breast-cancer-histopathological-breakhis (2).ipynb

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      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from keras.layers import *\n",
    "from keras.activations import *\n",
    "from keras.optimizers import *\n",
    "from keras.activations import *\n",
    "from keras.layers.core import Reshape \n",
    "\n",
    "import seaborn as sns\n",
    "import sklearn\n",
    "from sklearn.utils import resample\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix,classification_report\n",
    "\n",
    "\n",
    "import os \n",
    "from os import listdir\n",
    "from tqdm import tqdm\n",
    "import shutil\n",
    "import cv2\n",
    "\n",
    "import keras \n",
    "from keras import Model\n",
    "from tensorflow.keras import backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.utils import np_utils, to_categorical\n",
    "from keras.preprocessing import image\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.callbacks import Callback, LearningRateScheduler\n",
    "\n",
    "from math import *\n",
    "\n",
    "from tensorflow.keras.models import load_model"
   ]
  },
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      "2.12.0\n",
      "2.12.0\n",
      "Sat Nov  4 09:01:51 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 470.161.03   Driver Version: 470.161.03   CUDA Version: 11.4     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   31C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "|   1  Tesla T4            Off  | 00000000:00:05.0 Off |                    0 |\n",
      "| N/A   32C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)\n",
    "print(keras.__version__)\n",
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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   "source": [
    "#intersection over union\n",
    "def iou(y_true, y_pred, smooth = 0.5):\n",
    "    intersection = K.sum(y_true * y_pred)\n",
    "    sum_ = K.sum(y_true + y_pred)\n",
    "    jac = (intersection + smooth) / (sum_ - intersection + smooth)\n",
    "    return jac\n",
    "\n",
    "# Computing Sensitivity      \n",
    "def sensitivity(y_true, y_pred):\n",
    "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
    "    return true_positives / (possible_positives + K.epsilon())\n",
    "\n",
    "# Computing Specificity\n",
    "def specificity(y_true, y_pred):\n",
    "    true_negatives = K.sum(K.round(K.clip((1-y_true) * (1-y_pred), 0, 1)))\n",
    "    possible_negatives = K.sum(K.round(K.clip(1-y_true, 0, 1)))\n",
    "    return true_negatives / (possible_negatives + K.epsilon())\n",
    "\n",
    "def f1_score(y_true, y_pred):\n",
    "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
    "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
    "    precision = true_positives / (predicted_positives + K.epsilon())\n",
    "    recall = true_positives / (possible_positives + K.epsilon())\n",
    "    f1_val = 2*(precision*recall)/(precision+recall+K.epsilon())\n",
    "    return f1_val\n",
    "\n",
    "# Define Dice coefficient metric\n",
    "def dice_coefficient(y_true, y_pred, epsilon=1e-6):\n",
    "    y_true_flat = tf.reshape(y_true, [-1])\n",
    "    y_pred_flat = tf.reshape(y_pred, [-1])\n",
    "    intersection = tf.reduce_sum(y_true_flat * y_pred_flat)\n",
    "    dice_score = (2.0 * intersection + epsilon) / (tf.reduce_sum(y_true_flat) + tf.reduce_sum(y_pred_flat) + epsilon)\n",
    "    return dice_score\n",
    "\n",
    "def tversky(y_true, y_pred, smooth=1, alpha=0.7):\n",
    "    y_true_pos = K.flatten(y_true)\n",
    "    y_pred_pos = K.flatten(y_pred)\n",
    "    true_pos = K.sum(y_true_pos * y_pred_pos)\n",
    "    false_neg = K.sum(y_true_pos * (1 - y_pred_pos))\n",
    "    false_pos = K.sum((1 - y_true_pos) * y_pred_pos)\n",
    "    return (true_pos + smooth) / (true_pos + alpha * false_neg + (1 - alpha) * false_pos + smooth)\n",
    "\n",
    "def tversky_loss(y_true, y_pred):\n",
    "    return 1 - tversky(y_true, y_pred)\n",
    "\n",
    "def focal_tversky_loss(y_true, y_pred, gamma=0.75):\n",
    "    tv = tversky(y_true, y_pred)\n",
    "    return K.pow((1 - tv), gamma)"
   ]
  },
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   "source": [
    "def confusion_matrix_img(y_pred, y_true, savename):\n",
    "    cm = sklearn.metrics.confusion_matrix(y_true, y_pred)\n",
    "    labels = ['benign', 'malignant']\n",
    "    title='Confusion matrix'\n",
    "    print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(labels))\n",
    "    plt.xticks(tick_marks, labels, rotation=45)\n",
    "    plt.yticks(tick_marks, labels)\n",
    "    fmt = 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    \n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(savename)\n",
    "    plt.show()"
   ]
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   "source": [
    "epochs = 50\n",
    "\n",
    "earlystop = keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                          min_delta=0,\n",
    "                          patience=epochs,\n",
    "                          verbose=1,\n",
    "                          restore_best_weights=True\n",
    "                          )\n",
    "\n",
    "reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss',\n",
    "                              factor=0.2,\n",
    "                              patience=5,\n",
    "                              verbose=1,\n",
    "                              min_delta=0.0001)\n",
    "csv_logger = keras.callbacks.CSVLogger('Model.log')\n",
    "\n",
    "\n",
    "callbacks = [earlystop, csv_logger, reduce_lr]"
   ]
  },
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   "source": [
    "size= 128\n",
    "os.mkdir('augmented')\n",
    "os.mkdir('./augmented/benign')\n",
    "os.mkdir('./augmented/malignant')"
   ]
  },
  {
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   "source": [
    "def getListOfFiles(dirName):\n",
    "    listOfFile = os.listdir(dirName)\n",
    "    allFiles = list()\n",
    "    for entry in listOfFile:\n",
    "        fullPath = os.path.join(dirName, entry)\n",
    "        if os.path.isdir(fullPath):\n",
    "            allFiles = allFiles + getListOfFiles(fullPath)\n",
    "        else:\n",
    "            allFiles.append(fullPath)\n",
    "                \n",
    "    return allFiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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     "shell.execute_reply.started": "2023-11-04T09:01:51.440572Z"
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   },
   "outputs": [],
   "source": [
    "files_benign = getListOfFiles('../input/breakhis/BreaKHis_v1/BreaKHis_v1/histology_slides/breast/benign')\n",
    "for f in files_benign:\n",
    "    if f.endswith('.png'):\n",
    "        shutil.copy(f,'augmented/benign')\n",
    "        \n",
    "files_malignant = getListOfFiles('../input/breakhis/BreaKHis_v1/BreaKHis_v1/histology_slides/breast/malignant')\n",
    "for f in files_malignant:\n",
    "    if f.endswith('.png'):\n",
    "        shutil.copy(f,'augmented/malignant')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 9,
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   },
   "outputs": [],
   "source": [
    "benign_images = getListOfFiles('./augmented/benign')\n",
    "malignent_images = getListOfFiles('./augmented/malignant')"
   ]
  },
  {
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=128x128>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.load_img(benign_images[9], target_size=(size,size,1), grayscale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:03:33.423545Z",
     "iopub.status.busy": "2023-11-04T09:03:33.423200Z",
     "iopub.status.idle": "2023-11-04T09:03:34.741874Z",
     "shell.execute_reply": "2023-11-04T09:03:34.740909Z",
     "shell.execute_reply.started": "2023-11-04T09:03:33.423518Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=128x128>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.load_img(malignent_images[45], target_size=(size,size,1), grayscale=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:03:34.743526Z",
     "iopub.status.busy": "2023-11-04T09:03:34.743151Z",
     "iopub.status.idle": "2023-11-04T09:03:34.749836Z",
     "shell.execute_reply": "2023-11-04T09:03:34.748799Z",
     "shell.execute_reply.started": "2023-11-04T09:03:34.743491Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7909"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_images = len(benign_images) + len(malignent_images)\n",
    "total_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:03:34.751709Z",
     "iopub.status.busy": "2023-11-04T09:03:34.751250Z",
     "iopub.status.idle": "2023-11-04T09:03:35.525497Z",
     "shell.execute_reply": "2023-11-04T09:03:35.524402Z",
     "shell.execute_reply.started": "2023-11-04T09:03:34.751674Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7909, 2)\n",
      "                                               image target\n",
      "0  ./augmented/malignant/SOB_M_DC-14-3909-40-010.png      1\n",
      "1  ./augmented/malignant/SOB_M_LC-14-15570-100-05...      1\n",
      "2  ./augmented/malignant/SOB_M_LC-14-12204-200-02...      1\n",
      "3  ./augmented/malignant/SOB_M_DC-14-14015-100-02...      1\n",
      "4  ./augmented/malignant/SOB_M_LC-14-15570C-400-0...      1\n"
     ]
    }
   ],
   "source": [
    "data = pd.DataFrame(index=np.arange(0, len(benign_images)+len(malignent_images)), columns=[\"image\", \"target\"])\n",
    "k=0\n",
    "for c in [0,1]:\n",
    "        if c==1:\n",
    "            for m in range(len(benign_images)):\n",
    "                data.iloc[k][\"image\"] = benign_images[m]\n",
    "                data.iloc[k][\"target\"] = 0\n",
    "                k += 1\n",
    "        else:\n",
    "            for m in range(len(malignent_images)):\n",
    "                data.iloc[k][\"image\"] = malignent_images[m]\n",
    "                data.iloc[k][\"target\"] = 1\n",
    "                k += 1\n",
    "print(data.shape)\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:03:35.526917Z",
     "iopub.status.busy": "2023-11-04T09:03:35.526665Z",
     "iopub.status.idle": "2023-11-04T09:03:35.544594Z",
     "shell.execute_reply": "2023-11-04T09:03:35.543716Z",
     "shell.execute_reply.started": "2023-11-04T09:03:35.526894Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    5429\n",
       "0    2480\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_data = data[\"target\"].value_counts()\n",
    "count_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:03:35.545861Z",
     "iopub.status.busy": "2023-11-04T09:03:35.545541Z",
     "iopub.status.idle": "2023-11-04T09:03:35.564892Z",
     "shell.execute_reply": "2023-11-04T09:03:35.564177Z",
     "shell.execute_reply.started": "2023-11-04T09:03:35.545828Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    5429\n",
       "0    5429\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ben_upsampled = resample(data[data['target']==0],n_samples=data[data['target']==1].shape[0], random_state=42)\n",
    "up_sampled = pd.concat([data[data['target']==1], ben_upsampled])\n",
    "up_sampled['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:03:35.566144Z",
     "iopub.status.busy": "2023-11-04T09:03:35.565887Z",
     "iopub.status.idle": "2023-11-04T09:03:35.572035Z",
     "shell.execute_reply": "2023-11-04T09:03:35.571102Z",
     "shell.execute_reply.started": "2023-11-04T09:03:35.566121Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KFold(n_splits=10, random_state=42, shuffle=True)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = KFold(n_splits=10,random_state=42, shuffle=True)\n",
    "kf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:03:35.573400Z",
     "iopub.status.busy": "2023-11-04T09:03:35.573075Z",
     "iopub.status.idle": "2023-11-04T09:05:53.119062Z",
     "shell.execute_reply": "2023-11-04T09:05:53.118182Z",
     "shell.execute_reply.started": "2023-11-04T09:03:35.573368Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10858/10858 [02:10<00:00, 83.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9773, 128, 128, 3)\n",
      "(977, 128, 128, 3)\n",
      "(108, 128, 128, 3)\n"
     ]
    }
   ],
   "source": [
    "train_image = []\n",
    "y = []\n",
    "\n",
    "for i in tqdm(range(up_sampled.shape[0])):\n",
    "    img = tf.keras.utils.load_img(up_sampled['image'].iloc[i], target_size=(size,size,1), grayscale=False)\n",
    "    img = tf.keras.utils.img_to_array(img)\n",
    "    img = img/255\n",
    "    train_image.append(img)\n",
    "        \n",
    "X = np.array(train_image)\n",
    "y = up_sampled.iloc[:,-1].values\n",
    "for train_index, test_index in kf.split(X,y):\n",
    "    X_train, X_test, y_train, y_test = X[train_index], X[test_index], \\\n",
    "                                       y[train_index], y[test_index]\n",
    "    #print('train: %s, test: %s' % (X[train_index], X[test_index]))\n",
    "\n",
    "for train_index, test_index in kf.split(X_test,y_test):\n",
    "    X2_test, X_val, y2_test, y_val = X_test[train_index], X_test[test_index], \\\n",
    "                                       y_test[train_index],y_test[test_index]\n",
    "    \n",
    "Y_train = np_utils.to_categorical(y_train, 2)\n",
    "Y_test = np_utils.to_categorical(y2_test, 2)\n",
    "Y_val = np_utils.to_categorical(y_val, 2)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X2_test.shape)\n",
    "print(X_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:05:53.120430Z",
     "iopub.status.busy": "2023-11-04T09:05:53.120112Z",
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     "shell.execute_reply": "2023-11-04T09:05:53.344761Z",
     "shell.execute_reply.started": "2023-11-04T09:05:53.120394Z"
    }
   },
   "outputs": [],
   "source": [
    "my_input= Input(shape=(size, size, 3))\n",
    "img_shape= (size, size, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:05:53.347277Z",
     "iopub.status.busy": "2023-11-04T09:05:53.346969Z",
     "iopub.status.idle": "2023-11-04T09:05:53.352179Z",
     "shell.execute_reply": "2023-11-04T09:05:53.351283Z",
     "shell.execute_reply.started": "2023-11-04T09:05:53.347243Z"
