GAN MNIST tf classification (with SIFT).ipynb

{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"R87TiToDDoYz","executionInfo":{"status":"ok","timestamp":1669866280577,"user_tz":-330,"elapsed":57314,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"e3d77b99-db11-4d96-963a-ec691f9aec2a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"icQNarJ2L3CI"},"outputs":[],"source":["%matplotlib inline\n","from matplotlib import pyplot as plt\n","import numpy as np\n","import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras import Sequential, layers\n","from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout, Activation, Flatten, BatchNormalization, Input ,concatenate\n","from tensorflow.keras.utils import to_categorical\n","from tensorflow.keras.regularizers import l2\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","from tensorflow.keras.callbacks import  EarlyStopping, ModelCheckpoint\n","import glob\n","import os\n","import PIL\n","import time\n","\n","from IPython import display"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BOKI_3ROOytg"},"outputs":[],"source":["def make_generator_model():\n","    model = tf.keras.Sequential()\n","    model.add(layers.Dense(7*7*256, use_bias=False, input_shape=(100,)))\n","    model.add(layers.BatchNormalization())\n","    model.add(layers.LeakyReLU())\n","\n","    model.add(layers.Reshape((7, 7, 256)))\n","    assert model.output_shape == (None, 7, 7, 256) # Note: None is the batch size\n","\n","    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))\n","    assert model.output_shape == (None, 7, 7, 128)\n","    model.add(layers.BatchNormalization())\n","    model.add(layers.LeakyReLU())\n","\n","    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))\n","    assert model.output_shape == (None, 14, 14, 64)\n","    model.add(layers.BatchNormalization())\n","    model.add(layers.LeakyReLU())\n","\n","    model.add(layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))\n","    assert model.output_shape == (None, 28, 28, 1)\n","\n","    return model"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ryv6SYPgKFvV"},"outputs":[],"source":["def make_discriminator_model():\n","    model = tf.keras.Sequential()\n","    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same',\n","                                     input_shape=[28, 28, 1]))\n","    model.add(layers.LeakyReLU())\n","    model.add(layers.Dropout(0.3))\n","\n","    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))\n","    model.add(layers.LeakyReLU())\n","    model.add(layers.Dropout(0.3))\n","\n","    model.add(layers.Flatten())\n","    model.add(layers.Dense(1))\n","\n","    return model"]},{"cell_type":"code","source":["generator = make_generator_model()\n","discriminator = make_discriminator_model()\n","generator_optimizer = tf.keras.optimizers.Adam(1e-4)\n","discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)"],"metadata":{"id":"XcHxjUjxn3oQ"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dfVvU4PWMbPP"},"outputs":[],"source":["checkpoint_dir = '/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/training_checkpoints_old'\n","checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\n","checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,\n","                                 discriminator_optimizer=discriminator_optimizer,\n","                                 generator=generator,\n","                                 discriminator=discriminator)"]},{"cell_type":"code","source":["checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Xb4OZi6zD9UQ","executionInfo":{"status":"ok","timestamp":1669866290049,"user_tz":-330,"elapsed":3373,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"b8055f33-312b-4181-dfbb-7bad72410bbb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f9ddb265fd0>"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["noi = tf.random.normal([1, 100])\n","sample = checkpoint.generator(noi, training=False)\n","plt.imshow(sample[0, :, :, 0], cmap='gray')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":283},"id":"dPEQYWZwEALt","executionInfo":{"status":"ok","timestamp":1669866290050,"user_tz":-330,"elapsed":28,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"63a43a36-48e1-41f1-8b65-2f0fbc070772"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7f9dc9cfbe50>"]},"metadata":{},"execution_count":8},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["disc = checkpoint.discriminator\n","disc.summary()"],"metadata":{"id":"81pDU2CZl5TE","executionInfo":{"status":"ok","timestamp":1669866290826,"user_tz":-330,"elapsed":799,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"8c207e6b-7fc6-40cb-8296-b7965f128124"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_1\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d (Conv2D)             (None, 14, 14, 64)        1664      \n","                                                                 \n"," leaky_re_lu_3 (LeakyReLU)   (None, 14, 14, 64)        0         \n","                                                                 \n"," dropout (Dropout)           (None, 14, 14, 64)        0         \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 7, 7, 128)         204928    \n","                                                                 \n"," leaky_re_lu_4 (LeakyReLU)   (None, 7, 7, 128)         0         \n","                                                                 \n"," dropout_1 (Dropout)         (None, 7, 7, 128)         0         \n","                                                                 \n"," flatten (Flatten)           (None, 6272)              0         \n","                                                                 \n"," dense_1 (Dense)             (None, 1)                 6273      \n","                                                                 \n","=================================================================\n","Total params: 212,865\n","Trainable params: 212,865\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["val = disc(sample)\n","print (val)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"41f9NtMvEEv_","executionInfo":{"status":"ok","timestamp":1669866290826,"user_tz":-330,"elapsed":39,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"f8f6de40-da68-4096-d616-43159154f19b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tf.Tensor([[0.1819657]], shape=(1, 1), dtype=float32)\n"]}]},{"cell_type":"markdown","source":["Now, we transfer the weights of discriminator to a new model, with the only difference being that the new model's last dense layer has 10 parameters instead of 1."],"metadata":{"id":"nc63XVrwEIfG"}},{"cell_type":"code","source":["new_model = Sequential()\n","\n","for i in range(len(disc.layers) - 1):\n","    new_model.add(disc.layers[i])\n","\n","# Freeze the layers \n","for layer in new_model.layers:\n","    layer.trainable = True  #Unfreezed as