNormalization)            64)                                                               \n",
-      "                                                                                                  \n",
-      " max_pooling3d_25 (MaxPooling3D  (None, 20, 20, 32,   0          ['instance_normalization_61[0][0]\n",
-      " )                              64)                              ']                               \n",
-      "                                                                                                  \n",
-      " global_average_pooling3d_3 (Gl  (None, 64)          0           ['max_pooling3d_25[0][0]']       \n",
-      " obalAveragePooling3D)                                                                            \n",
-      "                                                                                                  \n",
-      " dense_6 (Dense)                (None, 8)            512         ['global_average_pooling3d_3[0][0\n",
-      "                                                                 ]']                              \n",
-      "                                                                                                  \n",
-      " dense_7 (Dense)                (None, 64)           512         ['dense_6[0][0]']                \n",
-      "                                                                                                  \n",
-      " tf.math.multiply_3 (TFOpLambda  (None, 20, 20, 32,   0          ['max_pooling3d_25[0][0]',       \n",
-      " )                              64)                               'dense_7[0][0]']                \n",
-      "                                                                                                  \n",
-      " conv3d_78 (Conv3D)             (None, 20, 20, 32,   221312      ['tf.math.multiply_3[0][0]']     \n",
-      "                                128)                                                              \n",
-      "                                                                                                  \n",
-      " instance_normalization_62 (Ins  (None, 20, 20, 32,   256        ['conv3d_78[0][0]']              \n",
-      " tanceNormalization)            128)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_79 (Conv3D)             (None, 20, 20, 32,   442496      ['instance_normalization_62[0][0]\n",
-      "                                128)                             ']                               \n",
-      "                                                                                                  \n",
-      " instance_normalization_63 (Ins  (None, 20, 20, 32,   256        ['conv3d_79[0][0]']              \n",
-      " tanceNormalization)            128)                                                              \n",
-      "                                                                                                  \n",
-      " max_pooling3d_26 (MaxPooling3D  (None, 10, 10, 16,   0          ['instance_normalization_63[0][0]\n",
-      " )                              128)                             ']                               \n",
-      "                                                                                                  \n",
-      " up_sampling3d_9 (UpSampling3D)  (None, 20, 20, 32,   0          ['max_pooling3d_26[0][0]']       \n",
-      "                                128)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_82 (Conv3D)             (None, 20, 20, 32,   131200      ['up_sampling3d_9[0][0]']        \n",
-      "                                128)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_83 (Conv3D)             (None, 20, 20, 32,   442496      ['conv3d_82[0][0]']              \n",
-      "                                128)                                                              \n",
-      "                                                                                                  \n",
-      " instance_normalization_67 (Ins  (None, 20, 20, 32,   256        ['conv3d_83[0][0]']              \n",
-      " tanceNormalization)            128)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_84 (Conv3D)             (None, 20, 20, 32,   442496      ['instance_normalization_67[0][0]\n",
-      "                                128)                             ']                               \n",
-      "                                                                                                  \n",
-      " up_sampling3d_10 (UpSampling3D  (None, 40, 40, 64,   0          ['conv3d_84[0][0]']              \n",
-      " )                              128)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_85 (Conv3D)             (None, 40, 40, 64,   65600       ['up_sampling3d_10[0][0]']       \n",
-      "                                64)                                                               \n",
-      "                                                                                                  \n",
-      " conv3d_86 (Conv3D)             (None, 40, 40, 64,   110656      ['conv3d_85[0][0]']              \n",
-      "                                64)                                                               \n",
-      "                                                                                                  \n",
-      " instance_normalization_69 (Ins  (None, 40, 40, 64,   128        ['conv3d_86[0][0]']              \n",
-      " tanceNormalization)            64)                                                               \n",
-      "                                                                                                  \n",
-      " conv3d_87 (Conv3D)             (None, 40, 40, 64,   110656      ['instance_normalization_69[0][0]\n",
-      "                                64)                              ']                               \n",
-      "                                                                                                  \n",
-      " up_sampling3d_11 (UpSampling3D  (None, 80, 80, 128,  0          ['conv3d_87[0][0]']              \n",
-      " )                               64)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_88 (Conv3D)             (None, 80, 80, 128,  16416       ['up_sampling3d_11[0][0]']       \n",
-      "                                 32)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_89 (Conv3D)             (None, 80, 80, 128,  27680       ['conv3d_88[0][0]']              \n",
-      "                                 32)                                                              \n",
-      "                                                                                                  \n",
-      " instance_normalization_71 (Ins  (None, 80, 80, 128,  64         ['conv3d_89[0][0]']              \n",
-      " tanceNormalization)             32)                                                              \n",
-      "                                                                                                  \n",
-      " conv3d_90 (Conv3D)             (None, 80, 80, 128,  27680       ['instance_normalization_71[0][0]\n",
-      "                                 32)                             ']                               \n",
-      "                                                                                                  \n",
-      " conv3d_91 (Conv3D)             (None, 80, 80, 128,  132         ['conv3d_90[0][0]']              \n",
-      "                                 4)                                                               \n",
-      "                                                                                                  \n",
-      "==================================================================================================\n",
-      "Total params: 3,254,948\n",
-      "Trainable params: 3,254,948\n",
-      "Non-trainable params: 0\n",
-      "__________________________________________________________________________________________________\n",
-      "None\n",
-      "(None, 80, 80, 128, 3)\n",
-      "(None, 80, 80, 128, 4)\n",
-      "Epoch 1/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 13.1092 - accuracy: 0.7706 - dice_whole_metric: 0.7499 - dice_en_metric: 0.3096 - dice_core_metric: 0.5960\n",
-      "Epoch 1: val_dice_en_metric improved from -inf to 0.47007, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-001-0.4701.hdf5\n",
-      "174/174 [==============================] - 338s 2s/step - loss: 13.1092 - accuracy: 0.7706 - dice_whole_metric: 0.7499 - dice_en_metric: 0.3096 - dice_core_metric: 0.5960 - val_loss: 8.9312 - val_accuracy: 0.7963 - val_dice_whole_metric: 0.7664 - val_dice_en_metric: 0.4701 - val_dice_core_metric: 0.6430\n",
-      "Epoch 2/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 6.4279 - accuracy: 0.8139 - dice_whole_metric: 0.7970 - dice_en_metric: 0.3228 - dice_core_metric: 0.6705\n",
-      "Epoch 2: val_dice_en_metric improved from 0.47007 to 0.49778, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-002-0.4978.hdf5\n",
-      "174/174 [==============================] - 333s 2s/step - loss: 6.4279 - accuracy: 0.8139 - dice_whole_metric: 0.7970 - dice_en_metric: 0.3228 - dice_core_metric: 0.6705 - val_loss: 4.4780 - val_accuracy: 0.8163 - val_dice_whole_metric: 0.7919 - val_dice_en_metric: 0.4978 - val_dice_core_metric: 0.6732\n",
-      "Epoch 3/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 3.2806 - accuracy: 0.8318 - dice_whole_metric: 0.8182 - dice_en_metric: 0.4173 - dice_core_metric: 0.7049\n",
-      "Epoch 3: val_dice_en_metric improved from 0.49778 to 0.52682, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-003-0.5268.hdf5\n",
-      "174/174 [==============================] - 335s 2s/step - loss: 3.2806 - accuracy: 0.8318 - dice_whole_metric: 0.8182 - dice_en_metric: 0.4173 - dice_core_metric: 0.7049 - val_loss: 2.3682 - val_accuracy: 0.8277 - val_dice_whole_metric: 0.8151 - val_dice_en_metric: 0.5268 - val_dice_core_metric: 0.7097\n",
-      "Epoch 4/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 1.7950 - accuracy: 0.8419 - dice_whole_metric: 0.8324 - dice_en_metric: 0.4468 - dice_core_metric: 0.7239\n",
-      "Epoch 4: val_dice_en_metric improved from 0.52682 to 0.52941, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-004-0.5294.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 1.7950 - accuracy: 0.8419 - dice_whole_metric: 0.8324 - dice_en_metric: 0.4468 - dice_core_metric: 0.7239 - val_loss: 1.3815 - val_accuracy: 0.8304 - val_dice_whole_metric: 0.8222 - val_dice_en_metric: 0.5294 - val_dice_core_metric: 0.7190\n",
-      "Epoch 5/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 1.0816 - accuracy: 0.8493 - dice_whole_metric: 0.8443 - dice_en_metric: 0.4627 - dice_core_metric: 0.7361\n",
-      "Epoch 5: val_dice_en_metric improved from 0.52941 to 0.53130, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-005-0.5313.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 1.0816 - accuracy: 0.8493 - dice_whole_metric: 0.8443 - dice_en_metric: 0.4627 - dice_core_metric: 0.7361 - val_loss: 0.8979 - val_accuracy: 0.8336 - val_dice_whole_metric: 0.8302 - val_dice_en_metric: 0.5313 - val_dice_core_metric: 0.7182\n",
-      "Epoch 6/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.7266 - accuracy: 0.8539 - dice_whole_metric: 0.8527 - dice_en_metric: 0.4657 - dice_core_metric: 0.7508\n",
-      "Epoch 6: val_dice_en_metric improved from 0.53130 to 0.53617, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-006-0.5362.hdf5\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.7266 - accuracy: 0.8539 - dice_whole_metric: 0.8527 - dice_en_metric: 0.4657 - dice_core_metric: 0.7508 - val_loss: 0.6506 - val_accuracy: 0.8361 - val_dice_whole_metric: 0.8320 - val_dice_en_metric: 0.5362 - val_dice_core_metric: 0.7228\n",
-      "Epoch 7/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.5525 - accuracy: 0.8462 - dice_whole_metric: 0.8593 - dice_en_metric: 0.2487 - dice_core_metric: 0.7494\n",
-      "Epoch 7: val_dice_en_metric did not improve from 0.53617\n",
-      "174/174 [==============================] - 338s 2s/step - loss: 0.5525 - accuracy: 0.8462 - dice_whole_metric: 0.8593 - dice_en_metric: 0.2487 - dice_core_metric: 0.7494 - val_loss: 0.5439 - val_accuracy: 0.8169 - val_dice_whole_metric: 0.8350 - val_dice_en_metric: 0.0912 - val_dice_core_metric: 0.7232\n",
-      "Epoch 8/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.4329 - accuracy: 0.8606 - dice_whole_metric: 0.8622 - dice_en_metric: 0.4616 - dice_core_metric: 0.7620\n",
-      "Epoch 8: val_dice_en_metric improved from 0.53617 to 0.55568, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-008-0.5557.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.4329 - accuracy: 0.8606 - dice_whole_metric: 0.8622 - dice_en_metric: 0.4616 - dice_core_metric: 0.7620 - val_loss: 0.4313 - val_accuracy: 0.8421 - val_dice_whole_metric: 0.8432 - val_dice_en_metric: 0.5557 - val_dice_core_metric: 0.7563\n",
-      "Epoch 9/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.3681 - accuracy: 0.8617 - dice_whole_metric: 0.8670 - dice_en_metric: 0.4292 - dice_core_metric: 0.7722\n",
-      "Epoch 9: val_dice_en_metric did not improve from 0.55568\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.3681 - accuracy: 0.8617 - dice_whole_metric: 0.8670 - dice_en_metric: 0.4292 - dice_core_metric: 0.7722 - val_loss: 0.3881 - val_accuracy: 0.8410 - val_dice_whole_metric: 0.8453 - val_dice_en_metric: 0.5489 - val_dice_core_metric: 0.7484\n",
-      "Epoch 10/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.3170 - accuracy: 0.8706 - dice_whole_metric: 0.8741 - dice_en_metric: 0.4884 - dice_core_metric: 0.7852\n",
-      "Epoch 10: val_dice_en_metric improved from 0.55568 to 0.57321, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-010-0.5732.hdf5\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.3170 - accuracy: 0.8706 - dice_whole_metric: 0.8741 - dice_en_metric: 0.4884 - dice_core_metric: 0.7852 - val_loss: 0.3420 - val_accuracy: 0.8504 - val_dice_whole_metric: 0.8521 - val_dice_en_metric: 0.5732 - val_dice_core_metric: 0.7700\n",
-      "Epoch 11/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.2806 - accuracy: 0.8772 - dice_whole_metric: 0.8784 - dice_en_metric: 0.5299 - dice_core_metric: 0.7965\n",
-      "Epoch 11: val_dice_en_metric did not improve from 0.57321\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.2806 - accuracy: 0.8772 - dice_whole_metric: 0.8784 - dice_en_metric: 0.5299 - dice_core_metric: 0.7965 - val_loss: 0.3424 - val_accuracy: 0.8382 - val_dice_whole_metric: 0.8461 - val_dice_en_metric: 0.5571 - val_dice_core_metric: 0.7638\n",
-      "Epoch 12/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.2636 - accuracy: 0.8758 - dice_whole_metric: 0.8828 - dice_en_metric: 0.4864 - dice_core_metric: 0.7963\n",