    }
   },
   "outputs": [],
   "source": [
    "data_gen = ImageDataGenerator(\n",
    "        zoom_range=1.2,  # set range for random zoom\n",
    "        rotation_range = 90,\n",
    "        width_shift_range=0.5,\n",
    "        height_shift_range=0.5,\n",
    "        horizontal_flip=True,  # randomly flip images\n",
    "        vertical_flip=True,  # randomly flip images\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:05:53.353758Z",
     "iopub.status.busy": "2023-11-04T09:05:53.353474Z",
     "iopub.status.idle": "2023-11-04T09:05:54.029107Z",
     "shell.execute_reply": "2023-11-04T09:05:54.028106Z",
     "shell.execute_reply.started": "2023-11-04T09:05:53.353734Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9773"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_gen.fit(X_train)\n",
    "X_train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:05:54.030677Z",
     "iopub.status.busy": "2023-11-04T09:05:54.030354Z",
     "iopub.status.idle": "2023-11-04T09:05:54.040470Z",
     "shell.execute_reply": "2023-11-04T09:05:54.039541Z",
     "shell.execute_reply.started": "2023-11-04T09:05:54.030650Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_gen.fit(X_val)\n",
    "X_val.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:05:54.041886Z",
     "iopub.status.busy": "2023-11-04T09:05:54.041614Z",
     "iopub.status.idle": "2023-11-04T09:05:54.115686Z",
     "shell.execute_reply": "2023-11-04T09:05:54.114749Z",
     "shell.execute_reply.started": "2023-11-04T09:05:54.041862Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "977"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_gen.fit(X2_test)\n",
    "X2_test.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:05:54.117005Z",
     "iopub.status.busy": "2023-11-04T09:05:54.116712Z",
     "iopub.status.idle": "2023-11-04T09:05:54.121562Z",
     "shell.execute_reply": "2023-11-04T09:05:54.120512Z",
     "shell.execute_reply.started": "2023-11-04T09:05:54.116979Z"
    }
   },
   "outputs": [],
   "source": [
    "#from keras.layers.advanced_activations import ELU\n",
    "from keras.layers import ELU, PReLU, LeakyReLU,ThresholdedReLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:05:54.123262Z",
     "iopub.status.busy": "2023-11-04T09:05:54.122800Z",
     "iopub.status.idle": "2023-11-04T09:05:59.375406Z",
     "shell.execute_reply": "2023-11-04T09:05:59.374352Z",
     "shell.execute_reply.started": "2023-11-04T09:05:54.123209Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of devices: 2\n"
     ]
    }
   ],
   "source": [
    "strategy = tf.distribute.MirroredStrategy()\n",
    "print('Number of devices: {}'.format(strategy.num_replicas_in_sync))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:05:59.377061Z",
     "iopub.status.busy": "2023-11-04T09:05:59.376750Z",
     "iopub.status.idle": "2023-11-04T09:05:59.383433Z",
     "shell.execute_reply": "2023-11-04T09:05:59.382289Z",
     "shell.execute_reply.started": "2023-11-04T09:05:59.377032Z"
    }
   },
   "outputs": [],
   "source": [
    "# bottleneck section \n",
    "def bottleneck0(inputs):\n",
    "    backbone = tf.keras.applications.EfficientNetV2B0(weights='imagenet', include_top=False)\n",
    "    x = backbone(inputs)\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Dense(256, activation='relu')(x)  # Bottleneck layer with EfficientNet\n",
    "    x = Flatten()(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:05:59.385159Z",
     "iopub.status.busy": "2023-11-04T09:05:59.384763Z",
     "iopub.status.idle": "2023-11-04T09:05:59.400645Z",
     "shell.execute_reply": "2023-11-04T09:05:59.399813Z",
     "shell.execute_reply.started": "2023-11-04T09:05:59.385129Z"
    }
   },
   "outputs": [],
   "source": [
    "def neck_section(inputs):\n",
    "    x = Conv2D(256, (7, 7), strides=(2, 2), padding='same')(inputs)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('PReLU')(x)\n",
    "    x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)\n",
    "\n",
    "    x = Flatten()(x)\n",
    "\n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:05:59.402167Z",
     "iopub.status.busy": "2023-11-04T09:05:59.401841Z",
     "iopub.status.idle": "2023-11-04T09:05:59.413199Z",
     "shell.execute_reply": "2023-11-04T09:05:59.412280Z",
     "shell.execute_reply.started": "2023-11-04T09:05:59.402127Z"
    }
   },
   "outputs": [],
   "source": [
    "# Vision Transform Section\n",
    "def vision_transform(inputs, patch_size):\n",
    "    \n",
    "    # Reshape the input into patches\n",
    "    x = Reshape((patch_size[0], patch_size[1], -1))(inputs)\n",
    "    x = Lambda(lambda image: K.cast(image, 'float32') / 255.0)(x)\n",
    "\n",
    "    # architecture for each patch processing\n",
    "    x = Conv2D(64, (3, 3), activation='relu')(x)\n",
    "    x = Conv2D(64, (3, 3), activation='relu')(x)\n",
    "    x = MaxPooling2D((2, 2))(x)\n",
    "    x = Conv2D(128, (3, 3), activation='relu')(x)\n",
    "    x = Conv2D(128, (3, 3), activation='relu')(x)\n",
    "    x = MaxPooling2D((2, 2))(x)\n",
    "\n",
    "    # Flatten the output\n",
    "    x = Flatten()(x)\n",
    "    \n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:05:59.419691Z",
     "iopub.status.busy": "2023-11-04T09:05:59.419378Z",
     "iopub.status.idle": "2023-11-04T09:05:59.426090Z",
     "shell.execute_reply": "2023-11-04T09:05:59.425174Z",
     "shell.execute_reply.started": "2023-11-04T09:05:59.419652Z"
    }
   },
   "outputs": [],
   "source": [
    "# overall model incorporating patches in the vision transform section and the neck section\n",
    "def model(input_shape, num_classes, patch_size):\n",
    "    inputs = Input(shape=input_shape)\n",
    "    \n",
    "    efficientnet_bottleneck = bottleneck0(inputs)\n",
    "    patches_transform = vision_transform(inputs, patch_size)\n",
    "    neck = neck_section(inputs)\n",
    "\n",
    "    # Merging the bottleneck, patches transform, and neck sections\n",
    "    merged = tf.keras.layers.concatenate([efficientnet_bottleneck, patches_transform, neck])\n",
    "    \n",
    "    #bridge = Dense(64, activation='relu')(merged)\n",
    "    \n",
    "    outputs = Dense(num_classes, activation='softmax')(merged)\n",
    "\n",
    "    model = Model(inputs=inputs, outputs=outputs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:05:59.427943Z",
     "iopub.status.busy": "2023-11-04T09:05:59.427317Z",
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     "shell.execute_reply": "2023-11-04T09:05:59.437283Z",
     "shell.execute_reply.started": "2023-11-04T09:05:59.427908Z"
    }
   },
   "outputs": [],
   "source": [
    "input_shape = (size, size, 3)  # Example input shape\n",
    "num_classes = 2  # number of classes\n",
    "patch_size = (32, 32)   # Size of the image patches\n",
    "batch_size = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:05:59.439550Z",
     "iopub.status.busy": "2023-11-04T09:05:59.439218Z",
     "iopub.status.idle": "2023-11-04T09:06:06.153558Z",
     "shell.execute_reply": "2023-11-04T09:06:06.152720Z",
     "shell.execute_reply.started": "2023-11-04T09:05:59.439525Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/efficientnet_v2/efficientnetv2-b0_notop.h5\n",
      "24274472/24274472 [==============================] - 0s 0us/step\n",
      "(None, 128, 128, 3)\n",
      "(None, 2)\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_2 (InputLayer)           [(None, 128, 128, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " reshape (Reshape)              (None, 32, 32, 48)   0           ['input_2[0][0]']                \n",
      "                                                                                                  \n",
      " lambda (Lambda)                (None, 32, 32, 48)   0           ['reshape[0][0]']                \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 30, 30, 64)   27712       ['lambda[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 28, 28, 64)   36928       ['conv2d[0][0]']                 \n",
      "                                                                                                  \n",
      " efficientnetv2-b0 (Functional)  (None, None, None,   5919312    ['input_2[0][0]']                \n",
      "                                1280)                                                             \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 14, 14, 64)   0           ['conv2d_1[0][0]']               \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 64, 64, 256)  37888       ['input_2[0][0]']                \n",
      "                                                                                                  \n",
      " global_average_pooling2d (Glob  (None, 1280)        0           ['efficientnetv2-b0[0][0]']      \n",
      " alAveragePooling2D)                                                                              \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 12, 12, 128)  73856       ['max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 64, 64, 256)  1024       ['conv2d_4[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 1280)        5120        ['global_average_pooling2d[0][0]'\n",
      " alization)                                                      ]                                \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 10, 10, 128)  147584      ['conv2d_2[0][0]']               \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 64, 64, 256)  1048576     ['batch_normalization_1[0][0]']  \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 256)          327936      ['batch_normalization[0][0]']    \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 5, 5, 128)   0           ['conv2d_3[0][0]']               \n",
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 32, 32, 256)  0          ['activation[0][0]']             \n",
      "                                                                                                  \n",
      " flatten (Flatten)              (None, 256)          0           ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " flatten_1 (Flatten)            (None, 3200)         0           ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " flatten_2 (Flatten)            (None, 262144)       0           ['max_pooling2d_2[0][0]']        \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 265600)       0           ['flatten[0][0]',                \n",
      "                                                                  'flatten_1[0][0]',              \n",
      "                                                                  'flatten_2[0][0]']              \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 2)            531202      ['concatenate[0][0]']            \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 8,157,138\n",
      "Trainable params: 8,093,458\n",
      "Non-trainable params: 63,680\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "with strategy.scope():\n",
    "    model = model(input_shape, num_classes, patch_size)\n",
    "    model.compile(loss = \"categorical_crossentropy\",\n",
    "                optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07), \n",
    "                metrics = (['accuracy', tf.keras.metrics.Precision(name='precision'),\n",
    "                        tf.keras.metrics.Recall(name='recall'),\n",
    "                        tf.keras.metrics.AUC(name='AUC'),\n",
    "                        f1_score, specificity, sensitivity, iou,tversky]))\n",
    "\n",
    "print(model.input_shape)\n",
    "print(model.output_shape)\n",
    "model.summary()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:06:06.155099Z",
     "iopub.status.busy": "2023-11-04T09:06:06.154802Z",
     "iopub.status.idle": "2023-11-04T09:06:06.542696Z",
     "shell.execute_reply": "2023-11-04T09:06:06.541781Z",
     "shell.execute_reply.started": "2023-11-04T09:06:06.155073Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(model, \n",
    "           show_shapes = True,\n",
    "           show_dtype= True,\n",
    "           show_layer_names = True,\n",
    "           show_layer_activations= True,\n",
    "            show_trainable= True,\n",
    "           rankdir = 'TB', \n",
    "           expand_nested = False, \n",
    "           dpi = 70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:06:06.544631Z",
     "iopub.status.busy": "2023-11-04T09:06:06.544013Z",
     "iopub.status.idle": "2023-11-04T09:47:04.738983Z",
     "shell.execute_reply": "2023-11-04T09:47:04.738007Z",
     "shell.execute_reply.started": "2023-11-04T09:06:06.544597Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-04 09:07:00.304167: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inmodel/efficientnetv2-b0/block2b_drop/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "152/152 [==============================] - 155s 352ms/step - loss: 6.7915 - accuracy: 0.7002 - precision: 0.7002 - recall: 0.7002 - AUC: 0.7228 - f1_score: 0.7003 - specificity: 0.7003 - sensitivity: 0.7003 - iou: 0.5528 - tversky: 0.7079 - val_loss: 1.0102 - val_accuracy: 0.5648 - val_precision: 0.5648 - val_recall: 0.5648 - val_AUC: 0.5790 - val_f1_score: 0.5689 - val_specificity: 0.5689 - val_sensitivity: 0.5689 - val_iou: 0.3858 - val_tversky: 0.5616 - lr: 0.0010\n",
      "Epoch 2/50\n",
      "152/152 [==============================] - 48s 314ms/step - loss: 2.6652 - accuracy: 0.7223 - precision: 0.7223 - recall: 0.7223 - AUC: 0.7647 - f1_score: 0.7222 - specificity: 0.7222 - sensitivity: 0.7222 - iou: 0.5760 - tversky: 0.7286 - val_loss: 0.9563 - val_accuracy: 0.6944 - val_precision: 0.6944 - val_recall: 0.6944 - val_AUC: 0.7492 - val_f1_score: 0.6960 - val_specificity: 0.6960 - val_sensitivity: 0.6960 - val_iou: 0.5208 - val_tversky: 0.6888 - lr: 0.0010\n",
      "Epoch 3/50\n",
      "152/152 [==============================] - 47s 307ms/step - loss: 2.4187 - accuracy: 0.7471 - precision: 0.7471 - recall: 0.7471 - AUC: 0.7921 - f1_score: 0.7470 - specificity: 0.7470 - sensitivity: 0.7470 - iou: 0.6092 - tversky: 0.7539 - val_loss: 3.1323 - val_accuracy: 0.5926 - val_precision: 0.5926 - val_recall: 0.5926 - val_AUC: 0.6002 - val_f1_score: 0.5959 - val_specificity: 0.5959 - val_sensitivity: 0.5959 - val_iou: 0.4139 - val_tversky: 0.5917 - lr: 0.0010\n",