True\n","# new_model.add(Dense(128))\n","# new_model.add(Dense(10, activation='softmax'))\n","new_model.summary()"],"metadata":{"id":"zd_ernVsl5P0","executionInfo":{"status":"ok","timestamp":1669866290827,"user_tz":-330,"elapsed":36,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"de47ee72-3800-464b-9802-ee9a6b355afd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential_2\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d (Conv2D)             (None, 14, 14, 64)        1664      \n","                                                                 \n"," leaky_re_lu_3 (LeakyReLU)   (None, 14, 14, 64)        0         \n","                                                                 \n"," dropout (Dropout)           (None, 14, 14, 64)        0         \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 7, 7, 128)         204928    \n","                                                                 \n"," leaky_re_lu_4 (LeakyReLU)   (None, 7, 7, 128)         0         \n","                                                                 \n"," dropout_1 (Dropout)         (None, 7, 7, 128)         0         \n","                                                                 \n"," flatten (Flatten)           (None, 6272)              0         \n","                                                                 \n","=================================================================\n","Total params: 206,592\n","Trainable params: 206,592\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}]},{"cell_type":"code","source":["# view the initialized weights and bias of the last dense layer; weights are uniformly randomly generated \n","# and biases are all zeroes by default\n","new_model.layers[-1].weights"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TNMbK2kvl5NL","outputId":"c6a05dc8-7e2b-42ee-bd14-53c3896496c6","executionInfo":{"status":"ok","timestamp":1669866290827,"user_tz":-330,"elapsed":20,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[]"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["(train_img, train_lb), (test_img, test_lb) = tf.keras.datasets.mnist.load_data()\n","\n","print(train_img.shape, train_lb.shape, test_img.shape, test_lb.shape, '\\n')\n","\n","# set random seed\n","np.random.seed(42)\n","trainsize = 10000  # number of training images we use, selected randomly\n","randtrain = np.random.choice(train_img.shape[0], trainsize, replace=False)\n","X_train, y_train = train_img[randtrain], train_lb[randtrain]\n","\n","np.random.seed(10)\n","testsize = 10000  # number of testing images we use, selected randomly\n","randtest = np.random.choice(test_img.shape[0], testsize, replace=False)\n","X_test, y_test = test_img[randtest], test_lb[randtest]\n","\n","print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"],"metadata":{"id":"0AQXyczyl6Ah","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1669866290828,"user_tz":-330,"elapsed":18,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"a0674989-aa5e-4fea-8f3f-c13e74211fdf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n","11490434/11490434 [==============================] - 0s 0us/step\n","(60000, 28, 28) (60000,) (10000, 28, 28) (10000,) \n","\n","(10000, 28, 28) (10000,) (10000, 28, 28) (10000,)\n"]}]},{"cell_type":"code","source":["train_images = X_train.reshape((trainsize, 28, 28, 1))\n","train_images = (train_images.astype('float32') - 127.5) / 127.5\n","\n","test_images = X_test.reshape((testsize, 28, 28, 1))\n","test_images = (test_images.astype('float32') - 127.5) / 127.5\n","\n","train_labels = to_categorical(y_train)\n","test_labels = to_categorical(y_test)"],"metadata":{"id":"LvKJSkUpHrFk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import skimage.data as skid\n","import cv2\n","import pylab as plt\n","import numpy as np\n","import cv2\n","from sklearn.cluster import MiniBatchKMeans\n","import pickle\n","import tqdm.notebook as tqdm\n","\n","def generate_vector(gray):\n","  Detector = cv2.xfeatures2d.SIFT_create() ## cv2.FastFeatureDetector.create()\n","  Descriptor = cv2.xfeatures2d.SIFT_create()\n","  vector_64 = np.zeros(64,dtype='uint8')\n","  step_size = 5\n","  kp = Detector.detect(gray)\n","  if(len(kp)<8):\n","        return vector_64\n","  # kp = [cv2.KeyPoint(x, y, step_size) for y in range(0, gray.shape[0], step_size) \n","  #                                     for x in range(0, gray.shape[1], step_size)]\n","  kp, desc = Descriptor.compute(gray, kp)\n","  kmeans = MiniBatchKMeans(n_clusters=8, n_init=10)\n","  kmeans.fit(desc)\n","  predictions = kmeans.predict(X=desc,  sample_weight=None)\n","  for pre in predictions:\n","    vector_64[pre] +=1\n","  return vector_64\n","\n","# multiple images train\n","stack_tr = generate_vector(X_train[0])\n","count = 0\n","for img in tqdm.tqdm(X_train[1:]):\n","    vector = generate_vector(img)\n","    stack_tr = np.vstack((stack_tr,vector))\n","    count +=1\n","print(\"Histogram Generated for training set! \")\n","# multiple images train\n","stack_ts = generate_vector(X_test[0])\n","count = 0\n","for img in tqdm.tqdm(X_test[1:]):\n","    vector = generate_vector(img)\n","    stack_ts = np.vstack((stack_ts,vector))\n","    count +=1\n","print(\"Histogram Generated for testing set! \")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":117,"referenced_widgets":["6a724e55f63741c48325c35aefb0a50b","fd4a06f366db49278d593b49ddc6ca89","b2bb3f4cd8144ea79c6535c2b765be10","9976def1c7224c6f9f2b866372f81280","90c37f23e6534f558443456e97223569","c20ea1b64939419daf5dc18f4926753d","c29dd5db766f4cf7a230dd823b125eaa","bf825cf2b64e4bdd975f3a8e8f1b204b","f00b5d3c80b140dba380a423874ca2c7","b83643d9b9994277a4842cd4e9f0dc7d","72ec7ca0f22341428718e3493dece1d0","e49c3413352543509ad5a37b8e926237","757ff12bb98b4803b4c749aea5874a84","3159e4e13eec4bb58087be621de41505","708b985dcb344fc9adffc0c5972e064d","82fa95e68de74070a012990e5c64791b","1424f1777bae4e0e920b17014b74ce33","e8b0f5e0fa184481bbf2f93aa4443c96","96fe53f4509544eb8a41da12d8fb8978","be704319d15941879b6893131ff0b567","808ba9d787c04610a284d5a74ff8f1be","4073085411b34a4bb516e5a7a65c8031"]},"id":"Lfx_sT2pF9DJ","executionInfo":{"status":"ok","timestamp":1669867090135,"user_tz":-330,"elapsed":321631,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"8c243d99-6cb7-48e7-fee6-73086615cbda"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/9999 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6a724e55f63741c48325c35aefb0a50b"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Histogram Generated for training set! \n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/9999 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e49c3413352543509ad5a37b8e926237"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Histogram Generated for testing set! \n"]}]},{"cell_type":"code","source":["# np.save(\"/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/MNIST_SIFT_histogram_train.npy\",stack_tr)\n","# np.save(\"/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/MNIST_SIFT_histogram_test.npy\",stack_ts)\n","#\n","# stack_tr = np.load(\"/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/MNIST_SIFT_histogram_train.npy\")\n","# stack_ts = np.load(\"/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/MNIST_SIFT_histogram_test.npy\")"],"metadata":{"id":"tm5HsaXY-XWp"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def ExtractFeatures_Layer(dim):\n","    model = Sequential()\n","    model.add(Dense(64,input_dim=dim,kernel_regularizer=l2(0.01)\n","                    ))\n","    model.add(Dropout(0.5))\n","    return model\n","DSIFT = ExtractFeatures_Layer(stack_tr.shape[1])\n","MergeModel = concatenate([new_model.output, DSIFT.output])"],"metadata":{"id":"0meqFPqkF9A8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# new_model.add(Dense(10, activation='softmax'))\n","m = Dense(32, activation='relu')(MergeModel)\n","m = Dropout(0.5)(m)\n","m = Dense(10, activation='softmax')(m)\n","\n","fin_model = Model(inputs=[new_model.input, DSIFT.input], outputs=m)\n","fin_model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z2WWe08CGbGI","executionInfo":{"status":"ok","timestamp":1669867090135,"user_tz":-330,"elapsed":20,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"b5f49145-a9ef-4bcf-9196-4b84e75b6a2b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                   Output Shape         Param #     Connected to                     \n","==================================================================================================\n"," conv2d_input (InputLayer)      [(None, 28, 28, 1)]  0           []                               \n","                                                                                                  \n"," conv2d (Conv2D)                (None, 14, 14, 64)   1664        ['conv2d_input[0][0]']           \n","                                                                                                  \n"," leaky_re_lu_3 (LeakyReLU)      (None, 14, 14, 64)   0           ['conv2d[1][0]']                 \n","                                                                                                  \n"," dropout (Dropout)              (None, 14, 14, 64)   0           ['leaky_re_lu_3[1][0]']          \n","                                                                                                  \n"," conv2d_1 (Conv2D)              (None, 7, 7, 128)    204928      ['dropout[1][0]']                \n","                                                                                                  \n"," leaky_re_lu_4 (LeakyReLU)      (None, 7, 7, 128)    0           ['conv2d_1[1][0]']               \n","                                                                                                  \n"," dense_2_input (InputLayer)     [(None, 64)]         0           []                               \n","                                                                                                  \n"," dropout_1 (Dropout)            (None, 7, 7, 128)    0           ['leaky_re_lu_4[1][0]']          \n","                                                                                                  \n"," dense_2 (Dense)                (None, 64)           4160        ['dense_2_input[0][0]']          \n","                                                                                                  \n"," flatten (Flatten)              (None, 6272)         0           ['dropout_1[1][0]']              \n","                                                                                                  \n"," dropout_2 (Dropout)            (None, 64)           0           ['dense_2[0][0]']                \n","                                                                                                  \n"," concatenate (Concatenate)      (None, 6336)         0           ['flatten[1][0]',                \n","                                                                  'dropout_2[0][0]']              \n","                                                                                                  \n"," dense_3 (Dense)                (None, 32)           202784      ['concatenate[0][0]']            \n","                                                                                                  \n"," dropout_3 (Dropout)            (None, 32)           0           ['dense_3[0][0]']                \n","                                                                                                  \n"," dense_4 (Dense)                (None, 10)           330         ['dropout_3[0][0]']              \n","                                                                                                  \n","==================================================================================================\n","Total params: 413,866\n","Trainable params: 413,866\n","Non-trainable params: 0\n","__________________________________________________________________________________________________\n"]}]},{"cell_type":"code","source":["fin_model.compile(Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-7),\n","                  loss='categorical_crossentropy',\n","                  metrics=['accuracy',tf.keras.metrics.CategoricalCrossentropy()])"],"metadata":{"id":"PF3zGWrAIPc_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["train_images.shape, stack_tr.shape, train_labels.shape, stack_ts.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fgG29TvLHWD8","executionInfo":{"status":"ok","timestamp":1669867090136,"user_tz":-330,"elapsed":10,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"a2ac733a-3ddc-4b01-8b60-18026e1b1dce"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((10000, 28, 28, 1), (10000, 64), (10000, 10), (10000, 64))"]},"metadata":{},"execution_count":27}]},{"cell_type":"code","source":["filepath = \"/content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFT(only)NET_\"+\"MNIST\"+\"_best_weights.hdf5\"\n","early_stop = EarlyStopping(monitor='val_accuracy', patience=50,mode='max')\n","checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')\n","callbacks_list = [checkpoint,early_stop]\n","\n","history2 = fin_model.fit([train_images,stack_tr], train_labels, batch_size=100, epochs=20\n","                        ,validation_data=([test_images,stack_ts], test_labels),\n","                         callbacks = callbacks_list,\n","                        ) "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WkwbM5t2F8-R","executionInfo":{"status":"ok","timestamp":1669867593002,"user_tz":-330,"elapsed":502873,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"f451725a-3525-47ee-9e3e-052137a0c32a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","100/100 [==============================] - ETA: 0s - loss: 1.3030 - accuracy: 0.6829 - categorical_crossentropy: 0.9519\n","Epoch 1: val_accuracy improved from -inf to 0.92200, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFT(only)NET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 26s 255ms/step - loss: 1.3030 - accuracy: 0.6829 - categorical_crossentropy: 0.9519 - val_loss: 0.4392 - val_accuracy: 0.9220 - val_categorical_crossentropy: 0.2894\n","Epoch 2/20\n","100/100 [==============================] - ETA: 0s - loss: 0.6203 - accuracy: 