20210110 1.3K_CNN_ AlexNet_ InceptionV3_ Resnet%0.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"20210110 1.3K_CNN.ipynb","provenance":[{"file_id":"1oGXiOyJBZ9S22nZHXeddUf3fkRCl3YMG","timestamp":1610271341650},{"file_id":"1JBT3DRIEwBfdRSwPQJ7hTnAWiqqKZ1Z9","timestamp":1609308469392},{"file_id":"1GaSd0LQx0yojUQ_Kdp-QjjXySK6RCMLy","timestamp":1609254647181},{"file_id":"1SST23CtRcLrgqbEkJNgs3R9z6FUhU7bJ","timestamp":1609251908126},{"file_id":"1q75fHOUPB4bKrqEHTpfEUsWjSUhiX8xp","timestamp":1609250014278},{"file_id":"1nYCMuP2msk3eA089JEIWUMxfJPWoWWO5","timestamp":1609248492775},{"file_id":"1U9SsnFuWhVdKq8OnuqW1nSUMD6lp8RF3","timestamp":1609247399776},{"file_id":"1m36arT9KRLvSfv_NN3pc4fRQ9JwESi_P","timestamp":1609237071261},{"file_id":"15P0r-yf61IGCaSJcHgDIK3g2Wpay5_So","timestamp":1608685424545},{"file_id":"1S2ddiJY5dup4eZ3ss-41NnpfFutEgDoo","timestamp":1607757617948},{"file_id":"1vWe_Qe9_6OlTEYvqCOnrhSchPeS4D69l","timestamp":1607441165310},{"file_id":"1F-Uzd1x-BE5YqOkuDWOl0gOSKR2AL_6n","timestamp":1606010358190}],"collapsed_sections":["gDePEVziyYyd","qd0WKEtw9hcu","y1GxRcM8I-xj","PJPTjL2SNQjG","p_SeNpIPjYtZ","dkbHXpe19c7W","0dodQ-iwjL7Y"]},"kernelspec":{"display_name":"Python 3","name":"python3"}},"cells":[{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zyIIoGWfehGq","executionInfo":{"status":"ok","timestamp":1610588550921,"user_tz":-480,"elapsed":17777,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"aa319520-904b-455a-9dba-92b875a2f355"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"gDePEVziyYyd"},"source":["## Libraries"]},{"cell_type":"code","metadata":{"id":"wA5pUDPEvfix"},"source":["import numpy as np \n","import pandas as pd \n","import matplotlib.pyplot as plt\n","\n","from sklearn.preprocessing import LabelEncoder , OneHotEncoder\n","from sklearn.compose import ColumnTransformer\n","\n","import cv2\n","import os\n","from sklearn.utils import shuffle\n","from sklearn.model_selection import train_test_split\n","\n","import pandas as pd\n","from scipy.sparse import csr_matrix\n","\n","import random\n","import time\n","import pickle\n","import h5py\n","from h5py import File"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"yNGIVnOlvg8_"},"source":["import tensorflow as tf\n","from tensorflow import keras\n","from tensorflow.keras.layers import Input, Lambda, Dense, Flatten, Dropout\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.applications.resnet50 import ResNet50\n","from tensorflow.keras.applications.resnet50 import preprocess_input\n","from tensorflow.keras.preprocessing import image\n","from tensorflow.keras.models import Sequential\n","from keras.preprocessing.image import ImageDataGenerator\n","from tensorflow.keras.applications.inception_v3 import InceptionV3\n","from tensorflow.keras.applications.inception_v3 import preprocess_input\n","from tensorflow.keras.utils import to_categorical\n","from tensorflow.keras.applications import ResNet50V2"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"qd0WKEtw9hcu"},"source":["##Read H5 files"]},{"cell_type":"code","metadata":{"id":"hErEfQKT9mNa"},"source":["ftest=h5py.File(\"/content/drive/MyDrive/UITM/SEM 4/pickle file/1.3ktest.h5\",\"r\")\n","ftrain=h5py.File(\"/content/drive/MyDrive/UITM/SEM 4/pickle file/1.3ktrain.h5\",\"r\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"spyy1JAb-Ap7","executionInfo":{"status":"ok","timestamp":1610588554782,"user_tz":-480,"elapsed":21595,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"461224f0-e783-482e-d523-ec066c1c9319"},"source":["print(ftest.keys())\n","print(ftrain.keys())"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<KeysViewHDF5 ['test_x', 'test_y']>\n","<KeysViewHDF5 ['train_x', 'train_y']>\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"3APNL__P-THV"},"source":["train_x=ftrain.get('train_x')\n","train_y=ftrain.get('train_y')\n","test_x=ftest.get('test_x')\n","test_y=ftest.get('test_y')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"DIqg6iw3-xVb"},"source":["train_x = np.array(train_x)\n","train_y = np.array(train_y)\n","test_x = np.array(test_x)\n","test_y = np.array(test_y)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KvWB0sJ5_Be5","executionInfo":{"status":"ok","timestamp":1610588669291,"user_tz":-480,"elapsed":136084,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"453b23e6-fb00-4080-d31c-f0f2b102c217"},"source":["print(test_x.shape)\n","print(test_y.shape)\n","print(train_x.shape)\n","print(train_y.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["(2765, 224, 224, 3)\n","(2765, 7)\n","(6449, 224, 224, 3)\n","(6449, 7)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"y1GxRcM8I-xj"},"source":["##Optimizer\r\n"]},{"cell_type":"markdown","metadata":{"id":"9RNvZsceJBrc"},"source":["Adam & Adam-decay"]},{"cell_type":"code","metadata":{"id":"_lsSRG1SI9yP"},"source":["#decayed_lr = keras.optimizers.schedules.ExponentialDecay(initial_learning_rate=1e-2, decay_steps=10000,decay_rate=0.9)\r\n","#opt = keras.optimizers.Adam(learning_rate=decayed_lr)\r\n","#opt='Adam'"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ykaczhH3Jz3x"},"source":["SGD & SGD decay"]},{"cell_type":"code","metadata":{"id":"7yYEE9dV6WDz"},"source":["#opt = keras.optimizers.SGD(learning_rate=decayed_lr)\r\n","opt=keras.optimizers.SGD(learning_rate=0.01)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"seLxoPhr49eX"},"source":["RMSprop"]},{"cell_type":"code","metadata":{"id":"74rOMY1548Ev"},"source":["#opt=keras.optimizers.RMSprop(learning_rate=decayed_lr)\r\n","#opt='RMSprop'"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PJPTjL2SNQjG"},"source":["##Confusion Metrics"]},{"cell_type":"code","metadata":{"id":"wokwTMwsNPlN"},"source":["cmetrics = [\r\n","    tf.keras.metrics.TruePositives(name = 'tp'), #correctly predicted cancer cell\r\n","    tf.keras.metrics.FalsePositives(name = 'fp'), #incorrectly predicted cancer cell\r\n","    tf.keras.metrics.TrueNegatives(name = 'tn'),# correctly predicted non cancerous\r\n","    tf.keras.metrics.FalseNegatives(name='fn'),\r\n","    tf.keras.metrics.CategoricalAccuracy(name = 'accuracy'),\r\n","    tf.keras.metrics.Precision(name = 'precision'),\r\n","    tf.keras.metrics.Recall(name = 'recall'),\r\n","    tf.keras.metrics.AUC(name = 'auc')\r\n","]"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Gd4oaHbM1gvP"},"source":["##Resnet50V2"]},{"cell_type":"code","metadata":{"id":"v8efpxyI1gvP"},"source":["image_input=(224,224,3)\n","num_classes=7\n","EPOCH=50\n","BATCHSIZE=20\n","#es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', mode='min', patience=50) #early stopping is used to improve training time and reduce overfitting"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"67rhCk2O1gvR","executionInfo":{"status":"ok","timestamp":1610588676711,"user_tz":-480,"elapsed":143479,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"f13bb3a8-b0ac-4993-c7eb-d6f9c540e0eb"},"source":["# Import the Resnet library & add preprocessing layer to the front of resnet\n","# imagenet weights was use as Transfer learning\n","resnetv2 = ResNet50V2(input_shape=image_input, weights='imagenet', include_top=False)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n","94674944/94668760 [==============================] - 1s 0us/step\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"3kdJBJEx1gvR"},"source":["# freeze top layers of models and train another half\n","for layer in resnetv2.layers[:]:\n","    layer.trainable = False"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Bqbo8av51gvR"},"source":["# to avoid overfitting, dropout layer is added before fully conneted layers.\n","x = Flatten()(resnetv2.output)\n","x = Dropout(0.5)(x)  # dropout layer\n","out = Dense(num_classes, activation='softmax', name='output_layer')(x) #fully connected layer"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"vxafW5oW1gvR"},"source":["resnetv2TL = Model(inputs=resnetv2.input, outputs=out, name='ResNet50V2')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QEx2KQUg1gvS","executionInfo":{"status":"ok","timestamp":1610588676716,"user_tz":-480,"elapsed":143462,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"310e4065-183e-40f5-f954-a6b9b1597f03"},"source":["resnetv2TL.summary()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"ResNet50V2\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n","__________________________________________________________________________________________________\n","conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n","__________________________________________________________________________________________________\n","conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n","__________________________________________________________________________________________________\n","pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_conv[0][0]                 \n","__________________________________________________________________________________________________\n","pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n","__________________________________________________________________________________________________\n","conv2_block1_preact_bn (BatchNo (None, 56, 56, 64)   256         pool1_pool[0][0]                 \n","__________________________________________________________________________________________________\n","conv2_block1_preact_relu (Activ (None, 56, 56, 64)   0           conv2_block1_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4096        conv2_block1_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv2_block1_2_pad (ZeroPadding (None, 58, 58, 64)   0           conv2_block1_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36864       conv2_block1_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block1_out (Add)          (None, 56, 56, 256)  0           conv2_block1_0_conv[0][0]        \n","                                                                 conv2_block1_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block2_preact_bn (BatchNo (None, 56, 56, 256)  1024        conv2_block1_out[0][0]           \n","__________________________________________________________________________________________________\n","conv2_block2_preact_relu (Activ (None, 56, 56, 256)  0           conv2_block2_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16384       conv2_block2_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv2_block2_2_pad (ZeroPadding (None, 58, 58, 64)   0           conv2_block2_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36864       conv2_block2_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block2_out (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n","                                                                 conv2_block2_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block3_preact_bn (BatchNo (None, 56, 56, 256)  1024        conv2_block2_out[0][0]           \n","__________________________________________________________________________________________________\n","conv2_block3_preact_relu (Activ (None, 56, 56, 256)  0           conv2_block3_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16384       conv2_block3_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv2_block3_2_pad (ZeroPadding (None, 58, 58, 64)   0           conv2_block3_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block3_2_conv (Conv2D)    (None, 28, 28, 64)   36864       conv2_block3_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv2_block3_2_bn (BatchNormali (None, 28, 28, 64)   256         conv2_block3_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block3_2_relu (Activation (None, 28, 28, 64)   0           conv2_block3_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","max_pooling2d (MaxPooling2D)    (None, 28, 28, 256)  0           conv2_block2_out[0][0]           \n","__________________________________________________________________________________________________\n","conv2_block3_3_conv (Conv2D)    (None, 28, 28, 256)  16640       conv2_block3_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv2_block3_out (Add)          (None, 28, 28, 256)  0           max_pooling2d[0][0]              \n","                                                                 conv2_block3_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block1_preact_bn (BatchNo (None, 28, 28, 256)  1024        conv2_block3_out[0][0]           \n","__________________________________________________________________________________________________\n","conv3_block1_preact_relu (Activ (None, 28, 28, 256)  0           conv3_block1_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32768       conv3_block1_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block1_2_pad (ZeroPadding (None, 30, 30, 128)  0           conv3_block1_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147456      conv3_block1_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv3_block1_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block1_out (Add)          (None, 28, 28, 512)  0           conv3_block1_0_conv[0][0]        \n","                                                                 conv3_block1_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block2_preact_bn (BatchNo (None, 28, 28, 512)  2048        conv3_block1_out[0][0]           \n","__________________________________________________________________________________________________\n","conv3_block2_preact_relu (Activ (None, 28, 28, 512)  0           conv3_block2_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65536       conv3_block2_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block2_2_pad (ZeroPadding (None, 30, 30, 128)  0           conv3_block2_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147456      conv3_block2_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block2_out (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n","                                                                 conv3_block2_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block3_preact_bn (BatchNo (None, 28, 28, 512)  2048        conv3_block2_out[0][0]           \n","__________________________________________________________________________________________________\n","conv3_block3_preact_relu (Activ (None, 28, 28, 512)  0           conv3_block3_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65536       conv3_block3_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block3_2_pad (ZeroPadding (None, 30, 30, 128)  0           conv3_block3_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147456      conv3_block3_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block3_out (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n","                                                                 conv3_block3_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block4_preact_bn (BatchNo (None, 28, 28, 512)  2048        conv3_block3_out[0][0]           \n","__________________________________________________________________________________________________\n","conv3_block4_preact_relu (Activ (None, 28, 28, 512)  0           conv3_block4_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65536       conv3_block4_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv3_block4_2_pad (ZeroPadding (None, 30, 30, 128)  0           conv3_block4_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block4_2_conv (Conv2D)    (None, 14, 14, 128)  147456      conv3_block4_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv3_block4_2_bn (BatchNormali (None, 14, 14, 128)  512         conv3_block4_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block4_2_relu (Activation (None, 14, 14, 128)  0           conv3_block4_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","max_pooling2d_1 (MaxPooling2D)  (None, 14, 14, 512)  0           conv3_block3_out[0][0]           \n","__________________________________________________________________________________________________\n","conv3_block4_3_conv (Conv2D)    (None, 14, 14, 512)  66048       conv3_block4_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv3_block4_out (Add)          (None, 14, 14, 512)  0           max_pooling2d_1[0][0]            \n","                                                                 conv3_block4_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block1_preact_bn (BatchNo (None, 14, 14, 512)  2048        conv3_block4_out[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block1_preact_relu (Activ (None, 14, 14, 512)  0           conv4_block1_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131072      conv4_block1_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block1_2_pad (ZeroPadding (None, 16, 16, 256)  0           conv4_block1_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  589824      conv4_block1_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv4_block1_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block1_out (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_conv[0][0]        \n","                                                                 conv4_block1_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block2_preact_bn (BatchNo (None, 14, 14, 1024) 4096        conv4_block1_out[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block2_preact_relu (Activ (None, 14, 14, 1024) 0           conv4_block2_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262144      conv4_block2_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block2_2_pad (ZeroPadding (None, 16, 16, 256)  0           conv4_block2_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  589824      conv4_block2_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block2_out (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n","                                                                 conv4_block2_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block3_preact_bn (BatchNo (None, 14, 14, 1024) 4096        conv4_block2_out[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block3_preact_relu (Activ (None, 14, 14, 1024) 0           conv4_block3_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262144      conv4_block3_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block3_2_pad (ZeroPadding (None, 16, 16, 256)  0           conv4_block3_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  589824      conv4_block3_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block3_out (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n","                                                                 conv4_block3_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block4_preact_bn (BatchNo (None, 14, 14, 1024) 4096        conv4_block3_out[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block4_preact_relu (Activ (None, 14, 14, 1024) 0           conv4_block4_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262144      conv4_block4_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block4_2_pad (ZeroPadding (None, 16, 16, 256)  0           conv4_block4_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  589824      conv4_block4_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block4_out (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n","                                                                 conv4_block4_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block5_preact_bn (BatchNo (None, 14, 14, 1024) 4096        conv4_block4_out[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block5_preact_relu (Activ (None, 14, 14, 1024) 0           conv4_block5_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262144      conv4_block5_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block5_2_pad (ZeroPadding (None, 16, 16, 256)  0           conv4_block5_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  589824      conv4_block5_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block5_out (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n","                                                                 conv4_block5_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block6_preact_bn (BatchNo (None, 14, 14, 1024) 4096        conv4_block5_out[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block6_preact_relu (Activ (None, 14, 14, 1024) 0           conv4_block6_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262144      conv4_block6_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv4_block6_2_pad (ZeroPadding (None, 16, 16, 256)  0           conv4_block6_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block6_2_conv (Conv2D)    (None, 7, 7, 256)    589824      conv4_block6_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv4_block6_2_bn (BatchNormali (None, 7, 7, 256)    1024        conv4_block6_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block6_2_relu (Activation (None, 7, 7, 256)    0           conv4_block6_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","max_pooling2d_2 (MaxPooling2D)  (None, 7, 7, 1024)   0           conv4_block5_out[0][0]           \n","__________________________________________________________________________________________________\n","conv4_block6_3_conv (Conv2D)    (None, 7, 7, 1024)   263168      conv4_block6_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv4_block6_out (Add)          (None, 7, 7, 1024)   0           max_pooling2d_2[0][0]            \n","                                                                 conv4_block6_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block1_preact_bn (BatchNo (None, 7, 7, 1024)   4096        conv4_block6_out[0][0]           \n","__________________________________________________________________________________________________\n","conv5_block1_preact_relu (Activ (None, 7, 7, 1024)   0           conv5_block1_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524288      conv5_block1_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block1_2_pad (ZeroPadding (None, 9, 9, 512)    0           conv5_block1_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359296     conv5_block1_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv5_block1_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block1_out (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_conv[0][0]        \n","                                                                 conv5_block1_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block2_preact_bn (BatchNo (None, 7, 7, 2048)   8192        conv5_block1_out[0][0]           \n","__________________________________________________________________________________________________\n","conv5_block2_preact_relu (Activ (None, 7, 7, 2048)   0           conv5_block2_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1048576     conv5_block2_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block2_2_pad (ZeroPadding (None, 9, 9, 512)    0           conv5_block2_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359296     conv5_block2_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block2_out (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n","                                                                 