      "Epoch 4/50\n",
      "152/152 [==============================] - 47s 309ms/step - loss: 2.0151 - accuracy: 0.7662 - precision: 0.7662 - recall: 0.7662 - AUC: 0.8159 - f1_score: 0.7663 - specificity: 0.7663 - sensitivity: 0.7663 - iou: 0.6336 - tversky: 0.7726 - val_loss: 1.2312 - val_accuracy: 0.7593 - val_precision: 0.7593 - val_recall: 0.7593 - val_AUC: 0.7963 - val_f1_score: 0.7543 - val_specificity: 0.7543 - val_sensitivity: 0.7543 - val_iou: 0.6041 - val_tversky: 0.7575 - lr: 0.0010\n",
      "Epoch 5/50\n",
      "152/152 [==============================] - 47s 308ms/step - loss: 1.2016 - accuracy: 0.7954 - precision: 0.7954 - recall: 0.7954 - AUC: 0.8529 - f1_score: 0.7954 - specificity: 0.7954 - sensitivity: 0.7954 - iou: 0.6637 - tversky: 0.7958 - val_loss: 2.0871 - val_accuracy: 0.7315 - val_precision: 0.7315 - val_recall: 0.7315 - val_AUC: 0.7659 - val_f1_score: 0.7308 - val_specificity: 0.7308 - val_sensitivity: 0.7308 - val_iou: 0.5801 - val_tversky: 0.7357 - lr: 0.0010\n",
      "Epoch 6/50\n",
      "152/152 [==============================] - 46s 305ms/step - loss: 1.5672 - accuracy: 0.7914 - precision: 0.7914 - recall: 0.7914 - AUC: 0.8413 - f1_score: 0.7915 - specificity: 0.7915 - sensitivity: 0.7915 - iou: 0.6624 - tversky: 0.7945 - val_loss: 26.0227 - val_accuracy: 0.4630 - val_precision: 0.4630 - val_recall: 0.4630 - val_AUC: 0.4630 - val_f1_score: 0.4581 - val_specificity: 0.4581 - val_sensitivity: 0.4581 - val_iou: 0.3063 - val_tversky: 0.4783 - lr: 0.0010\n",
      "Epoch 7/50\n",
      "152/152 [==============================] - ETA: 0s - loss: 1.4338 - accuracy: 0.8083 - precision: 0.8083 - recall: 0.8083 - AUC: 0.8580 - f1_score: 0.8083 - specificity: 0.8083 - sensitivity: 0.8083 - iou: 0.6825 - tversky: 0.8091\n",
      "Epoch 7: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
      "152/152 [==============================] - 47s 307ms/step - loss: 1.4338 - accuracy: 0.8083 - precision: 0.8083 - recall: 0.8083 - AUC: 0.8580 - f1_score: 0.8083 - specificity: 0.8083 - sensitivity: 0.8083 - iou: 0.6825 - tversky: 0.8091 - val_loss: 45.1140 - val_accuracy: 0.5370 - val_precision: 0.5370 - val_recall: 0.5370 - val_AUC: 0.5370 - val_f1_score: 0.5419 - val_specificity: 0.5419 - val_sensitivity: 0.5419 - val_iou: 0.3824 - val_tversky: 0.5586 - lr: 0.0010\n",
      "Epoch 8/50\n",
      "152/152 [==============================] - 47s 308ms/step - loss: 0.6803 - accuracy: 0.8351 - precision: 0.8351 - recall: 0.8351 - AUC: 0.9003 - f1_score: 0.8351 - specificity: 0.8351 - sensitivity: 0.8351 - iou: 0.7061 - tversky: 0.8269 - val_loss: 1.1043 - val_accuracy: 0.8333 - val_precision: 0.8333 - val_recall: 0.8333 - val_AUC: 0.8362 - val_f1_score: 0.8274 - val_specificity: 0.8274 - val_sensitivity: 0.8274 - val_iou: 0.6636 - val_tversky: 0.7976 - lr: 2.0000e-04\n",
      "Epoch 9/50\n",
      "152/152 [==============================] - 47s 310ms/step - loss: 0.4497 - accuracy: 0.8491 - precision: 0.8491 - recall: 0.8491 - AUC: 0.9207 - f1_score: 0.8493 - specificity: 0.8493 - sensitivity: 0.8493 - iou: 0.7156 - tversky: 0.8340 - val_loss: 0.7001 - val_accuracy: 0.7500 - val_precision: 0.7500 - val_recall: 0.7500 - val_AUC: 0.8243 - val_f1_score: 0.7358 - val_specificity: 0.7358 - val_sensitivity: 0.7358 - val_iou: 0.5676 - val_tversky: 0.7240 - lr: 2.0000e-04\n",
      "Epoch 10/50\n",
      "152/152 [==============================] - 47s 309ms/step - loss: 0.4127 - accuracy: 0.8559 - precision: 0.8559 - recall: 0.8559 - AUC: 0.9266 - f1_score: 0.8561 - specificity: 0.8561 - sensitivity: 0.8561 - iou: 0.7215 - tversky: 0.8381 - val_loss: 0.4209 - val_accuracy: 0.8148 - val_precision: 0.8148 - val_recall: 0.8148 - val_AUC: 0.9215 - val_f1_score: 0.8118 - val_specificity: 0.8118 - val_sensitivity: 0.8118 - val_iou: 0.6915 - val_tversky: 0.8183 - lr: 2.0000e-04\n",
      "Epoch 11/50\n",
      "152/152 [==============================] - 47s 308ms/step - loss: 0.4007 - accuracy: 0.8588 - precision: 0.8588 - recall: 0.8588 - AUC: 0.9297 - f1_score: 0.8588 - specificity: 0.8588 - sensitivity: 0.8588 - iou: 0.7243 - tversky: 0.8397 - val_loss: 0.9371 - val_accuracy: 0.7407 - val_precision: 0.7407 - val_recall: 0.7407 - val_AUC: 0.8059 - val_f1_score: 0.7244 - val_specificity: 0.7244 - val_sensitivity: 0.7244 - val_iou: 0.5628 - val_tversky: 0.7202 - lr: 2.0000e-04\n",
      "Epoch 12/50\n",
      "152/152 [==============================] - 47s 306ms/step - loss: 0.3762 - accuracy: 0.8609 - precision: 0.8609 - recall: 0.8609 - AUC: 0.9345 - f1_score: 0.8608 - specificity: 0.8608 - sensitivity: 0.8608 - iou: 0.7279 - tversky: 0.8429 - val_loss: 0.4778 - val_accuracy: 0.8241 - val_precision: 0.8241 - val_recall: 0.8241 - val_AUC: 0.9066 - val_f1_score: 0.8196 - val_specificity: 0.8196 - val_sensitivity: 0.8196 - val_iou: 0.6844 - val_tversky: 0.8150 - lr: 2.0000e-04\n",
      "Epoch 13/50\n",
      "152/152 [==============================] - 46s 305ms/step - loss: 0.3484 - accuracy: 0.8678 - precision: 0.8678 - recall: 0.8678 - AUC: 0.9403 - f1_score: 0.8676 - specificity: 0.8676 - sensitivity: 0.8676 - iou: 0.7346 - tversky: 0.8473 - val_loss: 2.2520 - val_accuracy: 0.5926 - val_precision: 0.5926 - val_recall: 0.5926 - val_AUC: 0.6303 - val_f1_score: 0.5923 - val_specificity: 0.5923 - val_sensitivity: 0.5923 - val_iou: 0.4189 - val_tversky: 0.5958 - lr: 2.0000e-04\n",
      "Epoch 14/50\n",
      "152/152 [==============================] - 47s 307ms/step - loss: 0.3467 - accuracy: 0.8685 - precision: 0.8685 - recall: 0.8685 - AUC: 0.9394 - f1_score: 0.8686 - specificity: 0.8686 - sensitivity: 0.8686 - iou: 0.7334 - tversky: 0.8459 - val_loss: 0.4651 - val_accuracy: 0.8426 - val_precision: 0.8426 - val_recall: 0.8426 - val_AUC: 0.9162 - val_f1_score: 0.8281 - val_specificity: 0.8281 - val_sensitivity: 0.8281 - val_iou: 0.7001 - val_tversky: 0.8203 - lr: 2.0000e-04\n",
      "Epoch 15/50\n",
      "152/152 [==============================] - ETA: 0s - loss: 0.3308 - accuracy: 0.8712 - precision: 0.8712 - recall: 0.8712 - AUC: 0.9432 - f1_score: 0.8711 - specificity: 0.8711 - sensitivity: 0.8711 - iou: 0.7343 - tversky: 0.8469\n",
      "Epoch 15: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
      "152/152 [==============================] - 47s 307ms/step - loss: 0.3308 - accuracy: 0.8712 - precision: 0.8712 - recall: 0.8712 - AUC: 0.9432 - f1_score: 0.8711 - specificity: 0.8711 - sensitivity: 0.8711 - iou: 0.7343 - tversky: 0.8469 - val_loss: 0.5778 - val_accuracy: 0.7870 - val_precision: 0.7870 - val_recall: 0.7870 - val_AUC: 0.8736 - val_f1_score: 0.7955 - val_specificity: 0.7955 - val_sensitivity: 0.7955 - val_iou: 0.6321 - val_tversky: 0.7783 - lr: 2.0000e-04\n",
      "Epoch 16/50\n",
      "152/152 [==============================] - 47s 310ms/step - loss: 0.2766 - accuracy: 0.8897 - precision: 0.8897 - recall: 0.8897 - AUC: 0.9561 - f1_score: 0.8897 - specificity: 0.8897 - sensitivity: 0.8897 - iou: 0.7519 - tversky: 0.8589 - val_loss: 0.2596 - val_accuracy: 0.8981 - val_precision: 0.8981 - val_recall: 0.8981 - val_AUC: 0.9608 - val_f1_score: 0.9070 - val_specificity: 0.9070 - val_sensitivity: 0.9070 - val_iou: 0.7795 - val_tversky: 0.8768 - lr: 4.0000e-05\n",
      "Epoch 17/50\n",
      "152/152 [==============================] - 47s 310ms/step - loss: 0.2664 - accuracy: 0.8906 - precision: 0.8906 - recall: 0.8906 - AUC: 0.9575 - f1_score: 0.8904 - specificity: 0.8904 - sensitivity: 0.8904 - iou: 0.7477 - tversky: 0.8563 - val_loss: 0.2222 - val_accuracy: 0.8981 - val_precision: 0.8981 - val_recall: 0.8981 - val_AUC: 0.9708 - val_f1_score: 0.8963 - val_specificity: 0.8963 - val_sensitivity: 0.8963 - val_iou: 0.7917 - val_tversky: 0.8815 - lr: 4.0000e-05\n",
      "Epoch 18/50\n",
      "152/152 [==============================] - 47s 307ms/step - loss: 0.2582 - accuracy: 0.8941 - precision: 0.8941 - recall: 0.8941 - AUC: 0.9600 - f1_score: 0.8942 - specificity: 0.8942 - sensitivity: 0.8942 - iou: 0.7497 - tversky: 0.8577 - val_loss: 0.2796 - val_accuracy: 0.9074 - val_precision: 0.9074 - val_recall: 0.9074 - val_AUC: 0.9587 - val_f1_score: 0.9112 - val_specificity: 0.9112 - val_sensitivity: 0.9112 - val_iou: 0.7793 - val_tversky: 0.8763 - lr: 4.0000e-05\n",
      "Epoch 19/50\n",
      "152/152 [==============================] - 47s 306ms/step - loss: 0.2564 - accuracy: 0.8940 - precision: 0.8940 - recall: 0.8940 - AUC: 0.9603 - f1_score: 0.8939 - specificity: 0.8939 - sensitivity: 0.8939 - iou: 0.7503 - tversky: 0.8581 - val_loss: 0.2522 - val_accuracy: 0.8889 - val_precision: 0.8889 - val_recall: 0.8889 - val_AUC: 0.9669 - val_f1_score: 0.8849 - val_specificity: 0.8849 - val_sensitivity: 0.8849 - val_iou: 0.7750 - val_tversky: 0.8752 - lr: 4.0000e-05\n",
      "Epoch 20/50\n",
      "152/152 [==============================] - 47s 306ms/step - loss: 0.2544 - accuracy: 0.8980 - precision: 0.8980 - recall: 0.8980 - AUC: 0.9610 - f1_score: 0.8980 - specificity: 0.8980 - sensitivity: 0.8980 - iou: 0.7556 - tversky: 0.8614 - val_loss: 0.2622 - val_accuracy: 0.9074 - val_precision: 0.9074 - val_recall: 0.9074 - val_AUC: 0.9589 - val_f1_score: 0.9041 - val_specificity: 0.9041 - val_sensitivity: 0.9041 - val_iou: 0.7374 - val_tversky: 0.8488 - lr: 4.0000e-05\n",
      "Epoch 21/50\n",
      "152/152 [==============================] - 48s 314ms/step - loss: 0.2467 - accuracy: 0.8973 - precision: 0.8973 - recall: 0.8973 - AUC: 0.9633 - f1_score: 0.8972 - specificity: 0.8972 - sensitivity: 0.8972 - iou: 0.7582 - tversky: 0.8633 - val_loss: 0.2245 - val_accuracy: 0.9167 - val_precision: 0.9167 - val_recall: 0.9167 - val_AUC: 0.9680 - val_f1_score: 0.9119 - val_specificity: 0.9119 - val_sensitivity: 0.9119 - val_iou: 0.7737 - val_tversky: 0.8735 - lr: 4.0000e-05\n",
      "Epoch 22/50\n",
      "152/152 [==============================] - ETA: 0s - loss: 0.2690 - accuracy: 0.8955 - precision: 0.8955 - recall: 0.8955 - AUC: 0.9572 - f1_score: 0.8953 - specificity: 0.8953 - sensitivity: 0.8953 - iou: 0.7517 - tversky: 0.8588\n",
      "Epoch 22: ReduceLROnPlateau reducing learning rate to 8.000000525498762e-06.\n",
      "152/152 [==============================] - 47s 307ms/step - loss: 0.2690 - accuracy: 0.8955 - precision: 0.8955 - recall: 0.8955 - AUC: 0.9572 - f1_score: 0.8953 - specificity: 0.8953 - sensitivity: 0.8953 - iou: 0.7517 - tversky: 0.8588 - val_loss: 0.3713 - val_accuracy: 0.8519 - val_precision: 0.8519 - val_recall: 0.8519 - val_AUC: 0.9262 - val_f1_score: 0.8537 - val_specificity: 0.8537 - val_sensitivity: 0.8537 - val_iou: 0.7007 - val_tversky: 0.8251 - lr: 4.0000e-05\n",
      "Epoch 23/50\n",
      "152/152 [==============================] - 47s 312ms/step - loss: 0.2417 - accuracy: 0.8995 - precision: 0.8995 - recall: 0.8995 - AUC: 0.9648 - f1_score: 0.8995 - specificity: 0.8995 - sensitivity: 0.8995 - iou: 0.7586 - tversky: 0.8635 - val_loss: 0.2039 - val_accuracy: 0.9074 - val_precision: 0.9074 - val_recall: 0.9074 - val_AUC: 0.9758 - val_f1_score: 0.9077 - val_specificity: 0.9077 - val_sensitivity: 0.9077 - val_iou: 0.7876 - val_tversky: 0.8831 - lr: 8.0000e-06\n",
      "Epoch 24/50\n",
      "152/152 [==============================] - 47s 309ms/step - loss: 0.2475 - accuracy: 0.8996 - precision: 0.8996 - recall: 0.8996 - AUC: 0.9627 - f1_score: 0.8996 - specificity: 0.8996 - sensitivity: 0.8996 - iou: 0.7564 - tversky: 0.8620 - val_loss: 0.1893 - val_accuracy: 0.9167 - val_precision: 0.9167 - val_recall: 0.9167 - val_AUC: 0.9767 - val_f1_score: 0.9226 - val_specificity: 0.9226 - val_sensitivity: 0.9226 - val_iou: 0.8016 - val_tversky: 0.8911 - lr: 8.0000e-06\n",
      "Epoch 25/50\n",
      "152/152 [==============================] - 47s 307ms/step - loss: 0.2407 - accuracy: 0.8987 - precision: 0.8987 - recall: 0.8987 - AUC: 0.9647 - f1_score: 0.8986 - specificity: 0.8986 - sensitivity: 0.8986 - iou: 0.7581 - tversky: 0.8632 - val_loss: 0.2163 - val_accuracy: 0.9074 - val_precision: 0.9074 - val_recall: 0.9074 - val_AUC: 0.9715 - val_f1_score: 0.9006 - val_specificity: 0.9006 - val_sensitivity: 0.9006 - val_iou: 0.7728 - val_tversky: 0.8734 - lr: 8.0000e-06\n",
      "Epoch 26/50\n",
      "152/152 [==============================] - 47s 309ms/step - loss: 0.2465 - accuracy: 0.8982 - precision: 0.8982 - recall: 0.8982 - AUC: 0.9632 - f1_score: 0.8983 - specificity: 0.8983 - sensitivity: 0.8983 - iou: 0.7543 - tversky: 0.8607 - val_loss: 0.1771 - val_accuracy: 0.9074 - val_precision: 0.9074 - val_recall: 0.9074 - val_AUC: 0.9809 - val_f1_score: 0.9041 - val_specificity: 0.9041 - val_sensitivity: 0.9041 - val_iou: 0.8056 - val_tversky: 0.8939 - lr: 8.0000e-06\n",
      "Epoch 27/50\n",
      "152/152 [==============================] - 47s 306ms/step - loss: 0.2433 - accuracy: 0.9013 - precision: 0.9013 - recall: 0.9013 - AUC: 0.9639 - f1_score: 0.9012 - specificity: 0.9012 - sensitivity: 0.9012 - iou: 0.7570 - tversky: 0.8624 - val_loss: 0.2952 - val_accuracy: 0.8889 - val_precision: 0.8889 - val_recall: 0.8889 - val_AUC: 0.9533 - val_f1_score: 0.8885 - val_specificity: 0.8885 - val_sensitivity: 0.8885 - val_iou: 0.7560 - val_tversky: 0.8628 - lr: 8.0000e-06\n",
      "Epoch 28/50\n",
      "152/152 [==============================] - 47s 309ms/step - loss: 0.2423 - accuracy: 0.8991 - precision: 0.8991 - recall: 0.8991 - AUC: 0.9642 - f1_score: 0.8991 - specificity: 0.8991 - sensitivity: 0.8991 - iou: 0.7560 - tversky: 0.8618 - val_loss: 0.2686 - val_accuracy: 0.8889 - val_precision: 0.8889 - val_recall: 0.8889 - val_AUC: 0.9584 - val_f1_score: 0.8920 - val_specificity: 0.8920 - val_sensitivity: 0.8920 - val_iou: 0.7712 - val_tversky: 0.8730 - lr: 8.0000e-06\n",
      "Epoch 29/50\n",
      "152/152 [==============================] - 47s 308ms/step - loss: 0.2332 - accuracy: 0.9038 - precision: 0.9038 - recall: 0.9038 - AUC: 0.9668 - f1_score: 0.9037 - specificity: 0.9037 - sensitivity: 0.9037 - iou: 0.7620 - tversky: 0.8657 - val_loss: 0.2568 - val_accuracy: 0.8981 - val_precision: 0.8981 - val_recall: 0.8981 - val_AUC: 0.9593 - val_f1_score: 0.8963 - val_specificity: 0.8963 - val_sensitivity: 0.8963 - val_iou: 0.7617 - val_tversky: 0.8660 - lr: 8.0000e-06\n",
      "Epoch 30/50\n",