0.8246 - categorical_crossentropy: 0.5462\n","Epoch 2: val_accuracy improved from 0.92200 to 0.93920, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFT(only)NET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 23s 233ms/step - loss: 0.6203 - accuracy: 0.8246 - categorical_crossentropy: 0.5462 - val_loss: 0.2339 - val_accuracy: 0.9392 - val_categorical_crossentropy: 0.2064\n","Epoch 3/20\n","100/100 [==============================] - ETA: 0s - loss: 0.4527 - accuracy: 0.8565 - categorical_crossentropy: 0.4400\n","Epoch 3: val_accuracy improved from 0.93920 to 0.95230, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFT(only)NET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 23s 233ms/step - loss: 0.4527 - accuracy: 0.8565 - categorical_crossentropy: 0.4400 - val_loss: 0.1730 - val_accuracy: 0.9523 - val_categorical_crossentropy: 0.1688\n","Epoch 4/20\n","100/100 [==============================] - ETA: 0s - loss: 0.3826 - accuracy: 0.8758 - categorical_crossentropy: 0.3806\n","Epoch 4: val_accuracy did not improve from 0.95230\n","100/100 [==============================] - 23s 229ms/step - loss: 0.3826 - accuracy: 0.8758 - categorical_crossentropy: 0.3806 - val_loss: 0.1683 - val_accuracy: 0.9498 - val_categorical_crossentropy: 0.1675\n","Epoch 5/20\n","100/100 [==============================] - ETA: 0s - loss: 0.3460 - accuracy: 0.8826 - categorical_crossentropy: 0.3456\n","Epoch 5: val_accuracy improved from 0.95230 to 0.96310, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFT(only)NET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 23s 232ms/step - loss: 0.3460 - accuracy: 0.8826 - categorical_crossentropy: 0.3456 - val_loss: 0.1250 - val_accuracy: 0.9631 - val_categorical_crossentropy: 0.1248\n","Epoch 6/20\n","100/100 [==============================] - ETA: 0s - loss: 0.3014 - accuracy: 0.8975 - categorical_crossentropy: 0.3013\n","Epoch 6: val_accuracy improved from 0.96310 to 0.96460, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFT(only)NET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 24s 242ms/step - loss: 0.3014 - accuracy: 0.8975 - categorical_crossentropy: 0.3013 - val_loss: 0.1200 - val_accuracy: 0.9646 - val_categorical_crossentropy: 0.1199\n","Epoch 7/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2876 - accuracy: 0.9053 - categorical_crossentropy: 0.2875\n","Epoch 7: val_accuracy improved from 0.96460 to 0.96710, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFT(only)NET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 25s 244ms/step - loss: 0.2876 - accuracy: 0.9053 - categorical_crossentropy: 0.2875 - val_loss: 0.1107 - val_accuracy: 0.9671 - val_categorical_crossentropy: 0.1105\n","Epoch 8/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2706 - accuracy: 0.9101 - categorical_crossentropy: 0.2705\n","Epoch 8: val_accuracy improved from 0.96710 to 0.96800, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFT(only)NET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 24s 239ms/step - loss: 0.2706 - accuracy: 0.9101 - categorical_crossentropy: 0.2705 - val_loss: 0.1115 - val_accuracy: 0.9680 - val_categorical_crossentropy: 0.1114\n","Epoch 9/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2577 - accuracy: 0.9115 - categorical_crossentropy: 0.2576\n","Epoch 9: val_accuracy did not improve from 0.96800\n","100/100 [==============================] - 23s 233ms/step - loss: 0.2577 - accuracy: 0.9115 - categorical_crossentropy: 0.2576 - val_loss: 0.1116 - val_accuracy: 0.9676 - val_categorical_crossentropy: 0.1115\n","Epoch 10/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2398 - accuracy: 0.9231 - categorical_crossentropy: 0.2397\n","Epoch 10: val_accuracy did not improve from 0.96800\n","100/100 [==============================] - 23s 234ms/step - loss: 0.2398 - accuracy: 0.9231 - categorical_crossentropy: 0.2397 - val_loss: 0.1056 - val_accuracy: 0.9665 - val_categorical_crossentropy: 0.1055\n","Epoch 11/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2326 - accuracy: 0.9243 - categorical_crossentropy: 0.2325\n","Epoch 11: val_accuracy improved from 0.96800 to 0.97030, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFT(only)NET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 23s 235ms/step - loss: 0.2326 - accuracy: 0.9243 - categorical_crossentropy: 0.2325 - val_loss: 0.0991 - val_accuracy: 0.9703 - val_categorical_crossentropy: 0.0989\n","Epoch 12/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2269 - accuracy: 0.9200 - categorical_crossentropy: 0.2268\n","Epoch 12: val_accuracy improved from 0.97030 to 0.97090, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFT(only)NET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 23s 235ms/step - loss: 0.2269 - accuracy: 0.9200 - categorical_crossentropy: 0.2268 - val_loss: 0.1000 - val_accuracy: 0.9709 - val_categorical_crossentropy: 0.0999\n","Epoch 13/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2184 - accuracy: 0.9264 - categorical_crossentropy: 0.2183\n","Epoch 13: val_accuracy did not improve from 0.97090\n","100/100 [==============================] - 23s 232ms/step - loss: 0.2184 - accuracy: 0.9264 - categorical_crossentropy: 0.2183 - val_loss: 0.1029 - val_accuracy: 0.9708 - val_categorical_crossentropy: 0.1029\n","Epoch 14/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2106 - accuracy: 0.9280 - categorical_crossentropy: 0.2105\n","Epoch 14: val_accuracy did not improve from 0.97090\n","100/100 [==============================] - 23s 233ms/step - loss: 0.2106 - accuracy: 0.9280 - categorical_crossentropy: 0.2105 - val_loss: 0.1020 - val_accuracy: 0.9703 - val_categorical_crossentropy: 0.1018\n","Epoch 15/20\n","100/100 [==============================] - ETA: 0s - loss: 0.2209 - accuracy: 0.9250 - categorical_crossentropy: 0.2208\n","Epoch 15: val_accuracy did not improve from 0.97090\n","100/100 [==============================] - 25s 251ms/step - loss: 0.2209 - accuracy: 0.9250 - categorical_crossentropy: 0.2208 - val_loss: 0.1016 - val_accuracy: 0.9709 - val_categorical_crossentropy: 0.1015\n","Epoch 16/20\n","100/100 [==============================] - ETA: 0s - loss: 0.1989 - accuracy: 0.9321 - categorical_crossentropy: 0.1988\n","Epoch 16: val_accuracy improved from 0.97090 to 0.97230, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFT(only)NET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 23s 234ms/step - loss: 0.1989 - accuracy: 0.9321 - categorical_crossentropy: 0.1988 - val_loss: 0.0938 - val_accuracy: 0.9723 - val_categorical_crossentropy: 0.0937\n","Epoch 17/20\n","100/100 [==============================] - ETA: 0s - loss: 0.1827 - accuracy: 0.9358 - categorical_crossentropy: 0.1826\n","Epoch 17: val_accuracy did not improve from 0.97230\n","100/100 [==============================] - 23s 232ms/step - loss: 0.1827 - accuracy: 0.9358 - categorical_crossentropy: 0.1826 - val_loss: 0.1069 - val_accuracy: 0.9717 - val_categorical_crossentropy: 0.1068\n","Epoch 18/20\n","100/100 [==============================] - ETA: 0s - loss: 0.1936 - accuracy: 