-      "Epoch 12: val_dice_en_metric improved from 0.57321 to 0.57553, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-012-0.5755.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.2636 - accuracy: 0.8758 - dice_whole_metric: 0.8828 - dice_en_metric: 0.4864 - dice_core_metric: 0.7963 - val_loss: 0.3069 - val_accuracy: 0.8512 - val_dice_whole_metric: 0.8557 - val_dice_en_metric: 0.5755 - val_dice_core_metric: 0.7840\n",
-      "Epoch 13/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.2341 - accuracy: 0.8846 - dice_whole_metric: 0.8911 - dice_en_metric: 0.5299 - dice_core_metric: 0.8105\n",
-      "Epoch 13: val_dice_en_metric improved from 0.57553 to 0.57789, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-013-0.5779.hdf5\n",
-      "174/174 [==============================] - 335s 2s/step - loss: 0.2341 - accuracy: 0.8846 - dice_whole_metric: 0.8911 - dice_en_metric: 0.5299 - dice_core_metric: 0.8105 - val_loss: 0.3101 - val_accuracy: 0.8436 - val_dice_whole_metric: 0.8490 - val_dice_en_metric: 0.5779 - val_dice_core_metric: 0.7832\n",
-      "Epoch 14/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.2279 - accuracy: 0.8841 - dice_whole_metric: 0.8879 - dice_en_metric: 0.5309 - dice_core_metric: 0.8106\n",
-      "Epoch 14: val_dice_en_metric improved from 0.57789 to 0.58748, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-014-0.5875.hdf5\n",
-      "174/174 [==============================] - 335s 2s/step - loss: 0.2279 - accuracy: 0.8841 - dice_whole_metric: 0.8879 - dice_en_metric: 0.5309 - dice_core_metric: 0.8106 - val_loss: 0.2894 - val_accuracy: 0.8510 - val_dice_whole_metric: 0.8536 - val_dice_en_metric: 0.5875 - val_dice_core_metric: 0.7962\n",
-      "Epoch 15/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.2304 - accuracy: 0.8807 - dice_whole_metric: 0.8851 - dice_en_metric: 0.5082 - dice_core_metric: 0.8044\n",
-      "Epoch 15: val_dice_en_metric did not improve from 0.58748\n",
-      "174/174 [==============================] - 334s 2s/step - loss: 0.2304 - accuracy: 0.8807 - dice_whole_metric: 0.8851 - dice_en_metric: 0.5082 - dice_core_metric: 0.8044 - val_loss: 0.2974 - val_accuracy: 0.8469 - val_dice_whole_metric: 0.8467 - val_dice_en_metric: 0.5520 - val_dice_core_metric: 0.7664\n",
-      "Epoch 16/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.2129 - accuracy: 0.8855 - dice_whole_metric: 0.8919 - dice_en_metric: 0.5171 - dice_core_metric: 0.8126\n",
-      "Epoch 16: val_dice_en_metric did not improve from 0.58748\n",
-      "174/174 [==============================] - 334s 2s/step - loss: 0.2129 - accuracy: 0.8855 - dice_whole_metric: 0.8919 - dice_en_metric: 0.5171 - dice_core_metric: 0.8126 - val_loss: 0.2789 - val_accuracy: 0.8501 - val_dice_whole_metric: 0.8597 - val_dice_en_metric: 0.5847 - val_dice_core_metric: 0.7792\n",
-      "Epoch 17/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.2240 - accuracy: 0.8757 - dice_whole_metric: 0.8882 - dice_en_metric: 0.4041 - dice_core_metric: 0.7961\n",
-      "Epoch 17: val_dice_en_metric did not improve from 0.58748\n",
-      "174/174 [==============================] - 334s 2s/step - loss: 0.2240 - accuracy: 0.8757 - dice_whole_metric: 0.8882 - dice_en_metric: 0.4041 - dice_core_metric: 0.7961 - val_loss: 0.2771 - val_accuracy: 0.8532 - val_dice_whole_metric: 0.8574 - val_dice_en_metric: 0.5715 - val_dice_core_metric: 0.7817\n",
-      "Epoch 18/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.2057 - accuracy: 0.8866 - dice_whole_metric: 0.8934 - dice_en_metric: 0.5201 - dice_core_metric: 0.8133\n",
-      "Epoch 18: val_dice_en_metric did not improve from 0.58748\n",
-      "174/174 [==============================] - 334s 2s/step - loss: 0.2057 - accuracy: 0.8866 - dice_whole_metric: 0.8934 - dice_en_metric: 0.5201 - dice_core_metric: 0.8133 - val_loss: 0.2655 - val_accuracy: 0.8560 - val_dice_whole_metric: 0.8611 - val_dice_en_metric: 0.5855 - val_dice_core_metric: 0.7998\n",
-      "Epoch 19/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1973 - accuracy: 0.8878 - dice_whole_metric: 0.8946 - dice_en_metric: 0.5307 - dice_core_metric: 0.8177\n",
-      "Epoch 19: val_dice_en_metric did not improve from 0.58748\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1973 - accuracy: 0.8878 - dice_whole_metric: 0.8946 - dice_en_metric: 0.5307 - dice_core_metric: 0.8177 - val_loss: 0.2646 - val_accuracy: 0.8536 - val_dice_whole_metric: 0.8622 - val_dice_en_metric: 0.5816 - val_dice_core_metric: 0.7872\n",
-      "Epoch 20/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1878 - accuracy: 0.8931 - dice_whole_metric: 0.8984 - dice_en_metric: 0.5593 - dice_core_metric: 0.8286\n",
-      "Epoch 20: val_dice_en_metric improved from 0.58748 to 0.59029, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-020-0.5903.hdf5\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.1878 - accuracy: 0.8931 - dice_whole_metric: 0.8984 - dice_en_metric: 0.5593 - dice_core_metric: 0.8286 - val_loss: 0.2493 - val_accuracy: 0.8587 - val_dice_whole_metric: 0.8687 - val_dice_en_metric: 0.5903 - val_dice_core_metric: 0.7992\n",
-      "Epoch 21/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1750 - accuracy: 0.8966 - dice_whole_metric: 0.9013 - dice_en_metric: 0.5631 - dice_core_metric: 0.8352\n",
-      "Epoch 21: val_dice_en_metric did not improve from 0.59029\n",
-      "174/174 [==============================] - 338s 2s/step - loss: 0.1750 - accuracy: 0.8966 - dice_whole_metric: 0.9013 - dice_en_metric: 0.5631 - dice_core_metric: 0.8352 - val_loss: 0.2600 - val_accuracy: 0.8528 - val_dice_whole_metric: 0.8582 - val_dice_en_metric: 0.5890 - val_dice_core_metric: 0.7974\n",
-      "Epoch 22/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1638 - accuracy: 0.9002 - dice_whole_metric: 0.9061 - dice_en_metric: 0.5670 - dice_core_metric: 0.8437\n",
-      "Epoch 22: val_dice_en_metric improved from 0.59029 to 0.59267, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-022-0.5927.hdf5\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.1638 - accuracy: 0.9002 - dice_whole_metric: 0.9061 - dice_en_metric: 0.5670 - dice_core_metric: 0.8437 - val_loss: 0.2447 - val_accuracy: 0.8580 - val_dice_whole_metric: 0.8648 - val_dice_en_metric: 0.5927 - val_dice_core_metric: 0.8035\n",
-      "Epoch 23/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1764 - accuracy: 0.8931 - dice_whole_metric: 0.8984 - dice_en_metric: 0.5449 - dice_core_metric: 0.8306\n",
-      "Epoch 23: val_dice_en_metric did not improve from 0.59267\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.1764 - accuracy: 0.8931 - dice_whole_metric: 0.8984 - dice_en_metric: 0.5449 - dice_core_metric: 0.8306 - val_loss: 0.2540 - val_accuracy: 0.8537 - val_dice_whole_metric: 0.8661 - val_dice_en_metric: 0.5812 - val_dice_core_metric: 0.7883\n",
-      "Epoch 24/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1646 - accuracy: 0.9005 - dice_whole_metric: 0.9048 - dice_en_metric: 0.5679 - dice_core_metric: 0.8426\n",
-      "Epoch 24: val_dice_en_metric improved from 0.59267 to 0.59513, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-024-0.5951.hdf5\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.1646 - accuracy: 0.9005 - dice_whole_metric: 0.9048 - dice_en_metric: 0.5679 - dice_core_metric: 0.8426 - val_loss: 0.2388 - val_accuracy: 0.8614 - val_dice_whole_metric: 0.8667 - val_dice_en_metric: 0.5951 - val_dice_core_metric: 0.8074\n",
-      "Epoch 25/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1546 - accuracy: 0.9024 - dice_whole_metric: 0.9085 - dice_en_metric: 0.5582 - dice_core_metric: 0.8502\n",
-      "Epoch 25: val_dice_en_metric did not improve from 0.59513\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.1546 - accuracy: 0.9024 - dice_whole_metric: 0.9085 - dice_en_metric: 0.5582 - dice_core_metric: 0.8502 - val_loss: 0.2429 - val_accuracy: 0.8550 - val_dice_whole_metric: 0.8652 - val_dice_en_metric: 0.5928 - val_dice_core_metric: 0.8008\n",
-      "Epoch 26/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1476 - accuracy: 0.9047 - dice_whole_metric: 0.9117 - dice_en_metric: 0.5697 - dice_core_metric: 0.8499\n",
-      "Epoch 26: val_dice_en_metric did not improve from 0.59513\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.1476 - accuracy: 0.9047 - dice_whole_metric: 0.9117 - dice_en_metric: 0.5697 - dice_core_metric: 0.8499 - val_loss: 0.2400 - val_accuracy: 0.8581 - val_dice_whole_metric: 0.8639 - val_dice_en_metric: 0.5923 - val_dice_core_metric: 0.8011\n",
-      "Epoch 27/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1463 - accuracy: 0.9047 - dice_whole_metric: 0.9113 - dice_en_metric: 0.5681 - dice_core_metric: 0.8513\n",
-      "Epoch 27: val_dice_en_metric did not improve from 0.59513\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.1463 - accuracy: 0.9047 - dice_whole_metric: 0.9113 - dice_en_metric: 0.5681 - dice_core_metric: 0.8513 - val_loss: 0.2350 - val_accuracy: 0.8588 - val_dice_whole_metric: 0.8675 - val_dice_en_metric: 0.5946 - val_dice_core_metric: 0.8080\n",
-      "Epoch 28/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1710 - accuracy: 0.8956 - dice_whole_metric: 0.9009 - dice_en_metric: 0.5497 - dice_core_metric: 0.8374\n",
-      "Epoch 28: val_dice_en_metric improved from 0.59513 to 0.59987, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-028-0.5999.hdf5\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.1710 - accuracy: 0.8956 - dice_whole_metric: 0.9009 - dice_en_metric: 0.5497 - dice_core_metric: 0.8374 - val_loss: 0.2367 - val_accuracy: 0.8648 - val_dice_whole_metric: 0.8647 - val_dice_en_metric: 0.5999 - val_dice_core_metric: 0.8143\n",
-      "Epoch 29/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1476 - accuracy: 0.9061 - dice_whole_metric: 0.9104 - dice_en_metric: 0.5806 - dice_core_metric: 0.8545\n",
-      "Epoch 29: val_dice_en_metric did not improve from 0.59987\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1476 - accuracy: 0.9061 - dice_whole_metric: 0.9104 - dice_en_metric: 0.5806 - dice_core_metric: 0.8545 - val_loss: 0.2295 - val_accuracy: 0.8646 - val_dice_whole_metric: 0.8686 - val_dice_en_metric: 0.5973 - val_dice_core_metric: 0.8059\n",
-      "Epoch 30/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1390 - accuracy: 0.9086 - dice_whole_metric: 0.9135 - dice_en_metric: 0.5854 - dice_core_metric: 0.8568\n",
-      "Epoch 30: val_dice_en_metric improved from 0.59987 to 0.60630, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-030-0.6063.hdf5\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.1390 - accuracy: 0.9086 - dice_whole_metric: 0.9135 - dice_en_metric: 0.5854 - dice_core_metric: 0.8568 - val_loss: 0.2168 - val_accuracy: 0.8670 - val_dice_whole_metric: 0.8759 - val_dice_en_metric: 0.6063 - val_dice_core_metric: 0.8095\n",
-      "Epoch 31/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1343 - accuracy: 0.9097 - dice_whole_metric: 0.9143 - dice_en_metric: 0.5900 - dice_core_metric: 0.8604\n",
-      "Epoch 31: val_dice_en_metric did not improve from 0.60630\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.1343 - accuracy: 0.9097 - dice_whole_metric: 0.9143 - dice_en_metric: 0.5900 - dice_core_metric: 0.8604 - val_loss: 0.2194 - val_accuracy: 0.8668 - val_dice_whole_metric: 0.8712 - val_dice_en_metric: 0.5972 - val_dice_core_metric: 0.8114\n",
-      "Epoch 32/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1298 - accuracy: 0.9119 - dice_whole_metric: 0.9153 - dice_en_metric: 0.5946 - dice_core_metric: 0.8661\n",
-      "Epoch 32: val_dice_en_metric did not improve from 0.60630\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1298 - accuracy: 0.9119 - dice_whole_metric: 0.9153 - dice_en_metric: 0.5946 - dice_core_metric: 0.8661 - val_loss: 0.2203 - val_accuracy: 0.8654 - val_dice_whole_metric: 0.8711 - val_dice_en_metric: 0.6021 - val_dice_core_metric: 0.8105\n",
-      "Epoch 33/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1452 - accuracy: 0.9054 - dice_whole_metric: 0.9080 - dice_en_metric: 0.5799 - dice_core_metric: 0.8504\n",
-      "Epoch 33: val_dice_en_metric did not improve from 0.60630\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1452 - accuracy: 0.9054 - dice_whole_metric: 0.9080 - dice_en_metric: 0.5799 - dice_core_metric: 0.8504 - val_loss: 0.2233 - val_accuracy: 0.8651 - val_dice_whole_metric: 0.8766 - val_dice_en_metric: 0.5996 - val_dice_core_metric: 0.8092\n",
-      "Epoch 34/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1474 - accuracy: 0.9058 - dice_whole_metric: 0.9090 - dice_en_metric: 0.5832 - dice_core_metric: 0.8550\n",
-      "Epoch 34: val_dice_en_metric did not improve from 0.60630\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.1474 - accuracy: 0.9058 - dice_whole_metric: 0.9090 - dice_en_metric: 0.5832 - dice_core_metric: 0.8550 - val_loss: 0.2349 - val_accuracy: 0.8600 - val_dice_whole_metric: 0.8644 - val_dice_en_metric: 0.5920 - val_dice_core_metric: 0.8038\n",
-      "Epoch 35/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1330 - accuracy: 0.9115 - dice_whole_metric: 0.9146 - dice_en_metric: 0.5900 - dice_core_metric: 0.8634\n",
-      "Epoch 35: val_dice_en_metric did not improve from 0.60630\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1330 - accuracy: 0.9115 - dice_whole_metric: 0.9146 - dice_en_metric: 0.5900 - dice_core_metric: 0.8634 - val_loss: 0.2236 - val_accuracy: 0.8646 - val_dice_whole_metric: 0.8670 - val_dice_en_metric: 0.5977 - val_dice_core_metric: 0.8126\n",
-      "Epoch 36/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1280 - accuracy: 0.9126 - dice_whole_metric: 0.9144 - dice_en_metric: 0.6021 - dice_core_metric: 0.8675\n",
-      "Epoch 36: val_dice_en_metric did not improve from 0.60630\n",
-      "174/174 [==============================] - 335s 2s/step - loss: 0.1280 - accuracy: 0.9126 - dice_whole_metric: 0.9144 - dice_en_metric: 0.6021 - dice_core_metric: 0.8675 - val_loss: 0.2124 - val_accuracy: 0.8700 - val_dice_whole_metric: 0.8734 - val_dice_en_metric: 0.6056 - val_dice_core_metric: 0.8148\n",
-      "Epoch 37/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1536 - accuracy: 0.9024 - dice_whole_metric: 0.9039 - dice_en_metric: 0.5723 - dice_core_metric: 0.8444\n",
-      "Epoch 37: val_dice_en_metric did not improve from 0.60630\n",
-      "174/174 [==============================] - 335s 2s/step - loss: 0.1536 - accuracy: 0.9024 - dice_whole_metric: 0.9039 - dice_en_metric: 0.5723 - dice_core_metric: 0.8444 - val_loss: 0.2280 - val_accuracy: 0.8648 - val_dice_whole_metric: 0.8774 - val_dice_en_metric: 0.6023 - val_dice_core_metric: 0.7937\n",
-      "Epoch 38/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1421 - accuracy: 0.9094 - dice_whole_metric: 0.9115 - dice_en_metric: 0.5985 - dice_core_metric: 0.8632\n",
-      "Epoch 38: val_dice_en_metric improved from 0.60630 to 0.61037, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-038-0.6104.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1421 - accuracy: 0.9094 - dice_whole_metric: 0.9115 - dice_en_metric: 0.5985 - dice_core_metric: 0.8632 - val_loss: 0.2023 - val_accuracy: 0.8766 - val_dice_whole_metric: 0.8806 - val_dice_en_metric: 0.6104 - val_dice_core_metric: 0.8208\n",