      "152/152 [==============================] - 47s 308ms/step - loss: 0.2473 - accuracy: 0.8995 - precision: 0.8995 - recall: 0.8995 - AUC: 0.9627 - f1_score: 0.8995 - specificity: 0.8995 - sensitivity: 0.8995 - iou: 0.7571 - tversky: 0.8624 - val_loss: 0.2339 - val_accuracy: 0.9074 - val_precision: 0.9074 - val_recall: 0.9074 - val_AUC: 0.9693 - val_f1_score: 0.9077 - val_specificity: 0.9077 - val_sensitivity: 0.9077 - val_iou: 0.7853 - val_tversky: 0.8816 - lr: 8.0000e-06\n",
      "Epoch 31/50\n",
      "152/152 [==============================] - ETA: 0s - loss: 0.2434 - accuracy: 0.8976 - precision: 0.8976 - recall: 0.8976 - AUC: 0.9639 - f1_score: 0.8977 - specificity: 0.8977 - sensitivity: 0.8977 - iou: 0.7543 - tversky: 0.8608\n",
      "Epoch 31: ReduceLROnPlateau reducing learning rate to 1.6000001778593287e-06.\n",
      "152/152 [==============================] - 47s 306ms/step - loss: 0.2434 - accuracy: 0.8976 - precision: 0.8976 - recall: 0.8976 - AUC: 0.9639 - f1_score: 0.8977 - specificity: 0.8977 - sensitivity: 0.8977 - iou: 0.7543 - tversky: 0.8608 - val_loss: 0.2912 - val_accuracy: 0.8611 - val_precision: 0.8611 - val_recall: 0.8611 - val_AUC: 0.9513 - val_f1_score: 0.8686 - val_specificity: 0.8686 - val_sensitivity: 0.8686 - val_iou: 0.7409 - val_tversky: 0.8536 - lr: 8.0000e-06\n",
      "Epoch 32/50\n",
      "152/152 [==============================] - 47s 305ms/step - loss: 0.2382 - accuracy: 0.9004 - precision: 0.9004 - recall: 0.9004 - AUC: 0.9653 - f1_score: 0.9003 - specificity: 0.9003 - sensitivity: 0.9003 - iou: 0.7569 - tversky: 0.8626 - val_loss: 0.1889 - val_accuracy: 0.9352 - val_precision: 0.9352 - val_recall: 0.9352 - val_AUC: 0.9787 - val_f1_score: 0.9418 - val_specificity: 0.9418 - val_sensitivity: 0.9418 - val_iou: 0.7955 - val_tversky: 0.8876 - lr: 1.6000e-06\n",
      "Epoch 33/50\n",
      "152/152 [==============================] - 46s 305ms/step - loss: 0.2310 - accuracy: 0.9061 - precision: 0.9061 - recall: 0.9061 - AUC: 0.9674 - f1_score: 0.9061 - specificity: 0.9061 - sensitivity: 0.9061 - iou: 0.7607 - tversky: 0.8650 - val_loss: 0.2402 - val_accuracy: 0.8981 - val_precision: 0.8981 - val_recall: 0.8981 - val_AUC: 0.9662 - val_f1_score: 0.9034 - val_specificity: 0.9034 - val_sensitivity: 0.9034 - val_iou: 0.7819 - val_tversky: 0.8778 - lr: 1.6000e-06\n",
      "Epoch 34/50\n",
      "152/152 [==============================] - 46s 304ms/step - loss: 0.2363 - accuracy: 0.9025 - precision: 0.9025 - recall: 0.9025 - AUC: 0.9659 - f1_score: 0.9024 - specificity: 0.9024 - sensitivity: 0.9024 - iou: 0.7595 - tversky: 0.8641 - val_loss: 0.1986 - val_accuracy: 0.9352 - val_precision: 0.9352 - val_recall: 0.9352 - val_AUC: 0.9775 - val_f1_score: 0.9347 - val_specificity: 0.9347 - val_sensitivity: 0.9347 - val_iou: 0.8003 - val_tversky: 0.8903 - lr: 1.6000e-06\n",
      "Epoch 35/50\n",
      "152/152 [==============================] - 46s 305ms/step - loss: 0.2333 - accuracy: 0.9041 - precision: 0.9041 - recall: 0.9041 - AUC: 0.9667 - f1_score: 0.9040 - specificity: 0.9040 - sensitivity: 0.9040 - iou: 0.7604 - tversky: 0.8647 - val_loss: 0.1979 - val_accuracy: 0.9444 - val_precision: 0.9444 - val_recall: 0.9444 - val_AUC: 0.9758 - val_f1_score: 0.9531 - val_specificity: 0.9531 - val_sensitivity: 0.9531 - val_iou: 0.8092 - val_tversky: 0.8951 - lr: 1.6000e-06\n",
      "Epoch 36/50\n",
      "152/152 [==============================] - 47s 308ms/step - loss: 0.2357 - accuracy: 0.9027 - precision: 0.9027 - recall: 0.9027 - AUC: 0.9661 - f1_score: 0.9027 - specificity: 0.9027 - sensitivity: 0.9027 - iou: 0.7599 - tversky: 0.8644 - val_loss: 0.1608 - val_accuracy: 0.9259 - val_precision: 0.9259 - val_recall: 0.9259 - val_AUC: 0.9844 - val_f1_score: 0.9268 - val_specificity: 0.9268 - val_sensitivity: 0.9268 - val_iou: 0.8109 - val_tversky: 0.8973 - lr: 1.6000e-06\n",
      "Epoch 37/50\n",
      "152/152 [==============================] - 47s 306ms/step - loss: 0.2404 - accuracy: 0.8994 - precision: 0.8994 - recall: 0.8994 - AUC: 0.9647 - f1_score: 0.8995 - specificity: 0.8995 - sensitivity: 0.8995 - iou: 0.7555 - tversky: 0.8616 - val_loss: 0.2126 - val_accuracy: 0.8981 - val_precision: 0.8981 - val_recall: 0.8981 - val_AUC: 0.9748 - val_f1_score: 0.9070 - val_specificity: 0.9070 - val_sensitivity: 0.9070 - val_iou: 0.7996 - val_tversky: 0.8893 - lr: 1.6000e-06\n",
      "Epoch 38/50\n",
      "152/152 [==============================] - 47s 307ms/step - loss: 0.2355 - accuracy: 0.9032 - precision: 0.9032 - recall: 0.9032 - AUC: 0.9661 - f1_score: 0.9031 - specificity: 0.9031 - sensitivity: 0.9031 - iou: 0.7595 - tversky: 0.8641 - val_loss: 0.1756 - val_accuracy: 0.9167 - val_precision: 0.9167 - val_recall: 0.9167 - val_AUC: 0.9808 - val_f1_score: 0.9119 - val_specificity: 0.9119 - val_sensitivity: 0.9119 - val_iou: 0.8054 - val_tversky: 0.8929 - lr: 1.6000e-06\n",
      "Epoch 39/50\n",
      "152/152 [==============================] - 47s 306ms/step - loss: 0.2406 - accuracy: 0.9003 - precision: 0.9003 - recall: 0.9003 - AUC: 0.9646 - f1_score: 0.9002 - specificity: 0.9002 - sensitivity: 0.9002 - iou: 0.7575 - tversky: 0.8628 - val_loss: 0.1757 - val_accuracy: 0.9167 - val_precision: 0.9167 - val_recall: 0.9167 - val_AUC: 0.9814 - val_f1_score: 0.9226 - val_specificity: 0.9226 - val_sensitivity: 0.9226 - val_iou: 0.8048 - val_tversky: 0.8929 - lr: 1.6000e-06\n",
      "Epoch 40/50\n",
      "152/152 [==============================] - 47s 307ms/step - loss: 0.2344 - accuracy: 0.9001 - precision: 0.9001 - recall: 0.9001 - AUC: 0.9664 - f1_score: 0.8998 - specificity: 0.8998 - sensitivity: 0.8998 - iou: 0.7594 - tversky: 0.8640 - val_loss: 0.2046 - val_accuracy: 0.9074 - val_precision: 0.9074 - val_recall: 0.9074 - val_AUC: 0.9744 - val_f1_score: 0.9041 - val_specificity: 0.9041 - val_sensitivity: 0.9041 - val_iou: 0.7809 - val_tversky: 0.8772 - lr: 1.6000e-06\n",
      "Epoch 41/50\n",
      "152/152 [==============================] - ETA: 0s - loss: 0.2304 - accuracy: 0.9011 - precision: 0.9011 - recall: 0.9011 - AUC: 0.9677 - f1_score: 0.9010 - specificity: 0.9010 - sensitivity: 0.9010 - iou: 0.7601 - tversky: 0.8645\n",
      "Epoch 41: ReduceLROnPlateau reducing learning rate to 3.200000264769187e-07.\n",
      "152/152 [==============================] - 46s 304ms/step - loss: 0.2304 - accuracy: 0.9011 - precision: 0.9011 - recall: 0.9011 - AUC: 0.9677 - f1_score: 0.9010 - specificity: 0.9010 - sensitivity: 0.9010 - iou: 0.7601 - tversky: 0.8645 - val_loss: 0.2167 - val_accuracy: 0.9259 - val_precision: 0.9259 - val_recall: 0.9259 - val_AUC: 0.9719 - val_f1_score: 0.9197 - val_specificity: 0.9197 - val_sensitivity: 0.9197 - val_iou: 0.7982 - val_tversky: 0.8897 - lr: 1.6000e-06\n",
      "Epoch 42/50\n",
      "152/152 [==============================] - 47s 307ms/step - loss: 0.2333 - accuracy: 0.9050 - precision: 0.9050 - recall: 0.9050 - AUC: 0.9667 - f1_score: 0.9049 - specificity: 0.9049 - sensitivity: 0.9049 - iou: 0.7605 - tversky: 0.8648 - val_loss: 0.2232 - val_accuracy: 0.8889 - val_precision: 0.8889 - val_recall: 0.8889 - val_AUC: 0.9704 - val_f1_score: 0.8849 - val_specificity: 0.8849 - val_sensitivity: 0.8849 - val_iou: 0.7727 - val_tversky: 0.8735 - lr: 3.2000e-07\n",
      "Epoch 43/50\n",
      "152/152 [==============================] - 47s 305ms/step - loss: 0.2287 - accuracy: 0.9053 - precision: 0.9053 - recall: 0.9053 - AUC: 0.9681 - f1_score: 0.9054 - specificity: 0.9054 - sensitivity: 0.9054 - iou: 0.7612 - tversky: 0.8652 - val_loss: 0.2674 - val_accuracy: 0.9074 - val_precision: 0.9074 - val_recall: 0.9074 - val_AUC: 0.9589 - val_f1_score: 0.9041 - val_specificity: 0.9041 - val_sensitivity: 0.9041 - val_iou: 0.7520 - val_tversky: 0.8590 - lr: 3.2000e-07\n",
      "Epoch 44/50\n",
      "152/152 [==============================] - 46s 305ms/step - loss: 0.2313 - accuracy: 0.9030 - precision: 0.9030 - recall: 0.9030 - AUC: 0.9673 - f1_score: 0.9030 - specificity: 0.9030 - sensitivity: 0.9030 - iou: 0.7601 - tversky: 0.8645 - val_loss: 0.2291 - val_accuracy: 0.9074 - val_precision: 0.9074 - val_recall: 0.9074 - val_AUC: 0.9674 - val_f1_score: 0.9112 - val_specificity: 0.9112 - val_sensitivity: 0.9112 - val_iou: 0.7877 - val_tversky: 0.8822 - lr: 3.2000e-07\n",
      "Epoch 45/50\n",
      "152/152 [==============================] - 47s 307ms/step - loss: 0.2351 - accuracy: 0.9015 - precision: 0.9015 - recall: 0.9015 - AUC: 0.9662 - f1_score: 0.9015 - specificity: 0.9015 - sensitivity: 0.9015 - iou: 0.7600 - tversky: 0.8644 - val_loss: 0.2289 - val_accuracy: 0.9074 - val_precision: 0.9074 - val_recall: 0.9074 - val_AUC: 0.9688 - val_f1_score: 0.9077 - val_specificity: 0.9077 - val_sensitivity: 0.9077 - val_iou: 0.7766 - val_tversky: 0.8752 - lr: 3.2000e-07\n",
      "Epoch 46/50\n",
      "152/152 [==============================] - ETA: 0s - loss: 0.2433 - accuracy: 0.8984 - precision: 0.8984 - recall: 0.8984 - AUC: 0.9638 - f1_score: 0.8984 - specificity: 0.8984 - sensitivity: 0.8984 - iou: 0.7559 - tversky: 0.8616\n",
      "Epoch 46: ReduceLROnPlateau reducing learning rate to 6.400000529538374e-08.\n",
      "152/152 [==============================] - 47s 305ms/step - loss: 0.2433 - accuracy: 0.8984 - precision: 0.8984 - recall: 0.8984 - AUC: 0.9638 - f1_score: 0.8984 - specificity: 0.8984 - sensitivity: 0.8984 - iou: 0.7559 - tversky: 0.8616 - val_loss: 0.2474 - val_accuracy: 0.9074 - val_precision: 0.9074 - val_recall: 0.9074 - val_AUC: 0.9627 - val_f1_score: 0.9041 - val_specificity: 0.9041 - val_sensitivity: 0.9041 - val_iou: 0.7523 - val_tversky: 0.8603 - lr: 3.2000e-07\n",
      "Epoch 47/50\n",
      "152/152 [==============================] - 47s 308ms/step - loss: 0.2339 - accuracy: 0.9017 - precision: 0.9017 - recall: 0.9017 - AUC: 0.9665 - f1_score: 0.9016 - specificity: 0.9016 - sensitivity: 0.9016 - iou: 0.7570 - tversky: 0.8624 - val_loss: 0.2064 - val_accuracy: 0.8981 - val_precision: 0.8981 - val_recall: 0.8981 - val_AUC: 0.9747 - val_f1_score: 0.8963 - val_specificity: 0.8963 - val_sensitivity: 0.8963 - val_iou: 0.7766 - val_tversky: 0.8759 - lr: 6.4000e-08\n",
      "Epoch 48/50\n",
      "152/152 [==============================] - 47s 307ms/step - loss: 0.2332 - accuracy: 0.9040 - precision: 0.9040 - recall: 0.9040 - AUC: 0.9667 - f1_score: 0.9039 - specificity: 0.9039 - sensitivity: 0.9039 - iou: 0.7591 - tversky: 0.8639 - val_loss: 0.2410 - val_accuracy: 0.8889 - val_precision: 0.8889 - val_recall: 0.8889 - val_AUC: 0.9664 - val_f1_score: 0.8814 - val_specificity: 0.8814 - val_sensitivity: 0.8814 - val_iou: 0.7586 - val_tversky: 0.8640 - lr: 6.4000e-08\n",
      "Epoch 49/50\n",
      "152/152 [==============================] - 47s 307ms/step - loss: 0.2388 - accuracy: 0.9003 - precision: 0.9003 - recall: 0.9003 - AUC: 0.9652 - f1_score: 0.9003 - specificity: 0.9003 - sensitivity: 0.9003 - iou: 0.7558 - tversky: 0.8618 - val_loss: 0.2718 - val_accuracy: 0.8796 - val_precision: 0.8796 - val_recall: 0.8796 - val_AUC: 0.9644 - val_f1_score: 0.8842 - val_specificity: 0.8842 - val_sensitivity: 0.8842 - val_iou: 0.7703 - val_tversky: 0.8711 - lr: 6.4000e-08\n",
      "Epoch 50/50\n",
      "152/152 [==============================] - 47s 306ms/step - loss: 0.2338 - accuracy: 0.9064 - precision: 0.9064 - recall: 0.9064 - AUC: 0.9666 - f1_score: 0.9063 - specificity: 0.9063 - sensitivity: 0.9063 - iou: 0.7601 - tversky: 0.8646 - val_loss: 0.2030 - val_accuracy: 0.8981 - val_precision: 0.8981 - val_recall: 0.8981 - val_AUC: 0.9759 - val_f1_score: 0.8963 - val_specificity: 0.8963 - val_sensitivity: 0.8963 - val_iou: 0.7836 - val_tversky: 0.8808 - lr: 6.4000e-08\n",
      "CPU times: user 1h 8min 20s, sys: 3min 2s, total: 1h 11min 23s\n",
      "Wall time: 40min 58s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "history = model.fit(data_gen.flow(X_train,Y_train,\n",
    "                                       batch_size = batch_size, \n",
    "                                       seed=27,\n",
    "                                       shuffle=True),\n",
    "                    callbacks = callbacks,\n",
    "                    epochs=50,\n",
    "                    steps_per_epoch = X_train.shape[0] // batch_size,\n",
    "                    validation_data =(data_gen.flow(X_val,Y_val,\n",
    "                                       batch_size = batch_size, \n",
    "                                       seed=27,\n",
    "                                       shuffle=True)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:49:12.033818Z",
     "iopub.status.busy": "2023-11-04T09:49:12.033035Z",
     "iopub.status.idle": "2023-11-04T09:49:13.284191Z",
     "shell.execute_reply": "2023-11-04T09:49:13.283271Z",
     "shell.execute_reply.started": "2023-11-04T09:49:12.033783Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "model.save(\"modelBH.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:55:37.039092Z",
     "iopub.status.busy": "2023-11-04T09:55:37.038321Z",
     "iopub.status.idle": "2023-11-04T09:55:37.146135Z",
     "shell.execute_reply": "2023-11-04T09:55:37.145175Z",
     "shell.execute_reply.started": "2023-11-04T09:55:37.039053Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>AUC</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>specificity</th>\n",
       "      <th>sensitivity</th>\n",
       "      <th>iou</th>\n",
       "      <th>tversky</th>\n",
       "      <th>...</th>\n",
       "      <th>val_accuracy</th>\n",
       "      <th>val_precision</th>\n",
       "      <th>val_recall</th>\n",
       "      <th>val_AUC</th>\n",
       "      <th>val_f1_score</th>\n",
       "      <th>val_specificity</th>\n",
       "      <th>val_sensitivity</th>\n",
       "      <th>val_iou</th>\n",
       "      <th>val_tversky</th>\n",
       "      <th>lr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.791473</td>\n",
       "      <td>0.700175</td>\n",
       "      <td>0.700175</td>\n",
       "      <td>0.700175</td>\n",
       "      <td>0.722848</td>\n",
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       "      <td>0.700319</td>\n",
       "      <td>0.700319</td>\n",
       "      <td>0.552804</td>\n",
       "      <td>0.707909</td>\n",
       "      <td>...</td>\n",
       "      <td>0.564815</td>\n",