conv5_block2_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block3_preact_bn (BatchNo (None, 7, 7, 2048)   8192        conv5_block2_out[0][0]           \n","__________________________________________________________________________________________________\n","conv5_block3_preact_relu (Activ (None, 7, 7, 2048)   0           conv5_block3_preact_bn[0][0]     \n","__________________________________________________________________________________________________\n","conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1048576     conv5_block3_preact_relu[0][0]   \n","__________________________________________________________________________________________________\n","conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block3_2_pad (ZeroPadding (None, 9, 9, 512)    0           conv5_block3_1_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359296     conv5_block3_2_pad[0][0]         \n","__________________________________________________________________________________________________\n","conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n","__________________________________________________________________________________________________\n","conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n","__________________________________________________________________________________________________\n","conv5_block3_out (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n","                                                                 conv5_block3_3_conv[0][0]        \n","__________________________________________________________________________________________________\n","post_bn (BatchNormalization)    (None, 7, 7, 2048)   8192        conv5_block3_out[0][0]           \n","__________________________________________________________________________________________________\n","post_relu (Activation)          (None, 7, 7, 2048)   0           post_bn[0][0]                    \n","__________________________________________________________________________________________________\n","flatten (Flatten)               (None, 100352)       0           post_relu[0][0]                  \n","__________________________________________________________________________________________________\n","dropout (Dropout)               (None, 100352)       0           flatten[0][0]                    \n","__________________________________________________________________________________________________\n","output_layer (Dense)            (None, 7)            702471      dropout[0][0]                    \n","==================================================================================================\n","Total params: 24,267,271\n","Trainable params: 702,471\n","Non-trainable params: 23,564,800\n","__________________________________________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"hYHaPzYc1gvV"},"source":["resnetv2TL.compile(loss='categorical_crossentropy',optimizer=opt, metrics=cmetrics)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mroPt5101gvV","executionInfo":{"status":"ok","timestamp":1610590107894,"user_tz":-480,"elapsed":1574623,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"27ce6bf7-96c7-4fd3-fa1b-cd8ea1cfee39"},"source":["t1 = time.time()\n","modelfit_resnet = resnetv2TL.fit(train_x, train_y, epochs=EPOCH, batch_size=BATCHSIZE, verbose=2, validation_data=(test_x, test_y) \n","                                 #callbacks=es\n","                                 )\n","# Testing has finished \n","t2 = time.time()\n","print( 'Time taken was {} seconds'.format( t2 - t1))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch 1/50\n","323/323 - 40s - loss: 31.5596 - tp: 3677.0000 - fp: 2765.0000 - tn: 35929.0000 - fn: 2772.0000 - accuracy: 0.5705 - precision: 0.5708 - recall: 0.5702 - auc: 0.7599 - val_loss: 42.5735 - val_tp: 1723.0000 - val_fp: 1042.0000 - val_tn: 15548.0000 - val_fn: 1042.0000 - val_accuracy: 0.6231 - val_precision: 0.6231 - val_recall: 0.6231 - val_auc: 0.7867\n","Epoch 2/50\n","323/323 - 27s - loss: 18.6172 - tp: 4556.0000 - fp: 1893.0000 - tn: 36801.0000 - fn: 1893.0000 - accuracy: 0.7065 - precision: 0.7065 - recall: 0.7065 - auc: 0.8382 - val_loss: 25.7242 - val_tp: 1780.0000 - val_fp: 985.0000 - val_tn: 15605.0000 - val_fn: 985.0000 - val_accuracy: 0.6438 - val_precision: 0.6438 - val_recall: 0.6438 - val_auc: 0.7998\n","Epoch 3/50\n","323/323 - 27s - loss: 13.8445 - tp: 4912.0000 - fp: 1535.0000 - tn: 37159.0000 - fn: 1537.0000 - accuracy: 0.7618 - precision: 0.7619 - recall: 0.7617 - auc: 0.8698 - val_loss: 55.4680 - val_tp: 1512.0000 - val_fp: 1253.0000 - val_tn: 15337.0000 - val_fn: 1253.0000 - val_accuracy: 0.5468 - val_precision: 0.5468 - val_recall: 0.5468 - val_auc: 0.7407\n","Epoch 4/50\n","323/323 - 28s - loss: 11.3942 - tp: 5169.0000 - fp: 1280.0000 - tn: 37414.0000 - fn: 1280.0000 - accuracy: 0.8015 - precision: 0.8015 - recall: 0.8015 - auc: 0.8911 - val_loss: 31.5672 - val_tp: 1886.0000 - val_fp: 879.0000 - val_tn: 15711.0000 - val_fn: 879.0000 - val_accuracy: 0.6821 - val_precision: 0.6821 - val_recall: 0.6821 - val_auc: 0.8207\n","Epoch 5/50\n","323/323 - 28s - loss: 9.4707 - tp: 5325.0000 - fp: 1123.0000 - tn: 37571.0000 - fn: 1124.0000 - accuracy: 0.8257 - precision: 0.8258 - recall: 0.8257 - auc: 0.9063 - val_loss: 43.9793 - val_tp: 1693.0000 - val_fp: 1072.0000 - val_tn: 15518.0000 - val_fn: 1072.0000 - val_accuracy: 0.6123 - val_precision: 0.6123 - val_recall: 0.6123 - val_auc: 0.7782\n","Epoch 6/50\n","323/323 - 28s - loss: 8.3085 - tp: 5448.0000 - fp: 1001.0000 - tn: 37693.0000 - fn: 1001.0000 - accuracy: 0.8448 - precision: 0.8448 - recall: 0.8448 - auc: 0.9173 - val_loss: 21.2596 - val_tp: 2005.0000 - val_fp: 760.0000 - val_tn: 15830.0000 - val_fn: 760.0000 - val_accuracy: 0.7251 - val_precision: 0.7251 - val_recall: 0.7251 - val_auc: 0.8468\n","Epoch 7/50\n","323/323 - 28s - loss: 6.6391 - tp: 5571.0000 - fp: 878.0000 - tn: 37816.0000 - fn: 878.0000 - accuracy: 0.8639 - precision: 0.8639 - recall: 0.8639 - auc: 0.9278 - val_loss: 16.1937 - val_tp: 2105.0000 - val_fp: 659.0000 - val_tn: 15931.0000 - val_fn: 660.0000 - val_accuracy: 0.7613 - val_precision: 0.7616 - val_recall: 0.7613 - val_auc: 0.8690\n","Epoch 8/50\n","323/323 - 28s - loss: 5.4863 - tp: 5741.0000 - fp: 708.0000 - tn: 37986.0000 - fn: 708.0000 - accuracy: 0.8902 - precision: 0.8902 - recall: 0.8902 - auc: 0.9410 - val_loss: 14.3545 - val_tp: 2113.0000 - val_fp: 651.0000 - val_tn: 15939.0000 - val_fn: 652.0000 - val_accuracy: 0.7642 - val_precision: 0.7645 - val_recall: 0.7642 - val_auc: 0.8712\n","Epoch 9/50\n","323/323 - 28s - loss: 5.0741 - tp: 5730.0000 - fp: 719.0000 - tn: 37975.0000 - fn: 719.0000 - accuracy: 0.8885 - precision: 0.8885 - recall: 0.8885 - auc: 0.9410 - val_loss: 43.0853 - val_tp: 1744.0000 - val_fp: 1021.0000 - val_tn: 15569.0000 - val_fn: 1021.0000 - val_accuracy: 0.6307 - val_precision: 0.6307 - val_recall: 0.6307 - val_auc: 0.7905\n","Epoch 10/50\n","323/323 - 28s - loss: 3.9622 - tp: 5854.0000 - fp: 595.0000 - tn: 38099.0000 - fn: 595.0000 - accuracy: 0.9077 - precision: 0.9077 - recall: 0.9077 - auc: 0.9513 - val_loss: 14.8670 - val_tp: 2129.0000 - val_fp: 636.0000 - val_tn: 15954.0000 - val_fn: 636.0000 - val_accuracy: 0.7700 - val_precision: 0.7700 - val_recall: 0.7700 - val_auc: 0.8745\n","Epoch 11/50\n","323/323 - 28s - loss: 4.0816 - tp: 5843.0000 - fp: 606.0000 - tn: 38088.0000 - fn: 606.0000 - accuracy: 0.9060 - precision: 0.9060 - recall: 0.9060 - auc: 0.9503 - val_loss: 16.1017 - val_tp: 2084.0000 - val_fp: 680.0000 - val_tn: 15910.0000 - val_fn: 681.0000 - val_accuracy: 0.7541 - val_precision: 0.7540 - val_recall: 0.7537 - val_auc: 0.8648\n","Epoch 12/50\n","323/323 - 28s - loss: 3.7819 - tp: 5861.0000 - fp: 588.0000 - tn: 38106.0000 - fn: 588.0000 - accuracy: 0.9088 - precision: 0.9088 - recall: 0.9088 - auc: 0.9523 - val_loss: 20.5545 - val_tp: 2026.0000 - val_fp: 739.0000 - val_tn: 15851.0000 - val_fn: 739.0000 - val_accuracy: 0.7327 - val_precision: 0.7327 - val_recall: 0.7327 - val_auc: 0.8499\n","Epoch 13/50\n","323/323 - 28s - loss: 3.3586 - tp: 5923.0000 - fp: 526.0000 - tn: 38168.0000 - fn: 526.0000 - accuracy: 0.9184 - precision: 0.9184 - recall: 0.9184 - auc: 0.9579 - val_loss: 14.9993 - val_tp: 2127.0000 - val_fp: 638.0000 - val_tn: 15952.0000 - val_fn: 638.0000 - val_accuracy: 0.7693 - val_precision: 0.7693 - val_recall: 0.7693 - val_auc: 0.8727\n","Epoch 14/50\n","323/323 - 29s - loss: 3.2983 - tp: 5941.0000 - fp: 507.0000 - tn: 38187.0000 - fn: 508.0000 - accuracy: 0.9212 - precision: 0.9214 - recall: 0.9212 - auc: 0.9580 - val_loss: 15.8086 - val_tp: 2121.0000 - val_fp: 644.0000 - val_tn: 15946.0000 - val_fn: 644.0000 - val_accuracy: 0.7671 - val_precision: 0.7671 - val_recall: 0.7671 - val_auc: 0.8703\n","Epoch 15/50\n","323/323 - 29s - loss: 3.0358 - tp: 5979.0000 - fp: 470.0000 - tn: 38224.0000 - fn: 470.0000 - accuracy: 0.9271 - precision: 0.9271 - recall: 0.9271 - auc: 0.9625 - val_loss: 14.2849 - val_tp: 2149.0000 - val_fp: 616.0000 - val_tn: 15974.0000 - val_fn: 616.0000 - val_accuracy: 0.7772 - val_precision: 0.7772 - val_recall: 0.7772 - val_auc: 0.8777\n","Epoch 16/50\n","323/323 - 28s - loss: 2.8329 - tp: 5985.0000 - fp: 464.0000 - tn: 38230.0000 - fn: 464.0000 - accuracy: 0.9281 - precision: 0.9281 - recall: 0.9281 - auc: 0.9626 - val_loss: 18.1734 - val_tp: 2113.0000 - val_fp: 652.0000 - val_tn: 15938.0000 - val_fn: 652.0000 - val_accuracy: 0.7642 - val_precision: 0.7642 - val_recall: 0.7642 - val_auc: 0.8695\n","Epoch 17/50\n","323/323 - 29s - loss: 2.6249 - tp: 6020.0000 - fp: 429.0000 - tn: 38265.0000 - fn: 429.0000 - accuracy: 0.9335 - precision: 0.9335 - recall: 0.9335 - auc: 0.9655 - val_loss: 15.1882 - val_tp: 2142.0000 - val_fp: 623.0000 - val_tn: 15967.0000 - val_fn: 623.0000 - val_accuracy: 0.7747 - val_precision: 0.7747 - val_recall: 0.7747 - val_auc: 0.8751\n","Epoch 18/50\n","323/323 - 29s - loss: 2.0888 - tp: 6074.0000 - fp: 375.0000 - tn: 38319.0000 - fn: 375.0000 - accuracy: 0.9419 - precision: 0.9419 - recall: 0.9419 - auc: 0.9706 - val_loss: 15.4859 - val_tp: 2128.0000 - val_fp: 637.0000 - val_tn: 15953.0000 - val_fn: 637.0000 - val_accuracy: 0.7696 - val_precision: 0.7696 - val_recall: 0.7696 - val_auc: 0.8748\n","Epoch 19/50\n","323/323 - 28s - loss: 2.1798 - tp: 6080.0000 - fp: 369.0000 - tn: 38325.0000 - fn: 369.0000 - accuracy: 0.9428 - precision: 0.9428 - recall: 0.9428 - auc: 0.9715 - val_loss: 15.6157 - val_tp: 2171.0000 - val_fp: 593.0000 - val_tn: 15997.0000 - val_fn: 594.0000 - val_accuracy: 0.7855 - val_precision: 0.7855 - val_recall: 0.7852 - val_auc: 0.8799\n","Epoch 20/50\n","323/323 - 28s - loss: 2.5138 - tp: 6052.0000 - fp: 397.0000 - tn: 38297.0000 - fn: 397.0000 - accuracy: 0.9384 - precision: 0.9384 - recall: 0.9384 - auc: 0.9672 - val_loss: 15.3580 - val_tp: 2173.0000 - val_fp: 592.0000 - val_tn: 15998.0000 - val_fn: 592.0000 - val_accuracy: 0.7859 - val_precision: 0.7859 - val_recall: 0.7859 - val_auc: 0.8814\n","Epoch 21/50\n","323/323 - 28s - loss: 1.9984 - tp: 6127.0000 - fp: 322.0000 - tn: 38372.0000 - fn: 322.0000 - accuracy: 0.9501 - precision: 0.9501 - recall: 0.9501 - auc: 0.9742 - val_loss: 27.3679 - val_tp: 1997.0000 - val_fp: 768.0000 - val_tn: 15822.0000 - val_fn: 768.0000 - val_accuracy: 0.7222 - val_precision: 0.7222 - val_recall: 0.7222 - val_auc: 0.8435\n","Epoch 22/50\n","323/323 - 28s - loss: 1.9182 - tp: 6115.0000 - fp: 334.0000 - tn: 38360.0000 - fn: 334.0000 - accuracy: 0.9482 - precision: 0.9482 - recall: 0.9482 - auc: 0.9734 - val_loss: 14.9385 - val_tp: 2168.0000 - val_fp: 597.0000 - val_tn: 15993.0000 - val_fn: 597.0000 - val_accuracy: 0.7841 - val_precision: 0.7841 - val_recall: 0.7841 - val_auc: 0.8797\n","Epoch 23/50\n","323/323 - 28s - loss: 1.6543 - tp: 6128.0000 - fp: 321.0000 - tn: 38373.0000 - fn: 321.0000 - accuracy: 0.9502 - precision: 0.9502 - recall: 0.9502 - auc: 0.9742 - val_loss: 15.2731 - val_tp: 2162.0000 - val_fp: 603.0000 - val_tn: 15987.0000 - val_fn: 603.0000 - val_accuracy: 0.7819 - val_precision: 0.7819 - val_recall: 0.7819 - val_auc: 0.8791\n","Epoch 24/50\n","323/323 - 28s - loss: 1.7668 - tp: 6150.0000 - fp: 299.0000 - tn: 38395.0000 - fn: 299.0000 - accuracy: 0.9536 - precision: 0.9536 - recall: 0.9536 - auc: 0.9760 - val_loss: 14.9406 - val_tp: 2176.0000 - val_fp: 589.0000 - val_tn: 16001.0000 - val_fn: 589.0000 - val_accuracy: 0.7870 - val_precision: 0.7870 - val_recall: 0.7870 - val_auc: 0.8836\n","Epoch 25/50\n","323/323 - 28s - loss: 1.4432 - tp: 6176.0000 - fp: 273.0000 - tn: 38421.0000 - fn: 273.0000 - accuracy: 0.9577 - precision: 0.9577 - recall: 0.9577 - auc: 0.9786 - val_loss: 17.5321 - val_tp: 2130.0000 - val_fp: 635.0000 - val_tn: 15955.0000 - val_fn: 635.0000 - val_accuracy: 0.7703 - val_precision: 0.7703 - val_recall: 0.7703 - val_auc: 0.8728\n","Epoch 26/50\n","323/323 - 28s - loss: 1.4832 - tp: 6164.0000 - fp: 285.0000 - tn: 38409.0000 - fn: 285.0000 - accuracy: 0.9558 - precision: 0.9558 - recall: 0.9558 - auc: 0.9778 - val_loss: 18.0555 - val_tp: 2152.0000 - val_fp: 613.0000 - val_tn: 15977.0000 - val_fn: 613.0000 - val_accuracy: 0.7783 - val_precision: 0.7783 - val_recall: 0.7783 - val_auc: 0.8783\n","Epoch 27/50\n","323/323 - 28s - loss: 1.6458 - tp: 6165.0000 - fp: 284.0000 - tn: 38410.0000 - fn: 284.0000 - accuracy: 0.9560 - precision: 0.9560 - recall: 0.9560 - auc: 0.9767 - val_loss: 15.1444 - val_tp: 2197.0000 - val_fp: 568.0000 - val_tn: 16022.0000 - val_fn: 568.0000 - val_accuracy: 0.7946 - val_precision: 0.7946 - val_recall: 0.7946 - val_auc: 0.8853\n","Epoch 28/50\n","323/323 - 28s - loss: 1.4280 - tp: 6211.0000 - fp: 238.0000 - tn: 38456.0000 - fn: 238.0000 - accuracy: 0.9631 - precision: 0.9631 - recall: 0.9631 - auc: 0.9814 - val_loss: 16.3127 - val_tp: 2183.0000 - val_fp: 582.0000 - val_tn: 16008.0000 - val_fn: 582.0000 - val_accuracy: 0.7895 - val_precision: 0.7895 - val_recall: 0.7895 - val_auc: 0.8832\n","Epoch 29/50\n","323/323 - 28s - loss: 1.3494 - tp: 6199.0000 - fp: 250.0000 - tn: 38444.0000 - fn: 250.0000 - accuracy: 0.9612 - precision: 0.9612 - recall: 0.9612 - auc: 0.9810 - val_loss: 15.1545 - val_tp: 2189.0000 - val_fp: 576.0000 - val_tn: 16014.0000 - val_fn: 576.0000 - val_accuracy: 0.7917 - val_precision: 0.7917 - val_recall: 0.7917 - val_auc: 0.8833\n","Epoch 30/50\n","323/323 - 28s - loss: 1.2368 - tp: 6209.0000 - fp: 240.0000 - tn: 38454.0000 - fn: 240.0000 - accuracy: 0.9628 - precision: 0.9628 - recall: 0.9628 - auc: 0.9814 - val_loss: 17.3244 - val_tp: 2116.0000 - val_fp: 649.0000 - val_tn: 15941.0000 - val_fn: 649.0000 - val_accuracy: 0.7653 - val_precision: 0.7653 - val_recall: 0.7653 - val_auc: 0.8695\n","Epoch 31/50\n","323/323 - 28s - loss: 1.2454 - tp: 6212.0000 - fp: 237.0000 - tn: 38457.0000 - fn: 237.0000 - accuracy: 0.9633 - precision: 0.9633 - recall: 0.9633 - auc: 0.9808 - val_loss: 14.9816 - val_tp: 2182.0000 - val_fp: 582.0000 - val_tn: 16008.0000 - val_fn: 583.0000 - val_accuracy: 0.7895 - val_precision: 0.7894 - val_recall: 0.7892 - val_auc: 0.8821\n","Epoch 32/50\n","323/323 - 28s - loss: 1.3130 - tp: 6197.0000 - fp: 252.0000 - tn: 38442.0000 - fn: 252.0000 - accuracy: 0.9609 - precision: 0.9609 - recall: 0.9609 - auc: 0.9800 - val_loss: 18.5388 - val_tp: 2128.0000 - val_fp: 637.0000 - val_tn: 15953.0000 - val_fn: 637.0000 - val_accuracy: 0.7696 - val_precision: 0.7696 - val_recall: 0.7696 - val_auc: 0.8702\n","Epoch 33/50\n","323/323 - 28s - loss: 1.2047 - tp: 6226.0000 - fp: 223.0000 - tn: 38471.0000 - fn: 223.0000 - accuracy: 0.9654 - precision: 0.9654 - recall: 0.9654 - auc: 0.9830 - val_loss: 16.9397 - val_tp: 2170.0000 - val_fp: 594.0000 - val_tn: 15996.0000 - val_fn: 595.0000 - val_accuracy: 0.7848 - val_precision: 0.7851 - val_recall: 0.7848 - val_auc: 0.8796\n","Epoch 34/50\n","323/323 - 28s - loss: 1.3087 - tp: 6208.0000 - fp: 241.0000 - tn: 38453.0000 - fn: 241.0000 - accuracy: 0.9626 - precision: 0.9626 - recall: 0.9626 - auc: 0.9815 - val_loss: 16.0654 - val_tp: 2208.0000 - val_fp: 557.0000 - val_tn: 16033.0000 - val_fn: 557.0000 - val_accuracy: 0.7986 - val_precision: 0.7986 - val_recall: 0.7986 - val_auc: 0.8868\n","Epoch 35/50\n","323/323 - 28s - loss: 1.0449 - tp: 6245.0000 - fp: 204.0000 - tn: 38490.0000 - fn: 204.0000 - accuracy: 0.9684 - precision: 0.9684 - recall: 0.9684 - auc: 0.9840 - val_loss: 20.1816 - val_tp: 2064.0000 - val_fp: 701.0000 - val_tn: 15889.0000 - val_fn: 701.0000 - val_accuracy: 0.7465 - val_precision: 0.7465 - val_recall: 0.7465 - val_auc: 0.8586\n","Epoch 36/50\n","323/323 - 28s - loss: 1.1682 - tp: 6219.0000 - fp: 230.0000 - tn: 38464.0000 - fn: 230.0000 - accuracy: 0.9643 - precision: 0.9643 - recall: 0.9643 - auc: 0.9821 - val_loss: 15.0175 - val_tp: 2197.0000 - val_fp: 568.0000 - val_tn: 16022.0000 - val_fn: 568.0000 - val_accuracy: 0.7946 - val_precision: 0.7946 - val_recall: 0.7946 - val_auc: 0.8846\n","Epoch 37/50\n","323/323 - 28s - loss: 0.8769 - tp: 6262.0000 - fp: 187.0000 - tn: 38507.0000 - fn: 187.0000 - accuracy: 0.9710 - precision: 0.9710 - recall: 0.9710 - auc: 0.9855 - val_loss: 15.2550 - val_tp: 2192.0000 - val_fp: 573.0000 - val_tn: 16017.0000 - val_fn: 573.0000 - val_accuracy: 0.7928 - val_precision: 0.7928 - val_recall: 0.7928 - val_auc: 0.8858\n","Epoch 38/50\n","323/323 - 28s - loss: 1.0691 - tp: 6236.0000 - fp: 213.0000 - tn: 38481.0000 - fn: 213.0000 - accuracy: 0.9670 - precision: 0.9670 - recall: 0.9670 - auc: 0.9831 - val_loss: 15.6913 - val_tp: 2185.0000 - val_fp: 580.0000 - val_tn: 16010.0000 - val_fn: 580.0000 - val_accuracy: 0.7902 - val_precision: 0.7902 - val_recall: 0.7902 - val_auc: 0.8855\n","Epoch 39/50\n","323/323 - 28s - loss: 0.8609 - tp: 6264.0000 - fp: 185.0000 - tn: 38509.0000 - fn: 185.0000 - accuracy: 0.9713 - precision: 0.9713 - recall: 0.9713 - auc: 0.9855 - val_loss: 20.8886 - val_tp: 2081.0000 - val_fp: 684.0000 - val_tn: 15906.0000 - val_fn: 684.0000 - val_accuracy: 0.7526 - val_precision: 0.7526 - val_recall: 0.7526 - val_auc: 0.8624\n","Epoch 40/50\n","323/323 - 29s - loss: 1.0890 - tp: 6238.0000 - fp: 211.0000 - tn: 38483.0000 - fn: 211.0000 - accuracy: 0.9673 - precision: 0.9673 - recall: 0.9673 - auc: 0.9835 - val_loss: 15.3155 - val_tp: 2195.0000 - val_fp: 570.0000 - val_tn: 16020.0000 - val_fn: 570.0000 - val_accuracy: 0.7939 - val_precision: 0.7939 - val_recall: 0.7939 - val_auc: 0.8852\n","Epoch 41/50\n","323/323 - 29s - loss: 0.9662 - tp: 6266.0000 - fp: 183.0000 - tn: 38511.0000 - fn: 183.0000 - accuracy: 0.9716 - precision: 0.9716 - recall: 0.9716 - auc: 0.9862 - val_loss: 16.2310 - val_tp: 2179.0000 - val_fp: 586.0000 - val_tn: 16004.0000 - val_fn: 586.0000 - val_accuracy: 0.7881 - val_precision: 0.7881 - val_recall: 0.7881 - val_auc: 0.8839\n","Epoch 42/50\n","323/323 - 28s - loss: 1.0094 - tp: 6268.0000 - fp: 181.0000 - tn: 38513.0000 - fn: 181.0000 - accuracy: 0.9719 - precision: 0.9719 - recall: 0.9719 - auc: 0.9858 - val_loss: 15.7899 - val_tp: 2187.0000 - val_fp: 578.0000 - val_tn: 16012.0000 - val_fn: 578.0000 - val_accuracy: 0.7910 - val_precision: 0.7910 - val_recall: 0.7910 - val_auc: 0.8824\n","Epoch 43/50\n","323/323 - 28s - loss: 0.9101 - tp: 6262.0000 - fp: 187.0000 - tn: 38507.0000 - fn: 187.0000 - accuracy: 0.9710 - precision: 0.9710 - recall: 0.9710 - auc: 0.9853 - val_loss: 15.4002 - val_tp: 2217.0000 - val_fp: 547.0000 - val_tn: 16043.0000 - val_fn: 548.0000 - val_accuracy: 0.8018 - val_precision: 0.8021 - val_recall: 0.8018 - val_auc: 0.8898\n","Epoch 44/50\n","323/323 - 28s - loss: 0.8665 - tp: 6282.0000 - fp: 167.0000 - tn: 38527.0000 - fn: 167.0000 - accuracy: 0.9741 - precision: 0.9741 - recall: 0.9741 - auc: 0.9874 - val_loss: 17.1660 - val_tp: 2162.0000 - val_fp: 602.0000 - val_tn: 15988.0000 - val_fn: 603.0000 - val_accuracy: 0.7819 - val_precision: 0.7822 - val_recall: 0.7819 - val_auc: 0.8796\n","Epoch 45/50\n","323/323 - 28s - loss: 1.0825 - tp: 6259.0000 - fp: 189.0000 - tn: 38505.0000 - fn: 190.0000 - accuracy: 0.9705 - precision: 0.9707 - recall: 0.9705 - auc: 0.9848 - val_loss: 17.3443 - val_tp: 2142.0000 - val_fp: 623.0000 - val_tn: 15967.0000 - val_fn: 623.0000 - val_accuracy: 0.7747 - val_precision: 0.7747 - val_recall: 0.7747 - val_auc: 0.8770\n","Epoch 46/50\n","323/323 - 28s - loss: 0.7923 - tp: 6283.0000 - fp: 166.0000 - tn: 38528.0000 - fn: 166.0000 - accuracy: 0.9743 - precision: 0.9743 - recall: 0.9743 - auc: 0.9870 - val_loss: 15.6650 - val_tp: 2191.0000 - val_fp: 573.0000 - val_tn: 16017.0000 - val_fn: 574.0000 - val_accuracy: 0.7928 - val_precision: 0.7927 - val_recall: 0.7924 - val_auc: 0.8844\n","Epoch 47/50\n","323/323 - 28s - loss: 0.8451 - tp: 6267.0000 - fp: 182.0000 - tn: 38512.0000 - fn: 182.0000 - accuracy: 0.9718 - precision: 0.9718 - recall: 0.9718 - auc: 0.9857 - val_loss: 16.1066 - val_tp: 2193.0000 - val_fp: 572.0000 - val_tn: 16018.0000 - val_fn: 572.0000 - val_accuracy: 0.7931 - val_precision: 0.7931 - val_recall: 0.7931 - val_auc: 0.8848\n","Epoch 48/50\n","323/323 - 29s - loss: 0.6801 - tp: 6303.0000 - fp: 146.0000 - tn: 38548.0000 - fn: 146.0000 - accuracy: 0.9774 - precision: 0.9774 - recall: 0.9774 - auc: 0.9890 - val_loss: 15.4266 - val_tp: 2202.0000 - val_fp: 563.0000 - val_tn: 16027.0000 - val_fn: 563.0000 - val_accuracy: 0.7964 - val_precision: 0.7964 - val_recall: 0.7964 - val_auc: 0.8880\n","Epoch 49/50\n","323/323 - 29s - loss: 0.8517 - tp: 6306.0000 - fp: 143.0000 - tn: 38551.0000 - fn: 143.0000 - accuracy: 0.9778 - precision: 0.9778 - recall: 0.9778 - auc: 0.9885 - val_loss: 14.8524 - val_tp: 2212.0000 - val_fp: 553.0000 - val_tn: 16037.0000 - val_fn: 553.0000 - val_accuracy: 0.8000 - val_precision: 0.8000 - val_recall: 0.8000 - val_auc: 0.8898\n","Epoch 50/50\n","323/323 - 29s - loss: 0.9114 - tp: 6293.0000 - fp: 156.0000 - tn: 38538.0000 - fn: 156.0000 - accuracy: 0.9758 - precision: 0.9758 - recall: 0.9758 - auc: 0.9872 - val_loss: 19.5507 - val_tp: 2149.0000 - val_fp: 616.0000 - val_tn: 15974.0000 - val_fn: 616.0000 - val_accuracy: 0.7772 - val_precision: 0.7772 - val_recall: 0.7772 - val_auc: 0.8759\n","Time taken was 1430.5135748386383 seconds\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"p_SeNpIPjYtZ"},"source":["##Resnet50-V2 -Graph Performance"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kdUohOz3O7v4","executionInfo":{"status":"ok","timestamp":1610590107898,"user_tz":-480,"elapsed":1574609,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"703c1c2c-b5af-4e8f-da69-4a11eb531f84"},"source":["print(modelfit_resnet.history.keys())\r\n","plt.plot(modelfit_resnet.history['accuracy'])\r\n","plt.plot(modelfit_resnet.history['val_accuracy'])\r\n","plt.title('ResNet50-V2 accuracy with average 1300 per class')\r\n","plt.ylabel('accuracy')\r\n","plt.xlabel('epoch')\r\n","plt.legend(['train', 'test'], loc='upper left')\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dict_keys(['loss', 'tp', 'fp', 'tn', 'fn', 'accuracy', 'precision', 'recall', 'auc', 'val_loss', 'val_tp', 'val_fp', 'val_tn', 'val_fn', 'val_accuracy', 'val_precision', 'val_recall', 