-      "Epoch 39/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1216 - accuracy: 0.9162 - dice_whole_metric: 0.9182 - dice_en_metric: 0.6042 - dice_core_metric: 0.8736\n",
-      "Epoch 39: val_dice_en_metric did not improve from 0.61037\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1216 - accuracy: 0.9162 - dice_whole_metric: 0.9182 - dice_en_metric: 0.6042 - dice_core_metric: 0.8736 - val_loss: 0.2117 - val_accuracy: 0.8675 - val_dice_whole_metric: 0.8737 - val_dice_en_metric: 0.6009 - val_dice_core_metric: 0.8044\n",
-      "Epoch 40/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1190 - accuracy: 0.9160 - dice_whole_metric: 0.9188 - dice_en_metric: 0.6079 - dice_core_metric: 0.8716\n",
-      "Epoch 40: val_dice_en_metric did not improve from 0.61037\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1190 - accuracy: 0.9160 - dice_whole_metric: 0.9188 - dice_en_metric: 0.6079 - dice_core_metric: 0.8716 - val_loss: 0.2042 - val_accuracy: 0.8714 - val_dice_whole_metric: 0.8761 - val_dice_en_metric: 0.6074 - val_dice_core_metric: 0.8117\n",
-      "Epoch 41/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1162 - accuracy: 0.9173 - dice_whole_metric: 0.9195 - dice_en_metric: 0.6071 - dice_core_metric: 0.8731\n",
-      "Epoch 41: val_dice_en_metric did not improve from 0.61037\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1162 - accuracy: 0.9173 - dice_whole_metric: 0.9195 - dice_en_metric: 0.6071 - dice_core_metric: 0.8731 - val_loss: 0.2037 - val_accuracy: 0.8712 - val_dice_whole_metric: 0.8766 - val_dice_en_metric: 0.6071 - val_dice_core_metric: 0.8159\n",
-      "Epoch 42/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1106 - accuracy: 0.9196 - dice_whole_metric: 0.9212 - dice_en_metric: 0.6127 - dice_core_metric: 0.8795\n",
-      "Epoch 42: val_dice_en_metric improved from 0.61037 to 0.61179, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-042-0.6118.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1106 - accuracy: 0.9196 - dice_whole_metric: 0.9212 - dice_en_metric: 0.6127 - dice_core_metric: 0.8795 - val_loss: 0.1896 - val_accuracy: 0.8775 - val_dice_whole_metric: 0.8817 - val_dice_en_metric: 0.6118 - val_dice_core_metric: 0.8241\n",
-      "Epoch 43/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1190 - accuracy: 0.9160 - dice_whole_metric: 0.9178 - dice_en_metric: 0.6064 - dice_core_metric: 0.8741\n",
-      "Epoch 43: val_dice_en_metric did not improve from 0.61179\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1190 - accuracy: 0.9160 - dice_whole_metric: 0.9178 - dice_en_metric: 0.6064 - dice_core_metric: 0.8741 - val_loss: 0.2069 - val_accuracy: 0.8700 - val_dice_whole_metric: 0.8747 - val_dice_en_metric: 0.6042 - val_dice_core_metric: 0.8159\n",
-      "Epoch 44/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1112 - accuracy: 0.9198 - dice_whole_metric: 0.9213 - dice_en_metric: 0.6100 - dice_core_metric: 0.8801\n",
-      "Epoch 44: val_dice_en_metric improved from 0.61179 to 0.61470, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-044-0.6147.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1112 - accuracy: 0.9198 - dice_whole_metric: 0.9213 - dice_en_metric: 0.6100 - dice_core_metric: 0.8801 - val_loss: 0.1877 - val_accuracy: 0.8788 - val_dice_whole_metric: 0.8830 - val_dice_en_metric: 0.6147 - val_dice_core_metric: 0.8268\n",
-      "Epoch 45/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1083 - accuracy: 0.9208 - dice_whole_metric: 0.9218 - dice_en_metric: 0.6145 - dice_core_metric: 0.8816\n",
-      "Epoch 45: val_dice_en_metric did not improve from 0.61470\n",
-      "174/174 [==============================] - 335s 2s/step - loss: 0.1083 - accuracy: 0.9208 - dice_whole_metric: 0.9218 - dice_en_metric: 0.6145 - dice_core_metric: 0.8816 - val_loss: 0.2044 - val_accuracy: 0.8729 - val_dice_whole_metric: 0.8719 - val_dice_en_metric: 0.6068 - val_dice_core_metric: 0.8216\n",
-      "Epoch 46/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1105 - accuracy: 0.9196 - dice_whole_metric: 0.9208 - dice_en_metric: 0.6112 - dice_core_metric: 0.8800\n",
-      "Epoch 46: val_dice_en_metric did not improve from 0.61470\n",
-      "174/174 [==============================] - 335s 2s/step - loss: 0.1105 - accuracy: 0.9196 - dice_whole_metric: 0.9208 - dice_en_metric: 0.6112 - dice_core_metric: 0.8800 - val_loss: 0.2141 - val_accuracy: 0.8658 - val_dice_whole_metric: 0.8702 - val_dice_en_metric: 0.6033 - val_dice_core_metric: 0.8084\n",
-      "Epoch 47/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1195 - accuracy: 0.9161 - dice_whole_metric: 0.9171 - dice_en_metric: 0.6103 - dice_core_metric: 0.8773\n",
-      "Epoch 47: val_dice_en_metric did not improve from 0.61470\n",
-      "174/174 [==============================] - 335s 2s/step - loss: 0.1195 - accuracy: 0.9161 - dice_whole_metric: 0.9171 - dice_en_metric: 0.6103 - dice_core_metric: 0.8773 - val_loss: 0.2206 - val_accuracy: 0.8620 - val_dice_whole_metric: 0.8678 - val_dice_en_metric: 0.5905 - val_dice_core_metric: 0.8021\n",
-      "Epoch 48/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1161 - accuracy: 0.9178 - dice_whole_metric: 0.9198 - dice_en_metric: 0.6122 - dice_core_metric: 0.8746\n",
-      "Epoch 48: val_dice_en_metric did not improve from 0.61470\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1161 - accuracy: 0.9178 - dice_whole_metric: 0.9198 - dice_en_metric: 0.6122 - dice_core_metric: 0.8746 - val_loss: 0.2005 - val_accuracy: 0.8745 - val_dice_whole_metric: 0.8754 - val_dice_en_metric: 0.6110 - val_dice_core_metric: 0.8180\n",
-      "Epoch 49/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1077 - accuracy: 0.9209 - dice_whole_metric: 0.9227 - dice_en_metric: 0.6175 - dice_core_metric: 0.8788\n",
-      "Epoch 49: val_dice_en_metric did not improve from 0.61470\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1077 - accuracy: 0.9209 - dice_whole_metric: 0.9227 - dice_en_metric: 0.6175 - dice_core_metric: 0.8788 - val_loss: 0.2052 - val_accuracy: 0.8699 - val_dice_whole_metric: 0.8731 - val_dice_en_metric: 0.6116 - val_dice_core_metric: 0.8156\n",
-      "Epoch 50/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1031 - accuracy: 0.9235 - dice_whole_metric: 0.9246 - dice_en_metric: 0.6230 - dice_core_metric: 0.8856\n",
-      "Epoch 50: val_dice_en_metric improved from 0.61470 to 0.62144, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-050-0.6214.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1031 - accuracy: 0.9235 - dice_whole_metric: 0.9246 - dice_en_metric: 0.6230 - dice_core_metric: 0.8856 - val_loss: 0.1830 - val_accuracy: 0.8814 - val_dice_whole_metric: 0.8832 - val_dice_en_metric: 0.6214 - val_dice_core_metric: 0.8255\n",
-      "Epoch 51/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0995 - accuracy: 0.9243 - dice_whole_metric: 0.9249 - dice_en_metric: 0.6230 - dice_core_metric: 0.8863\n",
-      "Epoch 51: val_dice_en_metric did not improve from 0.62144\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0995 - accuracy: 0.9243 - dice_whole_metric: 0.9249 - dice_en_metric: 0.6230 - dice_core_metric: 0.8863 - val_loss: 0.1952 - val_accuracy: 0.8741 - val_dice_whole_metric: 0.8759 - val_dice_en_metric: 0.6132 - val_dice_core_metric: 0.8173\n",
-      "Epoch 52/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1004 - accuracy: 0.9237 - dice_whole_metric: 0.9248 - dice_en_metric: 0.6211 - dice_core_metric: 0.8858\n",
-      "Epoch 52: val_dice_en_metric did not improve from 0.62144\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1004 - accuracy: 0.9237 - dice_whole_metric: 0.9248 - dice_en_metric: 0.6211 - dice_core_metric: 0.8858 - val_loss: 0.1942 - val_accuracy: 0.8736 - val_dice_whole_metric: 0.8788 - val_dice_en_metric: 0.6062 - val_dice_core_metric: 0.8167\n",
-      "Epoch 53/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1230 - accuracy: 0.9140 - dice_whole_metric: 0.9150 - dice_en_metric: 0.6069 - dice_core_metric: 0.8681\n",
-      "Epoch 53: val_dice_en_metric did not improve from 0.62144\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1230 - accuracy: 0.9140 - dice_whole_metric: 0.9150 - dice_en_metric: 0.6069 - dice_core_metric: 0.8681 - val_loss: 0.2302 - val_accuracy: 0.8584 - val_dice_whole_metric: 0.8671 - val_dice_en_metric: 0.5934 - val_dice_core_metric: 0.7999\n",
-      "Epoch 54/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1581 - accuracy: 0.9026 - dice_whole_metric: 0.9023 - dice_en_metric: 0.5827 - dice_core_metric: 0.8429\n",
-      "Epoch 54: val_dice_en_metric did not improve from 0.62144\n",
-      "174/174 [==============================] - 335s 2s/step - loss: 0.1581 - accuracy: 0.9026 - dice_whole_metric: 0.9023 - dice_en_metric: 0.5827 - dice_core_metric: 0.8429 - val_loss: 0.2336 - val_accuracy: 0.8635 - val_dice_whole_metric: 0.8708 - val_dice_en_metric: 0.5970 - val_dice_core_metric: 0.8002\n",
-      "Epoch 55/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1410 - accuracy: 0.9123 - dice_whole_metric: 0.9108 - dice_en_metric: 0.6001 - dice_core_metric: 0.8657\n",
-      "Epoch 55: val_dice_en_metric did not improve from 0.62144\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1410 - accuracy: 0.9123 - dice_whole_metric: 0.9108 - dice_en_metric: 0.6001 - dice_core_metric: 0.8657 - val_loss: 0.2177 - val_accuracy: 0.8692 - val_dice_whole_metric: 0.8727 - val_dice_en_metric: 0.6060 - val_dice_core_metric: 0.8124\n",
-      "Epoch 56/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1134 - accuracy: 0.9210 - dice_whole_metric: 0.9221 - dice_en_metric: 0.6143 - dice_core_metric: 0.8808\n",
-      "Epoch 56: val_dice_en_metric did not improve from 0.62144\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1134 - accuracy: 0.9210 - dice_whole_metric: 0.9221 - dice_en_metric: 0.6143 - dice_core_metric: 0.8808 - val_loss: 0.1943 - val_accuracy: 0.8755 - val_dice_whole_metric: 0.8809 - val_dice_en_metric: 0.6146 - val_dice_core_metric: 0.8129\n",
-      "Epoch 57/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1188 - accuracy: 0.9160 - dice_whole_metric: 0.9177 - dice_en_metric: 0.6012 - dice_core_metric: 0.8732\n",
-      "Epoch 57: val_dice_en_metric did not improve from 0.62144\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1188 - accuracy: 0.9160 - dice_whole_metric: 0.9177 - dice_en_metric: 0.6012 - dice_core_metric: 0.8732 - val_loss: 0.2246 - val_accuracy: 0.8613 - val_dice_whole_metric: 0.8673 - val_dice_en_metric: 0.5998 - val_dice_core_metric: 0.7979\n",
-      "Epoch 58/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1108 - accuracy: 0.9198 - dice_whole_metric: 0.9220 - dice_en_metric: 0.6174 - dice_core_metric: 0.8790\n",
-      "Epoch 58: val_dice_en_metric did not improve from 0.62144\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1108 - accuracy: 0.9198 - dice_whole_metric: 0.9220 - dice_en_metric: 0.6174 - dice_core_metric: 0.8790 - val_loss: 0.1875 - val_accuracy: 0.8774 - val_dice_whole_metric: 0.8832 - val_dice_en_metric: 0.6169 - val_dice_core_metric: 0.8152\n",
-      "Epoch 59/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0988 - accuracy: 0.9242 - dice_whole_metric: 0.9255 - dice_en_metric: 0.6232 - dice_core_metric: 0.8868\n",
-      "Epoch 59: val_dice_en_metric did not improve from 0.62144\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0988 - accuracy: 0.9242 - dice_whole_metric: 0.9255 - dice_en_metric: 0.6232 - dice_core_metric: 0.8868 - val_loss: 0.1831 - val_accuracy: 0.8775 - val_dice_whole_metric: 0.8858 - val_dice_en_metric: 0.6137 - val_dice_core_metric: 0.8099\n",
-      "Epoch 60/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0917 - accuracy: 0.9275 - dice_whole_metric: 0.9275 - dice_en_metric: 0.6289 - dice_core_metric: 0.8924\n",
-      "Epoch 60: val_dice_en_metric did not improve from 0.62144\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0917 - accuracy: 0.9275 - dice_whole_metric: 0.9275 - dice_en_metric: 0.6289 - dice_core_metric: 0.8924 - val_loss: 0.1765 - val_accuracy: 0.8808 - val_dice_whole_metric: 0.8854 - val_dice_en_metric: 0.6201 - val_dice_core_metric: 0.8232\n",
-      "Epoch 61/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0890 - accuracy: 0.9284 - dice_whole_metric: 0.9283 - dice_en_metric: 0.6331 - dice_core_metric: 0.8943\n",
-      "Epoch 61: val_dice_en_metric did not improve from 0.62144\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0890 - accuracy: 0.9284 - dice_whole_metric: 0.9283 - dice_en_metric: 0.6331 - dice_core_metric: 0.8943 - val_loss: 0.1866 - val_accuracy: 0.8752 - val_dice_whole_metric: 0.8807 - val_dice_en_metric: 0.6158 - val_dice_core_metric: 0.8151\n",
-      "Epoch 62/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0886 - accuracy: 0.9287 - dice_whole_metric: 0.9286 - dice_en_metric: 0.6353 - dice_core_metric: 0.8943\n",
-      "Epoch 62: val_dice_en_metric improved from 0.62144 to 0.62357, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-062-0.6236.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0886 - accuracy: 0.9287 - dice_whole_metric: 0.9286 - dice_en_metric: 0.6353 - dice_core_metric: 0.8943 - val_loss: 0.1751 - val_accuracy: 0.8827 - val_dice_whole_metric: 0.8837 - val_dice_en_metric: 0.6236 - val_dice_core_metric: 0.8270\n",
-      "Epoch 63/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0894 - accuracy: 0.9282 - dice_whole_metric: 0.9276 - dice_en_metric: 0.6354 - dice_core_metric: 0.8942\n",
-      "Epoch 63: val_dice_en_metric improved from 0.62357 to 0.62446, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-063-0.6245.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0894 - accuracy: 0.9282 - dice_whole_metric: 0.9276 - dice_en_metric: 0.6354 - dice_core_metric: 0.8942 - val_loss: 0.1817 - val_accuracy: 0.8801 - val_dice_whole_metric: 0.8802 - val_dice_en_metric: 0.6245 - val_dice_core_metric: 0.8225\n",
-      "Epoch 64/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0876 - accuracy: 0.9291 - dice_whole_metric: 0.9283 - dice_en_metric: 0.6363 - dice_core_metric: 0.8968\n",
-      "Epoch 64: val_dice_en_metric did not improve from 0.62446\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0876 - accuracy: 0.9291 - dice_whole_metric: 0.9283 - dice_en_metric: 0.6363 - dice_core_metric: 0.8968 - val_loss: 0.1766 - val_accuracy: 0.8817 - val_dice_whole_metric: 0.8822 - val_dice_en_metric: 0.6133 - val_dice_core_metric: 0.8254\n",
-      "Epoch 65/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1016 - accuracy: 0.9226 - dice_whole_metric: 0.9218 - dice_en_metric: 0.6229 - dice_core_metric: 0.8830\n",
-      "Epoch 65: val_dice_en_metric did not improve from 0.62446\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1016 - accuracy: 0.9226 - dice_whole_metric: 0.9218 - dice_en_metric: 0.6229 - dice_core_metric: 0.8830 - val_loss: 0.2153 - val_accuracy: 0.8672 - val_dice_whole_metric: 0.8640 - val_dice_en_metric: 0.6069 - val_dice_core_metric: 0.8098\n",