       "      <td>0.564815</td>\n",
       "      <td>0.564815</td>\n",
       "      <td>0.579047</td>\n",
       "      <td>0.568892</td>\n",
       "      <td>0.568892</td>\n",
       "      <td>0.568892</td>\n",
       "      <td>0.385750</td>\n",
       "      <td>0.561625</td>\n",
       "      <td>1.000000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.665192</td>\n",
       "      <td>0.722319</td>\n",
       "      <td>0.722319</td>\n",
       "      <td>0.722319</td>\n",
       "      <td>0.764692</td>\n",
       "      <td>0.722219</td>\n",
       "      <td>0.722219</td>\n",
       "      <td>0.722219</td>\n",
       "      <td>0.575986</td>\n",
       "      <td>0.728646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.694444</td>\n",
       "      <td>0.694444</td>\n",
       "      <td>0.694444</td>\n",
       "      <td>0.749186</td>\n",
       "      <td>0.696023</td>\n",
       "      <td>0.696023</td>\n",
       "      <td>0.696023</td>\n",
       "      <td>0.520768</td>\n",
       "      <td>0.688771</td>\n",
       "      <td>1.000000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.418709</td>\n",
       "      <td>0.747142</td>\n",
       "      <td>0.747142</td>\n",
       "      <td>0.747142</td>\n",
       "      <td>0.792137</td>\n",
       "      <td>0.747027</td>\n",
       "      <td>0.747027</td>\n",
       "      <td>0.747027</td>\n",
       "      <td>0.609182</td>\n",
       "      <td>0.753917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.600223</td>\n",
       "      <td>0.595881</td>\n",
       "      <td>0.595881</td>\n",
       "      <td>0.595881</td>\n",
       "      <td>0.413942</td>\n",
       "      <td>0.591716</td>\n",
       "      <td>1.000000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.015099</td>\n",
       "      <td>0.766196</td>\n",
       "      <td>0.766196</td>\n",
       "      <td>0.766196</td>\n",
       "      <td>0.815858</td>\n",
       "      <td>0.766284</td>\n",
       "      <td>0.766284</td>\n",
       "      <td>0.766284</td>\n",
       "      <td>0.633613</td>\n",
       "      <td>0.772643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.759259</td>\n",
       "      <td>0.759259</td>\n",
       "      <td>0.759259</td>\n",
       "      <td>0.796296</td>\n",
       "      <td>0.754261</td>\n",
       "      <td>0.754261</td>\n",
       "      <td>0.754261</td>\n",
       "      <td>0.604145</td>\n",
       "      <td>0.757513</td>\n",
       "      <td>1.000000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.201626</td>\n",
       "      <td>0.795448</td>\n",
       "      <td>0.795448</td>\n",
       "      <td>0.795448</td>\n",
       "      <td>0.852938</td>\n",
       "      <td>0.795419</td>\n",
       "      <td>0.795419</td>\n",
       "      <td>0.795419</td>\n",
       "      <td>0.663715</td>\n",
       "      <td>0.795834</td>\n",
       "      <td>...</td>\n",
       "      <td>0.731481</td>\n",
       "      <td>0.731481</td>\n",
       "      <td>0.731481</td>\n",
       "      <td>0.765861</td>\n",
       "      <td>0.730824</td>\n",
       "      <td>0.730824</td>\n",
       "      <td>0.730824</td>\n",
       "      <td>0.580116</td>\n",
       "      <td>0.735673</td>\n",
       "      <td>1.000000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.567214</td>\n",
       "      <td>0.791431</td>\n",
       "      <td>0.791431</td>\n",
       "      <td>0.791431</td>\n",
       "      <td>0.841341</td>\n",
       "      <td>0.791530</td>\n",
       "      <td>0.791530</td>\n",
       "      <td>0.791530</td>\n",
       "      <td>0.662370</td>\n",
       "      <td>0.794494</td>\n",
       "      <td>...</td>\n",
       "      <td>0.462963</td>\n",
       "      <td>0.462963</td>\n",
       "      <td>0.462963</td>\n",
       "      <td>0.462963</td>\n",
       "      <td>0.458097</td>\n",
       "      <td>0.458097</td>\n",
       "      <td>0.458097</td>\n",
       "      <td>0.306323</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>1.000000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.433812</td>\n",
       "      <td>0.808322</td>\n",
       "      <td>0.808322</td>\n",
       "      <td>0.808322</td>\n",
       "      <td>0.857980</td>\n",
       "      <td>0.808308</td>\n",
       "      <td>0.808309</td>\n",
       "      <td>0.808309</td>\n",
       "      <td>0.682492</td>\n",
       "      <td>0.809062</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537037</td>\n",
       "      <td>0.537037</td>\n",
       "      <td>0.537037</td>\n",
       "      <td>0.537037</td>\n",
       "      <td>0.541903</td>\n",
       "      <td>0.541903</td>\n",
       "      <td>0.541903</td>\n",
       "      <td>0.382419</td>\n",
       "      <td>0.558630</td>\n",
       "      <td>1.000000e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.680253</td>\n",
       "      <td>0.835101</td>\n",
       "      <td>0.835101</td>\n",
       "      <td>0.835101</td>\n",
       "      <td>0.900264</td>\n",
       "      <td>0.835109</td>\n",
       "      <td>0.835109</td>\n",
       "      <td>0.835109</td>\n",
       "      <td>0.706130</td>\n",
       "      <td>0.826939</td>\n",
       "      <td>...</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.836163</td>\n",
       "      <td>0.827415</td>\n",
       "      <td>0.827415</td>\n",
       "      <td>0.827415</td>\n",
       "      <td>0.663596</td>\n",
       "      <td>0.797574</td>\n",
       "      <td>2.000000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.449699</td>\n",
       "      <td>0.849109</td>\n",
       "      <td>0.849109</td>\n",
       "      <td>0.849109</td>\n",
       "      <td>0.920733</td>\n",
       "      <td>0.849270</td>\n",
       "      <td>0.849270</td>\n",
       "      <td>0.849270</td>\n",
       "      <td>0.715640</td>\n",
       "      <td>0.834042</td>\n",
       "      <td>...</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.824331</td>\n",
       "      <td>0.735795</td>\n",
       "      <td>0.735795</td>\n",
       "      <td>0.735795</td>\n",
       "      <td>0.567582</td>\n",
       "      <td>0.724030</td>\n",
       "      <td>2.000000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.412741</td>\n",
       "      <td>0.855907</td>\n",
       "      <td>0.855907</td>\n",
       "      <td>0.855907</td>\n",
       "      <td>0.926578</td>\n",
       "      <td>0.856061</td>\n",
       "      <td>0.856061</td>\n",
       "      <td>0.856061</td>\n",
       "      <td>0.721530</td>\n",
       "      <td>0.838085</td>\n",
       "      <td>...</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>0.921468</td>\n",
       "      <td>0.811790</td>\n",
       "      <td>0.811790</td>\n",
       "      <td>0.811790</td>\n",
       "      <td>0.691525</td>\n",
       "      <td>0.818349</td>\n",
       "      <td>2.000000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.400688</td>\n",
       "      <td>0.858791</td>\n",
       "      <td>0.858791</td>\n",
       "      <td>0.858791</td>\n",
       "      <td>0.929697</td>\n",
       "      <td>0.858799</td>\n",
       "      <td>0.858799</td>\n",
       "      <td>0.858799</td>\n",
       "      <td>0.724274</td>\n",
       "      <td>0.839750</td>\n",
       "      <td>...</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.805898</td>\n",
       "      <td>0.724432</td>\n",
       "      <td>0.724432</td>\n",
       "      <td>0.724432</td>\n",
       "      <td>0.562800</td>\n",
       "      <td>0.720188</td>\n",
       "      <td>2.000000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.376237</td>\n",
       "      <td>0.860851</td>\n",
       "      <td>0.860851</td>\n",
       "      <td>0.860851</td>\n",
       "      <td>0.934536</td>\n",
       "      <td>0.860849</td>\n",
       "      <td>0.860849</td>\n",
       "      <td>0.860849</td>\n",
       "      <td>0.727906</td>\n",
       "      <td>0.842855</td>\n",
       "      <td>...</td>\n",
       "      <td>0.824074</td>\n",
       "      <td>0.824074</td>\n",
       "      <td>0.824074</td>\n",
       "      <td>0.906550</td>\n",
       "      <td>0.819602</td>\n",
       "      <td>0.819602</td>\n",
       "      <td>0.819602</td>\n",
       "      <td>0.684393</td>\n",
       "      <td>0.814992</td>\n",
       "      <td>2.000000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.348359</td>\n",
       "      <td>0.867752</td>\n",
       "      <td>0.867752</td>\n",
       "      <td>0.867752</td>\n",
       "      <td>0.940262</td>\n",
       "      <td>0.867615</td>\n",
       "      <td>0.867615</td>\n",
       "      <td>0.867615</td>\n",
       "      <td>0.734613</td>\n",
       "      <td>0.847263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.630315</td>\n",
       "      <td>0.592330</td>\n",
       "      <td>0.592330</td>\n",
       "      <td>0.592330</td>\n",
       "      <td>0.418895</td>\n",
       "      <td>0.595819</td>\n",
       "      <td>2.000000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.346721</td>\n",
       "      <td>0.868473</td>\n",
       "      <td>0.868473</td>\n",
       "      <td>0.868473</td>\n",
       "      <td>0.939367</td>\n",
       "      <td>0.868609</td>\n",
       "      <td>0.868609</td>\n",
       "      <td>0.868609</td>\n",
       "      <td>0.733411</td>\n",
       "      <td>0.845949</td>\n",
       "      <td>...</td>\n",
       "      <td>0.842593</td>\n",
       "      <td>0.842593</td>\n",
       "      <td>0.842593</td>\n",
       "      <td>0.916238</td>\n",
       "      <td>0.828125</td>\n",
       "      <td>0.828125</td>\n",
       "      <td>0.828125</td>\n",
       "      <td>0.700058</td>\n",
       "      <td>0.820269</td>\n",
       "      <td>2.000000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.330760</td>\n",
       "      <td>0.871150</td>\n",
       "      <td>0.871150</td>\n",
       "      <td>0.871150</td>\n",
       "      <td>0.943197</td>\n",
       "      <td>0.871054</td>\n",
       "      <td>0.871054</td>\n",
       "      <td>0.871054</td>\n",
       "      <td>0.734286</td>\n",
       "      <td>0.846926</td>\n",
       "      <td>...</td>\n",
       "      <td>0.787037</td>\n",
       "      <td>0.787037</td>\n",
       "      <td>0.787037</td>\n",
       "      <td>0.873628</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>0.632141</td>\n",
       "      <td>0.778283</td>\n",
       "      <td>2.000000e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.276572</td>\n",
       "      <td>0.889690</td>\n",
       "      <td>0.889690</td>\n",
       "      <td>0.889690</td>\n",
       "      <td>0.956060</td>\n",
       "      <td>0.889678</td>\n",
       "      <td>0.889678</td>\n",
       "      <td>0.889678</td>\n",
       "      <td>0.751927</td>\n",
       "      <td>0.858934</td>\n",
       "      <td>...</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.960820</td>\n",
       "      <td>0.906960</td>\n",
       "      <td>0.906960</td>\n",
       "      <td>0.906960</td>\n",
       "      <td>0.779536</td>\n",
       "      <td>0.876828</td>\n",
       "      <td>4.000000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.266435</td>\n",
       "      <td>0.890617</td>\n",
       "      <td>0.890617</td>\n",
       "      <td>0.890617</td>\n",
       "      <td>0.957542</td>\n",
       "      <td>0.890449</td>\n",
       "      <td>0.890449</td>\n",
       "      <td>0.890449</td>\n",
       "      <td>0.747680</td>\n",
       "      <td>0.856263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.970765</td>\n",
       "      <td>0.896307</td>\n",
       "      <td>0.896307</td>\n",
       "      <td>0.896307</td>\n",
       "      <td>0.791695</td>\n",
       "      <td>0.881485</td>\n",
       "      <td>4.000000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.258191</td>\n",
       "      <td>0.894119</td>\n",
       "      <td>0.894119</td>\n",
       "      <td>0.894119</td>\n",
       "      <td>0.959998</td>\n",
       "      <td>0.894192</td>\n",
       "      <td>0.894192</td>\n",
       "      <td>0.894192</td>\n",
       "      <td>0.749728</td>\n",
       "      <td>0.857664</td>\n",
       "      <td>...</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.958676</td>\n",
       "      <td>0.911222</td>\n",
       "      <td>0.911222</td>\n",
       "      <td>0.911222</td>\n",
       "      <td>0.779273</td>\n",
       "      <td>0.876265</td>\n",
       "      <td>4.000000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.256382</td>\n",
       "      <td>0.894016</td>\n",
       "      <td>0.894016</td>\n",
       "      <td>0.894016</td>\n",
       "      <td>0.960283</td>\n",
       "      <td>0.893922</td>\n",
       "      <td>0.893922</td>\n",
       "      <td>0.893922</td>\n",
       "      <td>0.750297</td>\n",
       "      <td>0.858061</td>\n",
       "      <td>...</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.966907</td>\n",
       "      <td>0.884943</td>\n",
       "      <td>0.884943</td>\n",
       "      <td>0.884943</td>\n",
       "      <td>0.774951</td>\n",
       "      <td>0.875231</td>\n",
       "      <td>4.000000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.254435</td>\n",
       "      <td>0.898033</td>\n",
       "      <td>0.898033</td>\n",
       "      <td>0.898033</td>\n",
       "      <td>0.961029</td>\n",
       "      <td>0.897984</td>\n",
       "      <td>0.897984</td>\n",
       "      <td>0.897984</td>\n",
       "      <td>0.755614</td>\n",
       "      <td>0.861351</td>\n",
       "      <td>...</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.958933</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.737419</td>\n",
       "      <td>0.848817</td>\n",
       "      <td>4.000000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.246652</td>\n",
       "      <td>0.897312</td>\n",
       "      <td>0.897312</td>\n",
       "      <td>0.897312</td>\n",
       "      <td>0.963285</td>\n",
       "      <td>0.897205</td>\n",
       "      <td>0.897205</td>\n",
       "      <td>0.897205</td>\n",
       "      <td>0.758234</td>\n",
       "      <td>0.863299</td>\n",