'val_auc'])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydd3ib1bnAf6/3iLeT2LETxxlkkElCApSQsEoYZZfdQgcpXZeWVWiBUmh7aW8v65ZSWgqUPctOKRASNiF772ln2I7j7Xif+8f5ZCuOZMu2ZNnS+3sePZK+eb5Pn8573nnEGIOiKIoSvkQEuwGKoihKcFFBoCiKEuaoIFAURQlzVBAoiqKEOSoIFEVRwhwVBIqiKGGOCgJF6QYiUi0iIzpYv1NETuvNNil9CxG5S0SeCXY7fCEsBIHzpzzk/Hn3i8iTIjKgh8e8RkSMiNzSbnmhiMzxYf/hzv5RbsvmiEiL007X62q39eki8pqI1IjILhG5wsux/yoiT3lYPllE6p3jXC0iy0Sk0mnzH93bonSMMWaAMWY7gPM8/TbYbepriMg9IrJGRJpE5K5260521pWLSKnzXOe4rY8Vkced53O/iNzQbv9TRWSjiNSKyEIRyeulywpJwkIQOHzDGDMAmAJMBW7zwzEPAreISJIfjuVir9PJuF7/dFv3MNAADAauBB4RkaM9HOOfwIUikthu+beAt40xB4EE4GdAJjATOBW4yY/X4TdUQPmHINzHrcAtwDse1q0HzjDGpAJDgC3AI27r7wJGA3nAydj/2VwAEckE/gXcAaQDS4EXA3MJbYT0c2iMCfkXsBM4ze37H4F33L4fB3wOlAOrgDlu664BtgNVwA7gSrflnwJvAb92277QtT9W0N4KbANKgZeAdGfdbsAA1c7reGAOUOjlGhKxQuAot2VPA/d62X4T8G2375HAXuA8L9vfALzVwT18ECgAKoFlwKx2x/6lc51VzvqhzrqjgfexQrMI+KWz/Engt27HOOzand/sF8BqoB6IcruXVdiO5IJ2bbwW2OC2/hjgZuDVdts9BDzo4Rq/434PsJ3Ty27fC4ApzmcDjALmAY3Ob1Pt2t9p/01O+yuwHVWcl3s7EvjQeUYOAM8Cqc66XwCvePgtHnI+pwD/APYBe4DfApFuz+hnwP3OsX/b0bmcfY4BVjj38GWn3e6/0znASux/5XNgkg//v2eAuzpYHwv8N7Debdle4Otu3+8BXnA+zwM+b/ffOASM7eD/f5vzTJQBT7j/Fh1dk6fn0MPxvT3jdwHPuG33MrDfeR4+Bo52W3eW074q53e8yVmeCbzttO0g8AkQ0dU+sNPfyN8H7Isv3AQBkAuswekIgBznT3EWtuM+3fk+0HnAKoExzrbZrh+PNkEwxXm4XB28uyC4HvjSOWcs8CjwvLNuOLYziXJr5xxsh1KEFTr3A4nOuqlAbbvrugkvnTfwK+ADt+9nACVAtJftX8eLUHHWXwVkYDvkG50HOs5Zd7NzT8cAAkx2tk3CdlA3AnHO95nOPk/SuSBYCQwF4p1l38SOHiOAS4EaINtt3R7gWKcNo7CjyWxnO1fHGgUUA9M8XOMI7B8uwjnPLlebnHVlOH9C57cb5ela3Nr/lXOcdKyAus7LvR2Ffe5isc/dx8ADzro8oBZIcr5HOvf0OOf7a9jnKhEY5JzzB27PaBPwU+e64zs5V4xzzdcD0cCF2Ofxt27PYDFWg4wErnauM7aT/59HQQAMc+53C1aYXuMsT3Pu72C3bS8G1jifHwQeaXestcBFHfz/1zrPUjpWOPp0TXh4Dtsdu6Nn/C4OFwTfddbHAg8AK93W7cMZXDnXf4zz+b+Bvzq/RzQwCxC/95H+PmBffDk/ZjVW2hpgAYePuJ5ut/1/nAci0XlQL2r/EDh/sk+dzy8Bf3A+uwuCDcCpbvtkOw98FJ4FQRYwHtsR5WP/pI8662YB+9u14VpgkZdrHuacK9f5/iweRsFuD2ghkNmFe1oGTHY+b8KDpgFcDqzwsv+TdC4IvttJG1a6zuv8Ztd72e7fwLXO53NwG3l62LYAOyq+DPgbtmMdi9UW3nTbzhdBcJXb9z8Cf/Xx3p7vft+wA45vO59PB7Y5nwdjR6nxbtteDix0e0Z3+3ou4CSsMJV253Z1mo8A97TbfxMwu5NzdKYRpGP/hy7hNtS5v+6j9tOBnc7nf9Bu0ILt3K/xcvyduAlh7KBvmy/X1Nlz2MkzfhdugqDdulTnGlOc77uBHwDJ7ba7G3jD9awF6hVOPoLzjTFJ2A5nLFblAjvi+qbjtCoXkXLgROxIswY78rwO2Cci74jIWA/HvhP4oYgMbrc8D3jN7bgbgGbsH/gIjDH7jTHrjTEtxpgdWPvqRc7qaiC53S7JWOGGiKxzczDPMsbsxgqSqxzH+PmAJwfy+dhRx5nGmAOe2uVsd5OIbBCRCudaUmi7h0OxJpv2eFvuKwXt2vBtEVnpdj8n+NAGsD6Tq5zPV2FNat74CPuMnOR8XgTMdl4fdbH9+90+1wIeAxREZLCIvCAie0SkEttxZrpt8hy2wwG4wvkO9vmKxj6brnvyKFYzcNH+HnZ0riHAHuP0QB72zwNubPdfGers122M9Vn9E3jDscNXO6vcn/fWZ51O/gtecL+OXbS12ZdrOuwetsOnZ1xEIkXkXhHZ5tz3nc4q172/CCugdonIRyJyvLP8f7C+lvdEZLuI3NrZubpDOAkCAIwxH2FHcH9yFhVgNYJUt1eiMeZeZ/v/GGNOx47mNwJ/93DMjVjn1a/arSrAdrDux44zxuzBjgY6bS5tv9FmIEpERrutnwysc9pwtGlzMH/irP8n1kF8EbDDGLPM/eCO8+3vWEf6Gm+NEJFZWKF0CZBmrIOvAmuCcV3nSA+7FmBNKp6owTqsXWR52Kb1HjlRIX8HfgJkOG1Y60MbwJq9JonIBKxG8KyX7aBNEMxyPn9E54LAl9+yI37vHGOiMSYZK6zEbf3LwBwRyQUuoE0QFGA1gky35yvZGOMeQNC+bR2dax+QIyLu5x7q9rkA+F275znBGPN8dy/cjSisAEs2xpQ5bZnstr71WXfeW9c5QREj3dZ7wv06hmF9EODbNXX0+3b0jLtzBXAecBp2EDXc1XwAY8wSY8x52HvwOtbKgDGmyhhzozFmBHAucIOInOrD+bpE2AkChweA00VkMnZE9A0ROcOR2nFOGGeuM3o6z3nQ6rEjkRYvx/wN1nyQ6rbsr8DvXKFtIjJQRM5z1pU4x2p9iJyQujyxDAXuxaqFONrJv4C7RSRRRL6GfbA6Gt2+in3of4MVCq2IyCnYDvEiY8xXHd0srF2zyWlzlIjcyeEjsseAe0RktNP2SSKSgXVyZYvIz5xwwCQRmenssxI4S2woaxY2gqkjErF/yBKn/d/BagTubbhJRKY5bRjluu/GmDrgFWwH+pWjLXnjI2yUSrwxphDrnJuL9Xms8LJPEb51Bt5Iwj5bFU4I5c3uK40xJVjN5AmsQN/gLN8HvAf8r4gki0iEiIwUkdndPNcXWI31JyIS5TyrM9zW/x24TkRmOvc4UUTO9hY1JyLRIhKH7WeinP9WpLPuQhEZ47R5IHAf1sRy0Nn9KeB2EUlztPBrsQM4sH6RCSJykXP8O4HVzoDMGz92/tPp2AGbK8qoS9fkgY6ecXeSsH1IKXYA9Hu3+xQjIleKSIoxphHrl2xx1p3jPMuCHXw1470P6j6BtDv1lRftooZMm23wVefzTGwHcBDb0byD7UCzneUVWF/BImC8s881OD4Ct2P+BdtZzXG+R2CjcTZh1dZtwO/dtr/bOV85NnLpBqyNthY70ngIx0nobJ+OHS3UYG2KV/hw7U9iO/Eh7ZYvdJZXu73+7eUYkcDj2Ad0H1Y7aL2nzvrbsQ7uKmAJbb6JCVifTBnWVHKrszwO+2esxEZk/JwjfQTtf7PfOb/RAWzH8RHwfbf11zn3uhqrLUx1W3ei89t8x4d7tg94wu370vb3hsN9BKNpizp53VP76dhefDQ20qraOc6NtIsew2p2Bri53fIU7LNciH1OVwCXdfCMdnguYLqzvBqrifwLuMNt/Vzn9y137tPLuD2jHp490+51jbPup87zUuM8Fy8AeW77xtL2zBUBN7Q79mlYDf0Q9n85vJP/vytqqBw7KErw5Zra/45eju/tGW/9zbFmwTew/49dwLddzxDWSf+us3+l05YTnf1+7rShxvmN7+ioLd19iXMyRQlpRGQYtuPIMsZUBrs9/QURWYx1cj8R7LZ0FxHZiR0wfBDstvRVwtU0pIQRIuLSzF5QIdAxIjJbRLIc09DVwCTsaFUJYUI3U05RaHUkFmHV8blBbk5/YAzWUZmITaS82FhfhBLCqGlIURQlzFHTkKIoSpjT70xDmZmZZvjw4cFuhqIoSr9i2bJlB4wxAz2t63eCYPjw4SxdujTYzVAURelXiMgub+vUNKQoihLmqCBQFEUJc1QQKIqihDn9zkfgicbGRgoLC6mrqwt2UwJKXFwcubm5REdHB7spiqKEECEhCAoLC0lKSmL48OEcXjgxdDDGUFpaSmFhIfn5+cFujqIoIURImIbq6urIyMgIWSEAICJkZGSEvNajKErvExKCAAhpIeAiHK5RUZTeJyRMQ4qiKP2J0up6lu8up66x2b6aWqh3PrcYyE2LZ8TAAeRnJpISH3ifoAoCP1BeXs5zzz3Hj370oy7td9ZZZ/Hcc8+Rmpra+caKovRrjDEs21XG01/u4t9r9tPQ7Nv8MhmJMeRnJpKfmcjF03KZOSLD721TQeAHysvL+ctf/nKEIGhqaiIqyvstnj9/fqCbpiiKD7S0GLYfqGbpzjKW7iqj4GAtU4alcuKoTKbnpRMfE3nEPnWNzSzdWcYnW0r4fFspcdERjMtObn2NGZxEfEwk1fVNvLZiD89+uYuN+6tIio3iipnDOGdSNinx0cRFRxIbFUFsdCRx0dZaX3DwEDsO1LDjQDU7DtSwraSGRZtLOH6k/4UAqCDwC7feeivbtm1jypQpREdHExcXR1paGhs3bmTz5s2cf/75FBQUUFdXx/XXX8+8efOAtnIZ1dXVnHnmmZx44ol8/vnn5OTk8MYbbxAfHx/kK1OUnuMaCRtg6tBUoiI7dk3WNzXz2dYDbCmq5rgRGUzMSSEiwrt/rK6xmU+3HGDBxmIO1tTT2GxobG6hoamFxuYWGpsNMVERpMRHkxofTXJ8NKkJ0aTER1Pb0MyyXWUs311GeW0jAGkJ0QxNT+DxT3fw6EfbiYmM4Jg8KxSmDktjw75KPt5ygMXbS6lvaiE6UjhmWBpNLYZXlxVS09AMgAjkZyRSVFlHTUMz47OT+e8LJ3LelCEkxHTc9Y4aNIBRgwYAg4+4l4Eg5ATBb95ax/q9/p17ZPyQZH79jaO9rr/33ntZu3YtK1euZNGiRZx99tmsXbu2Nczz8ccfJz09nUOHDnHsscdy0UUXkZFxuGTfsmULzz//PH//+9+55JJLePXVV7nqqqv8eh2K4g+MMRRV1jM4ObbDAAZjDB9tLuGhBVtYvrscgNSEaOYcNZBTxg1m9lEDW+3fNfVNLNpUwrvr9rNwYzHV9U2txxmYFMspYwZxyrhBnDgqk8TYKKrrm1i4sZh31+1n0cZiahqaSYqNIictnujICKIjhejICBJjo4iKEBqbDcVVdWwuqqLiUCNVdW3HHzkwkTPGZzFteBrT8tIYkZmIiFDb0MRXOw7y2dYDfLa1lD+9t/mwfa6YOYyTRg9k5oj01o69pcVQWHaI9fsq2eC8puWlcfnMYUwdmtrjgI9ABYyEnCDoC8yYMeOwWP+HHnqI1157DYCCggK2bNlyhCDIz89nypQpAEybNo2dO3f2WnsVxRdqG5p4fcVenvpiJxv3VzE4OZZTxg7ilLGD+dqojNbO0BjDhxuLeWjBFlYVVpCTGs89508gPSGGBRuLWLSphNdX7iUyQjh2eBoDYqP4eMsBGppaSE+M4ZxJ2cydkMW47GQ+33aABRuKmb92Hy8uLSAmMoJx2Uls2F9FQ1MLmQNiOG9qDnOPzuK4ERnERPkWCNnU3EJlXRORIqQkeHbGJsREMWfMIOaMGQRYB+/qPRUcNTiJnFTP2npEhDAsI4FhGQnMnZDV9ZscJEJOEHQ0cu8tEhMTWz8vWrSIDz74gC+++IKEhATmzJnjMRcgNja29XNkZCSHDh3qlbYq4UtNfRMfbizm32v3UV7byMScFCblpjIpN4XctPjW0eeu0hqe/mIXLy0toLKuiXHZydx8xhjW7a3grVX7eP6rAmKiIjhhZAYz8zN4Z81e1u6pJDctnnsvnMiFx+S2dtBnT8qmucWwsqCcDzcWsWBDMYVlh7hixjDmTsji2OHpRLqZgS6YmssFU3NpbG5h6c4yFmwoYvnuMq6amceZE7M4ZljaYdv7SlRkBOmJMV3aJ2NALCc7QiHUCDlBEAySkpKoqqryuK6iooK0tDQSEhLYuHEjX375ZS+3TunrFFXW8ciibewpP8Sw9ATyMhIYlp7A8IzEVlNHdX0Te8sPsbf8EPsq6thXfoiiynoOOSGH9U0trWGIjU0t5KbFMyYriaMGJzEmK4n8zESiIyOoqW9iwcZi5q/ex8JNxdQ3tTAoKZaslDie+GxnayRLemIME3NSAPh4SwmRIsydkMXVJwxnel5aq5BoaGphyc6DLNhQ3Draz8tI4I8XT+KCqTlEe/AHREYI0/KsGebmM8b6dI+iIyM4fmRGwJyl4Y4KAj+QkZHB1772NSZMmEB8fDyDB7c5eObOnctf//pXxo0bx5gxYzjuuOOC2FKlL1Fe28AjH23jn5/vpKnZMDwzkU+2lFDX2BZWGBkhJERHUuVmMweIEDtCTYyJtFEn0ZHEOQ7RqAhh+4EaFmwsprnFOhejI4W8jEQKDta2dv6XzxjGWROzmZZnR9UNTS1s3F/J6sIKVheWs7qwgqq6Jn56ymiumDGMrJS4I64hJiqCr43K5GujMrnjnHEUVdaTOSCmU4ew0rfod3MWT58+3bSfmGbDhg2MGzcuSC3qXcLpWvs6lXWNbCmqIislnuzkuA4jW9ypqW/iic928OjH26mub+L8KTn87LTR5GUkYoyhuKqeXaW17CqtYffBWqrqmshKiWNIajxDUuLITo1ncFKsT9E320tq2FxUxab9VWwpriYnNZ6zJ2UzbViaz+1VQgMRWWaMme5pnWoEitIFdhyoYcEGa9tesvMgTc6IOyYqgqFp8eRlJDIsPYGh6QlERQjNLYYWY2huMTQbQ019Ey8uKeBAdQOnjRvMTWccxdis5NbjiwiDk+MYnBzHjPz0HrU1NiqyNaZdUTpCBYGidEBtQxNLd5bx8eYSPtxYzPYDNQAcNXgA1540gmOGpVFcVcfu0lo7ij9Yy+Ltpa2x5J44fkQGj35rDNPy0nrrMhSlQ1QQKIobh5wEoy+3l/LF9lJWFZTT1GKIiYzguJEZXH3CcE4ZO4ih6Qlej2GMoby2kRZjiIwQIiKESBEiI+zLkwNVUYKJCgIlJFm3t4L/fW8zibFR5KXbuO7hGYnkZSQwKCmWmoZmdh6oYfuBGnaU2FT+7Qdq2LCvksZm24FPyk3h2pNGcNyIDKbnpZEY69vfRURI62JooqIEExUESsixYEMRP31+BfHRkSTGRjF/zb7W6BmAmMiIwwp+icCQlHhGDEzkuyfmc/yIDKYPT2eAjx2/ovR39ElXQop/fr6T37y1jqOHpPCPq6czKDmOxuYW9pQdYtfBWnaX1lBQdoiU+GhGDkwkP3MAeRkJxEUfWVRMUcIFFQR+oLtlqAEeeOAB5s2bR0KCd5tzOGGMoaahmfLaBsprG6k4ZF/ltY0kx0cx+6iBJMUdWRKgucVwz9vrefLznZw+fjAPXjalteRBdGQEwzMTGZ6ZCAzs5StSlL6PCgI/4K0MtS888MADXHXVVSoIgEWbirnrzXXsLK31uk1MZAQnjs5k7tFZnDZ+MOmJMdTUN3H9Cyv4YEMx3z8xn9vOGtetsgOKEq6oIPAD7mWoTz/9dAYNGsRLL71EfX09F1xwAb/5zW+oqanhkksuobCwkObmZu644w6KiorYu3cvJ598MpmZmSxcuDDYlxIUiirruPut9byzZh8jBiZy65ljSU+IaS0X7CoZXFh2iP+s3c+/1+7nw43FRPwLZuZnUFbbwOaiKu4572i+dfzwYF+OovQ7Qk8Q/PtW2L/Gv8fMmghn3ut1tXsZ6vfee49XXnmFr776CmMM5557Lh9//DElJSUMGTKEd955B7A1iFJSUrjvvvtYuHAhmZmZ/m1zP6C5xfD0Fzv503ubaWxu4aavH8W1J40gNsqzvT47JZ5jh6fzq7PHsW5vJe+u3c+76/ZTWl3PP64+lpPHhmZBMEUJNKEnCILMe++9x3vvvcfUqVMBqK6uZsuWLcyaNYsbb7yRX/ziF5xzzjnMmjUryC0NLqsKyrn99bWs2VPBrNGZ/Pb8CeRlJHa+IzY8c0JOChNyUrjpjDEYYwJWp11RwoHQEwQdjNx7A2MMt912Gz/4wQ+OWLd8+XLmz5/P7bffzqmnnsqdd94ZhBYGj7rGZt5du5/nFu/mq50HGZgUy5+vmMrZE7N71JGrEFCUnhF6giAIuJehPuOMM7jjjju48sorGTBgAHv27CE6OpqmpibS09O56qqrSE1N5bHHHjts31A2DW0trub5r3bz6vJCymsbyctI4Bdzx3LlccNI9hABpChK76KCwA+4l6E+88wzueKKKzj++OMBGDBgAM888wxbt27l5ptvJiIigujoaB555BEA5s2bx9y5cxkyZEi/dRZX1zexwpnztfxQIxW1Da0hnzsO1LB0VxlREcIZR2dxxcxhHD8iQytfKkofQstQ9zP60rXuLq3lyc938tLSgsPmmAWIj44kJT6azKQYzp44hIun5TIwKdbLkRRFCTRahlrxG8YYvtheyhOf7eSDDUVEinD2pGwunpZLVnIcKU6op7fIH0VR+h4qCJROqa5vYo0za9VrK/awcX8V6Ykx/HjOKL51fB6Dk4+cuUpRlP5DyAiCcAgh7C0z3tbiKj7dcsBOWbingm0l1bhOPS47mT9cNJHzpuRofR5FCRFCQhDExcVRWlpKRkZGyAoDYwylpaXExQVu9N3U3MKfF27l/z7cSnOLYWBSLJNzU/jGpCFMGprCpJwUMgaonV9RQo2QEAS5ubkUFhZSUlIS7KYElLi4OHJzcwNy7N2ltfzsxRUs313OhVNzuOmMMWSnxIWsYFUUpY2QEATR0dHk5+cHuxn9EmMM/1q+h1+/uQ4RePCyKZw3JSfYzVIUpRcJCUGgdI+K2kZ++foa3lm9jxn56dx3yWRy07QKqqKEGyoIQoSSqnqW7TrIkNR4ctMSSEuIPsKsU9vQxLq9lawqKGd1YQWfbztAeW0jN58xhutmj9TSzYoSpqggCAEam1v4/lNLWVVQ3rosMSaS3LQEctPiSY6PZsO+SjYXVeGasTE7JY7peen8cM5IJg9NDVLLFUXpCwRUEIjIXOBBIBJ4zBhzb7v1ecDj2GmjDgJXGWMKA9mmUOT/FmxhVUE5d593NFnJcRSWHXJetRSWHWLDvkqOykri60dnMTk3hYm5KQxK0th/RVEsARMEIhIJPAycDhQCS0TkTWPMerfN/gQ8ZYz5p4icAvw38K1AtSkUWbbrIH9euJULj8nh2zopi6Io3SAigMeeAWw1xmw3xjQALwDntdtmPPCh83mhh/VKB1TXN/GzF1cyJDWe35x7dLCboyhKPyWQgiAHKHD7Xugsc2cVcKHz+QIgSUQy2h9IROaJyFIRWRrquQJd4a4317Gn7BD3XzrF44TuiqIovhBIQeALNwGzRWQFMBvYAzS338gY8zdjzHRjzPSBAwf2dhv7JPPX7OOVZYX8aM4ojh2eHuzmKIrSjwmks3gPMNTte66zrBVjzF4cjUBEBgAXGWPKUTpkf0Udv3xtDZNyU7j+tNHBbo6iKP2cQGoES4DRIpIvIjHAZcCb7huISKaIuNpwGzaCSAHW7qngqx0H2Vt+iOaWtmJzLS2Gm19ZRX1jCw9cOoXoyGArdYqi9HcCphEYY5pE5CfAf7Dho48bY9aJyN3AUmPMm8Ac4L9FxAAfAz8OVHv6E7tLazn/4c9ocgRAVIQ4iWLxREdG8MmWA/z+gomMGDggyC1VFCUUCGgegTFmPjC/3bI73T6/ArwSyDb0Rx5csIXICOHhK4+htLqhNR+goKyWLcXVnDdlCJfPGNr5gRRFUXxAM4v7GFuLq3ltRSHfOzGfM47OCnZzFEUJA9TA3Md4cMEW4qIjuW72yGA3RVGUMEEFQR9i4/5K3lq1l2tOGK4TwCiK0muoIOhD3P/+ZpJio5h30ohgN0VRlDBCBUEfYU1hBf9ZV8T3ZuWTmhAT7OYoihJGqCDoI9z3/iZSE6L57ok605qiKL2LCoI+wLJdZSzcVMK8k0aQrDWDFEXpZVQQ9AHue38TmQNiuOaE4cFuiqIoYYgKgiDz+bYDfLa1lOtmjyQhRtM6FEXpfVQQBBFjDPe9t5nBybFcdVxesJujKEqYooIgiLy5ai9Ld5Xxk5NHERcdGezmKIoSpqggCBIFB2u5/bW1TMtL4/IZw4LdHEVRwhgVBEGgucVww0srMcADl04hSktJK4oSRNQ7GQQeWbSVJTvLuP/SyQxNTwh2cxRFCXN0KNrLrCwo5/4PtnDu5CGcP6X9FM6Koii9jwqCXqSmvomfvbCCrOQ47jl/AiIS7CYpiqKoaag3ufut9ew6WMsL1x5HSrxmECuK0jdQjaCX+Peafby4tIAfzRnJzBEZwW6OoihKKyoIeoH9FXXc+q81TMpN4WenHRXs5iiKohyGCoIA09JiuPHllTQ0tfDApVOI1lBRRVH6GNorBZh/fLqDz7aW8utvjGfEwAHBbo6iKMoRqCAIIOv2VvDH/2zkjKMHc+mxQ4PdHEVRFI+oIAgQhxqauf6FlaQlxHDvhZM0VFRRlD6Lho8GiN/P38DW4mqe/t4M0hJ16klFUfouqhEEgAUbinj6y118/8R8Zo0eGOzmKIqidIgKArLhWqIAACAASURBVD9TUlXPLa+sZmxWEjfPHRPs5iiKonSKmob8iDGGW15ZRXV9E8/PO47YKJ1jQFGUvo9qBH7k2cW7WbiphF+eNY6jBicFuzmKoig+oYLAT5TXNvA//9nECSMz+PbxOu2koij9BxUEfuKhBVupqmvkzm+M11BRRVH6FSoI/MDOAzU8/eVOLpk+lLFZycFujqIoSpdQQeAH7v33RqIjI7jh61pQTlGU/ocKgh6yeHsp767bzw9nj2RQUlywm6MoitJlVBD0gJYWw+/mbyArOY7vzxoR7OYoiqJ0CxUEPeDNVXtZXVjBzWeMIT5GcwYURemfqCDoJnWNzfzx3Y1MyEnmgqk6Cb2iKP0XFQTd5B+f7mBvRR2/Oms8EREaLqooSv/FJ0EgIv8SkbNFpEuCQ0TmisgmEdkqIrd6WD9MRBaKyAoRWS0iZ3Xl+MGipKqevyzcyunjB3P8SJ1/WFGU/o2vHftfgCuALSJyr4h0Wk1NRCKBh4EzgfHA5SIyvt1mtwMvGWOmApc55+nzPPDBZuqbWrjtzLHBboqiKN3BGPjyr/D2DdDSEuzWBB2fis4ZYz4APhCRFOBy53MB8HfgGWNMo4fdZgBbjTHbAUTkBeA8YL37oQFXBlYKsLdbV9GL1DY08a/le7h4Wq5OPamENk31ULUf0kKsZEpDLbz5U1j7iv2eMRKO/3Hw2tPSAkVrYPsi+yrdBle+AgN7Ly/J5+qjIpIBXAV8C1gBPAucCFwNzPGwSw5Q4Pa9EJjZbpu7gPdE5KdAInCal3PPA+YBDBs2zNcmB4QPNhRzqLGZ89VBrIQy9VXwzEWwbxXcsAES0oPdIv9QtgtevBL2r4VT7oDCpfDBb2DUaTCwl8rGGwNlO2Dnp7BtIez4CGpL7bqBY+FQGfz7ZvjW69BL5Wp8EgQi8howBnga+IYxZp+z6kURWdqD818OPGmM+V8ROR54WkQmGGMO09WMMX8D/gYwffp004Pz9Zi3V+1lUFIsxw4PkT+G0rtsnA9xyTDsBIjoo7Ea9dXw7CVQsNh+3/UZjPuGb/uu/Rc0N0BaPqQNhwGD/N+Z1VdDbDe08e2L4OXvQEszXPkyjD4dqorgL8fBa9fB996HyABU5m9pgZINsOvztlf1frtuQBaMOh1Gngz5syE5Gxb/zQqCDW/C+PP83x4P+HrVDxljFnpaYYyZ7mWfPYD7jO25zjJ3vgfMdY7zhYjEAZlAsY/t6lUq6xpZtLmEK2cOI1IjhZSusuoFeO0H9nPqMJh8BUy+DNLzve/TUAstTVZ49AYNNfDcpVYIXPh3eOt62P6Rb4KgdBu88p3Dl0XFW4GQNtyaOrImQdZEyBgFEd3Ivdm/Bh6dDUOmwIwfwNHnQ1Rsx/sYA188DO/fAZlHwWXPWXMQQNJgOOc+ePka+PR+mH1z19tkDHz+f7BvpTWnNTdCcz00Ndj30m1QV+6cbwgMPxHyToC8r1ktpL2gnP5dWP5PePeXVkjEJHS9TV3EV0EwXkRWGGPKAUQkDbjcGNORc3cJMFpE8rEC4DKsw9md3cCpwJMiMg6IA0q6cgG9yfvrimhoauGcSUOC3RSlv1HwlbVLD58FU78Fq56Dj/4AH91rO4QpV9jOsWQTHNjsvG+C8gJrlvnpcohP9e1cjYcgOr7rbWyohecvg92fWyEw8WIrvHZ87Nv+Wz+w799+w3aCZTvbXge32/UtjjsxKh4GjbNCYdIltnP0hdUv2Y6zrhJemwfv3Q7Tv2M7z6Sstu1qDsCeZdb0s/NTe03jvgHnPwKx7eYKOfoC2PCW/S2OOgOyJ/nWFhdL/2GFTOowiBkAkTFWOEXGQHSqPW/eCfaVmte5hhQZBWf9DzxxJnx6H5xye9fa0w3EmM4tLSKy0hgzpd2yFU60T0f7nQU8AEQCjxtjficidwNLjTFvOlFEfwcGYB3Htxhj3uvomNOnTzdLl/bEGtV9vvPEV2wuqubTX5yspaYV3ykvgL+fbDuJaz9ss7dXFNqOduVzcHBb2/ZRcZAx2o6gk3Pg84fgpFvglF91fq6i9fDYaXDybXDCT31vY2OdFQLbF8EFj8LkS+3yzx6E9++EGzZas0VHPPtNO/r9r+We1zc1WCG3f43zWg37VluN5+YtEJPY8fGNgQcnQ+ZouOJl2L4QFj8KW96z2sW4bwBiBUD5LruPRMCg8VbzOu7H3s1xtQetiSghE+Yt7FzLcLFvlb3f+bPhipf8a+579VpY/zr86Ms2DaYHiMgybxYcXzWCSBER40gNJzQ0prOdjDHzgfntlt3p9nk98DUf2xBUymsb+GTLAb53Yr4KgWBQUwq7Pm1Tt5vqrS26qd6OBCdf5v9zHiqDta/ajtq02JFuXErXjlFfDc9fbtt5zTuHO11TcuGkm2DWjXbkeuigNV2kDjvcbFK+G778C8y8DhI7yVt573ZorIEFd8PIU2Dw0Z23saneOlC3L4Lz/9ImBMB2cAA7P7Ejd2801sGOT+CYb3vfJioGsibYF5c7x/0MnjzL+k4mfbPjdu5bZTv4k26yHe6oU+3r4Hb46jFY+YwVtjnT4NjvQc50yJ7smz8hIR3O/T947hJYdC+c9uvO96mrhJeutsLjgkf97/P5+j2waT68extc+ZJ/j90OXwXBu1jH8KPO9x84y8KGd9fup6nFqFkoGNSUwmOnWBODN4YdZ+3QnWGMNck01Fj7bOYYO/J2dfDNTbDtQ1j5rP0TNjfAwHFQugVe/T5c/oLvtu2WFusTKF5nR7DeolJEYOix3o8z5zZY/wZ8/iCcfrf37bZ+ANsWWMGy/Cl77u9/aDtgbzQ3WQfq1g/g3D9bE5U7WRMhLtX6CToSBLs/h6ZDNvqmKww7HlKGwuoXOxcE698AiYSx5xy+PH0EzP09nPG7njmmjzrDmu0+ewDGnNXxb2IMvPVfVkhf807nAro7JGXBnFutcN/0LoyZ6/9zOPgqCH6B7fx/6Hx/H3gsIC3qo7y9eh/DMxKYkKMTz/QqTfXwwhU2nv3yF62K7G6DPbAZHj/DmgN8EQQVBbDov49cPiDLmhwObIbqIohPt3bnKVdYB+fSx+GdG+DDe+C0u3xr+8Lfwsa3Ye69MLqLHaQ7g8bCxG/aaJLjfmwdnO1paYb37rDROrNvhSHH2FH+x3/0bmM2BubfCJvegbP+BMd868htIiIhf5YNcTTGe0e75QOIjIXhXVTwIyLstX32IFQX2ygjb21d/zrkn+Q9lNUfmvoZv7dC77V5cOmzMLh9DqzD0sdh3Wv2Wcg7vufn9cbM62D50/DuL2DEHIgOTKl7n3QZY0yLMeYRY8zFzutRY0xzQFrUBzlQXc/n2w5wzqQhahbqTYyBN34CBV9aJ9+YubazTsuzo6WEdGsGiIyFwmW+HbNwiX3//ofWAXvZ8/bPPPIU62TNPdZ2ADdugjP/YE0LItbUMP27NrJkzSudn2f1S/DJ/8K0a+yfuafMudVqJ5/e73n9iqeheD2c/hurAYw7ByZfDp/c5/3efPw/sOxJq0HMuNb7ufNnWwFatsP7Nls/sM7Qzuz8nph0KZhma4bzRtE6awIKdDhlXDJc+KjVQh85AV77oR31u7NvlTXXjDodTrg+sO2JjLbPYdlOG5kUIHytNTRaRF4RkfUist31Clir+hj/XrOPFgPnTO7EWRaulGy2Tk8fAg+6xEd/hDUv2cSfCRd63iYy2nbWe3wVBEttxEr2JKtdjD0LTvw5XPAIXLsALnvWdqKezClz/2Dj/9/4Cexd6fn4jXW2g33jxzZC6Kw/+WekmjESplxuR6IV7aKw66vgw99ZM8u4c93ae68VmK9fZ4WcO8ufhoW/s8LilDs6PrfLT+Ateqh8t41w6qpZyMWgsfY3XP2i923Wv2Edv+3NQoEg7wS4fqV1tq99Ff5vmu34aw5Yv8DL10BCBlzw197JBRl5shWAn/zvkULJT/h6FU8AjwBNwMnAU8AzAWlRH+St1fsYPWgAYwYndb5xuLHiWXj0JGuPfsfHui3G2FFq1X7v26x5BRb93sbaz7qx4+PlTrejtGZPlU7aUbgEhky1AqSrRMXAJU9BYqY1V1W7pbsYAxvfgYdnwIe/tfbmS57q3nm8cdIt1mn9yZ8OX/7Zg1BTDF9vZyOPT7UO0AObYcE9bcs3v2fzA0aeatd3JqgyR1vT2faPPK/fusC+d1cQgNUK9q6wgwpPrH/DhtkOGNj9c3SFhHTrrP2vFbZti/8KD06Bp8612ckXP26fg97i67+zgnDzfwJyeF8FQbwxZgE23HSXMeYu4OyAtKiPsb+ijiU7D6pZqD0NNTYb840f2Y545g/taPWNH1t7tdf9auH1H1rn733jbMjhutfsSNrF7sXw+o/sH/8bD3beUeVMs47K4vUdb9dUbwVGrrccSB8YMNBqDbUH4aVv2yimks22HMMLV9j4/W+/AZc+4/+yDGl51o6//GnbGYHVDj7/M0y4GHKnHbnPqFNh+vds1NHOT60Afvlq6wS+5J++CSoRGDHbagSetL6tH0Bybs9KNEy4yHZ0azxExxRvsBpHL2XZHkZKDpz3ZxvCOXKOFVan3hFYv4AnUodaodSRCa8H+OosrndKUG8RkZ9gE8TCouLaO2v2YfqDWai5CRqqID4t8OcqWm87kwNbrGNy9i32TxyfZkfxTXVw4d+O7GRKt9nOs2hd2yh/1QtW1Y5LtZ3B6NOt6SUlx3amHUW8uMhxOsA9y6yJwRv711g7e24H0SC+kD0Zzn8YXvmudVTvXw3RidYUc+z3/asFtGfWTVYL+/iPcN7D1nltWjoOdzz9bhsJ9doPbWhp4kBbYqF9YlVH5M+2ppvi9YeHpDY3Wk1h4kU9M4ElZVln6OoX4eRfHX6s9W8A4nuZi0AwcIx9HmsO9K4m4I6nIAE/4asguB5IAP4LuAdrHro6UI3qS7y9ei/js5MZ2dcrjX78PzaN/rvvOnHa3cQY+Nc8qNxjY9oHjml7T86xTsn5t9hO5Nuv2z+vizm/sCPi9++wo+9vPtGWmLPxHatBRETayoquKJqTf2UjUlY+b0M2l/7DCoUrXvZ9RJ023NpsC5dZh643XI7ingoCsEKraL212069Ck79de+YLVJy7DV+9Tfr4F71PHztZzb3wBuxA6yz/Ykz7T391mveo3O8kX+Sfd/+0eGCoOArOwDpiVnIxaTLbLTO7i8PH3Gvf8P6P9wzh4NFsIRAgOlUEDjJY5caY24CqoHvdLJLyFBwsJYVu8u5ZW4vVSXsCaVb7B/yuUtt9mp3Rw/7Vln1PH2kHbm7aqSAdbI2HbKdwoWPeT7H1/7LCoP5N9lM1Uuesp3lp/db2/wlTx3eaUVE2g5t5ClQ9yebWDT4aMgc5XubRaxW0JnDuHCJNWF0liHrK6fcbssX93ZlzhN/bqN9XvmeFYCzbuh8n7zjrQBOHda9LNXUoTZef8fHcPyP2pZv/QAiotoERU8YezZEJ1itwCUISjZbLWTuH3p+fMUrnQoCY0yziPhYCCS0eGeNLbJ6zsReTCI7VG47tKJ1VhXuqCCZO1VFNjGnttR2wNe8071iVatesPH53//AmnpqStrq3pRstu2ZMa/jpKoZ19oyCW/+FO47GuorYNp3rOmkozjouBQbGdMdcqbBlvdtVIe3Am2FS3rmH2iPSHDKMycNtvf484dsspmv2c49yWUA29mv/Zc1Q7qqdG79AIbO7HrGtSdiB9iooHWv2ZDJqFjY8IZdF0yzUBjgq2lohYi8CbwM1LgWGmP+FZBW9RHeXr2XyUNTGZYRwOp/xRtt+r6rQFbplrZ11UU2W9IXqvdDzjFWvX7hChsyePGTXQtva26ENS/DmDPbOrgBg+wrf5bvxwHr1IyOh/d/DWfee2TGqr/JmQ4YWwHS0+i0qsiG3vkjpr8vMOc2K/x6I5zSRf5sq4nsW2kFalWR9Y+cemenu/rMpEutRrrlfRvGu/4NK2hSdP6PQOJrLxEHlAKnAN9wXr34BPY++yvqWLunkrMmBNAuuWeZTVqZf5MNwcscbWO6v/2GrVJY2YUJ26qKbIjf2LPg67+1f6AP7+l8P3e2vA+1B2zIpj+YeDHcsC7wQgCsEAQrTD2xx1nuD/9AXyAmwZZgDkT9fG+0+gkW2fdtH9p3f/gHXIyYA4mDrHmodJt18AcjWijM8HWqyrDxC7hYvMPOGHTCyAA6h5Y/ZdXfH35mSwO4R0qkDO04zt6dhhrrH3DZ7I//MZRutSVsM0ZaZ6YvrHrORpSMOrVr19EXSEi3NmxvfoKCryAi2paLULpHYiYMnmD9BCfdZM1CiYNg8ET/nSMyyg4gljzW5stwT5JTAoKvM5Q9gS0TfRjGmA5CNPo3i3ccJCk2ivFDAlRbqPGQtbeOO9d2YO1JyvI9W7a6yL4PcLQXEVvPvGynTRxKHda5M6/2oC1sNWNeYMMfA0nONBsr74nCpTabOEC1WsKG/Nk2squhxha4O2qu/7NrJ11i8x4+e8j+pqlDO99H6RG+/oJvA+84rwXYCeerA9WovsDi7aVMH54WuJnINr4D9ZXezSZJWVYj8KVsQ5UjCNyjeCKj4ZtP2slOXvyWrYnfEWtftZOGBKKcc2+RMx2q9h1pUmtugr3LQ8csFEzyT7J5IosftWW6/WkWcpE9xYYsm2Y1C/USvhade9Xt9SxwCeDH8Iu+xYHqeraV1DAjPwClZV2sfM6af4Z7ccImZdtQzbqKzo/lPv+pO/GpcPnzNolq/k0dC5WVz1m1v6uzM/UlXIll7f0ExeuhsVYFgT/IO8GWgv70AUBgxMn+P4eIrYEkkWoW6iW6q9ONBrqYkdJ/+GrHQQBmjghQaGDlXju70uTLvavVruQZX/wErRqBB8d2+gibtLX5XVvG1xMlm+yIeXI3Qzf7ClkTrR+gvUmtNZEsZMcuvUdcsnXM11fY90DU4Qc44b/gR1/4Hj6t9Ahfq49WiUil6wW8hZ2jICRZvL2U+OhIJub4ITbaE6tesGUBOoqZT3KSnqp9EATV+21ST7wXwTXzOqtuz7/FqvPtWfmcHX11NPFIfyA6zmZVHyEIlloneGpecNoVariqkQbCLOQiMqpntYuULuGraSjJGJPs9jrKGNNB8fD+zeIdB5mWl0Z0ZABKzBpjO95hx3t2ErvoqkYwYLB37SIyCs59yCabvd+uJk1Lsw3VG3Va18sO9EVyptnCYO6F7wqXWLOQFg30D2PPskmHmuQVMviqEVwgIilu31NF5PzANSt4lNc2sKmoipn5ATILuZLGOoutbxUE+zo/ZvV+Kwg6InuyLQ2w/J92nlgX2xfZc3Q3o7evkTMdGqpt6WWw0VClW9Qs5E9ypsFte6wpTgkJfB3y/toY0+q1NMaUAz7M7tz/WLKzDGNgRqAEwcpnbc2e8Z3I0ZhEiE3pmkbQGXNus+aRt663ReHAFi2LS4Gjzux8//5Ae4fxnuX2XR3F/sWXqrBKv8FXQeBpu15Maew9Fm8vJSYqgslDU/1/8MY6mzsw/lzv9XDcScryXSPwpchcTCKcc78dIX/yv7Yuz4a3bSXNUImvzxhlBajLT1C4xJbIHjI1uO1SlD6Mr535UhG5D3jY+f5jwMdsp/7F4h0HmTo0lbjoDoqqdZdN79hoC19LLrhyCTqiudHa/tuHjnpj1Km2nssn99mkoKZD/isp0ReIiICcqW0lJQqXwKDxXau9ryhhhq8awU+BBuBF4AWgDisMQoqqukbW7a0InH9g5XO2DPJwH0v2JmV3rhG4pkvsStnpM35vKz1+8WdbbjrU7Oc50+1cAQ01ViCE2vUpip/xtdZQDXBrgNsSdJbuKqPFwMwRAYiNrtxri3TNutH3lHz37GJvES/eksk6IjHTCoPXf2idxKEWTZMzzWalrnnFJuSpf0BROsTXWkPvA990nMSISBrwgjHmjEA2rrdZvP0gURHCMcMCMN3j6hdt7kBXkraSsm1W8KEy73XvPZWX8IXJl9u8gxGzu7Zff8DlMF78qH1XQaAoHeKraSjTJQQAjDFlhGBm8Vc7SpmUm0J8jJ/9A67cgaHHdW12KF9CSLujEYDVAsbMtXMGhBpJg235juJ11nGcMTrYLVKUPo2vgqBFRFrnFxSR4XioRtqfqW1oYnVhRWDMQnuW2bj2rtbld2UXdyQIqooACY1kMH/i0gpyp/m/OqaihBi+Rg39CvhURD4CBJgFzAtYq4LAit3lNLWYwDiKd31u37s6m5Qv2cXV++28tf21dHSgyJlmayupWUhROsVXZ/G7IjId2/mvAF4HDgWyYb3N4u2lRAhMywuAf6CiEGKTu16gyxfTUFWR52Jz4c5wZ5ptf0yqrighjq/O4u8D1wO5wErgOOAL7NSVIcGXOw4yISeFpLgAjKwrCiAlt+v7RcXaCeQ70wh8ySoON3KOgZ+v17luFcUHfDWeXg8cC+wyxpwMTAXKO96l/1DX2MzKgnJmDA9Q/kB5gXVedoek7I4FgWoE3lEhoCg+4asgqDPG1AGISKwxZiMQMjViVxWU09DUEhhHMUDF7u5Pt9dRmYmWFqgpVkexoig9wldncaGIpGJ9A++LSBmwK3DN6l2+2nEQEQKjEdRV2qSm7piGwGoEJZs8rzt0EFqauh46qiiK4oavzuILnI93ichCIAV4N2Ct6mUW7zjImMFJpCQEwj9QaN+7bRpysotbWo4Mg3SZjLqaTKYoiuJGlwOsjTEfGWPeNMY0BKJBvU1jcwvLdpVxXMDMQs6k8anDOt7OG0nZtlxC7YEj13U3mUxRFMWNsM+0Wb+3kkONzYGbf8AlCHqiEYBnP0F3y0soiqK4EVBBICJzRWSTiGwVkSOK1onI/SKy0nltFpFej0QqLLPpEPmZiYE5QXmBnVC9uyGerdnFHiKHVCNQFMUPBGxyGRGJxM5fcDpQCCwRkTeNMetd2xhjfu62/U+xYam9SnFVHQCDkwM0MUtFgQ1j7G6Zg840gthkiEnofvsURQl7AqkRzAC2GmO2O/6EF4DzOtj+cuD5ALbHI0WV9URHCmmBcBRDz3IIoE2T8KYRaDKZoig9JJCCIAcocPte6Cw7AhHJA/KBD72snyciS0VkaUlJiV8bWVxVx6CkOCRQNfkrCrvvKAZbQyhxoHeNQJPJFEXpIX3FWXwZ8IoxptnTSmPM34wx040x0wcOHOjXExdX1jMwKdavx2ylqcF24N3NIXDhbcpK1QgURfEDgRQEewB3m0ius8wTlxEEsxBYjWBwcoAEQeUewPTMNASep6w0xk5TqRqBoig9JJCCYAkwWkTyRSQG29m/2X4jERkLpGGL2PU6RZX1DEoKlKPYSSbrbnkJF540gvoqaKzV8hKKovSYgAkCY0wT8BPgP8AG4CVjzDoRuVtEznXb9DLstJe9PtFNXWMzFYcaA6cR9DSHwEVSth39Nze1Lat2cgg0dFRRlB4SsPBRAGPMfGB+u2V3tvt+VyDb0BElVfUAgdMIyh1BkNzDKphJWYCxBeaSh9hlWl5CURQ/0VecxUHBlUMwKGAawW7rzI3uoaDxNGWlagSKoviJsBYERZXd1AjevxMWP9r5dhWFPTcLgecpK1UjUBTFT4S1ICiudGUVd1EjWP0SLH+68+3KC3ruKAYvGsF+iIyFuNSeH19RlLAmrAVBUVU9URFCWkKM7zu1NFvHbckGaKrvYLsWRyPoYQ4B2IQyiWinERRZbSBQiXCKooQNYS0IiivrGZQUS0REFzrTmgO2LHRLExSv72C7Emiuh5QeZBW7iIi0vob2GoH6BxRF8QPhLQiq6hjY1WJz1W6j8n2rvG/nrxwCF+1zCVwagaIoSg8Jb0FQWc/grpaXcO+M9632vl3FbvvuD9MQHDmJfXWRagSKoviFsBYERVV1XQ8ddZlnUvM61gjK/ZRM5sJ9EvvGOqgrV41AURS/ELaCoL6pmfLaRgZ3NXTUNSvY6K9D0drDs33dqSi0cwXE+ymqJykbakutg7o1h0AFgaIoPSdsBUGxK4egOxpBQgbkHgtNdVC6xfN2FT2ch6A9rlyC6iJNJlMUxa+EryBwlZfosrO4yI7OsyfZ797MQ+UF/vMPwOFTVmoymaIofiR8BYGTTDaoy87ifXZ0njEaouK9C4KK3f6LGILDp6xUjUBRFD8SvoLA0Qi6PFdxlRO/HxkFWRM8Rw7VVUJdhZ9NQ+00AomAxEz/HV9RlLAlbAVBUWUdURFCeneyil2j86xJsH+1zSJ2x985BADx6RAR7WgE+yFxkE00UxRF6SFhKwiKq+wUld3KKnYJguzJUF8JZTsO385f8xC4ExHRllSmyWSKoviRsBUERZV13fMPwOGCAKxW4E4gBIHrvC4fgfoHFEXxE2ErCEqq6rsXMQRt9vpB4yAi6kiHcXkBRMb4P87fpRFUq0agKIr/CFtB0CONwNXBR8VaYdBeEFQU2FnJIvx8e5OyoXKvLWinGoGiKH4iLAVBfVMzZbWNNmLIGBv548uUyVUeMnqzJx+5v79zCFwMGGx9EqZFNQJFUfxGWAqCtrmKY2HJY/DoLNj9Rec7Vu2DhEyIcos0ypoMtQfsSN1FRSGk+qH8dHtcJinQ8hKKoviNsBQErhyCvIgD8P6v7cKSjZ3vWLW/zVHswuUwdpmHmhqswPC3oxgOP7eahhRF8RPhKQgq6wDDxBV32Bm+ImPh4PbOd6z2IAiyJgDSFjlUuQcw/s0hcOGuEahpSFEUPxGegqCqnksjFzFgz6dw+t2QPgIO7uh8R08aQUwiZI5u0whcyWSB8BEcphGoIFAUxT+EpSCoKdnF7VHPYPJOhGnfgfT8zjWClmbv8fvZk90EQYByCADi06z2Ep9mI5YURVH8QPgJAmOYs/n3REkLct7/2RBPl0bQUeRQzQEnWseLIKjcY7dpnZAmABqBiD2/+gcURfEj4ScIVr/EuOoveSbxaisAwGoETYcO54B9TQAACw1JREFUnwqyPa1ZxdlHrstyK0ldsduabQI1Yh80HgaNDcyxFUUJS6KC3YBepaoI/n0L6yLH8dXAi7nWtdwlEA5uh2QPHT24ZRV70gjcBUFhYMxCLi5+3GoGiqIofiJ8NAJj4J0boPEQd5jrGJiS0LbOXRB4o32dIXfi0+wcxvtXW9NQICKGXMQkQHR84I6vKErYET6CYP3rsPFtmmbfyvLagYfPVZyca2sGdSgIHLNR4iDP67Mnwd4VgdcIFEVR/Ez4CILYJBhzNkUTrEHosLmKI6PsiL59OWl3qvYfmVXsTvZkKNsJzfUqCBRF6VeEj49g1Gkw6jSKd5cBMLj9pPXpIzrXCDw5il1kT2n7HEjTkKIoip8JH43AoajSVWeoXQnqzkJIq/Z1nM3rihwC1QgURelXhJ0gKKlyJq0/QiPIt5U9a0s971hd5NlR7CJpcFt8v2oEiqL0I8JOEBRV1hMhkJHowTQEns1DrqzijkxDYP0EsckQl+KfxiqKovQC4eMjcCiuqmNgUiyR7ecqbhUEO2DojMPXubKKO6vvM+sGOLDFf41VFEXpBcJOEBRV1h/pHwA7f4BEeNYIOsoqdmfYcfalKIrSjwioaUhE5orIJhHZKiK3etnmEhFZLyLrROS5QLYHbOXRIyKGwJaESM71IgicHILOBIGiKEo/JGAagYhEAg8DpwOFwBIRedMYs95tm9HAbcDXjDFlIuIlW8t/FFfWMWVoqueV3qqQVrsEgZZ+VhQl9AikRjAD2GqM2W6MaQBeAM5rt821wMPGmDIAY0xxANtDY3MLpTUNnjUC8J5L4NIIdA4ARVFCkEAKghygwO17obPMnaOAo0TkMxH5UkTmBrA9bnMVe/ARgBUEhw7CofLDl7uyiiOjA9k8RVGUoBDs8NEoYDQwB7gc+LuIHGG3EZF5IrJURJaWlJR0+2SuuYq9awT59r19qYnOsooVRVH6MYEUBHsA98yqXGeZO4XAm8aYRmPMDmAzVjAchjHmb8aY6caY6QMHDux2g4oqnWSyjjQCONI8VLWv42QyRVGUfkwgBcESYLSI5ItIDHAZ8Ga7bV7HagOISCbWVOTDLPLdo1ONIG24fW8vCKqL1FGsKErIEjBBYIxpAn4C/AfYALxkjFknIneLyLnOZv8BSkVkPbAQuNkY46XGQ88prqyzWcUDvAiCmERrAjq4s22Zr1nFiqIo/ZSAJpQZY+YD89stu9PtswFucF4Bp7iynswBHrKK3UlrF0JaU+J9rmJFUZQQINjO4l6lqKruyGJz7WkfQtoaOqqCQFGU0CSsBEFxZf3hM5N5Ij3fJpA11NjvmlWsKEqIE16CwFeNAGzxOXDLKlaNQFGU0CRsBIErq9hr6KiL9rkEVfsBgQEBr36hKIoSFMJGEByorscYDxPStCfNEQQuP0HVPkjUrGJFUUKXsBEExc4UlZ36COJTISHDTRAUqaNYUZSQJnwEgavOUGcaARweOaRZxYqihDhhIwhc5SUGJ3eiEYCTS7DTfq7ar4JAUZSQJmwEQUxkBCMGJpKRGNP5xukjoKIAGmqhplgFgaIoIU3YTFV5ybFDueTYoZ1vCE4IqYE9SzWrWFGUkCdsNIIu4col2PWFfddkMkVRQhgVBJ5w5RLs+sy+a9SQoighjAoCTyRkQGwyFC6x39U0pChKCKOCwBMiVitorEWzihVFCXVUEHjD5SfQrGJFUUIcFQTecAkCNQspihLiqCDwhqvmkDqKFUUJcVQQeEM1AkVRwgQVBN5QQaAoSpgQNpnFXSYpC06+HcafF+yWKIqiBBQVBN4Qgdk3B7sViqIoAUdNQ4qiKGGOCgJFUZQwRwWBoihKmKOCQFEUJcxRQaAoihLmqCBQFEUJc1QQKIqihDkqCBRFUcIcMcYEuw1dQkRKgF3d3D0TOODH5vQXwvW6IXyvXa87vPDluvOMMQM9reh3gqAniMhSY8z0YLejtwnX64bwvXa97vCip9etpiFFUZQwRwWBoihKmBNuguBvwW5AkAjX64bwvXa97vCiR9cdVj4CRVEU5UjCTSNQFEVR2qGCQFEUJcwJG0EgInNFZJOIbBWRW4PdnkAhIo+LSLGIrHVbli4i74vIFuc9LZhtDAQiMlREForIehFZJyLXO8tD+tpFJE5EvhKRVc51/8ZZni8ii53n/UURiQl2WwOBiESKyAoRedv5HvLXLSI7RWSNiKwUkaXOsh4952EhCEQkEngYOBMYD1wuIuOD26qA8SQwt92yW4EFxpjRwALne6jRBNxojBkPHAf82PmNQ/3a64FTjDGTgSnAXBE5DvgDcL8xZhRQBnwviG0MJNcDG9y+h8t1n2yMmeKWO9Cj5zwsBAEwA9hqjNlujGkAXgBCcjJiY8zHwMF2i88D/ul8/idwfq82qhcwxuwzxix3PldhO4ccQvzajaXa+RrtvAxwCvCKszzkrhtARHKBs4HHnO9CGFy3F3r0nIeLIMgBCty+FzrLwoXBxph9zuf9wOBgNibQiMhwYCqwmDC4dsc8shIoBt4HtgHlxpgmZ5NQfd4fAG4BWpzvGYTHdRvgPRFZJiLznGU9es518vowwxhjRCRkY4ZFZADwKvAzY0ylHSRaQvXajTHNwBQRSQVeA8YGuUkBR0TOAYqNMctEZE6w29PLnGiM2SMig4D3RWSj+8ruPOfhohHsAYa6fc91loULRSKSDeC8Fwe5Pf/f3v2ESlnFYRz/PmqGf0IpXCkmagsRRAmEUkGUWkiICy1JRVq7aRGEogSC26KFoAsFxato4TXXqVx0ISklGtUqWtgiNyUoGKGPi3OGbvfe4OJt7uSc57OZmTMvL+cH7zu/9z1n3t/pCkkvUJLAgO3ztbmJ2AFs/wFcAd4A5krqXOj14/G+Btgs6RfKUO8G4HP6P25s/1pf71ES/2omeJy3kghuAK/VfxRMB7YDF3vcp8l0Edhd3+8GvuphX7qijg8fA360/emwr/o6dknz6p0AkmYAb1HmR64AW+tmfRe37b22F9heRDmfL9veQZ/HLWmWpJc674G3ge+Z4HHezJPFkjZRxhSnAsdtH+pxl7pC0hlgPaUs7W/AJ8AF4BywkFLC+13bIyeUn2uS1gJXgTv8PWa8jzJP0LexS1pBmRycSrmwO2f7oKTFlCvll4HvgJ22/+xdT7unDg19ZPudfo+7xjdYP04DTts+JOkVJnCcN5MIIiJibK0MDUVExL9IIoiIaFwSQURE45IIIiIal0QQEdG4JIKISSRpfadSZsT/RRJBRETjkggixiBpZ63zf0vS0VrY7YGkz2rd/0uS5tVtV0q6Lum2pMFOLXhJSyV9XdcK+FbSkrr72ZK+lPSTpAENL4gU0QNJBBEjSFoGvAessb0SeAzsAGYBN20vB4YoT20DnAQ+tr2C8mRzp30AOFzXCngT6FSHXAV8SFkbYzGlbk5Ez6T6aMRoG4HXgRv1Yn0GpYjXE+Bs3eYUcF7SHGCu7aHafgL4otaDmW97EMD2I4C6v29s362fbwGLgGvdDytibEkEEaMJOGF77z8apQMjtnvW+izDa988Judh9FiGhiJGuwRsrfXeO+vBvko5XzqVLd8Hrtm+D/wuaV1t3wUM1VXS7kraUvfxoqSZkxpFxDjlSiRiBNs/SNpPWQVqCvAXsAd4CKyu392jzCNAKft7pP7Q/wx8UNt3AUclHaz72DaJYUSMW6qPRoyTpAe2Z/e6HxH/tQwNRUQ0LncEERGNyx1BRETjkggiIhqXRBAR0bgkgoiIxiURREQ07ilQdDpAfoFoKAAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nLonE3C3beZ_","executionInfo":{"status":"ok","timestamp":1610590107903,"user_tz":-480,"elapsed":1574592,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"d3d2b84a-00e7-4d4d-de93-6c78fa51fc5b"},"source":["print(modelfit_resnet.history.keys())\r\n","plt.plot(modelfit_resnet.history['loss'])\r\n","plt.plot(modelfit_resnet.history['val_loss'])\r\n","plt.title('ResNet50-V2 