0.9339 - categorical_crossentropy: 0.1935\n","Epoch 18: val_accuracy improved from 0.97230 to 0.97240, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFT(only)NET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 24s 237ms/step - loss: 0.1936 - accuracy: 0.9339 - categorical_crossentropy: 0.1935 - val_loss: 0.0958 - val_accuracy: 0.9724 - val_categorical_crossentropy: 0.0957\n","Epoch 19/20\n","100/100 [==============================] - ETA: 0s - loss: 0.1804 - accuracy: 0.9402 - categorical_crossentropy: 0.1803\n","Epoch 19: val_accuracy did not improve from 0.97240\n","100/100 [==============================] - 23s 232ms/step - loss: 0.1804 - accuracy: 0.9402 - categorical_crossentropy: 0.1803 - val_loss: 0.0931 - val_accuracy: 0.9721 - val_categorical_crossentropy: 0.0930\n","Epoch 20/20\n","100/100 [==============================] - ETA: 0s - loss: 0.1896 - accuracy: 0.9335 - categorical_crossentropy: 0.1895\n","Epoch 20: val_accuracy improved from 0.97240 to 0.97340, saving model to /content/drive/MyDrive/Colab Notebooks/SIFT+GAN handwriting/GAN_SIFT(only)NET_MNIST_best_weights.hdf5\n","100/100 [==============================] - 24s 236ms/step - loss: 0.1896 - accuracy: 0.9335 - categorical_crossentropy: 0.1895 - val_loss: 0.0925 - val_accuracy: 0.9734 - val_categorical_crossentropy: 0.0925\n"]}]},{"cell_type":"code","source":["accuracy = history2.history['accuracy']\n","val_accuracy = history2.history['val_accuracy']\n","\n","plt.figure()\n","plt.plot(accuracy, label='accuracy')\n","plt.plot(val_accuracy, label = 'val_accuracy')\n","plt.xlabel('Epoch')\n","plt.ylabel('Accuracy')\n","plt.ylim([0.3, 1])\n","plt.legend(loc='lower right')\n","plt.show()"],"metadata":{"id":"InnQ47D8F82w","colab":{"base_uri":"https://localhost:8080/","height":283},"executionInfo":{"status":"ok","timestamp":1669867593003,"user_tz":-330,"elapsed":34,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"73dd3d50-9499-45e5-fa3c-e8db39879594"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["loss = history2.history['loss']\n","val_loss = history2.history['val_loss']\n","\n","plt.figure()\n","plt.plot(loss, label='loss')\n","plt.plot(val_loss, label = 'val_loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.legend(loc='upper right')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"33EHdh9URWcE","executionInfo":{"status":"ok","timestamp":1669867593004,"user_tz":-330,"elapsed":27,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"8c9ad1cb-d5b7-4910-f655-7c3bfc7a2947"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["We can see that, using the tuned discriminator model, both training accuracy and testing are approximately 0.90 after 15 epochs."],"metadata":{"id":"kuIT4USFEZ7F"}},{"cell_type":"code","source":["stack_ts[i].shape, stack_ts[i].reshape(1,64).shape, test_images[i].shape, test_images[i].reshape(1,28,28,1).shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_iIDnrQlQDGY","executionInfo":{"status":"ok","timestamp":1669867593004,"user_tz":-330,"elapsed":23,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"97f0dc23-d777-45d7-f0ee-65efa69cb941"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((64,), (1, 64), (28, 28, 1), (1, 28, 28, 1))"]},"metadata":{},"execution_count":31}]},{"cell_type":"code","source":["# demonstration: predict the ith test digit\n","i = 15\n","prediction = fin_model.predict([test_images[i].reshape(1,28,28,1),stack_ts[i].reshape(1,64)])\n","\n","# get probability distribution and classification of the test digit\n","print(prediction)\n","print('prediction:', np.argmax(prediction))\n","\n","# draw the barplot\n","plt.figure()\n","plt.bar(np.arange(0,10).astype('str'), prediction[0,:])\n","plt.show()"],"metadata":{"id":"R1K4JKmEoVx7","colab":{"base_uri":"https://localhost:8080/","height":338},"executionInfo":{"status":"ok","timestamp":1669867593456,"user_tz":-330,"elapsed":470,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"248dcc81-daab-451a-cece-e989579a4607"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 103ms/step\n","[[2.4720015e-10 5.0456350e-10 9.9999821e-01 1.2521690e-06 3.3182720e-11\n","  2.5728890e-13 9.7759517e-13 1.6705032e-07 3.0047642e-07 1.8595232e-10]]\n","prediction: 2\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# show the actual ith digit\n","print('actual label:', np.argmax(test_labels[i]))\n","plt.figure()\n","plt.imshow(test_images[i,:,:,0], cmap='gray')\n","plt.show()"],"metadata":{"id":"fhckSRTIoXEi","colab":{"base_uri":"https://localhost:8080/","height":283},"executionInfo":{"status":"ok","timestamp":1669867593458,"user_tz":-330,"elapsed":15,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"9422e951-addf-4341-8acd-77766e13fde0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["actual label: 2\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["pred_labels = fin_model.predict([test_images,stack_ts]).argmax(axis=1)\n","true_labels = test_labels.argmax(axis=1)"],"metadata":{"id":"IG-VgKNqoYuj","colab":{"base_uri":"https://localhost:8080/"},"outputId":"4bcc0af0-587d-4c24-9a1b-4012ed03bcdd","executionInfo":{"status":"ok","timestamp":1669867598418,"user_tz":-330,"elapsed":4973,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["313/313 [==============================] - 5s 17ms/step\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","confusion_mat = confusion_matrix(true_labels, pred_labels)\n","confusion_mat"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JZuxHfnFQxgA","executionInfo":{"status":"ok","timestamp":1669867598419,"user_tz":-330,"elapsed":10,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"9e1ffed7-555c-4678-8599-9b98aeeef523"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 969,    0,    0,    0,    0,    0,    7,    1,    3,    0],\n","       [   0, 1116,    7,    2,    1,    1,    2,    1,    5,    0],\n","       [   5,    1, 1002,    7,    3,    0,    0,    5,    9,    0],\n","       [   0,    0,    4,  998,    0,    2,    0,    2,    3,    1],\n","       [   1,    2,    3,    0,  954,    0,    6,    0,    2,   14],\n","       [   2,    0,    0,   10,    0,  870,    5,    0,    5,    0],\n","       [   5,    3,    0,    0,    1,    9,  936,    0,    4,    0],\n","       [   1,    4,   30,    7,    4,    0,    0,  970,    3,    9],\n","       [   2,    0,    2,    7,    1,    4,    2,    3,  950,    3],\n","       [   8,    2,    2,    9,    3,    6,    1,    2,    7,  969]])"]},"metadata":{},"execution_count":35}]},{"cell_type":"code","source":["import numpy as np\n","confusion_mat = [[ 969,    0,    0,    0,    0,    0,    7,    1,    3,    0],\n","       [   0, 1116,    7,    2,    1,    1,    2,    1,    5,    0],\n","       [   5,    1, 1002,    7,    3,    0,    0,    5,    9,    0],\n","       [   0,    0,    4,  998,    0,    2,    0,    2,    3,    1],\n","       [   1,    2,    3,    0,  954,    0,    6,    0,    2,   14],\n","       [   2,    0,    0,   10,    0,  870,    5,    0,    5,    0],\n","       [   5,    3,    0,    0,    1,    9,  936,    0,    4,    0],\n","       [   1,    4,   30,    