-      "Epoch 66/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0951 - accuracy: 0.9273 - dice_whole_metric: 0.9267 - dice_en_metric: 0.6324 - dice_core_metric: 0.8921\n",
-      "Epoch 66: val_dice_en_metric did not improve from 0.62446\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0951 - accuracy: 0.9273 - dice_whole_metric: 0.9267 - dice_en_metric: 0.6324 - dice_core_metric: 0.8921 - val_loss: 0.1840 - val_accuracy: 0.8788 - val_dice_whole_metric: 0.8810 - val_dice_en_metric: 0.6179 - val_dice_core_metric: 0.8242\n",
-      "Epoch 67/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0859 - accuracy: 0.9307 - dice_whole_metric: 0.9301 - dice_en_metric: 0.6382 - dice_core_metric: 0.8971\n",
-      "Epoch 67: val_dice_en_metric improved from 0.62446 to 0.62447, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-067-0.6245.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0859 - accuracy: 0.9307 - dice_whole_metric: 0.9301 - dice_en_metric: 0.6382 - dice_core_metric: 0.8971 - val_loss: 0.1765 - val_accuracy: 0.8807 - val_dice_whole_metric: 0.8838 - val_dice_en_metric: 0.6245 - val_dice_core_metric: 0.8197\n",
-      "Epoch 68/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0977 - accuracy: 0.9242 - dice_whole_metric: 0.9249 - dice_en_metric: 0.6188 - dice_core_metric: 0.8855\n",
-      "Epoch 68: val_dice_en_metric did not improve from 0.62447\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0977 - accuracy: 0.9242 - dice_whole_metric: 0.9249 - dice_en_metric: 0.6188 - dice_core_metric: 0.8855 - val_loss: 0.1795 - val_accuracy: 0.8808 - val_dice_whole_metric: 0.8858 - val_dice_en_metric: 0.6215 - val_dice_core_metric: 0.8268\n",
-      "Epoch 69/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0921 - accuracy: 0.9288 - dice_whole_metric: 0.9285 - dice_en_metric: 0.6330 - dice_core_metric: 0.8948\n",
-      "Epoch 69: val_dice_en_metric did not improve from 0.62447\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0921 - accuracy: 0.9288 - dice_whole_metric: 0.9285 - dice_en_metric: 0.6330 - dice_core_metric: 0.8948 - val_loss: 0.1905 - val_accuracy: 0.8758 - val_dice_whole_metric: 0.8762 - val_dice_en_metric: 0.6163 - val_dice_core_metric: 0.8172\n",
-      "Epoch 70/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0847 - accuracy: 0.9309 - dice_whole_metric: 0.9305 - dice_en_metric: 0.6404 - dice_core_metric: 0.8969\n",
-      "Epoch 70: val_dice_en_metric did not improve from 0.62447\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0847 - accuracy: 0.9309 - dice_whole_metric: 0.9305 - dice_en_metric: 0.6404 - dice_core_metric: 0.8969 - val_loss: 0.1819 - val_accuracy: 0.8775 - val_dice_whole_metric: 0.8812 - val_dice_en_metric: 0.6200 - val_dice_core_metric: 0.8154\n",
-      "Epoch 71/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0877 - accuracy: 0.9292 - dice_whole_metric: 0.9276 - dice_en_metric: 0.6351 - dice_core_metric: 0.8955\n",
-      "Epoch 71: val_dice_en_metric did not improve from 0.62447\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0877 - accuracy: 0.9292 - dice_whole_metric: 0.9276 - dice_en_metric: 0.6351 - dice_core_metric: 0.8955 - val_loss: 0.1899 - val_accuracy: 0.8725 - val_dice_whole_metric: 0.8798 - val_dice_en_metric: 0.6112 - val_dice_core_metric: 0.8200\n",
-      "Epoch 72/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0887 - accuracy: 0.9294 - dice_whole_metric: 0.9284 - dice_en_metric: 0.6359 - dice_core_metric: 0.8968\n",
-      "Epoch 72: val_dice_en_metric did not improve from 0.62447\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0887 - accuracy: 0.9294 - dice_whole_metric: 0.9284 - dice_en_metric: 0.6359 - dice_core_metric: 0.8968 - val_loss: 0.1742 - val_accuracy: 0.8827 - val_dice_whole_metric: 0.8828 - val_dice_en_metric: 0.6217 - val_dice_core_metric: 0.8259\n",
-      "Epoch 73/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0821 - accuracy: 0.9322 - dice_whole_metric: 0.9310 - dice_en_metric: 0.6411 - dice_core_metric: 0.8998\n",
-      "Epoch 73: val_dice_en_metric improved from 0.62447 to 0.62516, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-073-0.6252.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0821 - accuracy: 0.9322 - dice_whole_metric: 0.9310 - dice_en_metric: 0.6411 - dice_core_metric: 0.8998 - val_loss: 0.1729 - val_accuracy: 0.8808 - val_dice_whole_metric: 0.8855 - val_dice_en_metric: 0.6252 - val_dice_core_metric: 0.8195\n",
-      "Epoch 74/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0826 - accuracy: 0.9308 - dice_whole_metric: 0.9305 - dice_en_metric: 0.6339 - dice_core_metric: 0.8974\n",
-      "Epoch 74: val_dice_en_metric did not improve from 0.62516\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0826 - accuracy: 0.9308 - dice_whole_metric: 0.9305 - dice_en_metric: 0.6339 - dice_core_metric: 0.8974 - val_loss: 0.1862 - val_accuracy: 0.8762 - val_dice_whole_metric: 0.8748 - val_dice_en_metric: 0.6223 - val_dice_core_metric: 0.8181\n",
-      "Epoch 75/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1659 - accuracy: 0.8963 - dice_whole_metric: 0.8954 - dice_en_metric: 0.5698 - dice_core_metric: 0.8311\n",
-      "Epoch 75: val_dice_en_metric did not improve from 0.62516\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.1659 - accuracy: 0.8963 - dice_whole_metric: 0.8954 - dice_en_metric: 0.5698 - dice_core_metric: 0.8311 - val_loss: 0.2204 - val_accuracy: 0.8694 - val_dice_whole_metric: 0.8749 - val_dice_en_metric: 0.5921 - val_dice_core_metric: 0.8172\n",
-      "Epoch 76/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1215 - accuracy: 0.9168 - dice_whole_metric: 0.9194 - dice_en_metric: 0.6154 - dice_core_metric: 0.8753\n",
-      "Epoch 76: val_dice_en_metric did not improve from 0.62516\n",
-      "174/174 [==============================] - 338s 2s/step - loss: 0.1215 - accuracy: 0.9168 - dice_whole_metric: 0.9194 - dice_en_metric: 0.6154 - dice_core_metric: 0.8753 - val_loss: 0.1944 - val_accuracy: 0.8739 - val_dice_whole_metric: 0.8816 - val_dice_en_metric: 0.6110 - val_dice_core_metric: 0.8117\n",
-      "Epoch 77/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0936 - accuracy: 0.9275 - dice_whole_metric: 0.9278 - dice_en_metric: 0.6288 - dice_core_metric: 0.8934\n",
-      "Epoch 77: val_dice_en_metric did not improve from 0.62516\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0936 - accuracy: 0.9275 - dice_whole_metric: 0.9278 - dice_en_metric: 0.6288 - dice_core_metric: 0.8934 - val_loss: 0.1833 - val_accuracy: 0.8735 - val_dice_whole_metric: 0.8877 - val_dice_en_metric: 0.6186 - val_dice_core_metric: 0.7919\n",
-      "Epoch 78/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0834 - accuracy: 0.9312 - dice_whole_metric: 0.9307 - dice_en_metric: 0.6355 - dice_core_metric: 0.8993\n",
-      "Epoch 78: val_dice_en_metric did not improve from 0.62516\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0834 - accuracy: 0.9312 - dice_whole_metric: 0.9307 - dice_en_metric: 0.6355 - dice_core_metric: 0.8993 - val_loss: 0.1787 - val_accuracy: 0.8788 - val_dice_whole_metric: 0.8831 - val_dice_en_metric: 0.6182 - val_dice_core_metric: 0.8201\n",
-      "Epoch 79/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0774 - accuracy: 0.9334 - dice_whole_metric: 0.9328 - dice_en_metric: 0.6410 - dice_core_metric: 0.9019\n",
-      "Epoch 79: val_dice_en_metric improved from 0.62516 to 0.62654, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-079-0.6265.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0774 - accuracy: 0.9334 - dice_whole_metric: 0.9328 - dice_en_metric: 0.6410 - dice_core_metric: 0.9019 - val_loss: 0.1675 - val_accuracy: 0.8832 - val_dice_whole_metric: 0.8864 - val_dice_en_metric: 0.6265 - val_dice_core_metric: 0.8250\n",
-      "Epoch 80/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0752 - accuracy: 0.9344 - dice_whole_metric: 0.9329 - dice_en_metric: 0.6457 - dice_core_metric: 0.9043\n",
-      "Epoch 80: val_dice_en_metric improved from 0.62654 to 0.62803, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-080-0.6280.hdf5\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0752 - accuracy: 0.9344 - dice_whole_metric: 0.9329 - dice_en_metric: 0.6457 - dice_core_metric: 0.9043 - val_loss: 0.1725 - val_accuracy: 0.8803 - val_dice_whole_metric: 0.8848 - val_dice_en_metric: 0.6280 - val_dice_core_metric: 0.8096\n",
-      "Epoch 81/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0857 - accuracy: 0.9292 - dice_whole_metric: 0.9286 - dice_en_metric: 0.6368 - dice_core_metric: 0.8946\n",
-      "Epoch 81: val_dice_en_metric did not improve from 0.62803\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0857 - accuracy: 0.9292 - dice_whole_metric: 0.9286 - dice_en_metric: 0.6368 - dice_core_metric: 0.8946 - val_loss: 0.1799 - val_accuracy: 0.8769 - val_dice_whole_metric: 0.8814 - val_dice_en_metric: 0.6199 - val_dice_core_metric: 0.8185\n",
-      "Epoch 82/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0784 - accuracy: 0.9331 - dice_whole_metric: 0.9317 - dice_en_metric: 0.6438 - dice_core_metric: 0.9009\n",
-      "Epoch 82: val_dice_en_metric did not improve from 0.62803\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0784 - accuracy: 0.9331 - dice_whole_metric: 0.9317 - dice_en_metric: 0.6438 - dice_core_metric: 0.9009 - val_loss: 0.1725 - val_accuracy: 0.8810 - val_dice_whole_metric: 0.8840 - val_dice_en_metric: 0.6244 - val_dice_core_metric: 0.8217\n",
-      "Epoch 83/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0763 - accuracy: 0.9336 - dice_whole_metric: 0.9323 - dice_en_metric: 0.6453 - dice_core_metric: 0.9008\n",
-      "Epoch 83: val_dice_en_metric did not improve from 0.62803\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0763 - accuracy: 0.9336 - dice_whole_metric: 0.9323 - dice_en_metric: 0.6453 - dice_core_metric: 0.9008 - val_loss: 0.1598 - val_accuracy: 0.8855 - val_dice_whole_metric: 0.8885 - val_dice_en_metric: 0.6267 - val_dice_core_metric: 0.8237\n",
-      "Epoch 84/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0775 - accuracy: 0.9334 - dice_whole_metric: 0.9321 - dice_en_metric: 0.6431 - dice_core_metric: 0.9008\n",
-      "Epoch 84: val_dice_en_metric did not improve from 0.62803\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0775 - accuracy: 0.9334 - dice_whole_metric: 0.9321 - dice_en_metric: 0.6431 - dice_core_metric: 0.9008 - val_loss: 0.1655 - val_accuracy: 0.8853 - val_dice_whole_metric: 0.8864 - val_dice_en_metric: 0.6280 - val_dice_core_metric: 0.8212\n",
-      "Epoch 85/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0830 - accuracy: 0.9305 - dice_whole_metric: 0.9295 - dice_en_metric: 0.6367 - dice_core_metric: 0.8972\n",
-      "Epoch 85: val_dice_en_metric improved from 0.62803 to 0.63116, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-085-0.6312.hdf5\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0830 - accuracy: 0.9305 - dice_whole_metric: 0.9295 - dice_en_metric: 0.6367 - dice_core_metric: 0.8972 - val_loss: 0.1621 - val_accuracy: 0.8872 - val_dice_whole_metric: 0.8902 - val_dice_en_metric: 0.6312 - val_dice_core_metric: 0.8344\n",
-      "Epoch 86/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0850 - accuracy: 0.9304 - dice_whole_metric: 0.9289 - dice_en_metric: 0.6384 - dice_core_metric: 0.8954\n",
-      "Epoch 86: val_dice_en_metric did not improve from 0.63116\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0850 - accuracy: 0.9304 - dice_whole_metric: 0.9289 - dice_en_metric: 0.6384 - dice_core_metric: 0.8954 - val_loss: 0.1880 - val_accuracy: 0.8774 - val_dice_whole_metric: 0.8754 - val_dice_en_metric: 0.6197 - val_dice_core_metric: 0.8184\n",
-      "Epoch 87/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0830 - accuracy: 0.9322 - dice_whole_metric: 0.9304 - dice_en_metric: 0.6456 - dice_core_metric: 0.9009\n",
-      "Epoch 87: val_dice_en_metric did not improve from 0.63116\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0830 - accuracy: 0.9322 - dice_whole_metric: 0.9304 - dice_en_metric: 0.6456 - dice_core_metric: 0.9009 - val_loss: 0.1779 - val_accuracy: 0.8799 - val_dice_whole_metric: 0.8811 - val_dice_en_metric: 0.6161 - val_dice_core_metric: 0.8257\n",
-      "Epoch 88/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0777 - accuracy: 0.9334 - dice_whole_metric: 0.9323 - dice_en_metric: 0.6441 - dice_core_metric: 0.9022\n",
-      "Epoch 88: val_dice_en_metric did not improve from 0.63116\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0777 - accuracy: 0.9334 - dice_whole_metric: 0.9323 - dice_en_metric: 0.6441 - dice_core_metric: 0.9022 - val_loss: 0.1655 - val_accuracy: 0.8851 - val_dice_whole_metric: 0.8883 - val_dice_en_metric: 0.6307 - val_dice_core_metric: 0.8273\n",
-      "Epoch 89/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0707 - accuracy: 0.9368 - dice_whole_metric: 0.9353 - dice_en_metric: 0.6496 - dice_core_metric: 0.9076\n",
-      "Epoch 89: val_dice_en_metric improved from 0.63116 to 0.63341, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-089-0.6334.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0707 - accuracy: 0.9368 - dice_whole_metric: 0.9353 - dice_en_metric: 0.6496 - dice_core_metric: 0.9076 - val_loss: 0.1629 - val_accuracy: 0.8855 - val_dice_whole_metric: 0.8867 - val_dice_en_metric: 0.6334 - val_dice_core_metric: 0.8287\n",
-      "Epoch 90/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0691 - accuracy: 0.9373 - dice_whole_metric: 0.9352 - dice_en_metric: 0.6524 - dice_core_metric: 0.9081\n",
-      "Epoch 90: val_dice_en_metric improved from 0.63341 to 0.63344, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-090-0.6334.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0691 - accuracy: 0.9373 - dice_whole_metric: 0.9352 - dice_en_metric: 0.6524 - dice_core_metric: 0.9081 - val_loss: 0.1588 - val_accuracy: 0.8869 - val_dice_whole_metric: 0.8896 - val_dice_en_metric: 0.6334 - val_dice_core_metric: 0.8296\n",
-      "Epoch 91/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0740 - accuracy: 0.9342 - dice_whole_metric: 0.9332 - dice_en_metric: 0.6453 - dice_core_metric: 0.9032\n",
-      "Epoch 91: val_dice_en_metric did not improve from 0.63344\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0740 - accuracy: 0.9342 - dice_whole_metric: 0.9332 - dice_en_metric: 0.6453 - dice_core_metric: 0.9032 - val_loss: 0.1721 - val_accuracy: 0.8829 - val_dice_whole_metric: 0.8816 - val_dice_en_metric: 0.6258 - val_dice_core_metric: 0.8268\n",
-      "Epoch 92/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0721 - accuracy: 0.9358 - dice_whole_metric: 0.9340 - dice_en_metric: 0.6484 - dice_core_metric: 0.9045\n",
-      "Epoch 92: val_dice_en_metric did not improve from 0.63344\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0721 - accuracy: 0.9358 - dice_whole_metric: 0.9340 - dice_en_metric: 0.6484 - dice_core_metric: 0.9045 - val_loss: 0.1707 - val_accuracy: 0.8826 - val_dice_whole_metric: 0.8837 - val_dice_en_metric: 0.6312 - val_dice_core_metric: 0.8244\n",