       "      <td>...</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.968021</td>\n",
       "      <td>0.911932</td>\n",
       "      <td>0.911932</td>\n",
       "      <td>0.911932</td>\n",
       "      <td>0.773685</td>\n",
       "      <td>0.873537</td>\n",
       "      <td>4.000000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.268957</td>\n",
       "      <td>0.895458</td>\n",
       "      <td>0.895458</td>\n",
       "      <td>0.895458</td>\n",
       "      <td>0.957183</td>\n",
       "      <td>0.895314</td>\n",
       "      <td>0.895314</td>\n",
       "      <td>0.895314</td>\n",
       "      <td>0.751661</td>\n",
       "      <td>0.858770</td>\n",
       "      <td>...</td>\n",
       "      <td>0.851852</td>\n",
       "      <td>0.851852</td>\n",
       "      <td>0.851852</td>\n",
       "      <td>0.926183</td>\n",
       "      <td>0.853693</td>\n",
       "      <td>0.853693</td>\n",
       "      <td>0.853693</td>\n",
       "      <td>0.700727</td>\n",
       "      <td>0.825088</td>\n",
       "      <td>4.000000e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.241663</td>\n",
       "      <td>0.899475</td>\n",
       "      <td>0.899475</td>\n",
       "      <td>0.899475</td>\n",
       "      <td>0.964759</td>\n",
       "      <td>0.899457</td>\n",
       "      <td>0.899457</td>\n",
       "      <td>0.899457</td>\n",
       "      <td>0.758600</td>\n",
       "      <td>0.863511</td>\n",
       "      <td>...</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.975823</td>\n",
       "      <td>0.907670</td>\n",
       "      <td>0.907670</td>\n",
       "      <td>0.907670</td>\n",
       "      <td>0.787601</td>\n",
       "      <td>0.883061</td>\n",
       "      <td>8.000001e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.247462</td>\n",
       "      <td>0.899578</td>\n",
       "      <td>0.899578</td>\n",
       "      <td>0.899578</td>\n",
       "      <td>0.962672</td>\n",
       "      <td>0.899600</td>\n",
       "      <td>0.899600</td>\n",
       "      <td>0.899600</td>\n",
       "      <td>0.756395</td>\n",
       "      <td>0.861974</td>\n",
       "      <td>...</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.976680</td>\n",
       "      <td>0.922585</td>\n",
       "      <td>0.922585</td>\n",
       "      <td>0.922585</td>\n",
       "      <td>0.801590</td>\n",
       "      <td>0.891140</td>\n",
       "      <td>8.000001e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.240688</td>\n",
       "      <td>0.898651</td>\n",
       "      <td>0.898651</td>\n",
       "      <td>0.898651</td>\n",
       "      <td>0.964690</td>\n",
       "      <td>0.898588</td>\n",
       "      <td>0.898588</td>\n",
       "      <td>0.898588</td>\n",
       "      <td>0.758056</td>\n",
       "      <td>0.863183</td>\n",
       "      <td>...</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.971536</td>\n",
       "      <td>0.900568</td>\n",
       "      <td>0.900568</td>\n",
       "      <td>0.900568</td>\n",
       "      <td>0.772833</td>\n",
       "      <td>0.873367</td>\n",
       "      <td>8.000001e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.246451</td>\n",
       "      <td>0.898239</td>\n",
       "      <td>0.898239</td>\n",
       "      <td>0.898239</td>\n",
       "      <td>0.963225</td>\n",
       "      <td>0.898257</td>\n",
       "      <td>0.898257</td>\n",
       "      <td>0.898257</td>\n",
       "      <td>0.754324</td>\n",
       "      <td>0.860656</td>\n",
       "      <td>...</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.980881</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.805572</td>\n",
       "      <td>0.893898</td>\n",
       "      <td>8.000001e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.243303</td>\n",
       "      <td>0.901329</td>\n",
       "      <td>0.901329</td>\n",
       "      <td>0.901329</td>\n",
       "      <td>0.963901</td>\n",
       "      <td>0.901167</td>\n",
       "      <td>0.901167</td>\n",
       "      <td>0.901167</td>\n",
       "      <td>0.756976</td>\n",
       "      <td>0.862411</td>\n",
       "      <td>...</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.953275</td>\n",
       "      <td>0.888494</td>\n",
       "      <td>0.888494</td>\n",
       "      <td>0.888494</td>\n",
       "      <td>0.755957</td>\n",
       "      <td>0.862797</td>\n",
       "      <td>8.000001e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.242309</td>\n",
       "      <td>0.899063</td>\n",
       "      <td>0.899063</td>\n",
       "      <td>0.899063</td>\n",
       "      <td>0.964237</td>\n",
       "      <td>0.899052</td>\n",
       "      <td>0.899052</td>\n",
       "      <td>0.899052</td>\n",
       "      <td>0.755971</td>\n",
       "      <td>0.861775</td>\n",
       "      <td>...</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.958419</td>\n",
       "      <td>0.892045</td>\n",
       "      <td>0.892045</td>\n",
       "      <td>0.892045</td>\n",
       "      <td>0.771246</td>\n",
       "      <td>0.873027</td>\n",
       "      <td>8.000001e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.233174</td>\n",
       "      <td>0.903801</td>\n",
       "      <td>0.903801</td>\n",
       "      <td>0.903801</td>\n",
       "      <td>0.966793</td>\n",
       "      <td>0.903694</td>\n",
       "      <td>0.903694</td>\n",
       "      <td>0.903694</td>\n",
       "      <td>0.761951</td>\n",
       "      <td>0.865737</td>\n",
       "      <td>...</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.959319</td>\n",
       "      <td>0.896307</td>\n",
       "      <td>0.896307</td>\n",
       "      <td>0.896307</td>\n",
       "      <td>0.761718</td>\n",
       "      <td>0.866034</td>\n",
       "      <td>8.000001e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.247271</td>\n",
       "      <td>0.899475</td>\n",
       "      <td>0.899475</td>\n",
       "      <td>0.899475</td>\n",
       "      <td>0.962745</td>\n",
       "      <td>0.899510</td>\n",
       "      <td>0.899510</td>\n",
       "      <td>0.899510</td>\n",
       "      <td>0.757061</td>\n",
       "      <td>0.862351</td>\n",
       "      <td>...</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.969307</td>\n",
       "      <td>0.907670</td>\n",
       "      <td>0.907670</td>\n",
       "      <td>0.907670</td>\n",
       "      <td>0.785338</td>\n",
       "      <td>0.881625</td>\n",
       "      <td>8.000001e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.243436</td>\n",
       "      <td>0.897621</td>\n",
       "      <td>0.897621</td>\n",
       "      <td>0.897621</td>\n",
       "      <td>0.963911</td>\n",
       "      <td>0.897693</td>\n",
       "      <td>0.897694</td>\n",
       "      <td>0.897694</td>\n",
       "      <td>0.754316</td>\n",
       "      <td>0.860763</td>\n",
       "      <td>...</td>\n",
       "      <td>0.861111</td>\n",
       "      <td>0.861111</td>\n",
       "      <td>0.861111</td>\n",
       "      <td>0.951346</td>\n",
       "      <td>0.868608</td>\n",
       "      <td>0.868608</td>\n",
       "      <td>0.868608</td>\n",
       "      <td>0.740907</td>\n",
       "      <td>0.853581</td>\n",
       "      <td>8.000001e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.238229</td>\n",
       "      <td>0.900402</td>\n",
       "      <td>0.900402</td>\n",
       "      <td>0.900402</td>\n",
       "      <td>0.965321</td>\n",
       "      <td>0.900335</td>\n",
       "      <td>0.900335</td>\n",
       "      <td>0.900335</td>\n",
       "      <td>0.756943</td>\n",
       "      <td>0.862582</td>\n",
       "      <td>...</td>\n",
       "      <td>0.935185</td>\n",
       "      <td>0.935185</td>\n",
       "      <td>0.935185</td>\n",
       "      <td>0.978652</td>\n",
       "      <td>0.941761</td>\n",
       "      <td>0.941761</td>\n",
       "      <td>0.941761</td>\n",
       "      <td>0.795478</td>\n",
       "      <td>0.887639</td>\n",
       "      <td>1.600000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.230994</td>\n",
       "      <td>0.906067</td>\n",
       "      <td>0.906067</td>\n",
       "      <td>0.906067</td>\n",
       "      <td>0.967385</td>\n",
       "      <td>0.906076</td>\n",
       "      <td>0.906076</td>\n",
       "      <td>0.906076</td>\n",
       "      <td>0.760695</td>\n",
       "      <td>0.864984</td>\n",
       "      <td>...</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.966221</td>\n",
       "      <td>0.903409</td>\n",
       "      <td>0.903409</td>\n",
       "      <td>0.903409</td>\n",
       "      <td>0.781919</td>\n",
       "      <td>0.877758</td>\n",
       "      <td>1.600000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.236340</td>\n",
       "      <td>0.902462</td>\n",
       "      <td>0.902462</td>\n",
       "      <td>0.902462</td>\n",
       "      <td>0.965879</td>\n",
       "      <td>0.902431</td>\n",
       "      <td>0.902431</td>\n",
       "      <td>0.902431</td>\n",
       "      <td>0.759526</td>\n",
       "      <td>0.864127</td>\n",
       "      <td>...</td>\n",
       "      <td>0.935185</td>\n",
       "      <td>0.935185</td>\n",
       "      <td>0.935185</td>\n",
       "      <td>0.977538</td>\n",
       "      <td>0.934659</td>\n",
       "      <td>0.934659</td>\n",
       "      <td>0.934659</td>\n",
       "      <td>0.800301</td>\n",
       "      <td>0.890283</td>\n",
       "      <td>1.600000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.233264</td>\n",
       "      <td>0.904110</td>\n",
       "      <td>0.904110</td>\n",
       "      <td>0.904110</td>\n",
       "      <td>0.966706</td>\n",
       "      <td>0.904036</td>\n",
       "      <td>0.904036</td>\n",
       "      <td>0.904036</td>\n",
       "      <td>0.760393</td>\n",
       "      <td>0.864706</td>\n",
       "      <td>...</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.975823</td>\n",
       "      <td>0.953125</td>\n",
       "      <td>0.953125</td>\n",
       "      <td>0.953125</td>\n",
       "      <td>0.809162</td>\n",
       "      <td>0.895131</td>\n",
       "      <td>1.600000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.235730</td>\n",
       "      <td>0.902668</td>\n",
       "      <td>0.902668</td>\n",
       "      <td>0.902668</td>\n",
       "      <td>0.966138</td>\n",
       "      <td>0.902724</td>\n",
       "      <td>0.902724</td>\n",
       "      <td>0.902724</td>\n",
       "      <td>0.759885</td>\n",
       "      <td>0.864413</td>\n",
       "      <td>...</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0.984396</td>\n",
       "      <td>0.926847</td>\n",
       "      <td>0.926847</td>\n",
       "      <td>0.926847</td>\n",
       "      <td>0.810869</td>\n",
       "      <td>0.897283</td>\n",
       "      <td>1.600000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.240411</td>\n",
       "      <td>0.899372</td>\n",
       "      <td>0.899372</td>\n",
       "      <td>0.899372</td>\n",
       "      <td>0.964698</td>\n",
       "      <td>0.899521</td>\n",
       "      <td>0.899521</td>\n",
       "      <td>0.899521</td>\n",
       "      <td>0.755455</td>\n",
       "      <td>0.861579</td>\n",
       "      <td>...</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.974794</td>\n",
       "      <td>0.906960</td>\n",
       "      <td>0.906960</td>\n",
       "      <td>0.906960</td>\n",
       "      <td>0.799590</td>\n",
       "      <td>0.889317</td>\n",
       "      <td>1.600000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.235505</td>\n",
       "      <td>0.903183</td>\n",
       "      <td>0.903183</td>\n",
       "      <td>0.903183</td>\n",
       "      <td>0.966113</td>\n",
       "      <td>0.903111</td>\n",
       "      <td>0.903111</td>\n",
       "      <td>0.903111</td>\n",
       "      <td>0.759517</td>\n",
       "      <td>0.864125</td>\n",
       "      <td>...</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.980796</td>\n",
       "      <td>0.911932</td>\n",
       "      <td>0.911932</td>\n",
       "      <td>0.911932</td>\n",
       "      <td>0.805448</td>\n",
       "      <td>0.892942</td>\n",
       "      <td>1.600000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.240613</td>\n",
       "      <td>0.900299</td>\n",
       "      <td>0.900299</td>\n",
       "      <td>0.900299</td>\n",
       "      <td>0.964631</td>\n",
       "      <td>0.900192</td>\n",
       "      <td>0.900192</td>\n",
       "      <td>0.900192</td>\n",
       "      <td>0.757537</td>\n",
       "      <td>0.862779</td>\n",
       "      <td>...</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.981439</td>\n",
       "      <td>0.922585</td>\n",
       "      <td>0.922585</td>\n",
       "      <td>0.922585</td>\n",
       "      <td>0.804816</td>\n",
       "      <td>0.892930</td>\n",
       "      <td>1.600000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.234412</td>\n",
       "      <td>0.900093</td>\n",
       "      <td>0.900093</td>\n",
       "      <td>0.900093</td>\n",
       "      <td>0.966394</td>\n",
       "      <td>0.899813</td>\n",
       "      <td>0.899813</td>\n",
       "      <td>0.899813</td>\n",
       "      <td>0.759393</td>\n",
       "      <td>0.863960</td>\n",
       "      <td>...</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.974366</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.780908</td>\n",
       "      <td>0.877194</td>\n",
       "      <td>1.600000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.230390</td>\n",
       "      <td>0.901123</td>\n",
       "      <td>0.901123</td>\n",
       "      <td>0.901123</td>\n",
       "      <td>0.967690</td>\n",
       "      <td>0.901002</td>\n",
       "      <td>0.901002</td>\n",
       "      <td>0.901002</td>\n",
       "      <td>0.760081</td>\n",
       "      <td>0.864536</td>\n",
       "      <td>...</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>0.971879</td>\n",
       "      <td>0.919744</td>\n",
       "      <td>0.919744</td>\n",
       "      <td>0.919744</td>\n",
       "      <td>0.798235</td>\n",
       "      <td>0.889671</td>\n",