loss with average 1300 per class')\r\n","plt.ylabel('loss')\r\n","plt.xlabel('epoch')\r\n","plt.legend(['train', 'test'], loc='upper left')\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dict_keys(['loss', 'tp', 'fp', 'tn', 'fn', 'accuracy', 'precision', 'recall', 'auc', 'val_loss', 'val_tp', 'val_fp', 'val_tn', 'val_fn', 'val_accuracy', 'val_precision', 'val_recall', 'val_auc'])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"dkbHXpe19c7W"},"source":["##InceptionV3"]},{"cell_type":"code","metadata":{"id":"G1scGR1sL2SB"},"source":["image_input=(224,224,3)\n","num_classes=7\n","EPOCH=50\n","BATCHSIZE=20"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YZpDVauV9iDz","executionInfo":{"status":"ok","timestamp":1610590110157,"user_tz":-480,"elapsed":1576825,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"b6a49275-be95-4981-f49d-8e0b0a6b13ef"},"source":["inceptionv3 = InceptionV3(input_shape=image_input, weights='imagenet', include_top=False)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n","87916544/87910968 [==============================] - 1s 0us/step\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"5-wvPLYU-TRS"},"source":["for layer in inceptionv3.layers[:]:\n","    layer.trainable = False"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"J9_haSDp-WtA"},"source":["x = Flatten()(inceptionv3.output)\n","x = Dropout(0.5)(x)\n","outIncv3 = Dense(num_classes, activation='softmax', name='output_layer')(x)\n","inceptionV3TL = Model(inputs=inceptionv3.input, outputs=outIncv3, name='InceptionV3')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3Fef3Yhv-_fT","executionInfo":{"status":"ok","timestamp":1610590110162,"user_tz":-480,"elapsed":1576813,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"166d2b00-2b50-4507-f108-b7671d0785db"},"source":["inceptionV3TL.summary()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"InceptionV3\"\n","__________________________________________________________________________________________________\n","Layer (type)                    Output Shape         Param #     Connected to                     \n","==================================================================================================\n","input_2 (InputLayer)            [(None, 224, 224, 3) 0                                            \n","__________________________________________________________________________________________________\n","conv2d (Conv2D)                 (None, 111, 111, 32) 864         input_2[0][0]                    \n","__________________________________________________________________________________________________\n","batch_normalization (BatchNorma (None, 111, 111, 32) 96          conv2d[0][0]                     \n","__________________________________________________________________________________________________\n","activation (Activation)         (None, 111, 111, 32) 0           batch_normalization[0][0]        \n","__________________________________________________________________________________________________\n","conv2d_1 (Conv2D)               (None, 109, 109, 32) 9216        activation[0][0]                 \n","__________________________________________________________________________________________________\n","batch_normalization_1 (BatchNor (None, 109, 109, 32) 96          conv2d_1[0][0]                   \n","__________________________________________________________________________________________________\n","activation_1 (Activation)       (None, 109, 109, 32) 0           batch_normalization_1[0][0]      \n","__________________________________________________________________________________________________\n","conv2d_2 (Conv2D)               (None, 109, 109, 64) 18432       activation_1[0][0]               \n","__________________________________________________________________________________________________\n","batch_normalization_2 (BatchNor (None, 109, 109, 64) 192         conv2d_2[0][0]                   \n","__________________________________________________________________________________________________\n","activation_2 (Activation)       (None, 109, 109, 64) 0           batch_normalization_2[0][0]      \n","__________________________________________________________________________________________________\n","max_pooling2d_3 (MaxPooling2D)  (None, 54, 54, 64)   0           activation_2[0][0]               \n","__________________________________________________________________________________________________\n","conv2d_3 (Conv2D)               (None, 54, 54, 80)   5120        max_pooling2d_3[0][0]            \n","__________________________________________________________________________________________________\n","batch_normalization_3 (BatchNor (None, 54, 54, 80)   240         conv2d_3[0][0]                   \n","__________________________________________________________________________________________________\n","activation_3 (Activation)       (None, 54, 54, 80)   0           batch_normalization_3[0][0]      \n","__________________________________________________________________________________________________\n","conv2d_4 (Conv2D)               (None, 52, 52, 192)  138240      activation_3[0][0]               \n","__________________________________________________________________________________________________\n","batch_normalization_4 (BatchNor (None, 52, 52, 192)  576         conv2d_4[0][0]                   \n","__________________________________________________________________________________________________\n","activation_4 (Activation)       (None, 52, 52, 192)  0           batch_normalization_4[0][0]      \n","__________________________________________________________________________________________________\n","max_pooling2d_4 (MaxPooling2D)  (None, 25, 25, 192)  0           activation_4[0][0]               \n","__________________________________________________________________________________________________\n","conv2d_8 (Conv2D)               (None, 25, 25, 64)   12288       max_pooling2d_4[0][0]            \n","__________________________________________________________________________________________________\n","batch_normalization_8 (BatchNor (None, 25, 25, 64)   192         conv2d_8[0][0]                   \n","__________________________________________________________________________________________________\n","activation_8 (Activation)       (None, 25, 25, 64)   0           batch_normalization_8[0][0]      \n","__________________________________________________________________________________________________\n","conv2d_6 (Conv2D)               (None, 25, 25, 48)   9216        max_pooling2d_4[0][0]            \n","__________________________________________________________________________________________________\n","conv2d_9 (Conv2D)               (None, 25, 25, 96)   55296       activation_8[0][0]               \n","__________________________________________________________________________________________________\n","batch_normalization_6 (BatchNor (None, 25, 25, 48)   144         conv2d_6[0][0]                   \n","__________________________________________________________________________________________________\n","batch_normalization_9 (BatchNor (None, 25, 25, 96)   288         conv2d_9[0][0]                   \n","__________________________________________________________________________________________________\n","activation_6 (Activation)       (None, 25, 25, 48)   0           batch_normalization_6[0][0]      \n","__________________________________________________________________________________________________\n","activation_9 (Activation)       (None, 25, 25, 96)   0           batch_normalization_9[0][0]      \n","__________________________________________________________________________________________________\n","average_pooling2d (AveragePooli (None, 25, 25, 192)  0           max_pooling2d_4[0][0]            \n","__________________________________________________________________________________________________\n","conv2d_5 (Conv2D)               (None, 25, 25, 64)   12288       max_pooling2d_4[0][0]            \n","__________________________________________________________________________________________________\n","conv2d_7 (Conv2D)               (None, 25, 25, 64)   76800       activation_6[0][0]               \n","__________________________________________________________________________________________________\n","conv2d_10 (Conv2D)              (None, 25, 25, 96)   82944       activation_9[0][0]               \n","__________________________________________________________________________________________________\n","conv2d_11 (Conv2D)              (None, 25, 25, 32)   6144        average_pooling2d[0][0]          \n","__________________________________________________________________________________________________\n","batch_normalization_5 (BatchNor (None, 25, 25, 64)   192         conv2d_5[0][0]                   \n","__________________________________________________________________________________________________\n","batch_normalization_7 (BatchNor (None, 25, 25, 64)   192         conv2d_7[0][0]                   \n","__________________________________________________________________________________________________\n","batch_normalization_10 (BatchNo (None, 25, 25, 96)   288         conv2d_10[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_11 (BatchNo (None, 25, 25, 32)   96          conv2d_11[0][0]                  \n","__________________________________________________________________________________________________\n","activation_5 (Activation)       (None, 25, 25, 64)   0           batch_normalization_5[0][0]      \n","__________________________________________________________________________________________________\n","activation_7 (Activation)       (None, 25, 25, 64)   0           batch_normalization_7[0][0]      \n","__________________________________________________________________________________________________\n","activation_10 (Activation)      (None, 25, 25, 96)   0           batch_normalization_10[0][0]     \n","__________________________________________________________________________________________________\n","activation_11 (Activation)      (None, 25, 25, 32)   0           batch_normalization_11[0][0]     \n","__________________________________________________________________________________________________\n","mixed0 (Concatenate)            (None, 25, 25, 256)  0           activation_5[0][0]               \n","                                                                 activation_7[0][0]               \n","                                                                 activation_10[0][0]              \n","                                                                 activation_11[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_15 (Conv2D)              (None, 25, 25, 64)   16384       mixed0[0][0]                     \n","__________________________________________________________________________________________________\n","batch_normalization_15 (BatchNo (None, 25, 25, 64)   192         conv2d_15[0][0]                  \n","__________________________________________________________________________________________________\n","activation_15 (Activation)      (None, 25, 25, 64)   0           batch_normalization_15[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_13 (Conv2D)              (None, 25, 25, 48)   12288       mixed0[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_16 (Conv2D)              (None, 25, 25, 96)   55296       activation_15[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_13 (BatchNo (None, 25, 25, 48)   144         conv2d_13[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_16 (BatchNo (None, 25, 25, 96)   288         conv2d_16[0][0]                  \n","__________________________________________________________________________________________________\n","activation_13 (Activation)      (None, 25, 25, 48)   0           batch_normalization_13[0][0]     \n","__________________________________________________________________________________________________\n","activation_16 (Activation)      (None, 25, 25, 96)   0           batch_normalization_16[0][0]     \n","__________________________________________________________________________________________________\n","average_pooling2d_1 (AveragePoo (None, 25, 25, 256)  0           mixed0[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_12 (Conv2D)              (None, 25, 25, 64)   16384       mixed0[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_14 (Conv2D)              (None, 25, 25, 64)   76800       activation_13[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_17 (Conv2D)              (None, 25, 25, 96)   82944       activation_16[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_18 (Conv2D)              (None, 25, 25, 64)   16384       average_pooling2d_1[0][0]        \n","__________________________________________________________________________________________________\n","batch_normalization_12 (BatchNo (None, 25, 25, 64)   192         conv2d_12[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_14 (BatchNo (None, 25, 25, 64)   192         conv2d_14[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_17 (BatchNo (None, 25, 25, 96)   288         conv2d_17[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_18 (BatchNo (None, 25, 25, 64)   192         conv2d_18[0][0]                  \n","__________________________________________________________________________________________________\n","activation_12 (Activation)      (None, 25, 25, 64)   0           batch_normalization_12[0][0]     \n","__________________________________________________________________________________________________\n","activation_14 (Activation)      (None, 25, 25, 64)   0           batch_normalization_14[0][0]     \n","__________________________________________________________________________________________________\n","activation_17 (Activation)      (None, 25, 25, 96)   0           batch_normalization_17[0][0]     \n","__________________________________________________________________________________________________\n","activation_18 (Activation)      (None, 25, 25, 64)   0           batch_normalization_18[0][0]     \n","__________________________________________________________________________________________________\n","mixed1 (Concatenate)            (None, 25, 25, 288)  0           activation_12[0][0]              \n","                                                                 activation_14[0][0]              \n","                                                                 activation_17[0][0]              \n","                                                                 activation_18[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_22 (Conv2D)              (None, 25, 25, 64)   18432       mixed1[0][0]                     \n","__________________________________________________________________________________________________\n","batch_normalization_22 (BatchNo (None, 25, 25, 64)   192         conv2d_22[0][0]                  \n","__________________________________________________________________________________________________\n","activation_22 (Activation)      (None, 25, 25, 64)   0           batch_normalization_22[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_20 (Conv2D)              (None, 25, 25, 48)   13824       mixed1[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_23 (Conv2D)              (None, 25, 25, 96)   55296       activation_22[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_20 (BatchNo (None, 25, 25, 48)   144         conv2d_20[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_23 (BatchNo (None, 25, 25, 96)   288         conv2d_23[0][0]                  \n","__________________________________________________________________________________________________\n","activation_20 (Activation)      (None, 25, 25, 48)   0           batch_normalization_20[0][0]     \n","__________________________________________________________________________________________________\n","activation_23 (Activation)      (None, 25, 25, 96)   0           batch_normalization_23[0][0]     \n","__________________________________________________________________________________________________\n","average_pooling2d_2 (AveragePoo (None, 25, 25, 288)  0           mixed1[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_19 (Conv2D)              (None, 25, 25, 64)   18432       mixed1[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_21 (Conv2D)              (None, 25, 25, 64)   76800       activation_20[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_24 (Conv2D)              (None, 25, 25, 96)   82944       activation_23[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_25 (Conv2D)              (None, 25, 25, 64)   18432       average_pooling2d_2[0][0]        \n","__________________________________________________________________________________________________\n","batch_normalization_19 (BatchNo (None, 25, 25, 64)   192         conv2d_19[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_21 (BatchNo (None, 25, 25, 64)   192         conv2d_21[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_24 (BatchNo (None, 25, 25, 96)   288         conv2d_24[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_25 (BatchNo (None, 25, 25, 64)   192         conv2d_25[0][0]                  \n","__________________________________________________________________________________________________\n","activation_19 (Activation)      (None, 25, 25, 64)   0           batch_normalization_19[0][0]     \n","__________________________________________________________________________________________________\n","activation_21 (Activation)      (None, 25, 25, 64)   0           batch_normalization_21[0][0]     \n","__________________________________________________________________________________________________\n","activation_24 (Activation)      (None, 25, 25, 96)   0           batch_normalization_24[0][0]     \n","__________________________________________________________________________________________________\n","activation_25 (Activation)      (None, 25, 25, 64)   0           batch_normalization_25[0][0]     \n","__________________________________________________________________________________________________\n","mixed2 (Concatenate)            (None, 25, 25, 288)  0           activation_19[0][0]              \n","                                                                 activation_21[0][0]              \n","                                                                 activation_24[0][0]              \n","                                                                 activation_25[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_27 (Conv2D)              (None, 25, 25, 64)   18432       mixed2[0][0]                     \n","__________________________________________________________________________________________________\n","batch_normalization_27 (BatchNo (None, 25, 25, 64)   192         conv2d_27[0][0]                  \n","__________________________________________________________________________________________________\n","activation_27 (Activation)      (None, 25, 25, 64)   0           batch_normalization_27[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_28 (Conv2D)              (None, 25, 25, 96)   55296       activation_27[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_28 (BatchNo (None, 25, 25, 96)   288         conv2d_28[0][0]                  \n","__________________________________________________________________________________________________\n","activation_28 (Activation)      (None, 25, 25, 96)   0           batch_normalization_28[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_26 (Conv2D)              (None, 12, 12, 384)  995328      mixed2[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_29 (Conv2D)              (None, 12, 12, 96)   82944       activation_28[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_26 (BatchNo (None, 12, 12, 384)  1152        conv2d_26[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_29 (BatchNo (None, 12, 12, 96)   288         conv2d_29[0][0]                  \n","__________________________________________________________________________________________________\n","activation_26 (Activation)      (None, 12, 12, 384)  0           batch_normalization_26[0][0]     \n","__________________________________________________________________________________________________\n","activation_29 (Activation)      (None, 12, 12, 96)   0           batch_normalization_29[0][0]     \n","__________________________________________________________________________________________________\n","max_pooling2d_5 (MaxPooling2D)  (None, 12, 12, 288)  0           mixed2[0][0]                     \n","__________________________________________________________________________________________________\n","mixed3 (Concatenate)            (None, 12, 12, 768)  0           activation_26[0][0]              \n","                                                                 activation_29[0][0]              \n","                                                                 max_pooling2d_5[0][0]            \n","__________________________________________________________________________________________________\n","conv2d_34 (Conv2D)              (None, 12, 12, 128)  98304       mixed3[0][0]                     \n","__________________________________________________________________________________________________\n","batch_normalization_34 (BatchNo (None, 12, 12, 128)  384         conv2d_34[0][0]                  \n","__________________________________________________________________________________________________\n","activation_34 (Activation)      (None, 12, 12, 128)  0           batch_normalization_34[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_35 (Conv2D)              (None, 12, 12, 128)  114688      activation_34[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_35 (BatchNo (None, 12, 12, 128)  384         conv2d_35[0][0]                  \n","__________________________________________________________________________________________________\n","activation_35 (Activation)      (None, 12, 12, 128)  0           batch_normalization_35[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_31 (Conv2D)              (None, 12, 12, 128)  98304       mixed3[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_36 (Conv2D)              (None, 12, 12, 128)  114688      activation_35[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_31 (BatchNo (None, 12, 12, 128)  384         conv2d_31[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_36 (BatchNo (None, 12, 12, 128)  384         conv2d_36[0][0]                  \n","__________________________________________________________________________________________________\n","activation_31 (Activation)      (None, 12, 12, 128)  0           batch_normalization_31[0][0]     \n","__________________________________________________________________________________________________\n","activation_36 (Activation)      (None, 12, 12, 128)  0           batch_normalization_36[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_32 (Conv2D)              (None, 12, 12, 128)  114688      activation_31[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_37 (Conv2D)              (None, 12, 12, 128)  114688      activation_36[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_32 (BatchNo (None, 12, 12, 128)  384         conv2d_32[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_37 (BatchNo (None, 12, 12, 128)  384         conv2d_37[0][0]                  \n","__________________________________________________________________________________________________\n","activation_32 (Activation)      (None, 12, 12, 128)  0           batch_normalization_32[0][0]     \n","__________________________________________________________________________________________________\n","activation_37 (Activation)      (None, 12, 12, 128)  0           batch_normalization_37[0][0]     \n","__________________________________________________________________________________________________\n","average_pooling2d_3 (AveragePoo (None, 12, 12, 768)  0           mixed3[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_30 (Conv2D)              (None, 12, 12, 192)  147456      mixed3[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_33 (Conv2D)              (None, 12, 12, 192)  172032      activation_32[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_38 (Conv2D)              (None, 12, 12, 192)  172032      activation_37[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_39 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_3[0][0]        \n","__________________________________________________________________________________________________\n","batch_normalization_30 (BatchNo (None, 12, 12, 192)  576         conv2d_30[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_33 (BatchNo (None, 12, 12, 192)  576         conv2d_33[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_38 (BatchNo (None, 12, 12, 192)  576         conv2d_38[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_39 (BatchNo (None, 12, 12, 192)  576         conv2d_39[0][0]                  \n","__________________________________________________________________________________________________\n","activation_30 (Activation)      (None, 12, 12, 192)  0           batch_normalization_30[0][0]     \n","__________________________________________________________________________________________________\n","activation_33 (Activation)      (None, 12, 12, 192)  0           batch_normalization_33[0][0]     \n","__________________________________________________________________________________________________\n","activation_38 (Activation)      (None, 12, 12, 192)  0           batch_normalization_38[0][0]     \n","__________________________________________________________________________________________________\n","activation_39 (Activation)      (None, 12, 12, 192)  0           batch_normalization_39[0][0]     \n","__________________________________________________________________________________________________\n","mixed4 (Concatenate)            (None, 12, 12, 768)  0           activation_30[0][0]              \n","                                                                 activation_33[0][0]              \n","                                                                 activation_38[0][0]              \n","                                                                 activation_39[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_44 (Conv2D)              (None, 12, 12, 160)  122880      mixed4[0][0]                     \n","__________________________________________________________________________________________________\n","batch_normalization_44 (BatchNo (None, 12, 12, 160)  480         conv2d_44[0][0]                  \n","__________________________________________________________________________________________________\n","activation_44 (Activation)      (None, 12, 12, 160)  0           batch_normalization_44[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_45 (Conv2D)              (None, 12, 12, 160)  179200      activation_44[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_45 (BatchNo (None, 12, 12, 160)  480         conv2d_45[0][0]                  \n","__________________________________________________________________________________________________\n","activation_45 (Activation)      (None, 12, 12, 160)  0           batch_normalization_45[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_41 (Conv2D)              (None, 12, 12, 160)  122880      mixed4[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_46 (Conv2D)              (None, 12, 12, 160)  179200      activation_45[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_41 (BatchNo (None, 12, 12, 160)  480         conv2d_41[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_46 (BatchNo (None, 12, 12, 160)  480         conv2d_46[0][0]                  \n","__________________________________________________________________________________________________\n","activation_41 (Activation)      (None, 12, 12, 160)  0           batch_normalization_41[0][0]     \n","__________________________________________________________________________________________________\n","activation_46 (Activation)      (None, 12, 12, 160)  0           batch_normalization_46[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_42 (Conv2D)              (None, 12, 12, 160)  179200      activation_41[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_47 (Conv2D)              (None, 12, 12, 160)  179200      activation_46[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_42 (BatchNo (None, 12, 12, 160)  480         conv2d_42[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_47 (BatchNo (None, 12, 12, 160)  480         conv2d_47[0][0]                  \n","__________________________________________________________________________________________________\n","activation_42 (Activation)      (None, 12, 12, 160)  0           batch_normalization_42[0][0]     \n","__________________________________________________________________________________________________\n","activation_47 (Activation)      (None, 12, 12, 160)  0           batch_normalization_47[0][0]     \n","__________________________________________________________________________________________________\n","average_pooling2d_4 (AveragePoo (None, 12, 12, 768)  0           mixed4[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_40 (Conv2D)              (None, 12, 12, 192)  147456      mixed4[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_43 (Conv2D)              (None, 12, 12, 192)  215040      activation_42[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_48 (Conv2D)              (None, 12, 12, 192)  215040      activation_47[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_49 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_4[0][0]        \n","__________________________________________________________________________________________________\n","batch_normalization_40 (BatchNo (None, 12, 12, 192)  576         conv2d_40[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_43 (BatchNo (None, 12, 12, 192)  576         conv2d_43[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_48 (BatchNo (None, 12, 12, 192)  576         conv2d_48[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_49 (BatchNo (None, 12, 12, 192)  576         conv2d_49[0][0]                  \n","__________________________________________________________________________________________________\n","activation_40 (Activation)      (None, 12, 12, 192)  0           batch_normalization_40[0][0]     \n","__________________________________________________________________________________________________\n","activation_43 (Activation)      (None, 12, 12, 192)  0           batch_normalization_43[0][0]     \n","__________________________________________________________________________________________________\n","activation_48 (Activation)      (None, 12, 12, 192)  0           batch_normalization_48[0][0]     \n","__________________________________________________________________________________________________\n","activation_49 (Activation)      (None, 12, 12, 192)  0           batch_normalization_49[0][0]     \n","__________________________________________________________________________________________________\n","mixed5 (Concatenate)            (None, 12, 12, 768)  0           activation_40[0][0]              \n","                                                                 activation_43[0][0]              \n","                                                                 activation_48[0][0]              \n","                                                                 activation_49[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_54 (Conv2D)              (None, 12, 12, 160)  122880      mixed5[0][0]                     \n","__________________________________________________________________________________________________\n","batch_normalization_54 (BatchNo (None, 12, 12, 160)  480         conv2d_54[0][0]                  \n","__________________________________________________________________________________________________\n","activation_54 (Activation)      (None, 12, 12, 160)  0           batch_normalization_54[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_55 (Conv2D)              (None, 12, 12, 160)  179200      activation_54[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_55 (BatchNo (None, 12, 12, 160)  480         conv2d_55[0][0]                  \n","__________________________________________________________________________________________________\n","activation_55 (Activation)      (None, 12, 12, 160)  0           batch_normalization_55[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_51 (Conv2D)              (None, 12, 12, 160)  122880      mixed5[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_56 (Conv2D)              (None, 12, 12, 160)  179200      activation_55[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_51 (BatchNo (None, 12, 12, 160)  480         conv2d_51[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_56 (BatchNo (None, 12, 12, 160)  480         conv2d_56[0][0]                  \n","__________________________________________________________________________________________________\n","activation_51 (Activation)      (None, 12, 12, 160)  0           batch_normalization_51[0][0]     \n","__________________________________________________________________________________________________\n","activation_56 (Activation)      (None, 12, 12, 160)  0           batch_normalization_56[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_52 (Conv2D)              (None, 12, 12, 160)  179200      activation_51[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_57 (Conv2D)              (None, 12, 12, 160)  179200      activation_56[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_52 (BatchNo (None, 12, 12, 160)  480         conv2d_52[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_57 (BatchNo (None, 12, 12, 160)  480         conv2d_57[0][0]                  \n","__________________________________________________________________________________________________\n","activation_52 (Activation)      (None, 12, 12, 160)  0           batch_normalization_52[0][0]     \n","__________________________________________________________________________________________________\n","activation_57 (Activation)      (None, 12, 12, 160)  0           batch_normalization_57[0][0]     \n","__________________________________________________________________________________________________\n","average_pooling2d_5 (AveragePoo (None, 12, 12, 768)  0           mixed5[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_50 (Conv2D)              (None, 12, 12, 192)  147456      mixed5[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_53 (Conv2D)              (None, 12, 12, 192)  215040      activation_52[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_58 (Conv2D)              (None, 12, 12, 192)  215040      activation_57[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_59 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_5[0][0]        \n","__________________________________________________________________________________________________\n","batch_normalization_50 (BatchNo (None, 12, 12, 192)  576         conv2d_50[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_53 (BatchNo (None, 12, 12, 192)  576         conv2d_53[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_58 (BatchNo (None, 12, 12, 192)  576         conv2d_58[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_59 (BatchNo (None, 12, 12, 192)  576         conv2d_59[0][0]                  \n","__________________________________________________________________________________________________\n","activation_50 (Activation)      (None, 12, 12, 192)  0           batch_normalization_50[0][0]     \n","__________________________________________________________________________________________________\n","activation_53 (Activation)      (None, 12, 12, 192)  0           batch_normalization_53[0][0]     \n","__________________________________________________________________________________________________\n","activation_58 (Activation)      (None, 12, 12, 192)  0           batch_normalization_58[0][0]     \n","__________________________________________________________________________________________________\n","activation_59 (Activation)      (None, 12, 12, 192)  0           batch_normalization_59[0][0]     \n","__________________________________________________________________________________________________\n","mixed6 (Concatenate)            (None, 12, 12, 768)  0           activation_50[0][0]              \n","                                                                 activation_53[0][0]              \n","                                                                 activation_58[0][0]              \n","                                                                 activation_59[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_64 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n","__________________________________________________________________________________________________\n","batch_normalization_64 (BatchNo (None, 12, 12, 192)  576         conv2d_64[0][0]                  \n","__________________________________________________________________________________________________\n","activation_64 (Activation)      (None, 12, 12, 192)  0           batch_normalization_64[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_65 (Conv2D)              (None, 12, 12, 192)  258048      activation_64[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_65 (BatchNo (None, 12, 12, 192)  576         conv2d_65[0][0]                  \n","__________________________________________________________________________________________________\n","activation_65 (Activation)      (None, 12, 12, 192)  0           batch_normalization_65[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_61 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_66 (Conv2D)              (None, 12, 12, 192)  258048      activation_65[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_61 (BatchNo (None, 12, 12, 192)  576         conv2d_61[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_66 (BatchNo (None, 12, 12, 192)  576         conv2d_66[0][0]                  \n","__________________________________________________________________________________________________\n","activation_61 (Activation)      (None, 12, 12, 192)  0           batch_normalization_61[0][0]     \n","__________________________________________________________________________________________________\n","activation_66 (Activation)      (None, 12, 12, 192)  0           batch_normalization_66[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_62 (Conv2D)              (None, 12, 12, 192)  258048      activation_61[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_67 (Conv2D)              (None, 12, 12, 192)  258048      activation_66[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_62 (BatchNo (None, 12, 12, 192)  576         conv2d_62[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_67 (BatchNo (None, 12, 12, 192)  576         conv2d_67[0][0]                  \n","__________________________________________________________________________________________________\n","activation_62 (Activation)      (None, 12, 12, 192)  0           batch_normalization_62[0][0]     \n","__________________________________________________________________________________________________\n","activation_67 (Activation)      (None, 12, 12, 192)  0           batch_normalization_67[0][0]     \n","__________________________________________________________________________________________________\n","average_pooling2d_6 (AveragePoo (None, 12, 12, 768)  0           mixed6[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_60 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_63 (Conv2D)              (None, 12, 12, 192)  258048      activation_62[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_68 (Conv2D)              (None, 12, 12, 192)  258048      activation_67[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_69 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_6[0][0]        \n","__________________________________________________________________________________________________\n","batch_normalization_60 (BatchNo (None, 12, 12, 192)  576         conv2d_60[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_63 (BatchNo (None, 12, 12, 192)  576         conv2d_63[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_68 (BatchNo (None, 12, 12, 192)  576         conv2d_68[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_69 (BatchNo (None, 12, 12, 192)  576         conv2d_69[0][0]                  \n","__________________________________________________________________________________________________\n","activation_60 (Activation)      (None, 12, 12, 192)  0           batch_normalization_60[0][0]     \n","__________________________________________________________________________________________________\n","activation_63 (Activation)      (None, 12, 12, 192)  0           batch_normalization_63[0][0]     \n","__________________________________________________________________________________________________\n","activation_68 (Activation)      (None, 12, 12, 192)  0           batch_normalization_68[0][0]     \n","__________________________________________________________________________________________________\n","activation_69 (Activation)      (None, 12, 12, 192)  0           batch_normalization_69[0][0]     \n","__________________________________________________________________________________________________\n","mixed7 (Concatenate)            (None, 12, 12, 768)  0           activation_60[0][0]              \n","                                                                 activation_63[0][0]              \n","                                                                 activation_68[0][0]              \n","                                                                 activation_69[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_72 (Conv2D)              (None, 12, 12, 192)  147456      mixed7[0][0]                     \n","__________________________________________________________________________________________________\n","batch_normalization_72 (BatchNo (None, 12, 12, 192)  576         conv2d_72[0][0]                  \n","__________________________________________________________________________________________________\n","activation_72 (Activation)      (None, 12, 12, 192)  0           batch_normalization_72[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_73 (Conv2D)              (None, 12, 12, 192)  258048      