-      "Epoch 93/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0728 - accuracy: 0.9358 - dice_whole_metric: 0.9333 - dice_en_metric: 0.6494 - dice_core_metric: 0.9075\n",
-      "Epoch 93: val_dice_en_metric did not improve from 0.63344\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0728 - accuracy: 0.9358 - dice_whole_metric: 0.9333 - dice_en_metric: 0.6494 - dice_core_metric: 0.9075 - val_loss: 0.1803 - val_accuracy: 0.8807 - val_dice_whole_metric: 0.8741 - val_dice_en_metric: 0.6221 - val_dice_core_metric: 0.8241\n",
-      "Epoch 94/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0992 - accuracy: 0.9233 - dice_whole_metric: 0.9248 - dice_en_metric: 0.6307 - dice_core_metric: 0.8840\n",
-      "Epoch 94: val_dice_en_metric did not improve from 0.63344\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0992 - accuracy: 0.9233 - dice_whole_metric: 0.9248 - dice_en_metric: 0.6307 - dice_core_metric: 0.8840 - val_loss: 0.1704 - val_accuracy: 0.8848 - val_dice_whole_metric: 0.8868 - val_dice_en_metric: 0.6310 - val_dice_core_metric: 0.8327\n",
-      "Epoch 95/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0808 - accuracy: 0.9330 - dice_whole_metric: 0.9315 - dice_en_metric: 0.6358 - dice_core_metric: 0.9014\n",
-      "Epoch 95: val_dice_en_metric did not improve from 0.63344\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0808 - accuracy: 0.9330 - dice_whole_metric: 0.9315 - dice_en_metric: 0.6358 - dice_core_metric: 0.9014 - val_loss: 0.1725 - val_accuracy: 0.8825 - val_dice_whole_metric: 0.8837 - val_dice_en_metric: 0.6236 - val_dice_core_metric: 0.8255\n",
-      "Epoch 96/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0724 - accuracy: 0.9363 - dice_whole_metric: 0.9346 - dice_en_metric: 0.6502 - dice_core_metric: 0.9057\n",
-      "Epoch 96: val_dice_en_metric improved from 0.63344 to 0.63460, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-096-0.6346.hdf5\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0724 - accuracy: 0.9363 - dice_whole_metric: 0.9346 - dice_en_metric: 0.6502 - dice_core_metric: 0.9057 - val_loss: 0.1580 - val_accuracy: 0.8891 - val_dice_whole_metric: 0.8883 - val_dice_en_metric: 0.6346 - val_dice_core_metric: 0.8308\n",
-      "Epoch 97/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0685 - accuracy: 0.9376 - dice_whole_metric: 0.9350 - dice_en_metric: 0.6532 - dice_core_metric: 0.9083\n",
-      "Epoch 97: val_dice_en_metric did not improve from 0.63460\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0685 - accuracy: 0.9376 - dice_whole_metric: 0.9350 - dice_en_metric: 0.6532 - dice_core_metric: 0.9083 - val_loss: 0.1543 - val_accuracy: 0.8884 - val_dice_whole_metric: 0.8903 - val_dice_en_metric: 0.6316 - val_dice_core_metric: 0.8298\n",
-      "Epoch 98/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0681 - accuracy: 0.9374 - dice_whole_metric: 0.9350 - dice_en_metric: 0.6563 - dice_core_metric: 0.9092\n",
-      "Epoch 98: val_dice_en_metric did not improve from 0.63460\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0681 - accuracy: 0.9374 - dice_whole_metric: 0.9350 - dice_en_metric: 0.6563 - dice_core_metric: 0.9092 - val_loss: 0.1630 - val_accuracy: 0.8864 - val_dice_whole_metric: 0.8841 - val_dice_en_metric: 0.6342 - val_dice_core_metric: 0.8314\n",
-      "Epoch 99/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0690 - accuracy: 0.9374 - dice_whole_metric: 0.9356 - dice_en_metric: 0.6481 - dice_core_metric: 0.9085\n",
-      "Epoch 99: val_dice_en_metric did not improve from 0.63460\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0690 - accuracy: 0.9374 - dice_whole_metric: 0.9356 - dice_en_metric: 0.6481 - dice_core_metric: 0.9085 - val_loss: 0.1644 - val_accuracy: 0.8859 - val_dice_whole_metric: 0.8840 - val_dice_en_metric: 0.6316 - val_dice_core_metric: 0.8289\n",
-      "Epoch 100/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0784 - accuracy: 0.9325 - dice_whole_metric: 0.9307 - dice_en_metric: 0.6438 - dice_core_metric: 0.8967\n",
-      "Epoch 100: val_dice_en_metric did not improve from 0.63460\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0784 - accuracy: 0.9325 - dice_whole_metric: 0.9307 - dice_en_metric: 0.6438 - dice_core_metric: 0.8967 - val_loss: 0.2084 - val_accuracy: 0.8695 - val_dice_whole_metric: 0.8667 - val_dice_en_metric: 0.6115 - val_dice_core_metric: 0.7954\n",
-      "Epoch 101/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1127 - accuracy: 0.9184 - dice_whole_metric: 0.9198 - dice_en_metric: 0.6224 - dice_core_metric: 0.8735\n",
-      "Epoch 101: val_dice_en_metric did not improve from 0.63460\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.1127 - accuracy: 0.9184 - dice_whole_metric: 0.9198 - dice_en_metric: 0.6224 - dice_core_metric: 0.8735 - val_loss: 0.2062 - val_accuracy: 0.8704 - val_dice_whole_metric: 0.8704 - val_dice_en_metric: 0.6158 - val_dice_core_metric: 0.8158\n",
-      "Epoch 102/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0818 - accuracy: 0.9335 - dice_whole_metric: 0.9329 - dice_en_metric: 0.6479 - dice_core_metric: 0.9019\n",
-      "Epoch 102: val_dice_en_metric did not improve from 0.63460\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0818 - accuracy: 0.9335 - dice_whole_metric: 0.9329 - dice_en_metric: 0.6479 - dice_core_metric: 0.9019 - val_loss: 0.1660 - val_accuracy: 0.8841 - val_dice_whole_metric: 0.8869 - val_dice_en_metric: 0.6248 - val_dice_core_metric: 0.8207\n",
-      "Epoch 103/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0691 - accuracy: 0.9377 - dice_whole_metric: 0.9354 - dice_en_metric: 0.6523 - dice_core_metric: 0.9097\n",
-      "Epoch 103: val_dice_en_metric did not improve from 0.63460\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0691 - accuracy: 0.9377 - dice_whole_metric: 0.9354 - dice_en_metric: 0.6523 - dice_core_metric: 0.9097 - val_loss: 0.1608 - val_accuracy: 0.8854 - val_dice_whole_metric: 0.8879 - val_dice_en_metric: 0.6285 - val_dice_core_metric: 0.8304\n",
-      "Epoch 104/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0687 - accuracy: 0.9368 - dice_whole_metric: 0.9352 - dice_en_metric: 0.6485 - dice_core_metric: 0.9059\n",
-      "Epoch 104: val_dice_en_metric did not improve from 0.63460\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0687 - accuracy: 0.9368 - dice_whole_metric: 0.9352 - dice_en_metric: 0.6485 - dice_core_metric: 0.9059 - val_loss: 0.1631 - val_accuracy: 0.8843 - val_dice_whole_metric: 0.8870 - val_dice_en_metric: 0.6282 - val_dice_core_metric: 0.8279\n",
-      "Epoch 105/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0679 - accuracy: 0.9377 - dice_whole_metric: 0.9357 - dice_en_metric: 0.6510 - dice_core_metric: 0.9091\n",
-      "Epoch 105: val_dice_en_metric did not improve from 0.63460\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0679 - accuracy: 0.9377 - dice_whole_metric: 0.9357 - dice_en_metric: 0.6510 - dice_core_metric: 0.9091 - val_loss: 0.1575 - val_accuracy: 0.8876 - val_dice_whole_metric: 0.8889 - val_dice_en_metric: 0.6296 - val_dice_core_metric: 0.8284\n",
-      "Epoch 106/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0646 - accuracy: 0.9395 - dice_whole_metric: 0.9370 - dice_en_metric: 0.6561 - dice_core_metric: 0.9119\n",
-      "Epoch 106: val_dice_en_metric did not improve from 0.63460\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0646 - accuracy: 0.9395 - dice_whole_metric: 0.9370 - dice_en_metric: 0.6561 - dice_core_metric: 0.9119 - val_loss: 0.1580 - val_accuracy: 0.8859 - val_dice_whole_metric: 0.8887 - val_dice_en_metric: 0.6313 - val_dice_core_metric: 0.8293\n",
-      "Epoch 107/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0620 - accuracy: 0.9402 - dice_whole_metric: 0.9374 - dice_en_metric: 0.6518 - dice_core_metric: 0.9138\n",
-      "Epoch 107: val_dice_en_metric improved from 0.63460 to 0.63755, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-107-0.6376.hdf5\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0620 - accuracy: 0.9402 - dice_whole_metric: 0.9374 - dice_en_metric: 0.6518 - dice_core_metric: 0.9138 - val_loss: 0.1558 - val_accuracy: 0.8870 - val_dice_whole_metric: 0.8873 - val_dice_en_metric: 0.6376 - val_dice_core_metric: 0.8349\n",
-      "Epoch 108/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0626 - accuracy: 0.9396 - dice_whole_metric: 0.9374 - dice_en_metric: 0.6569 - dice_core_metric: 0.9104\n",
-      "Epoch 108: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0626 - accuracy: 0.9396 - dice_whole_metric: 0.9374 - dice_en_metric: 0.6569 - dice_core_metric: 0.9104 - val_loss: 0.1574 - val_accuracy: 0.8831 - val_dice_whole_metric: 0.8904 - val_dice_en_metric: 0.6281 - val_dice_core_metric: 0.8224\n",
-      "Epoch 109/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0700 - accuracy: 0.9364 - dice_whole_metric: 0.9343 - dice_en_metric: 0.6541 - dice_core_metric: 0.9064\n",
-      "Epoch 109: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0700 - accuracy: 0.9364 - dice_whole_metric: 0.9343 - dice_en_metric: 0.6541 - dice_core_metric: 0.9064 - val_loss: 0.1606 - val_accuracy: 0.8856 - val_dice_whole_metric: 0.8881 - val_dice_en_metric: 0.6319 - val_dice_core_metric: 0.8297\n",
-      "Epoch 110/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0764 - accuracy: 0.9339 - dice_whole_metric: 0.9311 - dice_en_metric: 0.6408 - dice_core_metric: 0.9019\n",
-      "Epoch 110: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0764 - accuracy: 0.9339 - dice_whole_metric: 0.9311 - dice_en_metric: 0.6408 - dice_core_metric: 0.9019 - val_loss: 0.1729 - val_accuracy: 0.8816 - val_dice_whole_metric: 0.8844 - val_dice_en_metric: 0.6247 - val_dice_core_metric: 0.8323\n",
-      "Epoch 111/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0773 - accuracy: 0.9342 - dice_whole_metric: 0.9323 - dice_en_metric: 0.6478 - dice_core_metric: 0.9028\n",
-      "Epoch 111: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0773 - accuracy: 0.9342 - dice_whole_metric: 0.9323 - dice_en_metric: 0.6478 - dice_core_metric: 0.9028 - val_loss: 0.1567 - val_accuracy: 0.8889 - val_dice_whole_metric: 0.8884 - val_dice_en_metric: 0.6327 - val_dice_core_metric: 0.8337\n",
-      "Epoch 112/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0650 - accuracy: 0.9396 - dice_whole_metric: 0.9370 - dice_en_metric: 0.6559 - dice_core_metric: 0.9119\n",
-      "Epoch 112: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0650 - accuracy: 0.9396 - dice_whole_metric: 0.9370 - dice_en_metric: 0.6559 - dice_core_metric: 0.9119 - val_loss: 0.1625 - val_accuracy: 0.8850 - val_dice_whole_metric: 0.8846 - val_dice_en_metric: 0.6336 - val_dice_core_metric: 0.8243\n",
-      "Epoch 113/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0667 - accuracy: 0.9378 - dice_whole_metric: 0.9350 - dice_en_metric: 0.6570 - dice_core_metric: 0.9102\n",
-      "Epoch 113: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0667 - accuracy: 0.9378 - dice_whole_metric: 0.9350 - dice_en_metric: 0.6570 - dice_core_metric: 0.9102 - val_loss: 0.1697 - val_accuracy: 0.8821 - val_dice_whole_metric: 0.8812 - val_dice_en_metric: 0.6313 - val_dice_core_metric: 0.8262\n",
-      "Epoch 114/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0639 - accuracy: 0.9396 - dice_whole_metric: 0.9374 - dice_en_metric: 0.6596 - dice_core_metric: 0.9101\n",
-      "Epoch 114: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0639 - accuracy: 0.9396 - dice_whole_metric: 0.9374 - dice_en_metric: 0.6596 - dice_core_metric: 0.9101 - val_loss: 0.1613 - val_accuracy: 0.8853 - val_dice_whole_metric: 0.8862 - val_dice_en_metric: 0.6327 - val_dice_core_metric: 0.8269\n",
-      "Epoch 115/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0855 - accuracy: 0.9319 - dice_whole_metric: 0.9268 - dice_en_metric: 0.6413 - dice_core_metric: 0.8976\n",
-      "Epoch 115: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0855 - accuracy: 0.9319 - dice_whole_metric: 0.9268 - dice_en_metric: 0.6413 - dice_core_metric: 0.8976 - val_loss: 0.2374 - val_accuracy: 0.8564 - val_dice_whole_metric: 0.8590 - val_dice_en_metric: 0.5908 - val_dice_core_metric: 0.7901\n",
-      "Epoch 116/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1058 - accuracy: 0.9224 - dice_whole_metric: 0.9222 - dice_en_metric: 0.6273 - dice_core_metric: 0.8865\n",
-      "Epoch 116: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.1058 - accuracy: 0.9224 - dice_whole_metric: 0.9222 - dice_en_metric: 0.6273 - dice_core_metric: 0.8865 - val_loss: 0.1893 - val_accuracy: 0.8731 - val_dice_whole_metric: 0.8800 - val_dice_en_metric: 0.6087 - val_dice_core_metric: 0.8176\n",
-      "Epoch 117/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0694 - accuracy: 0.9382 - dice_whole_metric: 0.9365 - dice_en_metric: 0.6494 - dice_core_metric: 0.9102\n",
-      "Epoch 117: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0694 - accuracy: 0.9382 - dice_whole_metric: 0.9365 - dice_en_metric: 0.6494 - dice_core_metric: 0.9102 - val_loss: 0.1609 - val_accuracy: 0.8840 - val_dice_whole_metric: 0.8901 - val_dice_en_metric: 0.6236 - val_dice_core_metric: 0.8242\n",
-      "Epoch 118/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0617 - accuracy: 0.9408 - dice_whole_metric: 0.9380 - dice_en_metric: 0.6539 - dice_core_metric: 0.9141\n",
-      "Epoch 118: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0617 - accuracy: 0.9408 - dice_whole_metric: 0.9380 - dice_en_metric: 0.6539 - dice_core_metric: 0.9141 - val_loss: 0.1623 - val_accuracy: 0.8829 - val_dice_whole_metric: 0.8867 - val_dice_en_metric: 0.6240 - val_dice_core_metric: 0.8256\n",
-      "Epoch 119/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0580 - accuracy: 0.9422 - dice_whole_metric: 0.9390 - dice_en_metric: 0.6570 - dice_core_metric: 0.9161\n",
-      "Epoch 119: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 341s 2s/step - loss: 0.0580 - accuracy: 0.9422 - dice_whole_metric: 0.9390 - dice_en_metric: 0.6570 - dice_core_metric: 0.9161 - val_loss: 0.1554 - val_accuracy: 0.8867 - val_dice_whole_metric: 0.8882 - val_dice_en_metric: 0.6335 - val_dice_core_metric: 0.8300\n",
-      "Epoch 120/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0587 - accuracy: 0.9413 - dice_whole_metric: 0.9381 - dice_en_metric: 0.6626 - dice_core_metric: 0.9130\n",
-      "Epoch 120: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0587 - accuracy: 0.9413 - dice_whole_metric: 0.9381 - dice_en_metric: 0.6626 - dice_core_metric: 0.9130 - val_loss: 0.1614 - val_accuracy: 0.8829 - val_dice_whole_metric: 0.8851 - val_dice_en_metric: 0.6301 - val_dice_core_metric: 0.8278\n",
-      "Epoch 121/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0604 - accuracy: 0.9401 - dice_whole_metric: 0.9379 - dice_en_metric: 0.6625 - dice_core_metric: 0.9114\n",