       "      <td>1.600000e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.233303</td>\n",
       "      <td>0.905037</td>\n",
       "      <td>0.905037</td>\n",
       "      <td>0.905037</td>\n",
       "      <td>0.966673</td>\n",
       "      <td>0.904948</td>\n",
       "      <td>0.904948</td>\n",
       "      <td>0.904948</td>\n",
       "      <td>0.760516</td>\n",
       "      <td>0.864790</td>\n",
       "      <td>...</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.970422</td>\n",
       "      <td>0.884943</td>\n",
       "      <td>0.884943</td>\n",
       "      <td>0.884943</td>\n",
       "      <td>0.772709</td>\n",
       "      <td>0.873547</td>\n",
       "      <td>3.200000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.228696</td>\n",
       "      <td>0.905346</td>\n",
       "      <td>0.905346</td>\n",
       "      <td>0.905346</td>\n",
       "      <td>0.968052</td>\n",
       "      <td>0.905356</td>\n",
       "      <td>0.905357</td>\n",
       "      <td>0.905357</td>\n",
       "      <td>0.761160</td>\n",
       "      <td>0.865193</td>\n",
       "      <td>...</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.958933</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.752016</td>\n",
       "      <td>0.858979</td>\n",
       "      <td>3.200000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.231341</td>\n",
       "      <td>0.902977</td>\n",
       "      <td>0.902977</td>\n",
       "      <td>0.902977</td>\n",
       "      <td>0.967293</td>\n",
       "      <td>0.902958</td>\n",
       "      <td>0.902959</td>\n",
       "      <td>0.902959</td>\n",
       "      <td>0.760070</td>\n",
       "      <td>0.864459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.967421</td>\n",
       "      <td>0.911222</td>\n",
       "      <td>0.911222</td>\n",
       "      <td>0.911222</td>\n",
       "      <td>0.787659</td>\n",
       "      <td>0.882151</td>\n",
       "      <td>3.200000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.235073</td>\n",
       "      <td>0.901535</td>\n",
       "      <td>0.901535</td>\n",
       "      <td>0.901535</td>\n",
       "      <td>0.966183</td>\n",
       "      <td>0.901513</td>\n",
       "      <td>0.901513</td>\n",
       "      <td>0.901513</td>\n",
       "      <td>0.759974</td>\n",
       "      <td>0.864383</td>\n",
       "      <td>...</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.968793</td>\n",
       "      <td>0.907670</td>\n",
       "      <td>0.907670</td>\n",
       "      <td>0.907670</td>\n",
       "      <td>0.776625</td>\n",
       "      <td>0.875176</td>\n",
       "      <td>3.200000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.243343</td>\n",
       "      <td>0.898445</td>\n",
       "      <td>0.898445</td>\n",
       "      <td>0.898445</td>\n",
       "      <td>0.963848</td>\n",
       "      <td>0.898382</td>\n",
       "      <td>0.898382</td>\n",
       "      <td>0.898382</td>\n",
       "      <td>0.755853</td>\n",
       "      <td>0.861631</td>\n",
       "      <td>...</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.907407</td>\n",
       "      <td>0.962706</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.904119</td>\n",
       "      <td>0.752301</td>\n",
       "      <td>0.860340</td>\n",
       "      <td>3.200000e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.233859</td>\n",
       "      <td>0.901741</td>\n",
       "      <td>0.901741</td>\n",
       "      <td>0.901741</td>\n",
       "      <td>0.966549</td>\n",
       "      <td>0.901638</td>\n",
       "      <td>0.901638</td>\n",
       "      <td>0.901638</td>\n",
       "      <td>0.756966</td>\n",
       "      <td>0.862418</td>\n",
       "      <td>...</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.974709</td>\n",
       "      <td>0.896307</td>\n",
       "      <td>0.896307</td>\n",
       "      <td>0.896307</td>\n",
       "      <td>0.776580</td>\n",
       "      <td>0.875932</td>\n",
       "      <td>6.400001e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.233229</td>\n",
       "      <td>0.904007</td>\n",
       "      <td>0.904007</td>\n",
       "      <td>0.904007</td>\n",
       "      <td>0.966707</td>\n",
       "      <td>0.903933</td>\n",
       "      <td>0.903933</td>\n",
       "      <td>0.903933</td>\n",
       "      <td>0.759052</td>\n",
       "      <td>0.863876</td>\n",
       "      <td>...</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.966392</td>\n",
       "      <td>0.881392</td>\n",
       "      <td>0.881392</td>\n",
       "      <td>0.881392</td>\n",
       "      <td>0.758589</td>\n",
       "      <td>0.863979</td>\n",
       "      <td>6.400001e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.238787</td>\n",
       "      <td>0.900299</td>\n",
       "      <td>0.900299</td>\n",
       "      <td>0.900299</td>\n",
       "      <td>0.965158</td>\n",
       "      <td>0.900313</td>\n",
       "      <td>0.900313</td>\n",
       "      <td>0.900313</td>\n",
       "      <td>0.755819</td>\n",
       "      <td>0.861792</td>\n",
       "      <td>...</td>\n",
       "      <td>0.879630</td>\n",
       "      <td>0.879630</td>\n",
       "      <td>0.879630</td>\n",
       "      <td>0.964420</td>\n",
       "      <td>0.884233</td>\n",
       "      <td>0.884233</td>\n",
       "      <td>0.884233</td>\n",
       "      <td>0.770299</td>\n",
       "      <td>0.871079</td>\n",
       "      <td>6.400001e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.233788</td>\n",
       "      <td>0.906376</td>\n",
       "      <td>0.906376</td>\n",
       "      <td>0.906376</td>\n",
       "      <td>0.966618</td>\n",
       "      <td>0.906264</td>\n",
       "      <td>0.906264</td>\n",
       "      <td>0.906264</td>\n",
       "      <td>0.760067</td>\n",
       "      <td>0.864566</td>\n",
       "      <td>...</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.898148</td>\n",
       "      <td>0.975866</td>\n",
       "      <td>0.896307</td>\n",
       "      <td>0.896307</td>\n",
       "      <td>0.896307</td>\n",
       "      <td>0.783618</td>\n",
       "      <td>0.880829</td>\n",
       "      <td>6.400001e-08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        loss  accuracy  precision    recall       AUC  f1_score  specificity  \\\n",
       "0   6.791473  0.700175   0.700175  0.700175  0.722848  0.700319     0.700319   \n",
       "1   2.665192  0.722319   0.722319  0.722319  0.764692  0.722219     0.722219   \n",
       "2   2.418709  0.747142   0.747142  0.747142  0.792137  0.747027     0.747027   \n",
       "3   2.015099  0.766196   0.766196  0.766196  0.815858  0.766284     0.766284   \n",
       "4   1.201626  0.795448   0.795448  0.795448  0.852938  0.795419     0.795419   \n",
       "5   1.567214  0.791431   0.791431  0.791431  0.841341  0.791530     0.791530   \n",
       "6   1.433812  0.808322   0.808322  0.808322  0.857980  0.808308     0.808309   \n",
       "7   0.680253  0.835101   0.835101  0.835101  0.900264  0.835109     0.835109   \n",
       "8   0.449699  0.849109   0.849109  0.849109  0.920733  0.849270     0.849270   \n",
       "9   0.412741  0.855907   0.855907  0.855907  0.926578  0.856061     0.856061   \n",
       "10  0.400688  0.858791   0.858791  0.858791  0.929697  0.858799     0.858799   \n",
       "11  0.376237  0.860851   0.860851  0.860851  0.934536  0.860849     0.860849   \n",
       "12  0.348359  0.867752   0.867752  0.867752  0.940262  0.867615     0.867615   \n",
       "13  0.346721  0.868473   0.868473  0.868473  0.939367  0.868609     0.868609   \n",
       "14  0.330760  0.871150   0.871150  0.871150  0.943197  0.871054     0.871054   \n",
       "15  0.276572  0.889690   0.889690  0.889690  0.956060  0.889678     0.889678   \n",
       "16  0.266435  0.890617   0.890617  0.890617  0.957542  0.890449     0.890449   \n",
       "17  0.258191  0.894119   0.894119  0.894119  0.959998  0.894192     0.894192   \n",
       "18  0.256382  0.894016   0.894016  0.894016  0.960283  0.893922     0.893922   \n",
       "19  0.254435  0.898033   0.898033  0.898033  0.961029  0.897984     0.897984   \n",
       "20  0.246652  0.897312   0.897312  0.897312  0.963285  0.897205     0.897205   \n",
       "21  0.268957  0.895458   0.895458  0.895458  0.957183  0.895314     0.895314   \n",
       "22  0.241663  0.899475   0.899475  0.899475  0.964759  0.899457     0.899457   \n",
       "23  0.247462  0.899578   0.899578  0.899578  0.962672  0.899600     0.899600   \n",
       "24  0.240688  0.898651   0.898651  0.898651  0.964690  0.898588     0.898588   \n",
       "25  0.246451  0.898239   0.898239  0.898239  0.963225  0.898257     0.898257   \n",
       "26  0.243303  0.901329   0.901329  0.901329  0.963901  0.901167     0.901167   \n",
       "27  0.242309  0.899063   0.899063  0.899063  0.964237  0.899052     0.899052   \n",
       "28  0.233174  0.903801   0.903801  0.903801  0.966793  0.903694     0.903694   \n",
       "29  0.247271  0.899475   0.899475  0.899475  0.962745  0.899510     0.899510   \n",
       "30  0.243436  0.897621   0.897621  0.897621  0.963911  0.897693     0.897694   \n",
       "31  0.238229  0.900402   0.900402  0.900402  0.965321  0.900335     0.900335   \n",
       "32  0.230994  0.906067   0.906067  0.906067  0.967385  0.906076     0.906076   \n",
       "33  0.236340  0.902462   0.902462  0.902462  0.965879  0.902431     0.902431   \n",
       "34  0.233264  0.904110   0.904110  0.904110  0.966706  0.904036     0.904036   \n",
       "35  0.235730  0.902668   0.902668  0.902668  0.966138  0.902724     0.902724   \n",
       "36  0.240411  0.899372   0.899372  0.899372  0.964698  0.899521     0.899521   \n",
       "37  0.235505  0.903183   0.903183  0.903183  0.966113  0.903111     0.903111   \n",
       "38  0.240613  0.900299   0.900299  0.900299  0.964631  0.900192     0.900192   \n",
       "39  0.234412  0.900093   0.900093  0.900093  0.966394  0.899813     0.899813   \n",
       "40  0.230390  0.901123   0.901123  0.901123  0.967690  0.901002     0.901002   \n",
       "41  0.233303  0.905037   0.905037  0.905037  0.966673  0.904948     0.904948   \n",
       "42  0.228696  0.905346   0.905346  0.905346  0.968052  0.905356     0.905357   \n",
       "43  0.231341  0.902977   0.902977  0.902977  0.967293  0.902958     0.902959   \n",
       "44  0.235073  0.901535   0.901535  0.901535  0.966183  0.901513     0.901513   \n",
       "45  0.243343  0.898445   0.898445  0.898445  0.963848  0.898382     0.898382   \n",
       "46  0.233859  0.901741   0.901741  0.901741  0.966549  0.901638     0.901638   \n",
       "47  0.233229  0.904007   0.904007  0.904007  0.966707  0.903933     0.903933   \n",
       "48  0.238787  0.900299   0.900299  0.900299  0.965158  0.900313     0.900313   \n",
       "49  0.233788  0.906376   0.906376  0.906376  0.966618  0.906264     0.906264   \n",
       "\n",
       "    sensitivity       iou   tversky  ...  val_accuracy  val_precision  \\\n",
       "0      0.700319  0.552804  0.707909  ...      0.564815       0.564815   \n",
       "1      0.722219  0.575986  0.728646  ...      0.694444       0.694444   \n",
       "2      0.747027  0.609182  0.753917  ...      0.592593       0.592593   \n",
       "3      0.766284  0.633613  0.772643  ...      0.759259       0.759259   \n",
       "4      0.795419  0.663715  0.795834  ...      0.731481       0.731481   \n",
       "5      0.791530  0.662370  0.794494  ...      0.462963       0.462963   \n",
       "6      0.808309  0.682492  0.809062  ...      0.537037       0.537037   \n",
       "7      0.835109  0.706130  0.826939  ...      0.833333       0.833333   \n",
       "8      0.849270  0.715640  0.834042  ...      0.750000       0.750000   \n",
       "9      0.856061  0.721530  0.838085  ...      0.814815       0.814815   \n",
       "10     0.858799  0.724274  0.839750  ...      0.740741       0.740741   \n",
       "11     0.860849  0.727906  0.842855  ...      0.824074       0.824074   \n",
       "12     0.867615  0.734613  0.847263  ...      0.592593       0.592593   \n",
       "13     0.868609  0.733411  0.845949  ...      0.842593       0.842593   \n",
       "14     0.871054  0.734286  0.846926  ...      0.787037       0.787037   \n",
       "15     0.889678  0.751927  0.858934  ...      0.898148       0.898148   \n",
       "16     0.890449  0.747680  0.856263  ...      0.898148       0.898148   \n",
       "17     0.894192  0.749728  0.857664  ...      0.907407       0.907407   \n",
       "18     0.893922  0.750297  0.858061  ...      0.888889       0.888889   \n",
       "19     0.897984  0.755614  0.861351  ...      0.907407       0.907407   \n",
       "20     0.897205  0.758234  0.863299  ...      0.916667       0.916667   \n",
       "21     0.895314  0.751661  0.858770  ...      0.851852       0.851852   \n",
       "22     0.899457  0.758600  0.863511  ...      0.907407       0.907407   \n",
       "23     0.899600  0.756395  0.861974  ...      0.916667       0.916667   \n",
       "24     0.898588  0.758056  0.863183  ...      0.907407       0.907407   \n",
       "25     0.898257  0.754324  0.860656  ...      0.907407       0.907407   \n",
       "26     0.901167  0.756976  0.862411  ...      0.888889       0.888889   \n",
       "27     0.899052  0.755971  0.861775  ...      0.888889       0.888889   \n",
       "28     0.903694  0.761951  0.865737  ...      0.898148       0.898148   \n",
       "29     0.899510  0.757061  0.862351  ...      0.907407       0.907407   \n",
       "30     0.897694  0.754316  0.860763  ...      0.861111       0.861111   \n",
       "31     0.900335  0.756943  0.862582  ...      0.935185       0.935185   \n",
       "32     0.906076  0.760695  0.864984  ...      0.898148       0.898148   \n",
       "33     0.902431  0.759526  0.864127  ...      0.935185       0.935185   \n",
       "34     0.904036  0.760393  0.864706  ...      0.944444       0.944444   \n",
       "35     0.902724  0.759885  0.864413  ...      0.925926       0.925926   \n",
       "36     0.899521  0.755455  0.861579  ...      0.898148       0.898148   \n",