activation_72[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_73 (BatchNo (None, 12, 12, 192)  576         conv2d_73[0][0]                  \n","__________________________________________________________________________________________________\n","activation_73 (Activation)      (None, 12, 12, 192)  0           batch_normalization_73[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_70 (Conv2D)              (None, 12, 12, 192)  147456      mixed7[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_74 (Conv2D)              (None, 12, 12, 192)  258048      activation_73[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_70 (BatchNo (None, 12, 12, 192)  576         conv2d_70[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_74 (BatchNo (None, 12, 12, 192)  576         conv2d_74[0][0]                  \n","__________________________________________________________________________________________________\n","activation_70 (Activation)      (None, 12, 12, 192)  0           batch_normalization_70[0][0]     \n","__________________________________________________________________________________________________\n","activation_74 (Activation)      (None, 12, 12, 192)  0           batch_normalization_74[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_71 (Conv2D)              (None, 5, 5, 320)    552960      activation_70[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_75 (Conv2D)              (None, 5, 5, 192)    331776      activation_74[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_71 (BatchNo (None, 5, 5, 320)    960         conv2d_71[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_75 (BatchNo (None, 5, 5, 192)    576         conv2d_75[0][0]                  \n","__________________________________________________________________________________________________\n","activation_71 (Activation)      (None, 5, 5, 320)    0           batch_normalization_71[0][0]     \n","__________________________________________________________________________________________________\n","activation_75 (Activation)      (None, 5, 5, 192)    0           batch_normalization_75[0][0]     \n","__________________________________________________________________________________________________\n","max_pooling2d_6 (MaxPooling2D)  (None, 5, 5, 768)    0           mixed7[0][0]                     \n","__________________________________________________________________________________________________\n","mixed8 (Concatenate)            (None, 5, 5, 1280)   0           activation_71[0][0]              \n","                                                                 activation_75[0][0]              \n","                                                                 max_pooling2d_6[0][0]            \n","__________________________________________________________________________________________________\n","conv2d_80 (Conv2D)              (None, 5, 5, 448)    573440      mixed8[0][0]                     \n","__________________________________________________________________________________________________\n","batch_normalization_80 (BatchNo (None, 5, 5, 448)    1344        conv2d_80[0][0]                  \n","__________________________________________________________________________________________________\n","activation_80 (Activation)      (None, 5, 5, 448)    0           batch_normalization_80[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_77 (Conv2D)              (None, 5, 5, 384)    491520      mixed8[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_81 (Conv2D)              (None, 5, 5, 384)    1548288     activation_80[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_77 (BatchNo (None, 5, 5, 384)    1152        conv2d_77[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_81 (BatchNo (None, 5, 5, 384)    1152        conv2d_81[0][0]                  \n","__________________________________________________________________________________________________\n","activation_77 (Activation)      (None, 5, 5, 384)    0           batch_normalization_77[0][0]     \n","__________________________________________________________________________________________________\n","activation_81 (Activation)      (None, 5, 5, 384)    0           batch_normalization_81[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_78 (Conv2D)              (None, 5, 5, 384)    442368      activation_77[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_79 (Conv2D)              (None, 5, 5, 384)    442368      activation_77[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_82 (Conv2D)              (None, 5, 5, 384)    442368      activation_81[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_83 (Conv2D)              (None, 5, 5, 384)    442368      activation_81[0][0]              \n","__________________________________________________________________________________________________\n","average_pooling2d_7 (AveragePoo (None, 5, 5, 1280)   0           mixed8[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_76 (Conv2D)              (None, 5, 5, 320)    409600      mixed8[0][0]                     \n","__________________________________________________________________________________________________\n","batch_normalization_78 (BatchNo (None, 5, 5, 384)    1152        conv2d_78[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_79 (BatchNo (None, 5, 5, 384)    1152        conv2d_79[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_82 (BatchNo (None, 5, 5, 384)    1152        conv2d_82[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_83 (BatchNo (None, 5, 5, 384)    1152        conv2d_83[0][0]                  \n","__________________________________________________________________________________________________\n","conv2d_84 (Conv2D)              (None, 5, 5, 192)    245760      average_pooling2d_7[0][0]        \n","__________________________________________________________________________________________________\n","batch_normalization_76 (BatchNo (None, 5, 5, 320)    960         conv2d_76[0][0]                  \n","__________________________________________________________________________________________________\n","activation_78 (Activation)      (None, 5, 5, 384)    0           batch_normalization_78[0][0]     \n","__________________________________________________________________________________________________\n","activation_79 (Activation)      (None, 5, 5, 384)    0           batch_normalization_79[0][0]     \n","__________________________________________________________________________________________________\n","activation_82 (Activation)      (None, 5, 5, 384)    0           batch_normalization_82[0][0]     \n","__________________________________________________________________________________________________\n","activation_83 (Activation)      (None, 5, 5, 384)    0           batch_normalization_83[0][0]     \n","__________________________________________________________________________________________________\n","batch_normalization_84 (BatchNo (None, 5, 5, 192)    576         conv2d_84[0][0]                  \n","__________________________________________________________________________________________________\n","activation_76 (Activation)      (None, 5, 5, 320)    0           batch_normalization_76[0][0]     \n","__________________________________________________________________________________________________\n","mixed9_0 (Concatenate)          (None, 5, 5, 768)    0           activation_78[0][0]              \n","                                                                 activation_79[0][0]              \n","__________________________________________________________________________________________________\n","concatenate (Concatenate)       (None, 5, 5, 768)    0           activation_82[0][0]              \n","                                                                 activation_83[0][0]              \n","__________________________________________________________________________________________________\n","activation_84 (Activation)      (None, 5, 5, 192)    0           batch_normalization_84[0][0]     \n","__________________________________________________________________________________________________\n","mixed9 (Concatenate)            (None, 5, 5, 2048)   0           activation_76[0][0]              \n","                                                                 mixed9_0[0][0]                   \n","                                                                 concatenate[0][0]                \n","                                                                 activation_84[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_89 (Conv2D)              (None, 5, 5, 448)    917504      mixed9[0][0]                     \n","__________________________________________________________________________________________________\n","batch_normalization_89 (BatchNo (None, 5, 5, 448)    1344        conv2d_89[0][0]                  \n","__________________________________________________________________________________________________\n","activation_89 (Activation)      (None, 5, 5, 448)    0           batch_normalization_89[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_86 (Conv2D)              (None, 5, 5, 384)    786432      mixed9[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_90 (Conv2D)              (None, 5, 5, 384)    1548288     activation_89[0][0]              \n","__________________________________________________________________________________________________\n","batch_normalization_86 (BatchNo (None, 5, 5, 384)    1152        conv2d_86[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_90 (BatchNo (None, 5, 5, 384)    1152        conv2d_90[0][0]                  \n","__________________________________________________________________________________________________\n","activation_86 (Activation)      (None, 5, 5, 384)    0           batch_normalization_86[0][0]     \n","__________________________________________________________________________________________________\n","activation_90 (Activation)      (None, 5, 5, 384)    0           batch_normalization_90[0][0]     \n","__________________________________________________________________________________________________\n","conv2d_87 (Conv2D)              (None, 5, 5, 384)    442368      activation_86[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_88 (Conv2D)              (None, 5, 5, 384)    442368      activation_86[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_91 (Conv2D)              (None, 5, 5, 384)    442368      activation_90[0][0]              \n","__________________________________________________________________________________________________\n","conv2d_92 (Conv2D)              (None, 5, 5, 384)    442368      activation_90[0][0]              \n","__________________________________________________________________________________________________\n","average_pooling2d_8 (AveragePoo (None, 5, 5, 2048)   0           mixed9[0][0]                     \n","__________________________________________________________________________________________________\n","conv2d_85 (Conv2D)              (None, 5, 5, 320)    655360      mixed9[0][0]                     \n","__________________________________________________________________________________________________\n","batch_normalization_87 (BatchNo (None, 5, 5, 384)    1152        conv2d_87[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_88 (BatchNo (None, 5, 5, 384)    1152        conv2d_88[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_91 (BatchNo (None, 5, 5, 384)    1152        conv2d_91[0][0]                  \n","__________________________________________________________________________________________________\n","batch_normalization_92 (BatchNo (None, 5, 5, 384)    1152        conv2d_92[0][0]                  \n","__________________________________________________________________________________________________\n","conv2d_93 (Conv2D)              (None, 5, 5, 192)    393216      average_pooling2d_8[0][0]        \n","__________________________________________________________________________________________________\n","batch_normalization_85 (BatchNo (None, 5, 5, 320)    960         conv2d_85[0][0]                  \n","__________________________________________________________________________________________________\n","activation_87 (Activation)      (None, 5, 5, 384)    0           batch_normalization_87[0][0]     \n","__________________________________________________________________________________________________\n","activation_88 (Activation)      (None, 5, 5, 384)    0           batch_normalization_88[0][0]     \n","__________________________________________________________________________________________________\n","activation_91 (Activation)      (None, 5, 5, 384)    0           batch_normalization_91[0][0]     \n","__________________________________________________________________________________________________\n","activation_92 (Activation)      (None, 5, 5, 384)    0           batch_normalization_92[0][0]     \n","__________________________________________________________________________________________________\n","batch_normalization_93 (BatchNo (None, 5, 5, 192)    576         conv2d_93[0][0]                  \n","__________________________________________________________________________________________________\n","activation_85 (Activation)      (None, 5, 5, 320)    0           batch_normalization_85[0][0]     \n","__________________________________________________________________________________________________\n","mixed9_1 (Concatenate)          (None, 5, 5, 768)    0           activation_87[0][0]              \n","                                                                 activation_88[0][0]              \n","__________________________________________________________________________________________________\n","concatenate_1 (Concatenate)     (None, 5, 5, 768)    0           activation_91[0][0]              \n","                                                                 activation_92[0][0]              \n","__________________________________________________________________________________________________\n","activation_93 (Activation)      (None, 5, 5, 192)    0           batch_normalization_93[0][0]     \n","__________________________________________________________________________________________________\n","mixed10 (Concatenate)           (None, 5, 5, 2048)   0           activation_85[0][0]              \n","                                                                 mixed9_1[0][0]                   \n","                                                                 concatenate_1[0][0]              \n","                                                                 activation_93[0][0]              \n","__________________________________________________________________________________________________\n","flatten_1 (Flatten)             (None, 51200)        0           mixed10[0][0]                    \n","__________________________________________________________________________________________________\n","dropout_1 (Dropout)             (None, 51200)        0           flatten_1[0][0]                  \n","__________________________________________________________________________________________________\n","output_layer (Dense)            (None, 7)            358407      dropout_1[0][0]                  \n","==================================================================================================\n","Total params: 22,161,191\n","Trainable params: 358,407\n","Non-trainable params: 21,802,784\n","__________________________________________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Ylx7OBYC_POz"},"source":["inceptionV3TL.compile(loss='categorical_crossentropy', optimizer=opt, metrics=cmetrics)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"022dPpLr_aur","executionInfo":{"status":"ok","timestamp":1610591289975,"user_tz":-480,"elapsed":802334,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"c946f164-66ac-4287-b218-733ea0860bc2"},"source":["import time\n","\n","t1 = time.time()\n","inceptionV3TL_fit= inceptionV3TL.fit(train_x, train_y, epochs=EPOCH, batch_size=BATCHSIZE, verbose=2, validation_data=(test_x, test_y)\n","                                     #callbacks=es\n","                                     )\n","# Testing has finished \n","t2 = time.time()\n","print( 'Time taken was {} seconds'.format( t2 - t1))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch 1/50\n","323/323 - 30s - loss: 12.6207 - tp: 5508.0000 - fp: 3684.0000 - tn: 51600.0000 - fn: 3706.0000 - accuracy: 0.5982 - precision: 0.5992 - recall: 0.5978 - auc: 0.7881 - val_loss: 11.2284 - val_tp: 1550.0000 - val_fp: 1211.0000 - val_tn: 15379.0000 - val_fn: 1215.0000 - val_accuracy: 0.5609 - val_precision: 0.5614 - val_recall: 0.5606 - val_auc: 0.7641\n","Epoch 2/50\n","323/323 - 24s - loss: 5.7607 - tp: 4390.0000 - fp: 2048.0000 - tn: 36646.0000 - fn: 2059.0000 - accuracy: 0.6812 - precision: 0.6819 - recall: 0.6807 - auc: 0.8457 - val_loss: 8.0845 - val_tp: 1741.0000 - val_fp: 1017.0000 - val_tn: 15573.0000 - val_fn: 1024.0000 - val_accuracy: 0.6304 - val_precision: 0.6313 - val_recall: 0.6297 - val_auc: 0.8110\n","Epoch 3/50\n","323/323 - 23s - loss: 3.7435 - tp: 4885.0000 - fp: 1558.0000 - tn: 37136.0000 - fn: 1564.0000 - accuracy: 0.7579 - precision: 0.7582 - recall: 0.7575 - auc: 0.8906 - val_loss: 20.6575 - val_tp: 1124.0000 - val_fp: 1640.0000 - val_tn: 14950.0000 - val_fn: 1641.0000 - val_accuracy: 0.4069 - val_precision: 0.4067 - val_recall: 0.4065 - val_auc: 0.6691\n","Epoch 4/50\n","323/323 - 23s - loss: 2.8888 - tp: 5132.0000 - fp: 1309.0000 - tn: 37385.0000 - fn: 1317.0000 - accuracy: 0.7961 - precision: 0.7968 - recall: 0.7958 - auc: 0.9084 - val_loss: 4.8647 - val_tp: 1883.0000 - val_fp: 874.0000 - val_tn: 15716.0000 - val_fn: 882.0000 - val_accuracy: 0.6821 - val_precision: 0.6830 - val_recall: 0.6810 - val_auc: 0.8510\n","Epoch 5/50\n","323/323 - 24s - loss: 2.2578 - tp: 5361.0000 - fp: 1084.0000 - tn: 37610.0000 - fn: 1088.0000 - accuracy: 0.8313 - precision: 0.8318 - recall: 0.8313 - auc: 0.9289 - val_loss: 4.6347 - val_tp: 1926.0000 - val_fp: 832.0000 - val_tn: 15758.0000 - val_fn: 839.0000 - val_accuracy: 0.6976 - val_precision: 0.6983 - val_recall: 0.6966 - val_auc: 0.8602\n","Epoch 6/50\n","323/323 - 24s - loss: 1.8717 - tp: 5495.0000 - fp: 950.0000 - tn: 37744.0000 - fn: 954.0000 - accuracy: 0.8521 - precision: 0.8526 - recall: 0.8521 - auc: 0.9389 - val_loss: 6.3456 - val_tp: 1799.0000 - val_fp: 957.0000 - val_tn: 15633.0000 - val_fn: 966.0000 - val_accuracy: 0.6510 - val_precision: 0.6528 - val_recall: 0.6506 - val_auc: 0.8267\n","Epoch 7/50\n","323/323 - 24s - loss: 1.5552 - tp: 5575.0000 - fp: 868.0000 - tn: 37826.0000 - fn: 874.0000 - accuracy: 0.8648 - precision: 0.8653 - recall: 0.8645 - auc: 0.9446 - val_loss: 6.8719 - val_tp: 1914.0000 - val_fp: 848.0000 - val_tn: 15742.0000 - val_fn: 851.0000 - val_accuracy: 0.6922 - val_precision: 0.6930 - val_recall: 0.6922 - val_auc: 0.8438\n","Epoch 8/50\n","323/323 - 23s - loss: 1.4056 - tp: 5682.0000 - fp: 761.0000 - tn: 37933.0000 - fn: 767.0000 - accuracy: 0.8815 - precision: 0.8819 - recall: 0.8811 - auc: 0.9515 - val_loss: 7.9089 - val_tp: 1871.0000 - val_fp: 893.0000 - val_tn: 15697.0000 - val_fn: 894.0000 - val_accuracy: 0.6770 - val_precision: 0.6769 - val_recall: 0.6767 - val_auc: 0.8336\n","Epoch 9/50\n","323/323 - 24s - loss: 1.0842 - tp: 5767.0000 - fp: 679.0000 - tn: 38015.0000 - fn: 682.0000 - accuracy: 0.8946 - precision: 0.8947 - recall: 0.8942 - auc: 0.9609 - val_loss: 4.5605 - val_tp: 1973.0000 - val_fp: 787.0000 - val_tn: 15803.0000 - val_fn: 792.0000 - val_accuracy: 0.7143 - val_precision: 0.7149 - val_recall: 0.7136 - val_auc: 0.8665\n","Epoch 10/50\n","323/323 - 23s - loss: 0.9826 - tp: 5827.0000 - fp: 617.0000 - tn: 38077.0000 - fn: 622.0000 - accuracy: 0.9039 - precision: 0.9043 - recall: 0.9036 - auc: 0.9634 - val_loss: 4.4669 - val_tp: 2013.0000 - val_fp: 745.0000 - val_tn: 15845.0000 - val_fn: 752.0000 - val_accuracy: 0.7291 - val_precision: 0.7299 - val_recall: 0.7280 - val_auc: 0.8737\n","Epoch 11/50\n","323/323 - 23s - loss: 0.9595 - tp: 5853.0000 - fp: 593.0000 - tn: 38101.0000 - fn: 596.0000 - accuracy: 0.9079 - precision: 0.9080 - recall: 0.9076 - auc: 0.9655 - val_loss: 5.4044 - val_tp: 1969.0000 - val_fp: 794.0000 - val_tn: 15796.0000 - val_fn: 796.0000 - val_accuracy: 0.7121 - val_precision: 0.7126 - val_recall: 0.7121 - val_auc: 0.8623\n","Epoch 12/50\n","323/323 - 23s - loss: 0.7379 - tp: 5927.0000 - fp: 518.0000 - tn: 38176.0000 - fn: 522.0000 - accuracy: 0.9194 - precision: 0.9196 - recall: 0.9191 - auc: 0.9721 - val_loss: 4.4063 - val_tp: 2004.0000 - val_fp: 758.0000 - val_tn: 15832.0000 - val_fn: 761.0000 - val_accuracy: 0.7251 - val_precision: 0.7256 - val_recall: 0.7248 - val_auc: 0.8724\n","Epoch 13/50\n","323/323 - 23s - loss: 0.8054 - tp: 5903.0000 - fp: 542.0000 - tn: 38152.0000 - fn: 546.0000 - accuracy: 0.9155 - precision: 0.9159 - recall: 0.9153 - auc: 0.9696 - val_loss: 6.5359 - val_tp: 1887.0000 - val_fp: 877.0000 - val_tn: 15713.0000 - val_fn: 878.0000 - val_accuracy: 0.6828 - val_precision: 0.6827 - val_recall: 0.6825 - val_auc: 0.8400\n","Epoch 14/50\n","323/323 - 23s - loss: 0.6865 - tp: 5983.0000 - fp: 463.0000 - tn: 38231.0000 - fn: 466.0000 - accuracy: 0.9281 - precision: 0.9282 - recall: 0.9277 - auc: 0.9739 - val_loss: 6.1473 - val_tp: 1843.0000 - val_fp: 920.0000 - val_tn: 15670.0000 - val_fn: 922.0000 - val_accuracy: 0.6669 - val_precision: 0.6670 - val_recall: 0.6665 - val_auc: 0.8366\n","Epoch 15/50\n","323/323 - 23s - loss: 0.6716 - tp: 5960.0000 - fp: 486.0000 - tn: 38208.0000 - fn: 489.0000 - accuracy: 0.9243 - precision: 0.9246 - recall: 0.9242 - auc: 0.9737 - val_loss: 4.4155 - val_tp: 2035.0000 - val_fp: 727.0000 - val_tn: 15863.0000 - val_fn: 730.0000 - val_accuracy: 0.7360 - val_precision: 0.7368 - val_recall: 0.7360 - val_auc: 0.8739\n","Epoch 16/50\n","323/323 - 23s - loss: 0.6951 - tp: 5978.0000 - fp: 468.0000 - tn: 38226.0000 - fn: 471.0000 - accuracy: 0.9270 - precision: 0.9274 - recall: 0.9270 - auc: 0.9733 - val_loss: 4.5223 - val_tp: 2066.0000 - val_fp: 697.0000 - val_tn: 15893.0000 - val_fn: 699.0000 - val_accuracy: 0.7476 - val_precision: 0.7477 - val_recall: 0.7472 - val_auc: 0.8754\n","Epoch 17/50\n","323/323 - 23s - loss: 0.5361 - tp: 6062.0000 - fp: 386.0000 - tn: 38308.0000 - fn: 387.0000 - accuracy: 0.9400 - precision: 0.9401 - recall: 0.9400 - auc: 0.9795 - val_loss: 4.6359 - val_tp: 2024.0000 - val_fp: 737.0000 - val_tn: 15853.0000 - val_fn: 741.0000 - val_accuracy: 0.7327 - val_precision: 0.7331 - val_recall: 0.7320 - val_auc: 0.8718\n","Epoch 18/50\n","323/323 - 23s - loss: 0.4894 - tp: 6103.0000 - fp: 345.0000 - tn: 38349.0000 - fn: 346.0000 - accuracy: 0.9463 - precision: 0.9465 - recall: 0.9463 - auc: 0.9817 - val_loss: 4.2077 - val_tp: 2078.0000 - val_fp: 686.0000 - val_tn: 15904.0000 - val_fn: 687.0000 - val_accuracy: 