-      "Epoch 121: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0604 - accuracy: 0.9401 - dice_whole_metric: 0.9379 - dice_en_metric: 0.6625 - dice_core_metric: 0.9114 - val_loss: 0.1517 - val_accuracy: 0.8870 - val_dice_whole_metric: 0.8897 - val_dice_en_metric: 0.6355 - val_dice_core_metric: 0.8295\n",
-      "Epoch 122/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0805 - accuracy: 0.9333 - dice_whole_metric: 0.9293 - dice_en_metric: 0.6448 - dice_core_metric: 0.9037\n",
-      "Epoch 122: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0805 - accuracy: 0.9333 - dice_whole_metric: 0.9293 - dice_en_metric: 0.6448 - dice_core_metric: 0.9037 - val_loss: 0.2169 - val_accuracy: 0.8662 - val_dice_whole_metric: 0.8715 - val_dice_en_metric: 0.6021 - val_dice_core_metric: 0.8108\n",
-      "Epoch 123/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0995 - accuracy: 0.9261 - dice_whole_metric: 0.9261 - dice_en_metric: 0.6279 - dice_core_metric: 0.8917\n",
-      "Epoch 123: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0995 - accuracy: 0.9261 - dice_whole_metric: 0.9261 - dice_en_metric: 0.6279 - dice_core_metric: 0.8917 - val_loss: 0.1758 - val_accuracy: 0.8803 - val_dice_whole_metric: 0.8871 - val_dice_en_metric: 0.6215 - val_dice_core_metric: 0.8223\n",
-      "Epoch 124/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0696 - accuracy: 0.9385 - dice_whole_metric: 0.9368 - dice_en_metric: 0.6544 - dice_core_metric: 0.9107\n",
-      "Epoch 124: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0696 - accuracy: 0.9385 - dice_whole_metric: 0.9368 - dice_en_metric: 0.6544 - dice_core_metric: 0.9107 - val_loss: 0.1606 - val_accuracy: 0.8859 - val_dice_whole_metric: 0.8892 - val_dice_en_metric: 0.6265 - val_dice_core_metric: 0.8302\n",
-      "Epoch 125/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0591 - accuracy: 0.9425 - dice_whole_metric: 0.9396 - dice_en_metric: 0.6596 - dice_core_metric: 0.9172\n",
-      "Epoch 125: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0591 - accuracy: 0.9425 - dice_whole_metric: 0.9396 - dice_en_metric: 0.6596 - dice_core_metric: 0.9172 - val_loss: 0.1658 - val_accuracy: 0.8814 - val_dice_whole_metric: 0.8840 - val_dice_en_metric: 0.6225 - val_dice_core_metric: 0.8269\n",
-      "Epoch 126/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0559 - accuracy: 0.9433 - dice_whole_metric: 0.9402 - dice_en_metric: 0.6641 - dice_core_metric: 0.9177\n",
-      "Epoch 126: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 338s 2s/step - loss: 0.0559 - accuracy: 0.9433 - dice_whole_metric: 0.9402 - dice_en_metric: 0.6641 - dice_core_metric: 0.9177 - val_loss: 0.1639 - val_accuracy: 0.8819 - val_dice_whole_metric: 0.8843 - val_dice_en_metric: 0.6310 - val_dice_core_metric: 0.8213\n",
-      "Epoch 127/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0590 - accuracy: 0.9412 - dice_whole_metric: 0.9380 - dice_en_metric: 0.6603 - dice_core_metric: 0.9149\n",
-      "Epoch 127: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 338s 2s/step - loss: 0.0590 - accuracy: 0.9412 - dice_whole_metric: 0.9380 - dice_en_metric: 0.6603 - dice_core_metric: 0.9149 - val_loss: 0.1608 - val_accuracy: 0.8834 - val_dice_whole_metric: 0.8856 - val_dice_en_metric: 0.6306 - val_dice_core_metric: 0.8331\n",
-      "Epoch 128/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0613 - accuracy: 0.9399 - dice_whole_metric: 0.9373 - dice_en_metric: 0.6577 - dice_core_metric: 0.9109\n",
-      "Epoch 128: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 338s 2s/step - loss: 0.0613 - accuracy: 0.9399 - dice_whole_metric: 0.9373 - dice_en_metric: 0.6577 - dice_core_metric: 0.9109 - val_loss: 0.1493 - val_accuracy: 0.8885 - val_dice_whole_metric: 0.8922 - val_dice_en_metric: 0.6344 - val_dice_core_metric: 0.8308\n",
-      "Epoch 129/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0601 - accuracy: 0.9406 - dice_whole_metric: 0.9376 - dice_en_metric: 0.6616 - dice_core_metric: 0.9136\n",
-      "Epoch 129: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0601 - accuracy: 0.9406 - dice_whole_metric: 0.9376 - dice_en_metric: 0.6616 - dice_core_metric: 0.9136 - val_loss: 0.1610 - val_accuracy: 0.8851 - val_dice_whole_metric: 0.8835 - val_dice_en_metric: 0.6363 - val_dice_core_metric: 0.8327\n",
-      "Epoch 130/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.1005 - accuracy: 0.9175 - dice_whole_metric: 0.9273 - dice_en_metric: 0.4486 - dice_core_metric: 0.8916\n",
-      "Epoch 130: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.1005 - accuracy: 0.9175 - dice_whole_metric: 0.9273 - dice_en_metric: 0.4486 - dice_core_metric: 0.8916 - val_loss: 0.2112 - val_accuracy: 0.8678 - val_dice_whole_metric: 0.8689 - val_dice_en_metric: 0.6072 - val_dice_core_metric: 0.8214\n",
-      "Epoch 131/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0741 - accuracy: 0.9377 - dice_whole_metric: 0.9365 - dice_en_metric: 0.6532 - dice_core_metric: 0.9091\n",
-      "Epoch 131: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 338s 2s/step - loss: 0.0741 - accuracy: 0.9377 - dice_whole_metric: 0.9365 - dice_en_metric: 0.6532 - dice_core_metric: 0.9091 - val_loss: 0.1706 - val_accuracy: 0.8813 - val_dice_whole_metric: 0.8826 - val_dice_en_metric: 0.6210 - val_dice_core_metric: 0.8267\n",
-      "Epoch 132/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0596 - accuracy: 0.9420 - dice_whole_metric: 0.9392 - dice_en_metric: 0.6630 - dice_core_metric: 0.9153\n",
-      "Epoch 132: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 338s 2s/step - loss: 0.0596 - accuracy: 0.9420 - dice_whole_metric: 0.9392 - dice_en_metric: 0.6630 - dice_core_metric: 0.9153 - val_loss: 0.1553 - val_accuracy: 0.8861 - val_dice_whole_metric: 0.8887 - val_dice_en_metric: 0.6316 - val_dice_core_metric: 0.8307\n",
-      "Epoch 133/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0577 - accuracy: 0.9418 - dice_whole_metric: 0.9393 - dice_en_metric: 0.6645 - dice_core_metric: 0.9149\n",
-      "Epoch 133: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0577 - accuracy: 0.9418 - dice_whole_metric: 0.9393 - dice_en_metric: 0.6645 - dice_core_metric: 0.9149 - val_loss: 0.1609 - val_accuracy: 0.8828 - val_dice_whole_metric: 0.8862 - val_dice_en_metric: 0.6288 - val_dice_core_metric: 0.8283\n",
-      "Epoch 134/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0561 - accuracy: 0.9428 - dice_whole_metric: 0.9395 - dice_en_metric: 0.6653 - dice_core_metric: 0.9164\n",
-      "Epoch 134: val_dice_en_metric did not improve from 0.63755\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0561 - accuracy: 0.9428 - dice_whole_metric: 0.9395 - dice_en_metric: 0.6653 - dice_core_metric: 0.9164 - val_loss: 0.1511 - val_accuracy: 0.8884 - val_dice_whole_metric: 0.8878 - val_dice_en_metric: 0.6362 - val_dice_core_metric: 0.8374\n",
-      "Epoch 135/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0572 - accuracy: 0.9418 - dice_whole_metric: 0.9388 - dice_en_metric: 0.6607 - dice_core_metric: 0.9153\n",
-      "Epoch 135: val_dice_en_metric improved from 0.63755 to 0.63780, saving model to C:/Users/c21097211/Desktop/part23channel\\part23cnn3-135-0.6378.hdf5\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0572 - accuracy: 0.9418 - dice_whole_metric: 0.9388 - dice_en_metric: 0.6607 - dice_core_metric: 0.9153 - val_loss: 0.1540 - val_accuracy: 0.8876 - val_dice_whole_metric: 0.8873 - val_dice_en_metric: 0.6378 - val_dice_core_metric: 0.8371\n",
-      "Epoch 136/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0548 - accuracy: 0.9434 - dice_whole_metric: 0.9399 - dice_en_metric: 0.6692 - dice_core_metric: 0.9180\n",
-      "Epoch 136: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 336s 2s/step - loss: 0.0548 - accuracy: 0.9434 - dice_whole_metric: 0.9399 - dice_en_metric: 0.6692 - dice_core_metric: 0.9180 - val_loss: 0.1680 - val_accuracy: 0.8794 - val_dice_whole_metric: 0.8786 - val_dice_en_metric: 0.6285 - val_dice_core_metric: 0.8281\n",
-      "Epoch 137/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0551 - accuracy: 0.9433 - dice_whole_metric: 0.9396 - dice_en_metric: 0.6622 - dice_core_metric: 0.9169\n",
-      "Epoch 137: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0551 - accuracy: 0.9433 - dice_whole_metric: 0.9396 - dice_en_metric: 0.6622 - dice_core_metric: 0.9169 - val_loss: 0.1663 - val_accuracy: 0.8807 - val_dice_whole_metric: 0.8811 - val_dice_en_metric: 0.6291 - val_dice_core_metric: 0.8280\n",
-      "Epoch 138/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0587 - accuracy: 0.9414 - dice_whole_metric: 0.9379 - dice_en_metric: 0.6627 - dice_core_metric: 0.9146\n",
-      "Epoch 138: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0587 - accuracy: 0.9414 - dice_whole_metric: 0.9379 - dice_en_metric: 0.6627 - dice_core_metric: 0.9146 - val_loss: 0.1698 - val_accuracy: 0.8784 - val_dice_whole_metric: 0.8801 - val_dice_en_metric: 0.6197 - val_dice_core_metric: 0.8252\n",
-      "Epoch 139/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0683 - accuracy: 0.9369 - dice_whole_metric: 0.9357 - dice_en_metric: 0.6556 - dice_core_metric: 0.9057\n",
-      "Epoch 139: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0683 - accuracy: 0.9369 - dice_whole_metric: 0.9357 - dice_en_metric: 0.6556 - dice_core_metric: 0.9057 - val_loss: 0.1639 - val_accuracy: 0.8849 - val_dice_whole_metric: 0.8846 - val_dice_en_metric: 0.6347 - val_dice_core_metric: 0.8387\n",
-      "Epoch 140/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0606 - accuracy: 0.9412 - dice_whole_metric: 0.9381 - dice_en_metric: 0.6663 - dice_core_metric: 0.9135\n",
-      "Epoch 140: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0606 - accuracy: 0.9412 - dice_whole_metric: 0.9381 - dice_en_metric: 0.6663 - dice_core_metric: 0.9135 - val_loss: 0.1692 - val_accuracy: 0.8789 - val_dice_whole_metric: 0.8806 - val_dice_en_metric: 0.6311 - val_dice_core_metric: 0.8243\n",
-      "Epoch 141/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0546 - accuracy: 0.9432 - dice_whole_metric: 0.9398 - dice_en_metric: 0.6685 - dice_core_metric: 0.9181\n",
-      "Epoch 141: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0546 - accuracy: 0.9432 - dice_whole_metric: 0.9398 - dice_en_metric: 0.6685 - dice_core_metric: 0.9181 - val_loss: 0.1539 - val_accuracy: 0.8858 - val_dice_whole_metric: 0.8868 - val_dice_en_metric: 0.6324 - val_dice_core_metric: 0.8381\n",
-      "Epoch 142/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0700 - accuracy: 0.9358 - dice_whole_metric: 0.9336 - dice_en_metric: 0.6441 - dice_core_metric: 0.9066\n",
-      "Epoch 142: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0700 - accuracy: 0.9358 - dice_whole_metric: 0.9336 - dice_en_metric: 0.6441 - dice_core_metric: 0.9066 - val_loss: 0.1811 - val_accuracy: 0.8777 - val_dice_whole_metric: 0.8790 - val_dice_en_metric: 0.6237 - val_dice_core_metric: 0.8307\n",
-      "Epoch 143/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0681 - accuracy: 0.9381 - dice_whole_metric: 0.9358 - dice_en_metric: 0.6525 - dice_core_metric: 0.9098\n",
-      "Epoch 143: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0681 - accuracy: 0.9381 - dice_whole_metric: 0.9358 - dice_en_metric: 0.6525 - dice_core_metric: 0.9098 - val_loss: 0.1634 - val_accuracy: 0.8819 - val_dice_whole_metric: 0.8866 - val_dice_en_metric: 0.6182 - val_dice_core_metric: 0.8269\n",
-      "Epoch 144/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0568 - accuracy: 0.9424 - dice_whole_metric: 0.9392 - dice_en_metric: 0.6631 - dice_core_metric: 0.9170\n",
-      "Epoch 144: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0568 - accuracy: 0.9424 - dice_whole_metric: 0.9392 - dice_en_metric: 0.6631 - dice_core_metric: 0.9170 - val_loss: 0.1654 - val_accuracy: 0.8795 - val_dice_whole_metric: 0.8832 - val_dice_en_metric: 0.6219 - val_dice_core_metric: 0.8266\n",
-      "Epoch 145/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0555 - accuracy: 0.9427 - dice_whole_metric: 0.9391 - dice_en_metric: 0.6662 - dice_core_metric: 0.9168\n",
-      "Epoch 145: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0555 - accuracy: 0.9427 - dice_whole_metric: 0.9391 - dice_en_metric: 0.6662 - dice_core_metric: 0.9168 - val_loss: 0.1509 - val_accuracy: 0.8870 - val_dice_whole_metric: 0.8898 - val_dice_en_metric: 0.6338 - val_dice_core_metric: 0.8337\n",
-      "Epoch 146/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0566 - accuracy: 0.9423 - dice_whole_metric: 0.9382 - dice_en_metric: 0.6655 - dice_core_metric: 0.9173\n",
-      "Epoch 146: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0566 - accuracy: 0.9423 - dice_whole_metric: 0.9382 - dice_en_metric: 0.6655 - dice_core_metric: 0.9173 - val_loss: 0.1534 - val_accuracy: 0.8868 - val_dice_whole_metric: 0.8877 - val_dice_en_metric: 0.6332 - val_dice_core_metric: 0.8317\n",
-      "Epoch 147/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0602 - accuracy: 0.9402 - dice_whole_metric: 0.9364 - dice_en_metric: 0.6626 - dice_core_metric: 0.9127\n",
-      "Epoch 147: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0602 - accuracy: 0.9402 - dice_whole_metric: 0.9364 - dice_en_metric: 0.6626 - dice_core_metric: 0.9127 - val_loss: 0.1603 - val_accuracy: 0.8837 - val_dice_whole_metric: 0.8854 - val_dice_en_metric: 0.6224 - val_dice_core_metric: 0.8345\n",
-      "Epoch 148/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0574 - accuracy: 0.9424 - dice_whole_metric: 0.9385 - dice_en_metric: 0.6668 - dice_core_metric: 0.9170\n",
-      "Epoch 148: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0574 - accuracy: 0.9424 - dice_whole_metric: 0.9385 - dice_en_metric: 0.6668 - dice_core_metric: 0.9170 - val_loss: 0.1697 - val_accuracy: 0.8803 - val_dice_whole_metric: 0.8778 - val_dice_en_metric: 0.6205 - val_dice_core_metric: 0.8242\n",
-      "Epoch 149/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0525 - accuracy: 0.9446 - dice_whole_metric: 0.9407 - dice_en_metric: 0.6720 - dice_core_metric: 0.9193\n",
-      "Epoch 149: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0525 - accuracy: 0.9446 - dice_whole_metric: 0.9407 - dice_en_metric: 0.6720 - dice_core_metric: 0.9193 - val_loss: 0.1571 - val_accuracy: 0.8854 - val_dice_whole_metric: 0.8833 - val_dice_en_metric: 0.6327 - val_dice_core_metric: 0.8315\n",
-      "Epoch 150/150\n",
-      "174/174 [==============================] - ETA: 0s - loss: 0.0514 - accuracy: 0.9446 - dice_whole_metric: 0.9406 - dice_en_metric: 0.6718 - dice_core_metric: 0.9194\n",
-      "Epoch 150: val_dice_en_metric did not improve from 0.63780\n",
-      "174/174 [==============================] - 337s 2s/step - loss: 0.0514 - accuracy: 0.9446 - dice_whole_metric: 0.9406 - dice_en_metric: 0.6718 - dice_core_metric: 0.9194 - val_loss: 0.1548 - val_accuracy: 0.8849 - val_dice_whole_metric: 0.8863 - val_dice_en_metric: 0.6327 - val_dice_core_metric: 0.8312\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Fit the model \n",
-    "\n",
-    "steps_per_epoch = len(train_img_list)//batch_size\n",
-    "val_steps_per_epoch = len(val_img_list)//batch_size\n",