       "37     0.903111  0.759517  0.864125  ...      0.916667       0.916667   \n",
       "38     0.900192  0.757537  0.862779  ...      0.916667       0.916667   \n",
       "39     0.899813  0.759393  0.863960  ...      0.907407       0.907407   \n",
       "40     0.901002  0.760081  0.864536  ...      0.925926       0.925926   \n",
       "41     0.904948  0.760516  0.864790  ...      0.888889       0.888889   \n",
       "42     0.905357  0.761160  0.865193  ...      0.907407       0.907407   \n",
       "43     0.902959  0.760070  0.864459  ...      0.907407       0.907407   \n",
       "44     0.901513  0.759974  0.864383  ...      0.907407       0.907407   \n",
       "45     0.898382  0.755853  0.861631  ...      0.907407       0.907407   \n",
       "46     0.901638  0.756966  0.862418  ...      0.898148       0.898148   \n",
       "47     0.903933  0.759052  0.863876  ...      0.888889       0.888889   \n",
       "48     0.900313  0.755819  0.861792  ...      0.879630       0.879630   \n",
       "49     0.906264  0.760067  0.864566  ...      0.898148       0.898148   \n",
       "\n",
       "    val_recall   val_AUC  val_f1_score  val_specificity  val_sensitivity  \\\n",
       "0     0.564815  0.579047      0.568892         0.568892         0.568892   \n",
       "1     0.694444  0.749186      0.696023         0.696023         0.696023   \n",
       "2     0.592593  0.600223      0.595881         0.595881         0.595881   \n",
       "3     0.759259  0.796296      0.754261         0.754261         0.754261   \n",
       "4     0.731481  0.765861      0.730824         0.730824         0.730824   \n",
       "5     0.462963  0.462963      0.458097         0.458097         0.458097   \n",
       "6     0.537037  0.537037      0.541903         0.541903         0.541903   \n",
       "7     0.833333  0.836163      0.827415         0.827415         0.827415   \n",
       "8     0.750000  0.824331      0.735795         0.735795         0.735795   \n",
       "9     0.814815  0.921468      0.811790         0.811790         0.811790   \n",
       "10    0.740741  0.805898      0.724432         0.724432         0.724432   \n",
       "11    0.824074  0.906550      0.819602         0.819602         0.819602   \n",
       "12    0.592593  0.630315      0.592330         0.592330         0.592330   \n",
       "13    0.842593  0.916238      0.828125         0.828125         0.828125   \n",
       "14    0.787037  0.873628      0.795455         0.795455         0.795455   \n",
       "15    0.898148  0.960820      0.906960         0.906960         0.906960   \n",
       "16    0.898148  0.970765      0.896307         0.896307         0.896307   \n",
       "17    0.907407  0.958676      0.911222         0.911222         0.911222   \n",
       "18    0.888889  0.966907      0.884943         0.884943         0.884943   \n",
       "19    0.907407  0.958933      0.904119         0.904119         0.904119   \n",
       "20    0.916667  0.968021      0.911932         0.911932         0.911932   \n",
       "21    0.851852  0.926183      0.853693         0.853693         0.853693   \n",
       "22    0.907407  0.975823      0.907670         0.907670         0.907670   \n",
       "23    0.916667  0.976680      0.922585         0.922585         0.922585   \n",
       "24    0.907407  0.971536      0.900568         0.900568         0.900568   \n",
       "25    0.907407  0.980881      0.904119         0.904119         0.904119   \n",
       "26    0.888889  0.953275      0.888494         0.888494         0.888494   \n",
       "27    0.888889  0.958419      0.892045         0.892045         0.892045   \n",
       "28    0.898148  0.959319      0.896307         0.896307         0.896307   \n",
       "29    0.907407  0.969307      0.907670         0.907670         0.907670   \n",
       "30    0.861111  0.951346      0.868608         0.868608         0.868608   \n",
       "31    0.935185  0.978652      0.941761         0.941761         0.941761   \n",
       "32    0.898148  0.966221      0.903409         0.903409         0.903409   \n",
       "33    0.935185  0.977538      0.934659         0.934659         0.934659   \n",
       "34    0.944444  0.975823      0.953125         0.953125         0.953125   \n",
       "35    0.925926  0.984396      0.926847         0.926847         0.926847   \n",
       "36    0.898148  0.974794      0.906960         0.906960         0.906960   \n",
       "37    0.916667  0.980796      0.911932         0.911932         0.911932   \n",
       "38    0.916667  0.981439      0.922585         0.922585         0.922585   \n",
       "39    0.907407  0.974366      0.904119         0.904119         0.904119   \n",
       "40    0.925926  0.971879      0.919744         0.919744         0.919744   \n",
       "41    0.888889  0.970422      0.884943         0.884943         0.884943   \n",
       "42    0.907407  0.958933      0.904119         0.904119         0.904119   \n",
       "43    0.907407  0.967421      0.911222         0.911222         0.911222   \n",
       "44    0.907407  0.968793      0.907670         0.907670         0.907670   \n",
       "45    0.907407  0.962706      0.904119         0.904119         0.904119   \n",
       "46    0.898148  0.974709      0.896307         0.896307         0.896307   \n",
       "47    0.888889  0.966392      0.881392         0.881392         0.881392   \n",
       "48    0.879630  0.964420      0.884233         0.884233         0.884233   \n",
       "49    0.898148  0.975866      0.896307         0.896307         0.896307   \n",
       "\n",
       "     val_iou  val_tversky            lr  \n",
       "0   0.385750     0.561625  1.000000e-03  \n",
       "1   0.520768     0.688771  1.000000e-03  \n",
       "2   0.413942     0.591716  1.000000e-03  \n",
       "3   0.604145     0.757513  1.000000e-03  \n",
       "4   0.580116     0.735673  1.000000e-03  \n",
       "5   0.306323     0.478261  1.000000e-03  \n",
       "6   0.382419     0.558630  1.000000e-03  \n",
       "7   0.663596     0.797574  2.000000e-04  \n",
       "8   0.567582     0.724030  2.000000e-04  \n",
       "9   0.691525     0.818349  2.000000e-04  \n",
       "10  0.562800     0.720188  2.000000e-04  \n",
       "11  0.684393     0.814992  2.000000e-04  \n",
       "12  0.418895     0.595819  2.000000e-04  \n",
       "13  0.700058     0.820269  2.000000e-04  \n",
       "14  0.632141     0.778283  2.000000e-04  \n",
       "15  0.779536     0.876828  4.000000e-05  \n",
       "16  0.791695     0.881485  4.000000e-05  \n",
       "17  0.779273     0.876265  4.000000e-05  \n",
       "18  0.774951     0.875231  4.000000e-05  \n",
       "19  0.737419     0.848817  4.000000e-05  \n",
       "20  0.773685     0.873537  4.000000e-05  \n",
       "21  0.700727     0.825088  4.000000e-05  \n",
       "22  0.787601     0.883061  8.000001e-06  \n",
       "23  0.801590     0.891140  8.000001e-06  \n",
       "24  0.772833     0.873367  8.000001e-06  \n",
       "25  0.805572     0.893898  8.000001e-06  \n",
       "26  0.755957     0.862797  8.000001e-06  \n",
       "27  0.771246     0.873027  8.000001e-06  \n",
       "28  0.761718     0.866034  8.000001e-06  \n",
       "29  0.785338     0.881625  8.000001e-06  \n",
       "30  0.740907     0.853581  8.000001e-06  \n",
       "31  0.795478     0.887639  1.600000e-06  \n",
       "32  0.781919     0.877758  1.600000e-06  \n",
       "33  0.800301     0.890283  1.600000e-06  \n",
       "34  0.809162     0.895131  1.600000e-06  \n",
       "35  0.810869     0.897283  1.600000e-06  \n",
       "36  0.799590     0.889317  1.600000e-06  \n",
       "37  0.805448     0.892942  1.600000e-06  \n",
       "38  0.804816     0.892930  1.600000e-06  \n",
       "39  0.780908     0.877194  1.600000e-06  \n",
       "40  0.798235     0.889671  1.600000e-06  \n",
       "41  0.772709     0.873547  3.200000e-07  \n",
       "42  0.752016     0.858979  3.200000e-07  \n",
       "43  0.787659     0.882151  3.200000e-07  \n",
       "44  0.776625     0.875176  3.200000e-07  \n",
       "45  0.752301     0.860340  3.200000e-07  \n",
       "46  0.776580     0.875932  6.400001e-08  \n",
       "47  0.758589     0.863979  6.400001e-08  \n",
       "48  0.770299     0.871079  6.400001e-08  \n",
       "49  0.783618     0.880829  6.400001e-08  \n",
       "\n",
       "[50 rows x 21 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(history.history) \n",
    "df.to_excel(\"output.xlsx\")\n",
    "#df.to_csv(\"output1.csv\")\n",
    "df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:47:05.712427Z",
     "iopub.status.busy": "2023-11-04T09:47:05.711482Z",
     "iopub.status.idle": "2023-11-04T09:47:09.009119Z",
     "shell.execute_reply": "2023-11-04T09:47:09.008259Z",
     "shell.execute_reply.started": "2023-11-04T09:47:05.712384Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x3000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Interpreting the Metrics\n",
    "fig, ax = plt.subplots(5, 2, figsize=(15, 30))\n",
    "ax = ax.ravel()\n",
    "\n",
    "for i, met in enumerate(['accuracy', 'loss', 'precision', 'recall', 'AUC', 'f1_score','sensitivity', 'specificity','iou', 'tversky']):\n",
    "    ax[i].plot(history.history[met])\n",
    "    ax[i].plot(history.history['val_' + met])\n",
    "    ax[i].set_title('Model {}'.format(met))\n",
    "    ax[i].set_xlabel('epochs')\n",
    "    ax[i].set_ylabel(met)\n",
    "    ax[i].legend(['train', 'val'])\n",
    "plt.savefig(\"Model-Results.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:47:09.011244Z",
     "iopub.status.busy": "2023-11-04T09:47:09.010469Z",
     "iopub.status.idle": "2023-11-04T09:47:09.015710Z",
     "shell.execute_reply": "2023-11-04T09:47:09.014838Z",
     "shell.execute_reply.started": "2023-11-04T09:47:09.011188Z"
    }
   },
   "outputs": [],
   "source": [
    "Test_data =(data_gen.flow(X2_test,Y_test,\n",
    "                                       batch_size=128, \n",
    "                                       seed=27,\n",
    "                                       shuffle=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:47:09.017216Z",
     "iopub.status.busy": "2023-11-04T09:47:09.016901Z",
     "iopub.status.idle": "2023-11-04T09:47:15.403563Z",
     "shell.execute_reply": "2023-11-04T09:47:15.402489Z",
     "shell.execute_reply.started": "2023-11-04T09:47:09.017186Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate on test data\n",
      "8/8 [==============================] - 6s 522ms/step - loss: 0.2269 - accuracy: 0.9048 - precision: 0.9048 - recall: 0.9048 - AUC: 0.9683 - f1_score: 0.9052 - specificity: 0.9052 - sensitivity: 0.9052 - iou: 0.7659 - tversky: 0.8681\n",
      "test loss test acc: [0.2269475758075714, 0.9048106670379639, 0.9048106670379639, 0.9048106670379639, 0.9683393239974976, 0.9051923751831055, 0.905192494392395, 0.905192494392395, 0.7659320831298828, 0.8680603504180908]\n",
      "CPU times: user 10.4 s, sys: 286 ms, total: 10.7 s\n",
      "Wall time: 6.37 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Evaluate the model on the test data using `evaluate`\n",
    "print(\"Evaluate on test data\")\n",
    "results = model.evaluate(Test_data, batch_size=32, callbacks= callbacks)\n",
    "print(\"test loss test acc:\", results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:47:15.405513Z",
     "iopub.status.busy": "2023-11-04T09:47:15.405067Z",
     "iopub.status.idle": "2023-11-04T09:47:22.755872Z",
     "shell.execute_reply": "2023-11-04T09:47:22.754968Z",
     "shell.execute_reply.started": "2023-11-04T09:47:15.405473Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 7s 56ms/step\n",
      "Predicted and true label values saved\n",
      "Accuracy on test set :93.03991811668372%\n"
     ]
    }
   ],
   "source": [
    "truey=[]\n",
    "predy=[]\n",
    "x = X2_test\n",
    "y = Y_test\n",
    "\n",
    "yhat= model.predict(x)\n",
    "yh = yhat.tolist()\n",
    "yt = y.tolist()\n",
    "count = 0\n",
    "\n",
    "for i in range(len(y)):\n",
    "    yy = max(yh[i])\n",
    "    yyt = max(yt[i])\n",
    "    predy.append(yh[i].index(yy))\n",
    "    truey.append(yt[i].index(yyt))\n",
    "    if(yh[i].index(yy)== yt[i].index(yyt)):\n",
    "        count+=1\n",
    "\n",
    "acc = (count/len(y))*100\n",
    "\n",
    "#saving values for confusion matrix and analysis\n",
    "np.save('truey', truey)\n",
    "np.save('predy', predy)\n",
    "print(\"Predicted and true label values saved\")\n",
    "print(\"Accuracy on test set :\"+str(acc)+\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:47:22.757834Z",
     "iopub.status.busy": "2023-11-04T09:47:22.757194Z",
     "iopub.status.idle": "2023-11-04T09:47:23.226290Z",
     "shell.execute_reply": "2023-11-04T09:47:23.225342Z",
     "shell.execute_reply.started": "2023-11-04T09:47:22.757796Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[465  33]\n",
      " [ 35 444]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "\n",
    "confusion_matrix_img(truey, predy, savename='Confusion Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-04T09:49:26.845875Z",
     "iopub.status.busy": "2023-11-04T09:49:26.844897Z",
     "iopub.status.idle": "2023-11-04T09:49:31.089858Z",
     "shell.execute_reply": "2023-11-04T09:49:31.088952Z",
     "shell.execute_reply.started": "2023-11-04T09:49:26.845834Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.22 s, sys: 68.5 ms, total: 4.29 s\n",
      "Wall time: 4.24 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model0 = tf.keras.models.load_model('modelBH.h5',\n",
    "                                           compile=False,\n",
    "                                           custom_objects={\n",
    "                                                'Accuracy': 'accuracy', \n",
    "                                                'Precision': tf.keras.metrics.Precision(), \n",
    "                                                'Recall': tf.keras.metrics.Recall(),\n",
    "                                                'F1_Score': f1_score,})\n",
    "\n",
    "model0.compile(Adam(learning_rate=0.001, amsgrad=True),\n",
    "              loss = \"categorical_crossentropy\")"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}