0.7519 - val_precision: 0.7518 - val_recall: 0.7515 - val_auc: 0.8822\n","Epoch 19/50\n","323/323 - 23s - loss: 0.5197 - tp: 6078.0000 - fp: 369.0000 - tn: 38325.0000 - fn: 371.0000 - accuracy: 0.9426 - precision: 0.9428 - recall: 0.9425 - auc: 0.9805 - val_loss: 6.2911 - val_tp: 1975.0000 - val_fp: 785.0000 - val_tn: 15805.0000 - val_fn: 790.0000 - val_accuracy: 0.7157 - val_precision: 0.7156 - val_recall: 0.7143 - val_auc: 0.8593\n","Epoch 20/50\n","323/323 - 23s - loss: 0.4779 - tp: 6106.0000 - fp: 341.0000 - tn: 38353.0000 - fn: 343.0000 - accuracy: 0.9468 - precision: 0.9471 - recall: 0.9468 - auc: 0.9814 - val_loss: 4.9459 - val_tp: 2011.0000 - val_fp: 751.0000 - val_tn: 15839.0000 - val_fn: 754.0000 - val_accuracy: 0.7277 - val_precision: 0.7281 - val_recall: 0.7273 - val_auc: 0.8692\n","Epoch 21/50\n","323/323 - 23s - loss: 0.4657 - tp: 6101.0000 - fp: 345.0000 - tn: 38349.0000 - fn: 348.0000 - accuracy: 0.9462 - precision: 0.9465 - recall: 0.9460 - auc: 0.9819 - val_loss: 7.9539 - val_tp: 1884.0000 - val_fp: 878.0000 - val_tn: 15712.0000 - val_fn: 881.0000 - val_accuracy: 0.6814 - val_precision: 0.6821 - val_recall: 0.6814 - val_auc: 0.8359\n","Epoch 22/50\n","323/323 - 23s - loss: 0.4178 - tp: 6156.0000 - fp: 291.0000 - tn: 38403.0000 - fn: 293.0000 - accuracy: 0.9546 - precision: 0.9549 - recall: 0.9546 - auc: 0.9842 - val_loss: 5.3452 - val_tp: 2026.0000 - val_fp: 738.0000 - val_tn: 15852.0000 - val_fn: 739.0000 - val_accuracy: 0.7327 - val_precision: 0.7330 - val_recall: 0.7327 - val_auc: 0.8692\n","Epoch 23/50\n","323/323 - 23s - loss: 0.4425 - tp: 6150.0000 - fp: 299.0000 - tn: 38395.0000 - fn: 299.0000 - accuracy: 0.9536 - precision: 0.9536 - recall: 0.9536 - auc: 0.9838 - val_loss: 5.7175 - val_tp: 1973.0000 - val_fp: 789.0000 - val_tn: 15801.0000 - val_fn: 792.0000 - val_accuracy: 0.7139 - val_precision: 0.7143 - val_recall: 0.7136 - val_auc: 0.8586\n","Epoch 24/50\n","323/323 - 23s - loss: 0.3841 - tp: 6159.0000 - fp: 288.0000 - tn: 38406.0000 - fn: 290.0000 - accuracy: 0.9552 - precision: 0.9553 - recall: 0.9550 - auc: 0.9855 - val_loss: 5.0494 - val_tp: 2040.0000 - val_fp: 723.0000 - val_tn: 15867.0000 - val_fn: 725.0000 - val_accuracy: 0.7378 - val_precision: 0.7383 - val_recall: 0.7378 - val_auc: 0.8717\n","Epoch 25/50\n","323/323 - 23s - loss: 0.4069 - tp: 6153.0000 - fp: 295.0000 - tn: 38399.0000 - fn: 296.0000 - accuracy: 0.9543 - precision: 0.9542 - recall: 0.9541 - auc: 0.9848 - val_loss: 4.5811 - val_tp: 2069.0000 - val_fp: 691.0000 - val_tn: 15899.0000 - val_fn: 696.0000 - val_accuracy: 0.7486 - val_precision: 0.7496 - val_recall: 0.7483 - val_auc: 0.8749\n","Epoch 26/50\n","323/323 - 23s - loss: 0.3056 - tp: 6211.0000 - fp: 236.0000 - tn: 38458.0000 - fn: 238.0000 - accuracy: 0.9634 - precision: 0.9634 - recall: 0.9631 - auc: 0.9877 - val_loss: 4.5083 - val_tp: 2061.0000 - val_fp: 701.0000 - val_tn: 15889.0000 - val_fn: 704.0000 - val_accuracy: 0.7454 - val_precision: 0.7462 - val_recall: 0.7454 - val_auc: 0.8802\n","Epoch 27/50\n","323/323 - 23s - loss: 0.4120 - tp: 6153.0000 - fp: 295.0000 - tn: 38399.0000 - fn: 296.0000 - accuracy: 0.9541 - precision: 0.9542 - recall: 0.9541 - auc: 0.9847 - val_loss: 4.4951 - val_tp: 2062.0000 - val_fp: 698.0000 - val_tn: 15892.0000 - val_fn: 703.0000 - val_accuracy: 0.7465 - val_precision: 0.7471 - val_recall: 0.7458 - val_auc: 0.8806\n","Epoch 28/50\n","323/323 - 23s - loss: 0.3219 - tp: 6193.0000 - fp: 256.0000 - tn: 38438.0000 - fn: 256.0000 - accuracy: 0.9603 - precision: 0.9603 - recall: 0.9603 - auc: 0.9867 - val_loss: 5.2374 - val_tp: 2044.0000 - val_fp: 718.0000 - val_tn: 15872.0000 - val_fn: 721.0000 - val_accuracy: 0.7392 - val_precision: 0.7400 - val_recall: 0.7392 - val_auc: 0.8720\n","Epoch 29/50\n","323/323 - 23s - loss: 0.3519 - tp: 6184.0000 - fp: 264.0000 - tn: 38430.0000 - fn: 265.0000 - accuracy: 0.9591 - precision: 0.9591 - recall: 0.9589 - auc: 0.9863 - val_loss: 6.2877 - val_tp: 1961.0000 - val_fp: 801.0000 - val_tn: 15789.0000 - val_fn: 804.0000 - val_accuracy: 0.7096 - val_precision: 0.7100 - val_recall: 0.7092 - val_auc: 0.8586\n","Epoch 30/50\n","323/323 - 23s - loss: 0.3100 - tp: 6209.0000 - fp: 239.0000 - tn: 38455.0000 - fn: 240.0000 - accuracy: 0.9628 - precision: 0.9629 - recall: 0.9628 - auc: 0.9882 - val_loss: 4.9465 - val_tp: 2059.0000 - val_fp: 705.0000 - val_tn: 15885.0000 - val_fn: 706.0000 - val_accuracy: 0.7447 - val_precision: 0.7449 - val_recall: 0.7447 - val_auc: 0.8777\n","Epoch 31/50\n","323/323 - 23s - loss: 0.3238 - tp: 6215.0000 - fp: 233.0000 - tn: 38461.0000 - fn: 234.0000 - accuracy: 0.9637 - precision: 0.9639 - recall: 0.9637 - auc: 0.9877 - val_loss: 5.0850 - val_tp: 2046.0000 - val_fp: 716.0000 - val_tn: 15874.0000 - val_fn: 719.0000 - val_accuracy: 0.7400 - val_precision: 0.7408 - val_recall: 0.7400 - val_auc: 0.8740\n","Epoch 32/50\n","323/323 - 23s - loss: 0.2653 - tp: 6249.0000 - fp: 199.0000 - tn: 38495.0000 - fn: 200.0000 - accuracy: 0.9690 - precision: 0.9691 - recall: 0.9690 - auc: 0.9900 - val_loss: 5.6570 - val_tp: 1983.0000 - val_fp: 781.0000 - val_tn: 15809.0000 - val_fn: 782.0000 - val_accuracy: 0.7175 - val_precision: 0.7174 - val_recall: 0.7172 - val_auc: 0.8624\n","Epoch 33/50\n","323/323 - 23s - loss: 0.2465 - tp: 6249.0000 - fp: 199.0000 - tn: 38495.0000 - fn: 200.0000 - accuracy: 0.9691 - precision: 0.9691 - recall: 0.9690 - auc: 0.9902 - val_loss: 7.8028 - val_tp: 1830.0000 - val_fp: 933.0000 - val_tn: 15657.0000 - val_fn: 935.0000 - val_accuracy: 0.6618 - val_precision: 0.6623 - val_recall: 0.6618 - val_auc: 0.8258\n","Epoch 34/50\n","323/323 - 23s - loss: 0.2480 - tp: 6247.0000 - fp: 200.0000 - tn: 38494.0000 - fn: 202.0000 - accuracy: 0.9687 - precision: 0.9690 - recall: 0.9687 - auc: 0.9906 - val_loss: 4.9799 - val_tp: 2059.0000 - val_fp: 702.0000 - val_tn: 15888.0000 - val_fn: 706.0000 - val_accuracy: 0.7447 - val_precision: 0.7457 - val_recall: 0.7447 - val_auc: 0.8758\n","Epoch 35/50\n","323/323 - 23s - loss: 0.2464 - tp: 6245.0000 - fp: 203.0000 - tn: 38491.0000 - fn: 204.0000 - accuracy: 0.9684 - precision: 0.9685 - recall: 0.9684 - auc: 0.9903 - val_loss: 5.7546 - val_tp: 2050.0000 - val_fp: 714.0000 - val_tn: 15876.0000 - val_fn: 715.0000 - val_accuracy: 0.7414 - val_precision: 0.7417 - val_recall: 0.7414 - val_auc: 0.8714\n","Epoch 36/50\n","323/323 - 23s - loss: 0.2885 - tp: 6236.0000 - fp: 213.0000 - tn: 38481.0000 - fn: 213.0000 - accuracy: 0.9670 - precision: 0.9670 - recall: 0.9670 - auc: 0.9891 - val_loss: 4.4260 - val_tp: 2095.0000 - val_fp: 668.0000 - val_tn: 15922.0000 - val_fn: 670.0000 - val_accuracy: 0.7580 - val_precision: 0.7582 - val_recall: 0.7577 - val_auc: 0.8818\n","Epoch 37/50\n","323/323 - 23s - loss: 0.2836 - tp: 6222.0000 - fp: 227.0000 - tn: 38467.0000 - fn: 227.0000 - accuracy: 0.9648 - precision: 0.9648 - recall: 0.9648 - auc: 0.9889 - val_loss: 5.2530 - val_tp: 2076.0000 - val_fp: 686.0000 - val_tn: 15904.0000 - val_fn: 689.0000 - val_accuracy: 0.7508 - val_precision: 0.7516 - val_recall: 0.7508 - val_auc: 0.8763\n","Epoch 38/50\n","323/323 - 23s - loss: 0.2080 - tp: 6276.0000 - fp: 173.0000 - tn: 38521.0000 - fn: 173.0000 - accuracy: 0.9732 - precision: 0.9732 - recall: 0.9732 - auc: 0.9921 - val_loss: 4.4815 - val_tp: 2094.0000 - val_fp: 665.0000 - val_tn: 15925.0000 - val_fn: 671.0000 - val_accuracy: 0.7577 - val_precision: 0.7590 - val_recall: 0.7573 - val_auc: 0.8845\n","Epoch 39/50\n","323/323 - 23s - loss: 0.2517 - tp: 6257.0000 - fp: 190.0000 - tn: 38504.0000 - fn: 192.0000 - accuracy: 0.9702 - precision: 0.9705 - recall: 0.9702 - auc: 0.9901 - val_loss: 4.5374 - val_tp: 2077.0000 - val_fp: 687.0000 - val_tn: 15903.0000 - val_fn: 688.0000 - val_accuracy: 0.7512 - val_precision: 0.7514 - val_recall: 0.7512 - val_auc: 0.8803\n","Epoch 40/50\n","323/323 - 23s - loss: 0.2596 - tp: 6265.0000 - fp: 183.0000 - tn: 38511.0000 - fn: 184.0000 - accuracy: 0.9715 - precision: 0.9716 - recall: 0.9715 - auc: 0.9899 - val_loss: 5.6379 - val_tp: 2038.0000 - val_fp: 724.0000 - val_tn: 15866.0000 - val_fn: 727.0000 - val_accuracy: 0.7371 - val_precision: 0.7379 - val_recall: 0.7371 - val_auc: 0.8704\n","Epoch 41/50\n","323/323 - 23s - loss: 0.2028 - tp: 6291.0000 - fp: 157.0000 - tn: 38537.0000 - fn: 158.0000 - accuracy: 0.9757 - precision: 0.9757 - recall: 0.9755 - auc: 0.9926 - val_loss: 5.0875 - val_tp: 2050.0000 - val_fp: 710.0000 - val_tn: 15880.0000 - val_fn: 715.0000 - val_accuracy: 0.7421 - val_precision: 0.7428 - val_recall: 0.7414 - val_auc: 0.8768\n","Epoch 42/50\n","323/323 - 24s - loss: 0.2441 - tp: 6248.0000 - fp: 201.0000 - tn: 38493.0000 - fn: 201.0000 - accuracy: 0.9688 - precision: 0.9688 - recall: 0.9688 - auc: 0.9898 - val_loss: 4.8757 - val_tp: 2091.0000 - val_fp: 673.0000 - val_tn: 15917.0000 - val_fn: 674.0000 - val_accuracy: 0.7562 - val_precision: 0.7565 - val_recall: 0.7562 - val_auc: 0.8788\n","Epoch 43/50\n","323/323 - 24s - loss: 0.2447 - tp: 6259.0000 - fp: 189.0000 - tn: 38505.0000 - fn: 190.0000 - accuracy: 0.9707 - precision: 0.9707 - recall: 0.9705 - auc: 0.9904 - val_loss: 4.9141 - val_tp: 2097.0000 - val_fp: 663.0000 - val_tn: 15927.0000 - val_fn: 668.0000 - val_accuracy: 0.7588 - val_precision: 0.7598 - val_recall: 0.7584 - val_auc: 0.8798\n","Epoch 44/50\n","323/323 - 24s - loss: 0.1976 - tp: 6280.0000 - fp: 167.0000 - tn: 38527.0000 - fn: 169.0000 - accuracy: 0.9739 - precision: 0.9741 - recall: 0.9738 - auc: 0.9925 - val_loss: 4.8642 - val_tp: 2079.0000 - val_fp: 686.0000 - val_tn: 15904.0000 - val_fn: 686.0000 - val_accuracy: 0.7519 - val_precision: 0.7519 - val_recall: 0.7519 - val_auc: 0.8775\n","Epoch 45/50\n","323/323 - 24s - loss: 0.1928 - tp: 6274.0000 - fp: 175.0000 - tn: 38519.0000 - fn: 175.0000 - accuracy: 0.9729 - precision: 0.9729 - recall: 0.9729 - auc: 0.9922 - val_loss: 4.9961 - val_tp: 2092.0000 - val_fp: 668.0000 - val_tn: 15922.0000 - val_fn: 673.0000 - val_accuracy: 0.7570 - val_precision: 0.7580 - val_recall: 0.7566 - val_auc: 0.8788\n","Epoch 46/50\n","323/323 - 24s - loss: 0.2000 - tp: 6283.0000 - fp: 166.0000 - tn: 38528.0000 - fn: 166.0000 - accuracy: 0.9743 - precision: 0.9743 - recall: 0.9743 - auc: 0.9922 - val_loss: 5.1924 - val_tp: 2034.0000 - val_fp: 729.0000 - val_tn: 15861.0000 - val_fn: 731.0000 - val_accuracy: 0.7363 - val_precision: 0.7362 - val_recall: 0.7356 - val_auc: 0.8708\n","Epoch 47/50\n","323/323 - 24s - loss: 0.2159 - tp: 6270.0000 - fp: 179.0000 - tn: 38515.0000 - fn: 179.0000 - accuracy: 0.9722 - precision: 0.9722 - recall: 0.9722 - auc: 0.9919 - val_loss: 4.7736 - val_tp: 2082.0000 - val_fp: 681.0000 - val_tn: 15909.0000 - val_fn: 683.0000 - val_accuracy: 0.7530 - val_precision: 0.7535 - val_recall: 0.7530 - val_auc: 0.8784\n","Epoch 48/50\n","323/323 - 24s - loss: 0.2070 - tp: 6287.0000 - fp: 162.0000 - tn: 38532.0000 - fn: 162.0000 - accuracy: 0.9749 - precision: 0.9749 - recall: 0.9749 - auc: 0.9914 - val_loss: 4.8592 - val_tp: 2089.0000 - val_fp: 675.0000 - val_tn: 15915.0000 - val_fn: 676.0000 - val_accuracy: 0.7555 - val_precision: 0.7558 - val_recall: 0.7555 - val_auc: 0.8788\n","Epoch 49/50\n","323/323 - 24s - loss: 0.1572 - tp: 6300.0000 - fp: 149.0000 - tn: 38545.0000 - fn: 149.0000 - accuracy: 0.9769 - precision: 0.9769 - recall: 0.9769 - auc: 0.9937 - val_loss: 4.7581 - val_tp: 2103.0000 - val_fp: 659.0000 - val_tn: 15931.0000 - val_fn: 662.0000 - val_accuracy: 0.7606 - val_precision: 0.7614 - val_recall: 0.7606 - val_auc: 0.8812\n","Epoch 50/50\n","323/323 - 24s - loss: 0.1733 - tp: 6295.0000 - fp: 153.0000 - tn: 38541.0000 - fn: 154.0000 - accuracy: 0.9761 - precision: 0.9763 - recall: 0.9761 - auc: 0.9928 - val_loss: 5.8091 - val_tp: 2061.0000 - val_fp: 703.0000 - val_tn: 15887.0000 - val_fn: 704.0000 - val_accuracy: 0.7454 - val_precision: 0.7457 - val_recall: 0.7454 - val_auc: 0.8722\n","Time taken was 1179.1724035739899 seconds\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"0dodQ-iwjL7Y"},"source":["##Inception V3 -Graph Performance"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HM3DiqyIbspA","executionInfo":{"status":"ok","timestamp":1610591289977,"user_tz":-480,"elapsed":73,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"de5a376b-c7d2-4486-bbc4-7668c6dd0182"},"source":["print(inceptionV3TL_fit.history.keys())\r\n","plt.plot(inceptionV3TL_fit.history['accuracy'])\r\n","plt.plot(inceptionV3TL_fit.history['val_accuracy'])\r\n","plt.title('Inception-V3 accuracy with average 1300 per class')\r\n","plt.ylabel('accuracy')\r\n","plt.xlabel('epoch')\r\n","plt.legend(['train', 'test'], loc='upper left')\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dict_keys(['loss', 'tp', 'fp', 'tn', 'fn', 'accuracy', 'precision', 'recall', 'auc', 'val_loss', 'val_tp', 'val_fp', 'val_tn', 'val_fn', 'val_accuracy', 'val_precision', 'val_recall', 'val_auc'])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ydufMt1IbtW1","executionInfo":{"status":"ok","timestamp":1610591290539,"user_tz":-480,"elapsed":597,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"172f26f5-0318-4da1-a93b-fb9ba9faa18d"},"source":["print(inceptionV3TL_fit.history.keys())\r\n","plt.plot(modelfit_resnet.history['loss'])\r\n","plt.plot(modelfit_resnet.history['val_loss'])\r\n","plt.title('Inception-V3 loss with average 1300 per class')\r\n","plt.ylabel('accuracy')\r\n","plt.xlabel('epoch')\r\n","plt.legend(['train', 'test'], loc='upper left')\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dict_keys(['loss', 'tp', 'fp', 'tn', 'fn', 'accuracy', 'precision', 'recall', 'auc', 'val_loss', 'val_tp', 'val_fp', 'val_tn', 'val_fn', 'val_accuracy', 'val_precision', 'val_recall', 'val_auc'])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"IyFYYHCLTB9G"},"source":["##Alexnet"]},{"cell_type":"code","metadata":{"id":"TAde11p96F9K"},"source":["model = keras.models.Sequential([\r\n","    keras.layers.Conv2D(filters=96, kernel_size=(11,11), strides=(4,4), activation='relu', input_shape=(224,224,3)),\r\n","    keras.layers.BatchNormalization(),\r\n","    keras.layers.MaxPool2D(pool_size=(3,3), strides=(2,2)),\r\n","    keras.layers.Conv2D(filters=256, kernel_size=(5,5), strides=(1,1), activation='relu', padding=\"same\"),\r\n","    keras.layers.BatchNormalization(),\r\n","    keras.layers.MaxPool2D(pool_size=(3,3), strides=(2,2)),\r\n","    keras.layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding=\"same\"),\r\n","    keras.layers.BatchNormalization(),\r\n","    keras.layers.Conv2D(filters=384, kernel_size=(1,1), strides=(1,1), activation='relu', padding=\"same\"),\r\n","    keras.layers.BatchNormalization(),\r\n","    keras.layers.Conv2D(filters=256, kernel_size=(1,1), strides=(1,1), activation='relu', padding=\"same\"),\r\n","    keras.layers.BatchNormalization(),\r\n","    keras.layers.MaxPool2D(pool_size=(3,3), strides=(2,2))\r\n","])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YtZoj2IqWGlA"},"source":["weights_path='/content/drive/MyDrive/UITM/SEM 4/weight/alexnet_weights.h5'\r\n","\r\n","\r\n","def load_trained_model(weights_path):\r\n","   model = create_model()\r\n","   model.load_weights(weights_path)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XA9Pl8XsTKQW","executionInfo":{"status":"ok","timestamp":1610591290541,"user_tz":-480,"elapsed":192,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"7f91f552-5804-4b25-8ea4-0fc5cbda256a"},"source":["print(\"Freezed layers:\")\r\n","for i, layer in enumerate(model.layers[:]):\r\n","    print(i, layer.name)\r\n","    layer.trainable = True"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Freezed layers:\n","0 conv2d_94\n","1 batch_normalization_94\n","2 max_pooling2d_7\n","3 conv2d_95\n","4 batch_normalization_95\n","5 max_pooling2d_8\n","6 conv2d_96\n","7 batch_normalization_96\n","8 conv2d_97\n","9 batch_normalization_97\n","10 conv2d_98\n","11 batch_normalization_98\n","12 max_pooling2d_9\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HYvgbn3LTbOi","executionInfo":{"status":"ok","timestamp":1610591290543,"user_tz":-480,"elapsed":176,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"fd7428ff-c40f-458d-f389-52edf4a6eb09"},"source":["model.summary()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d_94 (Conv2D)           (None, 54, 54, 96)        34944     \n","_________________________________________________________________\n","batch_normalization_94 (Batc (None, 54, 54, 96)        384       \n","_________________________________________________________________\n","max_pooling2d_7 (MaxPooling2 (None, 26, 26, 96)        0         \n","_________________________________________________________________\n","conv2d_95 (Conv2D)           (None, 26, 26, 256)       614656    \n","_________________________________________________________________\n","batch_normalization_95 (Batc (None, 26, 26, 256)       1024      \n","_________________________________________________________________\n","max_pooling2d_8 (MaxPooling2 (None, 12, 12, 256)       0         \n","_________________________________________________________________\n","conv2d_96 (Conv2D)           (None, 12, 12, 384)       885120    \n","_________________________________________________________________\n","batch_normalization_96 (Batc (None, 12, 12, 384)       1536      \n","_________________________________________________________________\n","conv2d_97 (Conv2D)           (None, 12, 12, 384)       147840    \n","_________________________________________________________________\n","batch_normalization_97 (Batc (None, 12, 12, 384)       1536      \n","_________________________________________________________________\n","conv2d_98 (Conv2D)           (None, 12, 12, 256)       98560     \n","_________________________________________________________________\n","batch_normalization_98 (Batc (None, 12, 12, 256)       1024      \n","_________________________________________________________________\n","max_pooling2d_9 (MaxPooling2 (None, 5, 5, 256)         0         \n","=================================================================\n","Total params: 1,786,624\n","Trainable params: 1,783,872\n","Non-trainable params: 2,752\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"06WNO2KWWr3P","executionInfo":{"status":"ok","timestamp":1610591290543,"user_tz":-480,"elapsed":160,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"486d2773-436f-4aa8-9e5a-826b930c7ba1"},"source":["print(\"Freezed layers:\")\r\n","for i, layer in enumerate(model.layers[:]):\r\n","    print(i, layer.name)\r\n","    layer.trainable = False"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Freezed layers:\n","0 conv2d_94\n","1 batch_normalization_94\n","2 max_pooling2d_7\n","3 conv2d_95\n","4 batch_normalization_95\n","5 max_pooling2d_8\n","6 conv2d_96\n","7 batch_normalization_96\n","8 conv2d_97\n","9 batch_normalization_97\n","10 conv2d_98\n","11 batch_normalization_98\n","12 max_pooling2d_9\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9DKAr4yeW9R8","executionInfo":{"status":"ok","timestamp":1610591290544,"user_tz":-480,"elapsed":145,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"1e3d98bd-9b81-4ff3-8875-02828cfaa2d3"},"source":["model.summary()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d_94 (Conv2D)           (None, 54, 54, 96)        34944     \n","_________________________________________________________________\n","batch_normalization_94 (Batc (None, 54, 54, 96)        384       \n","_________________________________________________________________\n","max_pooling2d_7 (MaxPooling2 (None, 26, 26, 96)        0         \n","_________________________________________________________________\n","conv2d_95 (Conv2D)           (None, 26, 26, 256)       614656    \n","_________________________________________________________________\n","batch_normalization_95 (Batc (None, 26, 26, 256)       1024      \n","_________________________________________________________________\n","max_pooling2d_8 (MaxPooling2 (None, 12, 12, 256)       0         \n","_________________________________________________________________\n","conv2d_96 (Conv2D)           (None, 12, 12, 384)       885120    \n","_________________________________________________________________\n","batch_normalization_96 (Batc (None, 12, 12, 384)       1536      \n","_________________________________________________________________\n","conv2d_97 (Conv2D)           (None, 12, 12, 384)       147840    \n","_________________________________________________________________\n","batch_normalization_97 (Batc (None, 12, 12, 384)       1536      \n","_________________________________________________________________\n","conv2d_98 (Conv2D)           (None, 12, 12, 256)       98560     \n","_________________________________________________________________\n","batch_normalization_98 (Batc (None, 12, 12, 256)       1024      \n","_________________________________________________________________\n","max_pooling2d_9 (MaxPooling2 (None, 5, 5, 256)         0         \n","=================================================================\n","Total params: 1,786,624\n","Trainable params: 0\n","Non-trainable params: 1,786,624\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"qwYcxTOOjn5w"},"source":["x = keras.layers.Flatten()(model.output)\r\n","x = keras.layers.Dense(4096, activation='relu')(x)\r\n","x = keras.layers.Dropout(0.5)(x)\r\n","x = keras.layers.Dense(4096, activation='relu')(x)\r\n","x = keras.layers.Dropout(0.5)(x)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"4KTbk773mDhY"},"source":["out = keras.layers.Dense(7, activation='softmax')(x)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"vEb4jHfRmC9h"},"source":["alexout = Model(inputs=model.input, outputs=out, name='AlexNet')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"akQWv500W-n2"},"source":["model.compile(loss=keras.losses.categorical_crossentropy, optimizer=opt, metrics=cmetrics)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Cwdimm6htPm7","executionInfo":{"status":"ok","timestamp":1610591290547,"user_tz":-480,"elapsed":71,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"ad4d56ba-2221-418a-d424-2c7d5bdfe68e"},"source":["alexout.summary()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Model: \"AlexNet\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","conv2d_94_input (InputLayer) [(None, 224, 224, 3)]     0         \n","_________________________________________________________________\n","conv2d_94 (Conv2D)           (None, 54, 54, 96)        34944     \n","_________________________________________________________________\n","batch_normalization_94 (Batc (None, 54, 54, 96)        384       \n","_________________________________________________________________\n","max_pooling2d_7 (MaxPooling2 (None, 26, 26, 96)        0         \n","_________________________________________________________________\n","conv2d_95 (Conv2D)           (None, 26, 26, 256)       614656    \n","_________________________________________________________________\n","batch_normalization_95 (Batc (None, 26, 26, 256)       1024      \n","_________________________________________________________________\n","max_pooling2d_8 (MaxPooling2 (None, 12, 12, 256)       0         \n","_________________________________________________________________\n","conv2d_96 (Conv2D)           (None, 12, 12, 384)       885120    \n","_________________________________________________________________\n","batch_normalization_96 (Batc (None, 12, 12, 384)       1536      \n","_________________________________________________________________\n","conv2d_97 (Conv2D)           (None, 12, 12, 384)       147840    \n","_________________________________________________________________\n","batch_normalization_97 (Batc (None, 12, 12, 384)       1536      \n","_________________________________________________________________\n","conv2d_98 (Conv2D)           (None, 12, 12, 256)       98560     \n","_________________________________________________________________\n","batch_normalization_98 (Batc (None, 12, 12, 256)       1024      \n","_________________________________________________________________\n","max_pooling2d_9 (MaxPooling2 (None, 5, 5, 256)         0         \n","_________________________________________________________________\n","flatten_2 (Flatten)          (None, 6400)              0         \n","_________________________________________________________________\n","dense (Dense)                (None, 4096)              26218496  \n","_________________________________________________________________\n","dropout_2 (Dropout)          (None, 4096)              0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 4096)              16781312  \n","_________________________________________________________________\n","dropout_3 (Dropout)          (None, 4096)              0         \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 7)                 28679     \n","=================================================================\n","Total params: 44,815,111\n","Trainable params: 43,028,487\n","Non-trainable params: 1,786,624\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Z5qDENpSt14t"},"source":["alexout.compile(loss=keras.losses.categorical_crossentropy, optimizer=opt, metrics=cmetrics)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"hPvtoKuyfViz"},"source":["num_classes=7\r\n","EPOCH=50\r\n","BATCHSIZE=20"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oO-yK8e6fXgR","executionInfo":{"status":"ok","timestamp":1610591659653,"user_tz":-480,"elapsed":369142,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"ca5d2bab-ba25-4e54-e907-488e3a7dc72f"},"source":["t1 = time.time()\r\n","modelfit_alex = alexout.fit(train_x, train_y, epochs=EPOCH, batch_size=BATCHSIZE, verbose=2, validation_data=(test_x, test_y)\r\n","                                 #callbacks=es\r\n","                                 )\r\n","# Testing has finished \r\n","t2 = time.time()\r\n","print( 'Time taken was {} seconds'.format( t2 - t1))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch 1/50\n","323/323 - 9s - loss: 1.9382 - tp: 2061.0000 - fp: 703.0000 - tn: 54581.0000 - fn: 7153.0000 - accuracy: 0.3462 - precision: 0.7457 - recall: 0.2237 - auc: 0.7040 - val_loss: 1.9305 - val_tp: 0.0000e+00 - val_fp: 0.0000e+00 - val_tn: 16590.0000 - val_fn: 2765.0000 - val_accuracy: 0.1949 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.5639\n","Epoch 2/50\n","323/323 - 8s - loss: 1.9231 - tp: 0.0000e+00 - fp: 0.0000e+00 - tn: 38694.0000 - fn: 6449.0000 - accuracy: 0.2301 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.5863 - val_loss: 1.9145 - val_tp: 0.0000e+00 - val_fp: 0.0000e+00 - val_tn: 16590.0000 - val_fn: 2765.0000 - val_accuracy: 0.1689 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.5955\n","Epoch 3/50\n","323/323 - 8s - loss: 1.9083 - tp: 0.0000e+00 - fp: 0.0000e+00 - tn: 38694.0000 - fn: 6449.0000 - accuracy: 0.2314 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.6200 - val_loss: 1.8953 - val_tp: 0.0000e+00 - val_fp: 0.0000e+00 - val_tn: 16590.0000 - val_fn: 2765.0000 - val_accuracy: 0.2311 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.6368\n","Epoch 4/50\n","323/323 - 8s - loss: 1.8856 - tp: 0.0000e+00 - fp: 0.0000e+00 - tn: 38694.0000 - fn: 6449.0000 - accuracy: 0.2503 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.6560 - val_loss: 1.8670 - val_tp: 0.0000e+00 - val_fp: 0.0000e+00 - val_tn: 16590.0000 - val_fn: 2765.0000 - val_accuracy: 0.2687 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.7030\n","Epoch 5/50\n","323/323 - 8s - loss: 1.8540 - tp: 0.0000e+00 - fp: 0.0000e+00 - tn: 38694.0000 - fn: 6449.0000 - accuracy: 0.2706 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.6971 - val_loss: 1.8316 - val_tp: 0.0000e+00 - val_fp: 0.0000e+00 - val_tn: 16590.0000 - val_fn: 2765.0000 - val_accuracy: 0.2915 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.7169\n","Epoch 6/50\n","323/323 - 8s - loss: 1.8168 - tp: 0.0000e+00 - fp: 0.0000e+00 - tn: 38694.0000 - fn: 6449.0000 - accuracy: 0.2942 - precision: 0.0000e+00 - recall: 0.0000e+00 - auc: 0.7124 - val_loss: 1.7886 - val_tp: 0.0000e+00 - val_fp: 0.0000e+00 - val_tn: 16590.0000 - val_fn: 2765.0000 - val_accuracy: 0.3045 - val_precision: 0.0000e+00 - val_recall: 0.0000e+00 - val_auc: 0.7350\n","Epoch 7/50\n","323/323 - 8s - loss: 1.7730 - tp: 18.0000 - fp: 0.0000e+00 - tn: 38694.0000 - fn: 6431.0000 - accuracy: 0.3022 - precision: 1.0000 - recall: 0.0028 - auc: 0.7274 - val_loss: 1.7442 - val_tp: 42.0000 - val_fp: 4.0000 - val_tn: 16586.0000 - val_fn: 2723.0000 - val_accuracy: 0.3353 - val_precision: 0.9130 - val_recall: 0.0152 - val_auc: 0.7375\n","Epoch 8/50\n","323/323 - 8s - loss: 1.7286 - tp: 186.0000 - fp: 23.0000 - tn: 38671.0000 - fn: 6263.0000 - accuracy: 0.3380 - precision: 0.8900 - recall: 0.0288 - auc: 0.7404 - val_loss: 1.7078 - val_tp: 179.0000 - val_fp: 25.0000 - val_tn: 16565.0000 - val_fn: 2586.0000 - val_accuracy: 0.3277 - val_precision: 0.8775 - val_recall: 0.0647 - val_auc: 0.7305\n","Epoch 9/50\n","323/323 - 8s - loss: 1.6911 - tp: 436.0000 - fp: 65.0000 - tn: 38629.0000 - fn: 6013.0000 - accuracy: 0.3387 - precision: 0.8703 - recall: 0.0676 - auc: 0.7465 - val_loss: 1.6701 - val_tp: 272.0000 - val_fp: 54.0000 - val_tn: 16536.0000 - val_fn: 2493.0000 - val_accuracy: 0.3656 - val_precision: 0.8344 - val_recall: 0.0984 - val_auc: 0.7549\n","Epoch 10/50\n","323/323 - 8s - loss: 1.6609 - tp: 543.0000 - fp: 96.0000 - tn: 38598.0000 - fn: 5906.0000 - accuracy: 0.3574 - precision: 0.8498 - recall: 0.0842 - auc: 0.7556 - val_loss: 1.6406 - val_tp: 273.0000 - val_fp: 51.0000 - val_tn: 16539.0000 - val_fn: 2492.0000 - val_accuracy: 0.3696 - val_precision: 0.8426 - val_recall: 0.0987 - val_auc: 0.7639\n","Epoch 11/50\n","323/323 - 7s - loss: 1.6308 - tp: 612.0000 - fp: 113.0000 - tn: 38581.0000 - fn: 5837.0000 - accuracy: 0.3687 - precision: 0.8441 - recall: 0.0949 - auc: 0.7655 - val_loss: 1.6165 - val_tp: 308.0000 - val_fp: 60.0000 - val_tn: 16530.0000 - val_fn: 2457.0000 - val_accuracy: 0.3740 - val_precision: 0.8370 - val_recall: 0.1114 - val_auc: 0.7644\n","Epoch 12/50\n","323/323 - 7s - loss: 1.6070 - tp: 640.0000 - fp: 134.0000 - tn: 38560.0000 - fn: 5809.0000 - accuracy: 0.3780 - precision: 0.8269 - recall: 0.0992 - auc: 0.7709 - val_loss: 1.5961 - val_tp: 331.0000 - val_fp: 95.0000 - val_tn: 16495.0000 - val_fn: 2434.0000 - val_accuracy: 0.3754 - val_precision: 0.7770 - val_recall: 0.1197 - val_auc: 0.7703\n","Epoch 13/50\n","323/323 - 7s - loss: 1.5837 - tp: 686.0000 - fp: 140.0000 - tn: 38554.0000 - fn: 5763.0000 - accuracy: 0.3886 - precision: 0.8305 - recall: 0.1064 - auc: 0.7774 - val_loss: 1.5717 - val_tp: 286.0000 - val_fp: 50.0000 - val_tn: 16540.0000 - val_fn: 2479.0000 - val_accuracy: 0.4058 - val_precision: 0.8512 - val_recall: 0.1034 - val_auc: 0.7805\n","Epoch 14/50\n","323/323 - 7s - loss: 1.5640 - tp: 685.0000 - fp: 144.0000 - tn: 38550.0000 - fn: 5764.0000 - accuracy: 0.3956 - precision: 0.8263 - recall: 0.1062 - auc: 0.7818 - val_loss: 1.5478 - val_tp: 334.0000 - val_fp: 88.0000 - val_tn: 16502.0000 - val_fn: 2431.0000 - val_accuracy: 0.4051 - val_precision: 0.7915 - val_recall: 0.1208 - val_auc: 0.7845\n","Epoch 15/50\n","323/323 - 7s - loss: 1.5443 - tp: 692.0000 - fp: 171.0000 - tn: 38523.0000 - fn: 5757.0000 - accuracy: 0.4022 - precision: 0.8019 - recall: 0.1073 - auc: 0.7876 - val_loss: 1.5344 - val_tp: 245.0000 - val_fp: 32.0000 - val_tn: 16558.0000 - val_fn: 2520.0000 - val_accuracy: 0.4163 - val_precision: 0.8845 - val_recall: 0.0886 - val_auc: 0.7948\n","Epoch 16/50\n","323/323 - 7s - loss: 1.5256 - tp: 710.0000 - fp: 173.0000 - tn: 38521.0000 - fn: 5739.0000 - accuracy: 0.4114 - precision: 0.8041 - recall: 0.1101 - auc: 0.7923 - val_loss: 1.5201 - val_tp: 332.0000 - val_fp: 128.0000 - val_tn: 16462.0000 - val_fn: 2433.0000 - val_accuracy: 0.3967 - val_precision: 0.7217 - val_recall: 0.1201 - val_auc: 0.7897\n","Epoch 17/50\n","323/323 - 7s - loss: 1.5103 - tp: 763.0000 - fp: 194.0000 - tn: 38500.0000 - fn: 5686.0000 - accuracy: 0.4171 - precision: 0.7973 - recall: 0.1183 - auc: 0.7958 - val_loss: 1.4900 - val_tp: 346.0000 - val_fp: 83.0000 - val_tn: 16507.0000 - val_fn: 2419.0000 - val_accuracy: 0.4137 - val_precision: 0.8065 - val_recall: 0.1251 - val_auc: 0.8025\n","Epoch 18/50\n","323/323 - 7s - loss: 1.4953 - tp: 801.0000 - fp: 212.0000 - tn: 38482.0000 - fn: 5648.0000 - accuracy: 0.4204 - precision: 0.7907 - recall: 0.1242 - auc: 0.8005 - val_loss: 1.4783 - val_tp: 433.0000 - val_fp: 126.0000 - val_tn: 16464.0000 - val_fn: 2332.0000 - val_accuracy: 0.4134 - val_precision: 0.7746 - val_recall: 0.1566 - val_auc: 0.8041\n","Epoch 19/50\n","323/323 - 7s - loss: 1.4818 - tp: 864.0000 - fp: 268.0000 - tn: 38426.0000 - fn: 5585.0000 - accuracy: 0.4204 - precision: 0.7633 - recall: 0.1340 - auc: 0.8036 - val_loss: 1.4687 - val_tp: 420.0000 - val_fp: 125.0000 - val_tn: 16465.0000 - val_fn: 2345.0000 - val_accuracy: 0.4159 - val_precision: 0.7706 - val_recall: 0.1519 - val_auc: 0.8057\n","Epoch 20/50\n","323/323 - 7s - loss: 1.4726 - tp: 892.0000 - fp: 285.0000 - tn: 38409.0000 - fn: 5557.0000 - accuracy: 0.4334 - precision: 0.7579 - recall: 0.1383 - auc: 0.8072 - val_loss: 1.4562 - val_tp: 424.0000 - val_fp: 106.0000 - val_tn: 16484.0000 - val_fn: 2341.0000 - val_accuracy: 0.4318 - val_precision: 0.8000 - val_recall: 0.1533 - val_auc: 0.8105\n","Epoch 21/50\n","323/323 - 7s - loss: 1.4609 - tp: 954.0000 - fp: 291.0000 - tn: 38403.0000 - fn: 5495.0000 - accuracy: 0.4376 - precision: 0.7663 - recall: 0.1479 - auc: 0.8099 - val_loss: 1.4546 - val_tp: 402.0000 - val_fp: 96.0000 - val_tn: 16494.0000 - val_fn: 2363.0000 - val_accuracy: 0.4333 - val_precision: 0.8072 - val_recall: 0.1454 - val_auc: 0.8122\n","Epoch 22/50\n","323/323 - 7s - loss: 1.4528 - tp: 974.0000 - fp: 303.0000 - tn: 38391.0000 - fn: 5475.0000 - accuracy: 0.4379 - precision: 0.7627 - recall: 0.1510 - auc: 0.8125 - val_loss: 1.4400 - val_tp: 535.0000 - val_fp: 177.0000 - val_tn: 16413.0000 - val_fn: 2230.0000 - val_accuracy: 0.4401 - val_precision: 0.7514 - val_recall: 0.1935 - val_auc: 0.8162\n","Epoch 23/50\n","323/323 - 7s - loss: 1.4395 - tp: 1028.0000 - fp: 312.0000 - tn: 38382.0000 - fn: 5421.0000 - accuracy: 0.4401 - precision: 0.7672 - recall: 0.1594 - auc: 0.8167 - val_loss: 1.4172 - val_tp: 470.0000 - val_fp: 111.0000 - val_tn: 16479.0000 - val_fn: 2295.0000 - val_accuracy: 0.4376 - val_precision: 0.8090 - val_recall: 0.1700 - val_auc: 0.8218\n","Epoch 24/50\n","323/323 - 7s - loss: 1.4317 - tp: 1047.0000 - fp: 316.0000 - tn: 38378.0000 - fn: 5402.0000 - accuracy: 0.4447 - precision: 0.7682 - recall: 0.1624 - auc: 0.8188 - val_loss: 1.4083 - val_tp: 524.0000 - val_fp: 141.0000 - val_tn: 16449.0000 - val_fn: 2241.0000 - val_accuracy: 0.4412 - val_precision: 0.7880 - val_recall: 0.1895 - val_auc: 0.8233\n","Epoch 25/50\n","323/323 - 7s - loss: 1.4172 - tp: 1114.0000 - fp: 346.0000 - tn: 38348.0000 - fn: 5335.0000 - accuracy: 0.4517 - precision: 0.7630 - recall: 0.1727 - auc: 0.8230 - val_loss: 1.3959 - val_tp: 538.0000 - val_fp: 133.0000 - val_tn: 16457.0000 - val_fn: 2227.0000 - val_accuracy: 0.4470 - val_precision: 0.8018 - val_recall: 0.1946 - val_auc: 0.8283\n","Epoch 26/50\n","323/323 - 7s - loss: 1.4035 - tp: 1112.0000 - fp: 326.0000 - tn: 38368.0000 - fn: 5337.0000 - accuracy: 0.4593 - precision: 0.7733 - recall: 0.1724 - auc: 0.8275 - val_loss: 1.4128 - val_tp: 545.0000 - val_fp: 173.0000 - val_tn: 16417.0000 - val_fn: 2220.0000 - val_accuracy: 0.4517 - val_precision: 0.7591 - val_recall: 0.1971 - val_auc: 0.8246\n","Epoch 27/50\n","323/323 - 7s - loss: 1.3960 - tp: 1158.0000 - fp: 357.0000 - tn: 38337.0000 - fn: 5291.0000 - accuracy: 0.4655 - precision: 0.7644 - recall: 0.1796 - auc: 0.8290 - val_loss: 1.3742 - val_tp: 514.0000 - val_fp: 116.0000 - val_tn: 16474.0000 - val_fn: 2251.0000 - val_accuracy: 0.4510 - val_precision: 0.8159 - val_recall: 0.1859 - val_auc: 0.8331\n","Epoch 28/50\n","323/323 - 7s - loss: 1.3862 - tp: 1189.0000 - fp: 348.0000 - tn: 38346.0000 - fn: 5260.0000 - accuracy: 0.4641 - precision: 0.7736 - recall: 0.1844 - auc: 0.8313 - val_loss: 1.3643 - val_tp: 545.0000 - val_fp: 151.0000 - val_tn: 16439.0000 - val_fn: 2220.0000 - val_accuracy: 0.4550 - val_precision: 0.7830 - val_recall: 0.1971 - val_auc: 0.8355\n","Epoch 29/50\n","323/323 - 7s - loss: 1.3753 - tp: 1229.0000 - fp: 345.0000 - tn: 38349.0000 - fn: 5220.0000 - accuracy: 0.4731 - precision: 0.7808 - recall: 0.1906 - auc: 0.8351 - val_loss: 1.3504 - val_tp: 603.0000 - val_fp: 153.0000 - val_tn: 16437.0000 - val_fn: 2162.0000 - val_accuracy: 0.4651 - val_precision: 0.7976 - val_recall: 0.2181 - val_auc: 0.8401\n","Epoch 30/50\n","323/323 - 7s - loss: 1.3607 - tp: 1258.0000 - fp: 350.0000 - tn: 38344.0000 - fn: 5191.0000 - accuracy: 0.4799 - precision: 0.7823 - recall: 0.1951 - auc: 0.8393 - val_loss: 1.3424 - val_tp: 606.0000 - val_fp: 172.0000 - val_tn: 16418.0000 - val_fn: 2159.0000 - val_accuracy: 0.4705 - val_precision: 0.7789 - val_recall: 0.2192 - val_auc: 0.8421\n","Epoch 31/50\n","323/323 - 7s - loss: 1.3490 - tp: 1286.0000 - fp: 360.0000 - tn: 38334.0000 - fn: 5163.0000 - accuracy: 0.4788 - precision: 0.7813 - recall: 0.1994 - auc: 0.8421 - val_loss: 1.3285 - val_tp: 567.0000 - val_fp: 140.0000 - val_tn: 16450.0000 - val_fn: 2198.0000 - val_accuracy: 0.4597 - val_precision: 0.8020 - val_recall: 0.2051 - val_auc: 0.8451\n","Epoch 32/50\n","323/323 - 7s - loss: 1.3357 - tp: 1333.0000 - fp: 355.0000 - tn: 38339.0000 - fn: 5116.0000 - accuracy: 0.4805 - precision: 0.7897 - recall: 0.2067 - auc: 0.8452 - val_loss: 1.3225 - val_tp: 629.0000 - val_fp: 170.0000 - val_tn: 16420.0000 - val_fn: 2136.0000 - val_accuracy: 0.4691 - val_precision: 0.7872 - val_recall: 0.2275 - val_auc: 0.8460\n","Epoch 33/50\n","323/323 - 7s - loss: 1.3275 - tp: 1350.0000 - fp: 358.0000 - tn: 38336.0000 - fn: 5099.0000 - accuracy: 0.4835 - precision: 0.7904 - recall: 0.2093 - auc: 0.8478 - val_loss: 1.3175 - val_tp: 605.0000 - val_fp: 134.0000 - val_tn: 16456.0000 - val_fn: 2160.0000 - val_accuracy: 0.4835 - val_precision: 0.8187 - val_recall: 0.2188 - val_auc: 0.8492\n","Epoch 34/50\n","323/323 - 7s - loss: 1.3168 - tp: 1388.0000 - fp: 351.0000 - tn: 38343.0000 - fn: 5061.0000 - accuracy: 0.4929 - precision: 0.7982 - recall: 0.2152 - auc: 0.8502 - val_loss: 1.3671 - val_tp: 459.0000 - val_fp: 97.0000 - val_tn: 16493.0000 - val_fn: 2306.0000 - val_accuracy: 0.4593 - val_precision: 0.8255 - val_recall: 0.1660 - val_auc: 0.8344\n","Epoch 35/50\n","323/323 - 7s - loss: 1.3050 - tp: 1379.0000 - fp: 355.0000 - tn: 38339.0000 - fn: 5070.0000 - accuracy: 0.4951 - precision: 0.7953 - recall: 0.2138 - auc: 0.8532 - val_loss: 1.3091 - val_tp: 698.0000 - val_fp: 217.0000 - val_tn: 16373.0000 - val_fn: 2067.0000 - val_accuracy: 0.4676 - val_precision: 0.7628 - val_recall: 0.2524 - val_auc: 0.8474\n","Epoch 36/50\n","323/323 - 7s - loss: 1.2922 - tp: 1453.0000 - fp: 353.0000 - tn: 38341.0000 - fn: 4996.0000 - accuracy: 0.4967 - precision: 0.8045 - recall: 0.2253 - auc: 0.8565 - val_loss: 1.2759 - val_tp: 675.0000 - val_fp: 158.0000 - val_tn: 16432.0000 - val_fn: 2090.0000 - val_accuracy: 0.4951 - val_precision: 0.8103 - val_recall: 0.2441 - val_auc: 0.8587\n","Epoch 37/50\n","323/323 - 7s - loss: 1.2824 - tp: 1499.0000 - fp: 372.0000 - tn: 38322.0000 - fn: 4950.0000 - accuracy: 0.4968 - precision: 0.8012 - recall: 0.2324 - auc: 0.8589 - val_loss: 1.2620 - val_tp: 657.0000 - val_fp: 143.0000 - val_tn: 16447.0000 - val_fn: 2108.0000 - val_accuracy: 0.4958 - val_precision: 0.8213 - val_recall: 0.2376 - val_auc: 0.8625\n","Epoch 38/50\n","323/323 - 7s - loss: 1.2716 - tp: 1496.0000 - fp: 355.0000 - tn: 38339.0000 - fn: 4953.0000 - accuracy: 0.5058 - precision: 0.8082 - recall: 0.2320 - auc: 0.8615 - val_loss: 1.2725 - val_tp: 654.0000 - val_fp: 128.0000 - val_tn: 16462.0000 - val_fn: 2111.0000 - val_accuracy: 0.4958 - val_precision: 0.8363 - val_recall: 0.2365 - val_auc: 0.8596\n","Epoch 39/50\n","323/323 - 7s - loss: 1.2611 - tp: 1522.0000 - fp: 359.0000 - tn: 38335.0000 - fn: 4927.0000 - accuracy: 0.5089 - precision: 0.8091 - recall: 0.2360 - auc: 0.8639 - val_loss: 1.2540 - val_tp: 704.0000 - val_fp: 194.0000 - val_tn: 16396.0000 - val_fn: 2061.0000 - val_accuracy: 0.4911 - val_precision: 0.7840 - val_recall: 0.2546 - val_auc: 0.8621\n","Epoch 40/50\n","323/323 - 7s - loss: 1.2536 - tp: 1579.0000 - fp: 374.0000 - tn: 38320.0000 - fn: 4870.0000 - accuracy: 0.5061 - precision: 0.8085 - recall: 0.2448 - auc: 0.8654 - val_loss: 1.2480 - val_tp: 656.0000 - val_fp: 131.0000 - val_tn: 16459.0000 - val_fn: 2109.0000 - val_accuracy: 0.5125 - val_precision: 0.8335 - val_recall: 0.2373 - val_auc: 0.8660\n","Epoch 41/50\n","323/323 - 7s - loss: 1.2437 - tp: 1600.0000 - fp: 381.0000 - tn: 38313.0000 - fn: 4849.0000 - accuracy: 0.5102 - precision: 0.8077 - recall: 0.2481 - auc: 0.8676 - val_loss: 1.2665 - val_tp: 709.0000 - val_fp: 217.0000 - val_tn: 16373.0000 - val_fn: 2056.0000 - val_accuracy: 0.4951 - val_precision: 0.7657 - val_recall: 0.2564 - val_auc: 0.8581\n","Epoch 42/50\n","323/323 - 7s - loss: 1.2381 - tp: 1615.0000 - fp: 383.0000 - tn: 38311.0000 - fn: 4834.0000 - accuracy: 0.5136 - precision: 0.8083 - recall: 0.2504 - auc: 0.8685 - val_loss: 1.2308 - val_tp: 738.0000 - val_fp: 203.0000 - val_tn: 16387.0000 - val_fn: 2027.0000 - val_accuracy: 0.5020 - val_precision: 0.7843 - val_recall: 0.2669 - val_auc: 0.8674\n","Epoch 43/50\n","323/323 - 7s - loss: 1.2268 - tp: 1662.0000 - fp: 380.0000 - tn: 38314.0000 - fn: 4787.0000 - accuracy: 0.5178 - precision: 0.8139 - recall: 0.2577 - auc: 0.8706 - val_loss: 1.2361 - val_tp: 737.0000 - val_fp: 230.0000 - val_tn: 16360.0000 - val_fn: 2028.0000 - val_accuracy: 0.4955 - val_precision: 0.7622 - val_recall: 0.2665 - val_auc: 0.8660\n","Epoch 44/50\n","323/323 - 7s - loss: 1.2198 - tp: 1666.0000 - fp: 394.0000 - tn: 38300.0000 - fn: 4783.0000 - accuracy: 0.5215 - precision: 0.8087 - recall: 0.2583 - auc: 0.8727 - val_loss: 1.2266 - val_tp: 752.0000 - val_fp: 201.0000 - val_tn: 16389.0000 - val_fn: 2013.0000 - val_accuracy: 0.5110 - val_precision: 0.7891 - val_recall: 0.2720 - val_auc: 0.8676\n","Epoch 45/50\n","323/323 - 7s - loss: 1.2125 - tp: 1680.0000 - fp: 408.0000 - tn: 38286.0000 - fn: 4769.0000 - accuracy: 0.5229 - precision: 0.8046 - recall: 0.2605 - auc: 0.8749 - val_loss: 1.1993 - val_tp: 800.0000 - val_fp: 221.0000 - val_tn: 16369.0000 - val_fn: 1965.0000 - val_accuracy: 0.5193 - val_precision: 0.7835 - val_recall: 0.2893 - val_auc: 0.8741\n","Epoch 46/50\n","323/323 - 7s - loss: 1.1996 - tp: 1719.0000 - fp: 426.0000 - tn: 38268.0000 - fn: 4730.0000 - accuracy: 0.5288 - precision: 0.8014 - recall: 0.2666 - auc: 0.8776 - val_loss: 1.1925 - val_tp: 787.0000 - val_fp: 205.0000 - val_tn: 16385.0000 - val_fn: 1978.0000 - val_accuracy: 0.5204 - val_precision: 0.7933 - val_recall: 0.2846 - val_auc: 0.8760\n","Epoch 47/50\n","323/323 - 7s - loss: 1.1945 - tp: 1756.0000 - fp: 428.0000 - tn: 38266.0000 - fn: 4693.0000 - accuracy: 0.5300 - precision: 0.8040 - recall: 0.2723 - auc: 0.8780 - val_loss: 1.2176 - val_tp: 760.0000 - val_fp: 213.0000 - val_tn: 16377.0000 - val_fn: 2005.0000 - val_accuracy: 0.5096 - val_precision: 0.7811 - val_recall: 0.2749 - val_auc: 0.8688\n","Epoch 48/50\n","323/323 - 7s - loss: 1.1795 - tp: 1760.0000 - fp: 415.0000 - tn: 38279.0000 - fn: 4689.0000 - accuracy: 0.5314 - precision: 0.8092 - recall: 0.2729 - auc: 0.8815 - val_loss: 1.2877 - val_tp: 628.0000 - val_fp: 165.0000 - val_tn: 16425.0000 - val_fn: 2137.0000 - val_accuracy: 0.4944 - val_precision: 0.7919 - val_recall: 0.2271 - val_auc: 0.8551\n","Epoch 49/50\n","323/323 - 7s - loss: 1.1823 - tp: 1792.0000 - fp: 440.0000 - tn: 38254.0000 - fn: 4657.0000 - accuracy: 0.5316 - precision: 0.8029 - recall: 0.2779 - auc: 0.8806 - val_loss: 1.1785 - val_tp: 814.0000 - val_fp: 193.0000 - val_tn: 16397.0000 - val_fn: 1951.0000 - val_accuracy: 0.5107 - val_precision: 0.8083 - val_recall: 0.2944 - val_auc: 0.8787\n","Epoch 50/50\n","323/323 - 7s - loss: 1.1761 - tp: 1813.0000 - fp: 442.0000 - tn: 38252.0000 - fn: 4636.0000 - accuracy: 0.5393 - precision: 0.8040 - recall: 0.2811 - auc: 0.8823 - val_loss: 1.1616 - val_tp: 833.0000 - val_fp: 191.0000 - val_tn: 16399.0000 - val_fn: 1932.0000 - val_accuracy: 0.5212 - val_precision: 0.8135 - val_recall: 0.3013 - val_auc: 0.8824\n","Time taken was 368.69788694381714 seconds\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"fPkiV9z9h9Zo"},"source":["##AlexNet-Graph Performance"]},{"cell_type":"code","metadata":{"id":"Ed7nnGyEhb0m"},"source":["print(modelfit_alex.history.keys())\r\n","plt.plot(modelfit_alex.history['accuracy'])\r\n","plt.plot(modelfit_alex.history['val_accuracy'])\r\n","plt.title('AlexNet accuracy with average 1300 per class')\r\n","plt.ylabel('accuracy')\r\n","plt.xlabel('epoch')\r\n","plt.legend(['train', 'test'], loc='upper left')\r\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Dy5KatEMhlM5","colab":{"base_uri":"https://localhost:8080/","height":311},"executionInfo":{"status":"ok","timestamp":1610594682703,"user_tz":-480,"elapsed":1052,"user":{"displayName":"hazwani hasnun","photoUrl":"","userId":"04936183839801346764"}},"outputId":"ed8d8b4c-9cd7-46a4-af93-5f82f88fb9b0"},"source":["print(modelfit_alex.history.keys())\r\n","plt.plot(modelfit_alex.history['loss'])\r\n","plt.plot(modelfit_alex.history['val_loss'])\r\n","plt.title('AlexNet Loss with average 1300 per class')\r\n","plt.ylabel('accuracy')\r\n","plt.xlabel('epoch')\r\n","plt.legend(['train', 'test'], loc='upper left')\r\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["dict_keys(['loss', 'tp', 'fp', 'tn', 'fn', 'accuracy', 'precision', 'recall', 'auc', 'val_loss', 'val_tp', 'val_fp', 'val_tn', 'val_fn', 'val_accuracy', 'val_precision', 'val_recall', 'val_auc'])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"Jx_97Mcllzoz"},"source":["##Save Model"]},{"cell_type":"code","metadata":{"id":"aqIiUBmZlyzJ"},"source":["modelfit_resnet.save('/content/drive/MyDrive/UITM/SEM 4/pickle file')\r\n","inceptionV3TL_fit.save('/content/drive/MyDrive/UITM/SEM 4/pickle file')\r\n","modelfit_alex.save('/content/drive/MyDrive/UITM/SEM 4/pickle file')"],"execution_count":null,"outputs":[]}]}