-    "\n",
-    "model =  CNN_Model(IMG_HEIGHT=80, \n",
-    "                          IMG_WIDTH=80, \n",
-    "                          IMG_DEPTH=128, \n",
-    "                          IMG_CHANNELS=3, \n",
-    "                          num_classes=4)\n",
-    "\n",
-    "model.compile(optimizer = optim, loss=generalised_wasserstein_dice_loss, metrics=metrics)\n",
-    "print(model.summary())\n",
-    "print(model.input_shape)\n",
-    "print(model.output_shape)\n",
-    "\n",
-    "checkpoint_filepath = 'C:/Users/c21097211/Desktop/part23channel/part23cnn3-{epoch:03d}-{val_dice_en_metric:.04f}.hdf5' # use your path \n",
-    "\n",
-    "model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_filepath,\n",
-    "                                                               monitor='val_dice_en_metric', verbose=1,\n",
-    "                                                               save_best_only=True, mode='max')\n",
-    "\n",
-    "history=model.fit(train_img_datagen1,\n",
-    "          steps_per_epoch=steps_per_epoch,\n",
-    "          epochs=150,\n",
-    "          verbose=1,\n",
-    "          validation_data=val_img_datagen1,\n",
-    "          validation_steps=val_steps_per_epoch,\n",
-    "          callbacks=[model_checkpoint_callback])\n",
-    "\n",
-    "np.save('part23cnn66_history.npy',history.history)\n",
-    "model.save('part23cnn66.hdf5')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "57733c9a",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#plot the training and validation IoU and loss at each epoch\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import keras\n",
-    "from matplotlib import pyplot as plt\n",
-    "import glob\n",
-    "import random\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "#my_model = load_model('C:/Users/c21097211/part23cnn.hdf5', \n",
-    " #                     compile=False)\n",
-    "\n",
-    "history=np.load('C:/Users/c21097211/part23cnn66_history.npy',allow_pickle='TRUE').item()\n",
-    "\n",
-    "loss = history['loss']\n",
-    "val_loss = history['val_loss']\n",
-    "epochs = range(1, len(loss) + 1)\n",
-    "plt.plot(epochs, loss, 'y', label='Training loss')\n",
-    "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
-    "plt.title('Training and validation loss')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Loss')\n",
-    "plt.legend()\n",
-    "plt.show()\n",
-    "\n",
-    "acc = history['accuracy']\n",
-    "val_acc = history['val_accuracy']\n",
-    "\n",
-    "plt.plot(epochs, acc, 'y', label='Training accuracy')\n",
-    "plt.plot(epochs, val_acc, 'r', label='Validation accuracy')\n",
-    "plt.title('Training and validation accuracy')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('Accuracy')\n",
-    "plt.legend()\n",
-    "plt.show()\n",
-    "\n",
-    "dice_whole_metric = history['dice_whole_metric']\n",
-    "val_dice_whole_metric = history['val_dice_whole_metric']\n",
-    "\n",
-    "plt.plot(epochs, dice_whole_metric, 'y', label='Training dice_whole_metric')\n",
-    "plt.plot(epochs,val_dice_whole_metric, 'r', label='Validation dice_whole_metric')\n",
-    "plt.title('Training and validation dice')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('dice_whole_metric')\n",
-    "plt.legend()\n",
-    "plt.show()\n",
-    "\n",
-    "dice_en_metric = history['dice_en_metric']\n",
-    "val_dice_en_metric = history['val_dice_en_metric']\n",
-    "\n",
-    "plt.plot(epochs, dice_en_metric, 'y', label='Training dice_en_metric')\n",
-    "plt.plot(epochs,val_dice_en_metric, 'r', label='Validation dice_en_metric')\n",
-    "plt.title('Training and validation dice_en_metric')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('dice_en_metric')\n",
-    "plt.legend()\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "dice_core_metric = history['dice_core_metric']\n",
-    "val_dice_core_metric = history['val_dice_core_metric']\n",
-    "\n",
-    "plt.plot(epochs, dice_core_metric, 'y', label='Training dice_core_metric')\n",
-    "plt.plot(epochs,val_dice_core_metric, 'r', label='Validation dice_core_metric')\n",
-    "plt.title('Training and validation dice_core_metric')\n",
-    "plt.xlabel('Epochs')\n",
-    "plt.ylabel('dice_core_metric')\n",
-    "plt.legend()\n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "8280723d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 1s 594ms/step\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import glob\n",
-    "import os \n",
-    "val_path=\"C:/Users/c21097211/Desktop/part23channel/val\"\n",
-    "\n",
-    "batch_size=8\n",
-    "\n",
-    "val_img_datagen1=imageLoader(val_path,batch_size)\n",
-    "\n",
-    "\n",
-    "val_img_list= os.listdir(val_path)\n",
-    "\n",
-    "imgv, mskv = val_img_datagen1.__next__()\n",
-    "\n",
-    "\n",
-    "img_num = random.randint(0,imgt.shape[0]-1)\n",
-    "test_img=imgv[img_num]\n",
-    "c=mskv[img_num]\n",
-    "test_mask=np.argmax(c, axis=3)\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "my_model = load_model('C:/Users/c21097211/part23cnn66.hdf5', \n",
-    "                      compile=False)\n",
-    "\n",
-    "\n",
-    "test_img_input = np.expand_dims(test_img, axis=0)\n",
-    "test_prediction = my_model.predict(test_img_input)\n",
-    "test_prediction_argmax=np.argmax(test_prediction, axis=4)[0,:,:,:]\n",
-    "\n",
-    "\n",
-    "# print(test_prediction_argmax.shape)\n",
-    "# print(test_mask_argmax.shape)\n",
-    "# print(np.unique(test_prediction_argmax))\n",
-    "\n",
-    "\n",
-    "#Plot individual slices from test predictions for verification\n",
-    "from matplotlib import pyplot as plt\n",
-    "import random\n",
-    "\n",
-    "#n_slice=random.randint(0, test_prediction_argmax.shape[2])\n",
-    "n_slice=50\n",
-    "plt.subplot(231)\n",
-    "plt.imshow(test_img[:,:,n_slice, 0], cmap='gray')\n",
-    "plt.title('Image flair')\n",
-    "plt.subplot(232)\n",
-    "plt.imshow(test_img[:,:,n_slice, 1], cmap='gray')\n",
-    "plt.title('Image t1ce')\n",
-    "plt.subplot(233)\n",
-    "plt.imshow(test_img[:,:,n_slice, 2], cmap='gray')\n",
-    "plt.title('Image t2')\n",
-    "plt.subplot(234)\n",
-    "plt.imshow(test_mask[:,:,n_slice])\n",
-    "plt.title('Mask')\n",
-    "plt.show()\n",
-    "\n",
-    "plt.subplot(235)\n",
-    "plt.title('Prediction on test image')\n",
-    "plt.imshow(test_prediction_argmax[:,:, n_slice])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e2ead2ba",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from skimage.transform import resize\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import keras\n",
-    "from matplotlib import pyplot as plt\n",
-    "import random\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "original_image =np.load(\"C:/Users/c21097211/Desktop/part23channel/val/train21/image_21.npy\")\n",
-    "original_mask  = np.load(\"C:/Users/c21097211/Desktop/part23channel/val/train21/mask_21.npy\")\n",
-    "predicted_mask = np.load(\"C:/Users/c21097211/Desktop/part23channel/val/train21/predicted_mask_21.npy\")\n",
-    "test_mask=np.argmax(original_mask, axis=3)\n",
-    "print(original_image.shape)\n",
-    "print(original_mask.shape)\n",
-    "print(predicted_mask.shape)\n",
-    "\n",
-    "\n",
-    "ptsx,ptsy,ptsz=np.where(predicted_mask >0)\n",
-    "xmin,xmax,ymin,ymax,zmin,zmax=ptsx.min(),ptsx.max(),ptsy.min(),ptsy.max(),ptsz.min(),ptsz.max()  \n",
-    "m2 = test_mask[xmin:xmax,ymin:ymax,zmin:zmax]\n",
-    "        \n",
-    "mask_=resize(m2,(80,80,128))\n",
-    "\n",
-    "       \n",
-    "image = original_image[xmin:xmax,ymin:ymax,zmin:zmax]\n",
-    "image_=resize(image,(80,80,128),mode='constant',preserve_range=True)\n",
-    "    \n",
-    "\n",
-    "\n",
-    "my_model = load_model('C:/Users/c21097211/part23cnn66.hdf5', \n",
-    "                      compile=False)\n",
-    "\n",
-    "\n",
-    "test_img_input = np.expand_dims(image_, axis=0)\n",
-    "test_prediction = my_model.predict(test_img_input)\n",
-    "test_prediction_argmax=np.argmax(test_prediction, axis=4)[0,:,:,:]\n",
-    "\n",
-    "\n",
-    "# print(test_prediction_argmax.shape)\n",
-    "# print(test_mask_argmax.shape)\n",
-    "# print(np.unique(test_prediction_argmax))\n",
-    "\n",
-    "\n",
-    "#Plot individual slices from test predictions for verification\n",
-    "from matplotlib import pyplot as plt\n",
-    "import random\n",
-    "\n",
-    "#n_slice=random.randint(0, test_prediction_argmax.shape[2])\n",
-    "n_slice=50\n",
-    "plt.subplot(231)\n",
-    "plt.imshow(image_[:,:,n_slice, 0], cmap='gray')\n",
-    "plt.title('Image flair')\n",
-    "plt.subplot(232)\n",
-    "plt.imshow(image_[:,:,n_slice, 1], cmap='gray')\n",
-    "plt.title('Image t1ce')\n",
-    "plt.subplot(233)\n",
-    "plt.imshow(image_[:,:,n_slice, 2], cmap='gray')\n",
-    "plt.title('Image t2')\n",
-    "plt.subplot(234)\n",
-    "plt.imshow(mask_[:,:,n_slice])\n",
-    "plt.title('Mask')\n",
-    "plt.show()\n",
-    "\n",
-    "plt.subplot(235)\n",
-    "plt.title('Prediction on test image')\n",
-    "plt.imshow(test_prediction_argmax[:,:, n_slice])\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6e9b7704",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(128, 128, 128, 3)\n",
-      "(128, 128, 128, 4)\n",
-      "(128, 128, 128)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import glob\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from skimage.transform import resize\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import keras\n",
-    "from matplotlib import pyplot as plt\n",
-    "import random\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "original_image =np.load(\"C:/Users/c21097211/Desktop/part23channel/val/train277/image_277.npy\")\n",
-    "original_mask  = np.load(\"C:/Users/c21097211/Desktop/part23channel/val/train277/mask_277.npy\")\n",
-    "predicted_mask = np.load(\"C:/Users/c21097211/Desktop/part23channel/val/train277/predicted_mask_277.npy\")\n",
-    "\n",
-    "print(original_image.shape)\n",
-    "print(original_mask.shape)\n",
-    "print(predicted_mask.shape)\n",
-    "\n",
-    "\n",
-    "ptsx,ptsy,ptsz=np.where(predicted_mask >0)\n",
-    "xmin,xmax,ymin,ymax,zmin,zmax=ptsx.min(),ptsx.max(),ptsy.min(),ptsy.max(),ptsz.min(),ptsz.max()  \n",
-    "m2 = predicted_mask[xmin:xmax,ymin:ymax,zmin:zmax]\n",
-    "        \n",
-    "mask_=resize(m2,(80,80,128))\n",
-    "\n",
-    "       \n",
-    "image = original_image[xmin:xmax,ymin:ymax,zmin:zmax]\n",
-    "image_=resize(image,(80,80,128),mode='constant',preserve_range=True)\n",
-    "    \n",
-    "\n",
-    "\n",
-    "my_model = load_model('C:/Users/c21097211/part23cnn66.hdf5', \n",
-    "                      compile=False)\n",
-    "\n",
-    "\n",
-    "test_img_input = np.expand_dims(image_, axis=0)\n",
-    "test_prediction = my_model.predict(test_img_input)\n",
-    "test_prediction_argmax=np.argmax(test_prediction, axis=4)[0,:,:]\n",
-    "\n",
-    "\n",
-    "# print(test_prediction_argmax.shape)\n",
-    "# print(test_mask_argmax.shape)\n",
-    "# print(np.unique(test_prediction_argmax))\n",
-    "\n",
-    "\n",
-    "#Plot individual slices from test predictions for verification\n",
-    "from matplotlib import pyplot as plt\n",
-    "import random\n",
-    "\n",
-    "#n_slice=random.randint(0, test_prediction_argmax.shape[2])\n",
-    "n_slice=50\n",
-    "plt.subplot(231)\n",
-    "plt.imshow(image_[:,:,n_slice, 0], cmap='gray')\n",
-    "plt.title('Image flair')\n",
-    "plt.subplot(232)\n",
-    "plt.imshow(image_[:,:,n_slice, 1], cmap='gray')\n",
-    "plt.title('Image t1ce')\n",
-    "plt.subplot(233)\n",
-    "plt.imshow(image_[:,:,n_slice, 2], cmap='gray')\n",
-    "plt.title('Image t2')\n",
-    "plt.subplot(234)\n",
-    "plt.imshow(mask_[:,:,n_slice])\n",
-    "plt.title('Mask')\n",
-    "plt.show()\n",
-    "\n",
-    "plt.subplot(235)\n",
-    "plt.title('Prediction on test image')\n",
-    "plt.imshow(test_prediction_argmax[:,:, n_slice])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "33c05e16",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import glob\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from skimage.transform import resize\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import keras\n",
-    "from matplotlib import pyplot as plt\n",
-    "import random\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "original_image =np.load(\"C:/Users/c21097211/Desktop/part23channel/val/train303/image_303.npy\")\n",
-    "original_mask  = np.load(\"C:/Users/c21097211/Desktop/part23channel/val/train303/mask_303.npy\")\n",
-    "predicted_mask = np.load(\"C:/Users/c21097211/Desktop/part23channel/val/train303/predicted_mask_303.npy\")\n",
-    "\n",
-    "print(original_image.shape)\n",
-    "print(original_mask.shape)\n",
-    "print(predicted_mask.shape)\n",
-    "\n",
-    "\n",
-    "ptsx,ptsy,ptsz=np.where(predicted_mask >0)\n",
-    "xmin,xmax,ymin,ymax,zmin,zmax=ptsx.min(),ptsx.max(),ptsy.min(),ptsy.max(),ptsz.min(),ptsz.max()  \n",
-    "m2 = original_mask[xmin:xmax,ymin:ymax,zmin:zmax]\n",
-    "        \n",
-    "mask_=resize(m2,(80,80,128))\n",
-    "\n",
-    "       \n",
-    "image = original_image[xmin:xmax,ymin:ymax,zmin:zmax]\n",
-    "image_=resize(image,(80,80,128),mode='constant',preserve_range=True)\n",
-    "    \n",
-    "\n",
-    "\n",
-    "my_model = load_model('C:/Users/c21097211/part23cnn66.hdf5', \n",
-    "                      compile=False)\n",
-    "\n",
-    "\n",
-    "test_img_input = np.expand_dims(image_, axis=0)\n",
-    "test_prediction = my_model.predict(test_img_input)\n",
-    "test_prediction_argmax=np.argmax(test_prediction, axis=4)[0,:,:,:]\n",
-    "\n",
-    "\n",
-    "# print(test_prediction_argmax.shape)\n",
-    "# print(test_mask_argmax.shape)\n",
-    "# print(np.unique(test_prediction_argmax))\n",
-    "\n",
-    "\n",
-    "#Plot individual slices from test predictions for verification\n",
-    "from matplotlib import pyplot as plt\n",
-    "import random\n",
-    "\n",
-    "#n_slice=random.randint(0, test_prediction_argmax.shape[2])\n",
-    "n_slice=70\n",
-    "plt.subplot(231)\n",
-    "plt.imshow(image_[:,:,n_slice, 0], cmap='gray')\n",
-    "plt.title('Image flair')\n",
-    "plt.subplot(232)\n",
-    "plt.imshow(image_[:,:,n_slice, 1], cmap='gray')\n",
-    "plt.title('Image t1ce')\n",
-    "plt.subplot(233)\n",
-    "plt.imshow(image_[:,:,n_slice, 2], cmap='gray')\n",
-    "plt.title('Image t2')\n",
-    "plt.subplot(234)\n",
-    "plt.imshow(mask_[:,:,n_slice])\n",
-    "plt.title('Mask')\n",
-    "plt.show()\n",
-    "\n",
-    "plt.subplot(235)\n",
-    "plt.title('Prediction on test image')\n",
-    "plt.imshow(test_prediction_argmax[:,:, n_slice])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "fc7b4d93",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(128, 128, 128, 3)\n",
-      "(128, 128, 128, 4)\n",
-      "(128, 128, 128)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n",