7,    4,    0,    0,  970,    3,    9],\n","       [   2,    0,    2,    7,    1,    4,    2,    3,  950,    3],\n","       [   8,    2,    2,    9,    3,    6,    1,    2,    7,  969]]\n","confusion_mat = np.array(confusion_mat)"],"metadata":{"id":"AWoWhwJoaRt-","executionInfo":{"status":"ok","timestamp":1670262817232,"user_tz":-330,"elapsed":8,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","import seaborn as sns\n","%matplotlib inline\n","from matplotlib import pyplot as plt\n","\n","confusion_mat_norm = np.around(confusion_mat.astype('float') / np.atleast_2d(confusion_mat.sum(axis=1)).T, decimals=2)\n","\n","classes = np.arange(0,10).astype('str')\n","confusion_mat_df = pd.DataFrame(confusion_mat_norm,\n","                                index = classes, \n","                                columns = classes)\n","\n","figure = plt.figure()\n","sns.heatmap(confusion_mat_df, annot=True, cmap=plt.cm.Blues)\n","# plt.tight_layout()\n","plt.ylabel('True label')\n","plt.xlabel('Predicted label')\n","# plt.show()"],"metadata":{"id":"XKXDeR3nocJB","colab":{"base_uri":"https://localhost:8080/","height":296},"executionInfo":{"status":"ok","timestamp":1670263322830,"user_tz":-330,"elapsed":1451,"user":{"displayName":"ROHAN DUBEY","userId":"11200230061670192260"}},"outputId":"ed5ff14a-e5fa-4738-9fa7-d8cf96c81071"},"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 15.0, 'Predicted label')"]},"metadata":{},"execution_count":17},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAWsAAAEGCAYAAACjLLT8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO2deXwVRfa3n5OEXQiLJOyyBEX2RcBlQEDZFIk44qCjjsuIGyo4KOC4vMPMAO7LKOMCKj/HZcQVBA2KgqCsgsiqrCMESRABRVEgnPeP7oQkkOQmud3pvpzHT3+43V1dp6pueW6luup8RVUxDMMwgk1cWRfAMAzDKBpz1oZhGCHAnLVhGEYIMGdtGIYRAsxZG4ZhhICEsi5AQVTqMMyXZSq7lzzphxkjZBz2cZVUnIhvtmKVigmUuhGL43P2L3/S9y/NRtaGYRghILAja8MwDF+RYI9dzVkbhmEAxMWXdQkKxZy1YRgGQMDfHZizNgzDAJsGMQzDCAUBH1kH+6ckF73PPJUVb9/DqnfvY+TVvY+636huDWY+fQuL/zuGtOduo35S9Zx7/7g1laVT72Lp1Lu4uE/HUpfls3mfMvD8vgzo15vJzz1b6vyCYCsW6xRtW5/Nn8eFA/oxsH8fnp90dF4HDhxg1F9GMLB/H6649BK2p28DYM+e3Vx39ZWc2bkjE/45tlRlyCmL9YvoI3GRH2VAKJx1XJzw2OhLSB02kQ6//weD+3WiRdM6edKMHzGIl2cspssfxjPu2fcZe8tAAPr9rhXtT21I1yET6H7FQwy/8hyqVqlY4rJkZWUx7p9jmfj0JN6eNoMPZr7Hxg0bSlW/srYVi3WKtq2srCwm/GMsT/77Od6c9h4fzJzBxo1583rnrTeoWq0a096fxR+v+BOPP/IwABXKV+CmW25jxMg7S12n7LJYv/AAkciPMiAUzrpz68Zs3Po9W9J3cfBQFlPTljGgR9s8aVo0rcvcxV8DMHfJNwzo0QaAU5vWYf6yDWRlHeaXXw+wcn06fc48tcRlWbXyKxo2PIkGDRtSrnx5+p13PnM+mV3yygXAVizWKdq2Vq38ioaNGjl5lStP3/7nMefjvHnN+Xg2F6ReCMC5ffqyeNECVJVKlSvToWMnKlQoX+o65ZTF+kX0iYuP/CgDPHPWItJCREaJyBPuMUpESuQl6yUlsi1jd855esZu6tdOzJNm5TfppPZqD0Bqr3ZUO6ESNROr8NU3jnOuVLEctapX4ezTTqZBnRolrldmRgZ16h4Z1SclJ5ORkVHi/IJgKxbrFG1bmZkZJNepm3OenFyHnZkZ+dJkUsdNk5CQwAknVGXPnj0lsldoWaxfeMPxOA0iIqOA1wABFruHAK+KyOhCnhsqIktFZOmh71cXy+aYR9+mW6cUFrw6im6dUkjP2E1W1mFmL1zHB/PX8MmLf2HK+KtZ9NVmsrIOl6J2hmHEJAGfBvFqNci1QCtVPZj7oog8AqwGJhzrIVV9FngW8u7T3565lwbJR0bD9ZNrkL5zb55nv9u5lyEjJwFQpVJ5LjynPXv37QfggclpPDA5DYAXx13F+m8zS1yxpORkdny3I+c8MyOD5OTkEucXBFuxWKdo20pKSiZjx3c55xkZO6idlJwvTRI7dnxHcp06HDp0iH37fqJ69er5syo11i88IuBL97wq3WGg3jGu13XvFYulq/9HSqPanFSvFuUS4hnctyMz5nyVJ02t6lUQ9xfvjmv6MuXdhYDzcrJmYhUAWjevR+vm9fhowbriFiGHVq3b8O23W9i2bSsHDxzgg5kzOLtnrxLnFwRbsVinaNty8vof6du2cfDgAdLen0mPfHmd3bMX0999B4CPZqXRuevpOX0ymli/8IiAT4N4NbIeDswWkfXAVvdaIyAFGFbczLKyDjPi/teZPvFm4uOEKe8uZO2mHdxz4/ksW/MtM+aupPtpzRl7y0BUYf6yDQwf/zoA5RLi+ej54QD8tO9XrvnrlFJNgyQkJDDmr/dy49A/c/hwFhcO+j0pKc1LnF8QbMVinaJtKyEhgVF33cNN11/L4azDpA76Pc1SmjPxySdo2ao1PXr24sKLLubuMXcysH8fqiUmMuHBR3KeP69PL37e9zMHDx7kk49nM/HZyTRrllLm9QqCHb9tFUh8sLebi1eCuSISB3QB6ruX0oElqpoVyfMWItUoSyxEariISojUc8ZFHiJ19l2+f2me7WBU1cPAQq/yNwzDiCoBn7O27eaGYRgQ+O3m5qwNwzDARtaGYRihwEbWhmEYIcDEBwzDMEKATYOUDL+W1NXoepsvdgB2LXzMN1u2HKx0WPsdhwT8Ow+sszYMw/AVG1kbhmGEAHPWhmEYIcBeMBqGYYQAm7M2DMMIATYNYhiGEQICPrIO9k9JMYimMnLvM1qw4s27WPXO3Yy86tyj7jeqU4OZ/76Zxa+NIu2ZYdRPOiIx9s9bB/LF66NZ/sYYHr7joqLLHRDF7FhVsTZb4bDjt61jISIRH2VBTDjraCojO0rqg0m99Rk6XDyewX070qJJXsWK8SNSHSX1IfczblIaY4ddAMDpbRtzRrsmdB5yP50umUCnlo3o1qngmMVBUcyOVRVrsxUOO37bKghz1j4QTWXkzq1OYuPWnUeU1Gcty1FKz6ZFkzrMXbIegLlL1jPgbOe+KlSoUI7y5RKoUD6BhIR4Mnf9VHi5A6CYHasq1mYrHHb8tlUQEicRH2VBTDjraCojO0rqRxSp0zP2HK2kvn47qb3aAZDasy3VTqhIzcTKLFq5hU+Xrmdz2lg2p/2djxas4+stBZcjKIrZsapibbbCYcdvWwVhI+t8iMjVhdzLUTcvizmrSBnz6Dt069iMBS/f4Sqp7yErS2na4EROaZJMSv/7aNbvXnp0bs5Z7ZuWdXENw4iAoDvrslgN8jfghWPdyK1u/ushIpbYiaYysqOkfkSRun5y9aOV1L//kSF3PA+4Suq92rF3336uGXQGi1du4ef9BwBI+3wtXds25rMvNx273AFRzI5VFWuzFQ47ftsqiLJywpHiychaRL4q4FgJRP0biKYy8tI135LSsDYn1avpKKn36ciMuavypMmjpH51b6ZMc9TLtu7YTbeOKcTHx5GQEEe3jims21zwn3JBUcyOVRVrsxUOO37bKhApxlEGeDWyTgb6ArvzXRfg82gbi6YyclbWYUY88CbTn7yR+Pi4I0rqN/Rn2ZqtzPh0Fd07pTB22AWoKvOXb2T4hKkAvDX7S87u3Jyl/x2FKnz4+VpmzltdaLmDoJgdqyrWZiscdvy2VRBBH1l7om4uIpOBF1R1/jHuvaKqlxWVR3GmQUqDhUg1jPATDXXzmle8ErHP+eGly2JD3VxVry3kXpGO2jAMw2+CPrKOiaV7hmEYpSaKc9Yi0k9EvhaRDSIy+hj3G4nIJyKy3H2fd15ReZqzNgzDIHpL90QkHngK6A+0BC4VkZb5kt0NvK6qHYAhwMSiymfO2jAMg6ius+4CbFDVTap6AHgNSM2XRoFq7udEYHtRmVrUPcMwDCjWNnIRGQoMzXXpWXefCEB9YGuue9uArvmy+H/ALBG5BagCHB0xLh/HvbPevehx32zVOHOkb7Z2ffagb7Zs5Ul4OOzB6q+CCFu/KM4Lxtwb+ErIpcCLqvqwiJwBvCQirVX1cEEPHPfO2jAMA6K6GiQdaJjrvIF7LTfXAv0AVHWBiFQETgQyC8rU5qwNwzCI6pz1EqC5iDQRkfI4LxCn5UvzLXCOa/dUoCKws7BMbWRtGIZB9EbWqnpIRIYBaUA88LyqrhaRscBSVZ0G/AV4TkRG4LxsvEqL2KFoztowDAOiGvNDVWcCM/NduzfX5zXAWcXJ05y1YRgGEBcX7Flhc9aGYRgEf7u5OWvDMAwos9CnkRLscX8xCKsKc+/TT2HF1DtZ9eZoRl7Z86j7jerUYOZT17P45dtJ+/eNOUrq3Ts1Y+F/RuQcu+eN54KzWxVeblNSN1v58wlInwBTNy+KmHDWYVVhjosTHrtzEKm3TaLDHx5kcN8ORyup3zaAl2d+QZc/PsK4yR8y9iYn3sunX2zk9Msf5fTLH6X/TU/zy68H+WjhN4WW25TUzVb+fILQJ6JZp9JgztoHwqrC3LlVIzZu28WW7T+4SupfMqB73tFxiybJR5TUl2446j7AoF5tmbVgHft/O1h4uU1J3WzlzycAfSKadSoNx62zFpEWInKOiJyQ73q/aNsKqwpzvdr5lNQzC1BS79kGgNQerXOU1HMzuE8HXp+1vPBym5K62cqfT0D6BARE3TxOIj7KAq80GG8F3gVuAVaJSO6IU+MKeS4U6uZ+Mubx9xwl9ZdG0K1jM1dJ/Uj4gDq1qtKqWR0+XPB1GZbSMMJP0EfWXq0GuQ7opKr7RKQx8IaINFbVxynknWsQ1M39tLV9Zz4l9aQClNRHTQFcJfWebdi779ec+78/tx3T5qziUFaB8V+ccpuSutnKn09A+gSYunkkeDUNEqeq+wBUdQvQA+gvIo/gwQKZsKowL12zlZSGJ+ZSUm/PjHwCu7USKx9RUr+qF1OmL8lz/5IIpkCOlNuU1M1W/nzKvk8cKUvZqpuLRH6UBV6NrDNEpL2qfgngjrAHAM8DbaJtLKwqzFlZhxnx4NtMf+I64uOEKdOXsHZTBvcM7cuytVuZMW+No6R+U38UmL98E8MfeCvn+UZ1a9AguTrzlm2KqNympG628ucThD4RzTqVhqCPrL1SN28AHFLVHce4d5aqflZUHn6pm/uJxbM2yppYjWcdDXXzU0alRdw4X9/fN2bUzbcVcq9IR20YhuE3QR9z2HZzwzAMnE1qQcactWEYBjayNgzDCAVBf8FoztowDAMbWRu52P35Q77ZqtF5mG+2di950jdbRumwlTsFY+IDhmEYISDov2PmrA3DMLA5a8MwjFAQcF9tztowDANsZG0YhhEKAu6rzVkbhmFA8HcwBnutSjEIo1ipn7Z6n3kqK96+h1Xv3sfIq3sfdb9R3RrMfPoWFv93DGnP3Ub9pCMxi/9xaypLp97F0ql3cXGfjiUuQzZhbL/j1VYs1qkggi4+EBPOOoxipX7aiosTHht9CanDJtLh9/9gcL9OtGhaJ0+a8SMG8fKMxXT5w3jGPfs+Y28ZCEC/37Wi/akN6TpkAt2veIjhV55D1SoVy7xOZst7W7FYp8IIejzrmHDWYRQr9dNW59aN2bj1e7ak73KEedOWMaBH2zxpWjSty9zFjjTY3CXfMKCHE3b81KZ1mL9sA1lZh/nl1wOsXJ9OnzNPLfM6mS3vbcVinQrjuB1Zi0gXEensfm4pIreLyHle2AqjWKmftuolJbItY3fOeXrG7qOFeb9JJ7VXewBSe7Wj2gmVqJlYha++cZxzpYrlqFW9CmefdjIN6tQoYY3C2X7Hq61YrFNhBH1k7ckLRhG5D+gPJIjIh0BX4BNgtIh0UNV/FvDcUGAowJMTn+Ha64Z6UTzjGIx59G0eHTWYywd25bNlG0jP2E1W1mFmL1xHp1Yn8cmLf+H73ftY9NXmPIK9hhErBP0Fo1erQS4G2gMVgB1AA1X9UUQeAhYBx3TWx5tgrl+2tmfupUHykdFw/eQaRwvz7tzLkJGTAFeY95z27N23H4AHJqfxwOQ0AF4cdxXrv80sdhmyCWP7Ha+2YrFOhRH0ddZeTYMcUtUsVf0F2KiqPwKo6n4g6sOyMIqV+mlr6er/kdKoNifVq+UI8/btyIw5X+VJU6t6lSPCvNf0Zcq7CwFntFEzsQoArZvXo3Xzeny0YF2Z18lseW8rFutUGEGfs/ZqZH1ARCq7zrpT9kURScQDZx1GsVI/bWVlHWbE/a8zfeLNjjDvuwtZu2kH99x4PsvWfMuMuSvpflpzxt4yEFWYv2wDw8e/DkC5hHg+en44AD/t+5Vr/jqlVNMgYWy/49VWLNapMAI+sPZMMLeCqv52jOsnAnVVdWVRecSiYK6fWIhU43giGoK5PR77PGKfM2f4mTEjmHuUo3avfw9874VNwzCM0hD0kbVtNzcMwyD4q0FiYlOMYRhGaYkTifgoChHpJyJfi8gGERldQJpLRGSNiKwWkVeKytNG1oZhGERvGkRE4oGngN7ANmCJiExT1TW50jQHxgBnqepuEUkqKl8bWRuGYRDVpXtdgA2quklVDwCvAan50lwHPKWquwFUtcjNC+asDcMwgDiJ/BCRoSKyNNeRe7t1fWBrrvNt7rXcnAycLCKfichCEelXVPkKnAYRkX9BwcvnVPXWojI3yg4/l9PVOHOkb7b8Uog/7MGS1oKIVcVxP9uQ0q/cK9YLxty7rUtIAtAc6AE0AD4VkTaquqewBwpiaSkKYhiGESokCg7fJR1omOu8gXstN9uARap6ENgsIt/gOO8lBWVaoLNW1Sm5z3PtSDQMw4g5orhybwnQXESa4DjpIcBl+dK8A1wKvOBuFjwZ2FRo+YqyKiJniMgaYJ173k5EJha//IZhGMElWi8YVfUQMAxIA9YCr6vqahEZKyID3WRpwC7Xt34C3KGquwrLN5Kle48BfYFpbkFWiEj3CJ4zDMMIDdF8daCqM4GZ+a7dm+uzAre7R0REtM5aVbfm+zXJitSAYRhGGAj6i95InPVWETkTUBEpB9yGM7Q3DMOIGWJhu/kNwM046wS344gK3OxloUpCrKowh1HFuvfpp7Bi6p2senM0I6/sedT9RnVqMPOp61n88u2k/ftG6ic5EmPdOzVj4X9G5By7543ngrNblaos0azXZ/PnceGAfgzs34fnJx2d14EDBxj1lxEM7N+HKy69hO3p2wDYs2c31119JWd27siEf44tVRlyyhLCfhGk9jsWQZf1KtJZq+r3qvpHVU1W1dqqenlRE+F+E6sqzGFUsY6LEx67cxCpt02iwx8eZHDfDrRoklfxY/xtA3h55hd0+eMjjJv8IWNvcqQ5P/1iI6df/iinX/4o/W96ml9+PchHC78JRL2ysrKY8I+xPPnv53hz2nt8MHMGGzfmzeudt96garVqTHt/Fn+84k88/sjDAFQoX4GbbrmNESPvLHFd8pclbP0iSO1XENGMDeJJ+YpKICJNRWS6iOwUkUwReVdEmvpRuEiJVRXmMKpYd27ViI3bdrFl+w+OkvqsLxnQPe/ouEWTZOYuWQ/A3KUbjroPMKhXW2YtWMf+3w6WqBwQ3XqtWvkVDRs1cvIqV56+/c9jzsd585rz8WwuSL0QgHP79GXxogWoKpUqV6ZDx05UqFC+xHU5qiwh6xdBar+CkGIcZUEk0yCvAK8DdYF6wFTg1eIaEpH/K+4zkRKrKsxhVLGuVzuRbRlHNmGlZ+45Wkl9/XZSe7YBILVHa6qdUJGaiZXzpBncpwOvz1peojJkE816ZWZmkFynbs55cnIddmZm5EuTSR03TUJCAiecUJU9ewrckFZiwtgvgtR+BRF0Wa9InHVlVX1JVQ+5x3+AioU9ICLT8h3TgYuyzwt5Lme/vddzwUbZMebx9+jWsRkLXhpBt47NSM/Yk0cqrE6tqrRqVocPF3xdhqU0jjeKExukLCgsNkhN9+P7bjzW13BihfyBfOsHj0EDYA0wyX1GgNOAhwt7yNTNy8ZWNO1s37mXBsnVc87rJ1U/Wkn9+x8ZMsrZIFulUnku7NmGvft+zbn/+3PbMW3OKg6VQusRoluvpKRkMnZ8l3OekbGD2knJ+dIksWPHdyTXqcOhQ4fYt+8nqlevnj+rUhPGfhGk9iuIMK8G+QInPsglwPU4u2zmADfiOOzCOM19/q/AXlWdA+xX1bmqOreUZT6KWFVhDqOK9dI1W0lpeCIn1avpKKn3ac+MeavzpKmVWPmIkvpVvZgyPW84hEuiMAUC0a2Xk9f/SN+2jYMHD5D2/kx65Mvr7J69mP7uOwB8NCuNzl1P9+RP5jD2iyC1X0EEfRqksNggTUqaqaoeBh4VkanuvxmF2SotsarCHEYV66ysw4x48G2mP3Gdo6Q+fQlrN2Vwz9C+LFu7lRnz1tC9Uwpjb+qPAvOXb2L4A2/lPN+obg0aJFdn3rJCwyT4Xq+EhARG3XUPN11/LYezDpM66Pc0S2nOxCefoGWr1vTo2YsLL7qYu8fcycD+faiWmMiEBx/Jef68Pr34ed/PHDx4kE8+ns3EZyfTrFlKmdfLLztBar+CCPjAOjJ1cxFpDbQk11y1qkb8wlBEzsdRRLgr0mdM3Tw8WIjU0hH0nXMlxc82rFyu9I149WsrIy7wC0PaBE/dXETuw4m52hJnrro/MB+I2Fmr6gxgRsmKaBiG4T1B/8mMZDXIxcA5wA5VvRpoByQW/ohhGEa4iI+TiI+yIJJ55P2qelhEDolINSCTvIG1DcMwQk9ZvTiMlEic9VIRqQ48h7PCYx+wwNNSGYZh+EzAfXXRzlpVb3I/Pi0iHwDVVPUrb4tlGIbhL0F/0VvYppiOhd1T1WXeFMkwDMN/Au6rCx1ZF7bbUAFvdoIYUcHPZVN+LacDqNH1Nl/s7Fr4mC92/MaWJBZMaOesVfXoQMSGYRgxSnxYnbVhGMbxRNB3MJqzNgzDwJy1YRhGKAj6nHUkSjEiIpeLyL3ueSMR6eJ90QzDMPwj6PGsI9luPhE4A7jUPf8JeMqzEhmGYZQBoRfMBbqq6s3ArwCquhvwVgytBMSi4ni0bQVFXTqqSupntGDFm3ex6p27GXnVuUfdb1SnBjP/fTOLXxtF2jPDcpTUAf5560C+eH00y98Yw8N3XFR0uQPSfhC9NozFOpWUBJGIj7IgEmd9UETicdZWIyK1gdJJeESZWFQcj7atoKhLR11JffRgUm99hg4Xj2dw345HK6mPSOXlGYvpMuR+xk1KY+ywCwA4vW1jzmjXhM5D7qfTJRPo1LIR3ToVHB85KO2XXZZotGEs1qk0xMLI+gngbSBJRP6JEx51nKelKiaxqDgebVtBUZeOrpL6SWzcupMt6btcJfVlDOjRJk+aFk3qHFFSX7KeAWc791WhQoVylC+XQIXyCSQkxJO566fCyx2A9sspSxTaMBbrVBriRCI+yoIinbWqvgzcCYwHvgMuVNWpxTEiIr8TkdtFpE/Jilk4sag4Hm1bQVGXjqqSelI+JfWMApTUe7UDILVn2xwl9UUrt/Dp0vVsThvL5rS/89GCdXy9peByBKX9IHptGIt1Kg2hH1mLSCPgF2A6MA342b1W2DOLc32+DngSqArc54rvFvScqZsbUWXMo+84Suov30G3TimukrrStMGJnNIkmZT+99Gs37306Nycs9o3LeviGmVI0FeDRLLOegZHFMorAk2Ar4FWhTxTLtfnoUBvVd0pIg8BC4EJx3rI1M29sxUUdemoKqln5lNSTy5ASf2O5wFXSb1XO/bu2881g85g8cot/Lz/AABpn6+la9vGfPblsbUfg9J+EL02jMU6lYayEhWIlEimQdqoalv33+ZAF4qOZx0nIjVEpBaOzuNON6+fgUOlLnU+YlFxPNq2gqIuHV0l9W9JaVg7l5J6R2bMXZUnTa3qVY4oqV/dmynTFgKwdcduunVMIT4+joSEOLp1TGHd5oL/7A5K+x0pS+nbMBbrVBpiYWSdB1VdJiJdi0iWiCNUIICKSF1V/U5ETsADqbNYVByPtq2gqEtHXUn9gTeZ/uSNxMfHMeXdhazdtIN7bujPsjVbmfHpKkdJfdgFqCrzl29k+ATndctbs7/k7M7NWfrfUajCh5+vZea81YWWOwjtl12WaLRhLNapNEjAVRiLVDcXkdtzncYBHYFaqtq32MZEKgPJqrq5qLSmbl46YjUUZiyGSPWz/WK1X1RMKL2nnfDxxogbZ3SvZsFTN8d5MZjNIZw57DdLYkxVfwGKdNSGYRh+E/Ap68KdtbsZpqqqjvSpPIZhGGVCaAM5iUiCqmYBZ/lYHsMwjDIhPi7yoyhEpJ+IfC0iG4pYrvx7EVEROa2oPAsbWS/GmZ/+UkSmAVOBn7NvqupbRRfZMAwjHERrjt2dkXgK6A1sA5aIyDRVXZMvXVXgNmBRJPlGMmddEdiFo7mYvd5aAXPWhmHEDFGcs+4CbFDVTQAi8hqQCqzJl+7vwP3AHZFkWpizTnJXgqziiJPOxvNXyrH61tovYrFO4N8qjVqnD/fFDsDuRY/7ZitW+0U0KE7TiMhQnA1/2TzrbuoDqA9szXVvG5BnubOIdAQaquoMESm1s44HCloXHTPL6qzzGoYBEFeM1X+5d1sXFxGJAx4BrirOc4U56+9UNTqBag3DMAJOFMdt6UDDXOcN3GvZVAVaA3PcFSh1gGkiMlBVlxaUaWHO2oachmEcNyREb9J6CdBcRJrgOOkhwGXZN1V1L3Bi9rmIzAFGFuaoofDYIOeUprSGYRhhIlohUlX1EDAMSAPWAq+r6moRGSsiA0tavgJH1qr6Q0kzNQzDCBvRfH+lqjOBmfmu3VtA2h6R5FnsQE6GYRixSNDXGpizNgzDIDKNw7Ik6OXL4XhVYfbLVljr5Ge/8FNJvSisX0Sf0GswBoHjVYXZL1thrZOf/cJPJfVI6m39IvqYs44Cx6sKs1+2wlonP/uFn0rqEdXb+kXUkWIcZYEnzlpEuopINfdzJRH5m4hMF5H7RSSxqOfzc7yqMPtlK6x18rNf+KmkXhTWL7wh9OrmJeR5HEV0gMdxZL7ud6+9UNBDudXNjzX/aBhBxpTUw42IRHyUBV6tBolzF4YDnKaqHd3P80Xky4Ieyr3f/peDRyI5Ha8qzH7ZCmud/OwXfiqpF4X1C28I+pywV+VbJSJXu59XZAfWFpGTgYPFzex4VWH2y1ZY6+Rnv/BTSb0orF94Q9BfMHo1sv4z8LiI3A18DywQka04YQP/XNzMjlcVZr9shbVOfvYLP5XUI6m39YvoE3RZryLVzUuVufOSsQnOj8I2VY14OJF7GsRLLERquPArznmsxrOOVaKhbv7Wiu8i7lwXtasbSHXzEqOqPwIrvLRhGIYRDYI+srbt5oZhGAQ/JrQ5a8MwDCDeRtaGYRjBJ+C+2py1YRgGgAR8IiSwzjoWV2kcPHTYN1vx8f61n5/flV+2/FyhUaPnMWPSe8LuT/yTVfVr5Y5D6ftF0F1OYJ21YRiGn8TzQ08AABW6SURBVBRH3bwsMGdtGIaBjawNwzBCQdCnXs1ZG4ZhAHHB9tXmrA3DMMBWgxiGYYSCgM+CBD6Ea8SEVdjz88/mcdHA/lw4oC8vTn7uqPsHDhxgzB0juHBAX/70xz+wPT0dcGSQLrtkEJddMohLB1/IJ7M/LLrcAREdDut35aet3l1SWPHyrax69TZG/rHbUfcbJScy87GrWPziTaQ9cTX1a1fLc79q5QpsePMvPDr8/FKVI6zixiVBivFfWRATzjqswp5ZWVncP+7vPDHxWaa+PZ20D2awKZ/g67tvv0HVaom8814al11+Jf967CEAUlKa83+vTOWV19/mXxOfZdzf/x+HDh06lpkcW0EQHQ7rd+Wnrbg44bHbB5A68iU6XPEkg89tQ4vGtfOkGX9zX17+4Eu6XDWRcS/OYez1edXW7/tzL+av+F+J6wPhFTcuKXES+VEWxISzDquw5+pVX9GwYSMaNHAEX/v0O4+5cz7Ok2buJx8zYGAqAOf07svixQtRVSpWqkRCgjOL9dtvB4qMGBYU0eGwfld+2up8agM2pv/Alu92O+K8s1cy4Hct8qRp0TiJucscpZm5yzbnud/h5Lok1TyBj5aU7ocprOLGJSXo4gNeCebeKiINvcj7WIRV2DMzM5PkOrnySkomMyO/4OsRUdhswde9ruDrqq9WcMmgAQy5OJUxd9+X47yPbSsYosNh/a78tFWvdlW2ZR6RDEvf+SP1T8w7zbFyww5Su7cEILX7qVSrUpGa1SohIkwY1o8xT6WVyHZuwipuXFKOS3Vz4O/AIhGZJyI3iUjtIp8gr2Cu1/OLsUDrtu14/e33+L9XXueFyc/x22+/lXWRDJ8Y81Qa3do3ZsHkG+nWvjHpmXvJOqxcP6gzaQvXk77zx7IuYugI+sjaq9Ugm4BOwLnAH4C/icgXwKvAW6r607Eeyi2Y++shIg4sEFZhz6SkJDJ25MorM4Ok5PyCr44obHLyEcHXxHyCr02aNqNy5cps3LCelq1aF2ArGKLDYf2u/LS1fedPNEhKzDmvX7sa6d/ndb7f7fqJIXe/BrjivGe3ZO++X+naqiFntTuJoRd2pkql8pQvF8++/Qe455miX0B7Waeg9L/CCPhiEM9G1qqqh1V1lqpeC9QDJgL9cBx5VAmrsGfLVm3YmkvwddYHM+l+ds88abr36Ml7094FYPaHaXTu4gi+pm/blvNC8bvt6WzZsol69eoXUe6yFx0O63flp62l69JJaVCTk+pWd8R5z2nDjPnr8qSplVj5iDjv5d2YMnM5AFf//U1OvvgRWlzyKGMmpvHKBytK5KijXaeg9L9CCfg8iFcj6zzVUdWDwDRgmohUjraxsAp7JiQkcMeYu7nlxj+TdfgwAy+8iGYpzXn6qSc4tVVrzu7Ri9RBF3PvX0dx4YC+VKuWyLgHnDfkXy7/ginPP0dCuXKICKPvupfqNWoUaisIosNh/a78tJWVdZgRj85g+sNXEh8Xx5QZy1i7ZSf3XNuLZevSmfHZ13Tv0JixQ3ujKPNX/I/hj7wX5RqFV9y4pAR9u7kngrkicrKqflOaPIozDRIWLESqcSwsRGrpqVyu9J1wyaa9ERe4c9PE2BDMLa2jNgzD8J2Ajzlsu7lhGAYWG8QwDCMUBH02LyZ2MBqGYZSWaC4GEZF+IvK1iGwQkdHHuH+7iKwRka9EZLaInFRUnuasDcMwABGJ+Cgin3jgKaA/0BK4VERa5ku2HDhNVdsCbwAPFFU+c9aGYRg40yCRHkXQBdigqptU9QDwGpCaO4GqfqKqv7inC4EGRWVqc9Y+Ui7Bv99Gf5WljdLg53K6Gl1v882Wnwrx0aA4U9YiMhQYmuvSs+4ObID6wNZc97YBXQvJ7lrg/aJsmrM2DMOAYnnr3KExSmVS5HLgNODsotKaszYMwyCqS/fSgdxRRxu41/LaEzkX+CtwtqoWGYXN5qwNwzCI6pz1EqC5iDQRkfLAEJxwG7lsSQfgGWCgqmZGUj4bWRuGYRC9ddaqekhEhgFpQDzwvKquFpGxwFJVnQY8CJwATHVXl3yrqgMLy9ectWEYBtHdwaiqM4GZ+a7dm+vzuUc9VATmrA3DMLAdjL4RVhVrP20FRV06rO0Xi7Z6n9GCFW/exap37mbkVUcP9hrVqcHMf9/M4tdGkfbMMOrnEkX4560D+eL10Sx/YwwP33FRicuQjZ/tdywCHs46Npx1WFWs/bQVFHXpsLZfLNqKixMeGz2Y1FufocPF4xnctyMtmuRVbxk/IpWXZyymy5D7GTcpjbHDLgDg9LaNOaNdEzoPuZ9Ol0ygU8tGdOtU8vjSfrZfgQTcW8eEsw6rirWftoKiLh3W9otFW51bncTGrTvZkr7LUVGftYwBPdrkSdOiSR3mLlkPwNwl6xlwtnNfFSpUKEf5cglUKJ9AQkI8mbuOqdbna51KQ9A1GL1SNy8vIle66wgRkctE5EkRuVlEykXbXlhVrP20FRR16bC2XyzaqpeUyLaMI99vesYe6tdOzJNm5frtpPZqB0Bqz7ZUO6EiNRMrs2jlFj5dup7NaWPZnPZ3Plqwjq+3lLy+frZfQQR8YO3ZyPoF4HzgNhF5CRgMLAI6A5MKesjUzQ0jWIx59B26dWzGgpfvoFunFNIz9pCVpTRtcCKnNEkmpf99NOt3Lz06N+es9k3LurilI+De2qvVIG1Uta2IJODs3Kmnqlki8h9gRUEPHW/q5n7aCoq6dFjbLxZtbc/cS4PkI99v/eTqpO/cmyfNd9//yJA7ngdcFfVe7di7bz/XDDqDxSu38PP+AwCkfb6Wrm0b89mXJdPD9rP9CiLo4gNejazj3J07VYHKQPbfVhWAqE+DhFXF2k9bQVGXDmv7xaKtpWu+JaVhbU6qV9NRUe/TkRlzV+VJU6t6lSMq6lf3Zsq0hQBs3bGbbh1TiI+PIyEhjm4dU1i3ueTTFn62X0FEcQejJ3g1sp4MrMPZvfNXnF06m4DTccIFRpWwqlj7aSso6tJhbb9YtJWVdZgRD7zJ9CdvJD4+jinvLmTtph3cc0N/lq3ZyoxPV9G9Uwpjh12AqjJ/+UaGT5gKwFuzv+Tszs1Z+t9RqMKHn69l5rzVZV6n0hDscbVH6uYAIlIPQFW3i0h14FycLZWLI3k+FtXN/cTPEKmmbh4eYjVEasWE0vvaTTt/jfh/mqa1K8aGujk4TjrX5z04agiGYRiBJOhjDttubhiGQfCnQcxZG4ZhQOC9tTlrwzAMgr90z5y1YRgGNmdtGIYRCuIC7qw9W7pXWn456E/BYnXZWawu3fOrXtYvSk+tbqN9s7V/4f2l/sK27T4QceM0qFE+dpbuGYZhhImg/z6bszYMwyDwi0HMWRuGYYCNrA3DMEJBtIOWRRtz1oZhGNg0iGEYRigI+MA6PBqMQVHmhnCqWENw2jAW6wTh7Bd+tl/v009mxX9HsmrqHYy8osdR9xvVqc7Mf13H4v8MJ23i0DwSYw2TqzP98WtZ/tpfWPbq7TSqW6NkFS4EKcZ/ZUEonHVQlLmzyxI2FevsvILQhrFYp+yyhK1f+Nl+cXHCYyMvJHXE83S49BEG92lHi8ZJedKMv+V8Xn7/C7pc/hjjJs9m7E39cu5Nuu8SHn35UzoMeZhu1zzJzh/2Fbu+RRJwWa9QOOugKHPnlCVkKtY5eQWgDWOxTjllCVm/8LP9OrdsyMZtu9iy/QdHSf3DFQzo3jJPmhZNkpm7dCMAc7/YmHO/ReMkEuLj+Hixo7L+8/4D7P/tYLHrWxQB99XeOWsRaSoiI0XkcRF5RERuEJFqJckrKMrcEE4VawhOG8ZinSCc/cLP9qtXO5FtmbmU1DP3HltJvUdrAFJ7tKJalYrUrFaZ5o1OZM9Pv/LahCtYMOVWxg07jzgP9obHiUR8lAWeOGsRuRV4GqiIo2heAWgILBSRHoU8l6Nufqz5M8MwYpcx/5pBt45NWTDlVrp1aEp65l6yDh8mIT6es9o3YfQTM/jdNU/SpH5Nrjj/tKjbP141GK8D2ruK5o8AM1W1h4g8A7wLdDjWQ7nVzXPHBgmKMjeEU8UagtOGsVgnCGe/8LP9tu/cS4OkXErqSYnHUFL/iSGjXwJcJfWebdi771fSM/fy1Tfb2bL9BwCmzV1Nl9aNmDK92MUINV7OWWf/EFQATgBQ1W8pgbp5UJS5j5QlXCrWR/Iq+zaMxTodKUu4+oWf7bd07TZSGtbipLo1HCX13u2YMW9tnjS1EisfUVL/U0+mTF/iPruVxKoVObF6FQB6nJbCus2ZxS5DUQR9ZO1J1D0RuQ24FlgEdAPuV9UXRKQ28Kaqdi8qj/xR9+Z9OpeH7h+Xo8z95+tvyKPM/dtvv3H3mDv5eu3aHGXuBg0bAnmVuatWq5pHmbsk80/zPp3LAxPG5agwX3f9jcXOw2tbx4quFpQ2LE375a9XUOpU2nr5Zcuv9oOjo+71PeMUHhxxAfFxcUx5bwkPvPgJ91zXm2XrtjFj3loG9WzD2Jv6OUrqX25m+IPvcOBgFgC9ujRnwq3nI8Dyr9O5efxbHDyUlZN3NKLu7d1/OGJnmFjJ/4CqXqqbtwJOBVap6rriPm8hUkuHhUgtHdYvSk/YQqT++GvkzrpaRf+dtZfq5quB1V7lbxiGEU2C/vts280NwzAwDUbDMIxQEPSRdSh2MBqGYXhNNHcwikg/EflaRDaIyFGT9yJSQUT+695fJCKNi8rTnLVhGAZEzVuLSDzwFNAfaAlcKiIt8yW7FtitqinAo8D9RRXPnLVhGAZR3W7eBdigqptU9QDwGpCaL00qMMX9/AZwjhS1gF1VY+oAhsaSHbMVLluxWKdYtlWaMgJLcx1Dc927GJiU6/wK4Ml8z68CGuQ63wicWJjNWBxZD40xO2YrXLZisU6xbKtEqOqzqnparsPzYEax6KwNwzDKknScwHXZNHCvHTONiCQAicCuwjI1Z20YhhFdlgDNRaSJiJQHhgDT8qWZBvzJ/Xwx8LG68yEFEYvrrP2KrepnDFezFR5bsVinWLYVdVT1kIgMA9KAeOB5VV0tImOBpao6DZgMvCQiG4AfcBx6oXgWG8QwDMOIHjYNYhiGEQLMWRuGYYSAmHHWRW3vjKKd50UkU0RWeWUjl62GIvKJiKwRkdVunHCvbFUUkcUissK19TevbLn24kVkuYi857GdLSKyUkS+FJGlHtuqLiJviMg6EVkrImd4ZOcUtz7Zx48iMtwjWyPc/rBKRF4VkYpe2HFt3ebaWe1VfUJNWS8uj9IC9XicReVNgfLACqClR7a6Ax1x4nR7Xa+6QEf3c1XgGw/rJcAJ7udyOMIRp3tYt9uBV4D3PG7DLRSx2SCKtqYAf3Y/lweq+2AzHtgBnORB3vWBzUAl9/x14CqP6tEaZ6NIZZyFDx8BKX58b2E5YmVkHcn2zqigqp/ivL31HFX9TlWXuZ9/Atbi/A/khS1V1X3uaTn38OTts4g0AM4HJnmRf1kgIok4P+STAVT1gKpGX0b9aM4BNqrq/zzKPwGo5K4Frgxs98jOqcAiVf1FVQ8Bc4GLPLIVSmLFWdcHtuY634ZHTq2scKNydcAZ8XplI15EvgQygQ9V1StbjwF3Aoc9yj83CswSkS9ExMudcU2AncAL7vTOJBGp4qG9bIYAr3qRsaqmAw8B3wLfAXtVdZYXtnBG1d1EpJaIVAbOI+/GkuOeWHHWMY2InAC8CQxX1R+9sqOqWaraHmfHVRcRaR1tGyIyAMhU1S+inXcB/E5VO+JEQLtZRIrU/ywhCTjTY/9W1Q7Az4CnulbuhouBwFSP8q+B8xdqE6AeUEVELvfClqquxYk8Nwv4APgSyCr0oeOMWHHWkWzvDCUiUg7HUb+sqm/5YdP98/0ToJ8H2Z8FDBSRLTjTVb1E5D8e2AFyRoeoaibwNs6UmRdsA7bl+mvkDRzn7SX9gWWqmuFR/ucCm1V1p6oeBN4CzvTIFqo6WVU7qSOovRvnHY3hEivOOpLtnaHDDZk4GVirqo94bKu2iFR3P1cCegPFFjouClUdo6oNVLUxzvf0sap6MloTkSoiUjX7M9AH58/tqKOqO4CtInKKe+kcYI0XtnJxKR5Ngbh8C5wuIpXdvngOznsTTxCRJPffRjjz1a94ZSuMxMR2cy1ge6cXtkTkVaAHcKKIbAPuU9XJXtjCGYVeAax055IB7lLVmR7YqgtMcQOnxwGvq6qny+p8IBl42w0TnAC8oqofeGjvFuBld8CwCbjaK0Puj09v4HqvbKjqIhF5A1gGHAKW4+1W8DdFpBZwELjZpxe0ocG2mxuGYYSAWJkGMQzDiGnMWRuGYYQAc9aGYRghwJy1YRhGCDBnbRiGEQLMWRuFIiJZbmS3VSIy1d0KXNK8XhSRi93Pk0SkZSFpe4hIsTdguFH2Toz0er40+wq7f4z0/09ERha3jIZREsxZG0WxX1Xbq2pr4ABwQ+6bboCfYqOqf1bVwjaN9MDD3XKGETbMWRvFYR6Q4o5654nINGCNGwDqQRFZIiJficj14OzAFJEn3TjjHwFJ2RmJyBwROc393E9ElrmxtGe7QatuAEa4o/pu7g7LN10bS0TkLPfZWiIyy42BPAkn1GuhiMg7bmCn1fmDO4nIo+712SJS273WTEQ+cJ+ZJyItotGYhlEcYmIHo+E97gi6P06QHXDiXrRW1c2uw9urqp1FpALwmYjMwokSeArQEmc34Rrg+Xz51gaeA7q7edVU1R9E5Glgn6o+5KZ7BXhUVee725HTcMJq3gfMV9WxInI+cG0E1bnGtVEJWCIib6rqLqAKjqDpCBG51817GM6uvRtUdb2IdAUmAr1K0IyGUWLMWRtFUSnXVvd5OLFKzgQWq+pm93ofoG32fDSQCDTHie/8qqpmAdtF5ONj5H868Gl2XqpaUKzwc4GW7tZxgGpuNMLuuHGPVXWGiOyOoE63isgg93NDt6y7cEK2/te9/h/gLdfGmcDUXLYrRGDDMKKKOWujKPa7YVNzcJ3Wz7kvAbeoalq+dOdFsRxxOMo1vx6jLBEjIj1wHP8ZqvqLiMwBCpKqUtfunvxtYBh+Y3PWRjRIA250w7kiIie7gYY+Bf7gzmnXBXoe49mFQHcRaeI+W9O9/hOOlFk2s3ACJeGmy3aenwKXudf6AzWKKGsisNt11C1wRvbZxAHZfx1chjO98iOwWUQGuzZERNoVYcMwoo45ayMaTMKZj14mjpDwMzh/tb0NrHfv/R+wIP+DqroTGIoz5bCCI9MQ04FB2S8YgVuB09wXmGs4sirlbzjOfjXOdMi3RZT1AyBBRNYCE3B+LLL5GUd0YRXOnPRY9/ofgWvd8q3GI8k4wygMi7pnGIYRAmxkbRiGEQLMWRuGYYQAc9aGYRghwJy1YRhGCDBnbRiGEQLMWRuGYYQAc9aGYRgh4P8DggKjoZv1szMAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["import pandas as pd\n","import seaborn as sns\n","%matplotlib inline\n","from matplotlib import pyplot as plt\n","\n","confusion_mat_norm = np.around(confusion_mat.astype('float') / np.atleast_2d(confusion_mat.sum(axis=1)).T, decimals=2)\n","fig, ax = plt.subplots(figsize=(12, 8))\n","import string\n","# classes = list(string.ascii_uppercase)\n","classes = np.arange(0,10).astype('str')\n","confusion_mat_df = pd.DataFrame(confusion_mat_norm,\n","                                index = classes, \n","                                columns = classes)\n","sns.heatmap(confusion_mat_df, ax=ax,annot=True, cbar_kws={'label': 'Accuracy %'}, cmap=sns.color_palette(\"Blues\",8))\n","# sns.heatmap(confusion_mat_df, annot=True, cmap=plt.cm.Blues, linewidths=.5, ax=ax, cbar_kws={'label': 'Accuracy %'})\n","plt.tight_layout()\n","plt.ylabel('True label')\n","plt.xlabel('Predicted label')\n","# plt.show()"],"metadata":{"id":"5FqM32UOoucd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"YVdFJSubhzE3"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyP+Jm/6aR0lx+oXqjduYXOt"},"gpuClass":"standard","kernelspec":{"display_name":"Python 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