-      "WARNING:root:The given value for groups will be overwritten.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000001946AE8C700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x000001946AE8C700> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 1s 571ms/step\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import glob\n",
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from skimage.transform import resize\n",
-    "import numpy as np\n",
-    "import os\n",
-    "import keras\n",
-    "from matplotlib import pyplot as plt\n",
-    "import random\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "original_image =np.load(\"C:/Users/c21097211/Desktop/part23channel/val/train55/image_55.npy\")\n",
-    "original_mask  = np.load(\"C:/Users/c21097211/Desktop/part23channel/val/train55/mask_55.npy\")\n",
-    "predicted_mask = np.load(\"C:/Users/c21097211/Desktop/part23channel/val/train55/predicted_mask_55.npy\")\n",
-    "\n",
-    "print(original_image.shape)\n",
-    "print(original_mask.shape)\n",
-    "print(predicted_mask.shape)\n",
-    "\n",
-    "test_mask=np.argmax(original_mask , axis=3)\n",
-    "\n",
-    "ptsx,ptsy,ptsz=np.where(predicted_mask >0)\n",
-    "xmin,xmax,ymin,ymax,zmin,zmax=ptsx.min(),ptsx.max(),ptsy.min(),ptsy.max(),ptsz.min(),ptsz.max()  \n",
-    "m2 = original_mask[xmin:xmax,ymin:ymax,zmin:zmax]\n",
-    "        \n",
-    "mask_=resize(m2,(80,80,128))\n",
-    "\n",
-    "       \n",
-    "image = original_image[xmin:xmax,ymin:ymax,zmin:zmax]\n",
-    "image_=resize(image,(80,80,128),mode='constant',preserve_range=True)\n",
-    "    \n",
-    "\n",
-    "\n",
-    "my_model = load_model('C:/Users/c21097211/part23cnn66.hdf5', \n",
-    "                      compile=False)\n",
-    "\n",
-    "\n",
-    "test_img_input = np.expand_dims(image_, axis=0)\n",
-    "test_prediction = my_model.predict(test_img_input)\n",
-    "test_prediction_argmax=np.argmax(test_prediction, axis=4)[0,:,:,:]\n",
-    "\n",
-    "np.save('C:/Users/c21097211/Desktop/'+\"/predicted_mask2_\"+\".npy\",test_prediction_argmax)        \n",
-    "\n",
-    "\n",
-    "# print(test_prediction_argmax.shape)\n",
-    "# print(test_mask_argmax.shape)\n",
-    "# print(np.unique(test_prediction_argmax))\n",
-    "\n",
-    "\n",
-    "#Plot individual slices from test predictions for verification\n",
-    "from matplotlib import pyplot as plt\n",
-    "import random\n",
-    "\n",
-    "#n_slice=random.randint(0, test_prediction_argmax.shape[2])\n",
-    "n_slice=60\n",
-    "plt.subplot(231)\n",
-    "plt.imshow(image_[:,:,n_slice, 0], cmap='gray')\n",
-    "plt.title('Image flair')\n",
-    "plt.subplot(232)\n",
-    "plt.imshow(image_[:,:,n_slice, 1], cmap='gray')\n",
-    "plt.title('Image t1ce')\n",
-    "plt.subplot(233)\n",
-    "plt.imshow(image_[:,:,n_slice, 2], cmap='gray')\n",
-    "plt.title('Image t2')\n",
-    "plt.subplot(234)\n",
-    "plt.imshow(mask_[:,:,n_slice])\n",
-    "plt.title('Mask')\n",
-    "plt.show()\n",
-    "\n",
-    "plt.show()\n",
-    "plt.subplot(235)\n",
-    "plt.title('Prediction on test image')\n",
-    "plt.imshow(test_prediction_argmax[:,:, n_slice])\n",
-    "plt.show()\n",
-    "\n",
-    "plt.figure(figsize=(12, 8))\n",
-    "\n",
-    "n_slice=70\n",
-    "plt.subplot(231)\n",
-    "plt.imshow(original_image[:,:,n_slice, 0], cmap='gray')\n",
-    "plt.title('Image flair')\n",
-    "plt.subplot(232)\n",
-    "plt.imshow(original_image[:,:,n_slice, 1], cmap='gray')\n",
-    "plt.title('Image t1ce')\n",
-    "plt.subplot(233)\n",
-    "plt.imshow(original_image[:,:,n_slice, 2], cmap='gray')\n",
-    "plt.title('Image t2')\n",
-    "plt.subplot(234)\n",
-    "plt.imshow(test_mask[:,:,70])\n",
-    "plt.title('Mask2')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "0078c722",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(128, 128, 128, 4)\n"
-     ]
-    }
-   ],
-   "source": [
-    "predicted_mask = np.load(\"C:/Users/c21097211/Desktop/mask_55.npy\")\n",
-    "print(predicted_mask.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "811e5dd8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(128, 128, 128, 3)\n",
-      "(128, 128, 128)\n",
-      "(128, 128)\n",
-      "(128, 128, 128, 3)\n",
-      "(128, 128, 3)\n",
-      "(128, 128, 3)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras.backend as K\n",
-    "original_mask  =np.load(\"C:/Users/c21097211/Desktop/Bratsdataset/part23channel/train/train3/mask_3.npy\")\n",
-    "n_slice=60\n",
-    "\n",
-    "c=np.argmax(original_mask, axis=3)\n",
-    "\n",
-    "y_true_f = tf.convert_to_tensor(c, 'float32')\n",
-    "\n",
-    "\n",
-    "y_core=K.sum(tf.gather(y_true_f, [1,2],axis =1),axis=1)\n",
-    "\n",
-    "print(original_mask.shape)\n",
-    "print(y_true_f.shape)\n",
-    "print(y_core.shape)\n",
-    "\n",
-    "y_true_f1 = tf.convert_to_tensor(original_mask, 'float32')\n",
-    "y_core1=K.sum(tf.gather(y_true_f1, [2],axis =1),axis=1)\n",
-    "p_enh=y_true_f1[:,-1]\n",
-    "\n",
-    "print(y_true_f1.shape)\n",
-    "print(y_core1.shape)\n",
-    "print(p_enh.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "96680b4e",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "'''\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "from keras.models import Model\n",
-    "from keras.layers import Input, Reshape, Dense, Conv3D, BatchNormalization, UpSampling3D, MaxPooling3D, concatenate, GlobalAveragePooling3D, Conv3DTranspose, BatchNormalization, Dropout, Lambda\n",
-    "from keras import regularizers\n",
-    "from tensorflow.keras.optimizers import Adam\n",
-    "from keras.metrics import MeanIoU\n",
-    "Lrelu = tf.keras.layers.LeakyReLU(alpha=0.1)\n",
-    "from tensorflow_addons.layers import InstanceNormalization\n",
-    "from tensorflow.python.keras.layers import Dropout, SpatialDropout3D\n",
-    "################################################################################################################################    \n",
-    "def SqueezeAndExcitation(inputs, ratio=8):\n",
-    "    b,_, _, _,c= inputs.shape\n",
-    "    x = GlobalAveragePooling3D()(inputs)\n",
-    "    x = Dense(c//ratio, activation=\"relu\", use_bias=False)(x)\n",
-    "    x = Dense(c, activation=\"sigmoid\", use_bias=False)(x)\n",
-    "    x = inputs * x\n",
-    "    return x\n",
-    "################################################################################################################################\n",
-    "#def TransitionBlock(inputs):\n",
-    "       # b,_, _, _,c= inputs.shape\n",
-    "       # x = tf.keras.layers.BatchNormalization()(inputs)\n",
-    "      #  x = tf.keras.layers.ReLU()(x)\n",
-    "      #  x = tf.keras.layers.Conv3D(3, kernel_size=1)(x)\n",
-    "     #   x = tf.keras.layers.AvgPool3D(pool_size=2, strides=2)\n",
-    "      #  return x\n",
-    "     \n",
-    "###############################################################################################################################    \n",
-    "def CNN_Model(IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS, num_classes):\n",
-    "    kernel_initializer =  'he_uniform'\n",
-    "    inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_DEPTH, IMG_CHANNELS))\n",
-    "    #s = Lambda(lambda x: x / 255)(inputs)   #No need for this if we normalize our inputs beforehand\n",
-    "    s = inputs\n",
-    "   \n",
-    "    conv = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(s)\n",
-    "    conv1 = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(conv)\n",
-    "    B1= InstanceNormalization(axis=-1)(conv1)\n",
-    "    conv1 = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(B1)\n",
-    "    pool1 = MaxPooling3D((2, 2, 2))(conv1)\n",
-    "   \n",
-    "    # 3x3 conv\n",
-    "    conv2 = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(conv)\n",
-    "    B2= InstanceNormalization(axis=-1)(conv2)\n",
-    "    conv2 = Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(B2)\n",
-    "    B2= InstanceNormalization(axis=-1)(conv2)\n",
-    "    pool2 = MaxPooling3D((2, 2, 2))(B2)\n",
-    "\n",
-    "    # 5x5 conv\n",
-    "    conv3 = Conv3D(64, (5, 5, 5), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(conv)\n",
-    "    B3= InstanceNormalization(axis=-1)(conv3)\n",
-    "    conv3 = Conv3D(64, (5, 5, 5), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(B3)\n",
-    "    B3= InstanceNormalization(axis=-1)(conv3)\n",
-    "    # 3x3 max pooling\n",
-    "    pool3 = MaxPooling3D((3, 3, 3), strides=(1,1,1), padding='same')(B3)\n",
-    "\n",
-    "   \n",
-    "    # concatenate filters, assumes filters/channels last\n",
-    "    layer_out = concatenate([conv1  , conv2, conv3], axis=-1)\n",
-    "    Bo= InstanceNormalization(axis=-1)(layer_out)\n",
-    "    poolo = MaxPooling3D((2, 2, 2))(Bo)  \n",
-    "    drop1 = SpatialDropout3D(0.2)(poolo)\n",
-    "   \n",
-    "    conv4 = Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(drop1)\n",
-    "    B4= InstanceNormalization(axis=-1)(conv4)\n",
-    "    conv4 = Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(B4)\n",
-    "    B4= InstanceNormalization(axis=-1)(conv4)\n",
-    "    pool4= MaxPooling3D((2, 2, 2))(B4)  \n",
-    "\n",
-    "   \n",
-    "    attention_layer1 = SqueezeAndExcitation( pool4)\n",
-    "    print(\"-----------------------------------------------------------\")\n",
-    "    print(attention_layer1)\n",
-    "    print(\"-----------------------------------------------------------\")\n",
-    "    #transition_layer1=TransitionBlock(attention_layer1)\n",
-    "\n",
-    "    conv5= Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(attention_layer1)\n",
-    "    B5= InstanceNormalization(axis=-1)(conv5)\n",
-    "    conv5= Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(B5)\n",
-    "    B5= InstanceNormalization(axis=-1)(conv5)\n",
-    "    pool5 = MaxPooling3D((2, 2, 2))(B5)\n",
-    "\n",
-    "\n",
-    " \n",
-    "    conv6= Conv3D(256, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(pool5)\n",
-    "    B6= InstanceNormalization(axis=-1)(conv5)\n",
-    "    conv6= Conv3D(256, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(B6)\n",
-    "    B6= InstanceNormalization(axis=-1)(conv6)\n",
-    "    pool6 = MaxPooling3D((2, 2, 2))(B6)\n",
-    "   \n",
-    "\n",
-    "################################# Upsampling ##########################################\n",
-    "\n",
-    "                                                                                                         \n",
-    " \n",
-    "    u8 = Conv3D(128, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(UpSampling3D(size =(2,2,2))(pool6))                                                                                                  \n",
-    "    c8 = InstanceNormalization(axis=-1)(u8)\n",
-    "    c8 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(u8)\n",
-    "    c8 = InstanceNormalization(axis=-1)(c8)\n",
-    "    c8 = Conv3D(128, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(c8)\n",
-    "                                                                                                         \n",
-    "    u9 = Conv3D(64, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(UpSampling3D(size =(2,2,2))(c8))\n",
-    "    c9 = InstanceNormalization(axis=-1)(u9)\n",
-    "    c9 = Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(u9)\n",
-    "    c9 = InstanceNormalization(axis=-1)(c9)\n",
-    "    c9 = Conv3D(64, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(c9)\n",
-    "\n",
-    "    u10= Conv3D(32, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(UpSampling3D(size =(2,2,2))(c9))\n",
-    "    c10 = InstanceNormalization(axis=-1)(u10)\n",
-    "    c10= Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(u10)\n",
-    "    c10= InstanceNormalization(axis=-1)(c10)                                                                                                    \n",
-    "    c10= Conv3D(32, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(c10)\n",
-    "                                                                                                       \n",
-    "    u11=Conv3D(16, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(UpSampling3D(size =(2,2,2))(c10))\n",
-    "    c11= InstanceNormalization(axis=-1)(u11)\n",
-    "    c11= Conv3D(16, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(u11)\n",
-    "    c11= InstanceNormalization(axis=-1)(c11)                                                                                                    \n",
-    "    c11= Conv3D(16, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(c11)\n",
-    "                             \n",
-    "    u12=Conv3D(8, (2, 2, 2), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(UpSampling3D(size =(2,2,2))(c11))\n",
-    "    c12= InstanceNormalization(axis=-1)(u12)\n",
-    "    c12= Conv3D(8, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(u12)\n",
-    "    c12= InstanceNormalization(axis=-1)(c12)                                                                                                    \n",
-    "    c12= Conv3D(8, (3, 3, 3), activation=Lrelu, kernel_initializer=kernel_initializer, padding='same')(c12)\n",
-    "                       \n",
-    "    outputs = Conv3D(num_classes, (1, 1, 1), kernel_regularizer=regularizers.l2(0.01),activation='softmax')(c12)\n",
-    "\n",
-    "    model = Model(inputs=[inputs], outputs=[outputs])\n",
-    "    #compile model outside of this function to make it flexible.\n",
-    "\n",
-    "    return model                \n",
-    "                     \n",
-    "\n",
-    "#Test if everything is working ok.\n",
-    "model = CNN_Model(80, 80, 128, 3, 3)\n",
-    "\n",
-    "\n",
-    "model.summary()\n",
-    "print(model.input_shape)\n",
-    "print(model.output_shape)\n",
-    "'''"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "878a8bcf",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "'''\n",
-    "# Setting up a loss function\n",
-    "labels = tf.reshape(labels['y'], [-1, NUM_CLASSES])\n",
-    "\n",
-    "#Using tf.cast to convert label type from int32 to float32, as logit is off type float32\n",
-    "labels = tf.cast(labels, tf.float32) \n",
-    "\n",
-    "loss = tf.nn.weighted_cross_entropy_with_logits(\n",
-    "            targets = labels,\n",
-    "            logits = net_output_ops['logits'],\n",
-    "            pos_weight = 0.49,\n",
-    "            name=None)\n",
-    "            \n",
-    "'''"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..8f6c006
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,18 @@
+# requirements.txt
+#
+# SPDX-FileCopyrightText: Copyright (C) 2022 Frank C Langbein <frank@langbein.org>, Cardiff University
+# SPDX-License-Identifier: AGPL-3.0-or-later
+numpy>=1.23
+nibabel>=5.0.1
+tensorflow>=2.10
+#tensorflow-addons>=0.19
+matplotlib>=3.5
+scikit-learn>=1.1
+ipywidgets>=7.7.1
+ipympl>=0.9.2
+screeninfo>=0.8
+segmentation-models-3D>=1.0.4
+pydot>=1.4.2
+pandas>=1.3.5
+seaborn>=0.12.2
+psutil>=5.9.6