{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Activation,MaxPooling2D,Dropout\nfrom keras.layers.core import Dense, Flatten\nfrom keras.optimizers import Adam\nfrom keras.optimizers import SGD\nfrom keras.metrics import categorical_crossentropy\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.layers.convolutional import Conv2D\nfrom tensorflow.keras.models import load_model\nfrom keras.callbacks import ModelCheckpoint,ReduceLROnPlateau,EarlyStopping,LearningRateScheduler,CSVLogger,LambdaCallback,TensorBoard\nfrom keras import regularizers\nimport itertools\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix\nfrom keras.regularizers import l2\nimport seaborn as sns\nimport math\nimport os\nfrom keras.applications.resnet50 import ResNet50\nfrom keras.applications.vgg16 import VGG16\nfrom keras.applications.vgg19 import VGG19\n%matplotlib inline","execution_count":null,"outputs":[]},{"metadata":{"scrolled":true,"trusted":true},"cell_type":"code","source":"#os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\ntrain_path = '../input/catdog10000train5000validate/train/train'\nvalid_path = '../input/catdog10000train5000validate/valid/valid'\ntest_path  = '../input/catdog10000train5000validate/test/test'\ntrain_gen = ImageDataGenerator(\n    shear_range=0.2,\n    zoom_range=0.2,\n    rotation_range=20,\n    width_shift_range=0.2,\n    height_shift_range=0.2,\n    horizontal_flip=True,\n    channel_shift_range=10.,\n    fill_mode=\"nearest\"\n)\ngen = ImageDataGenerator(rotation_range=10, width_shift_range=0.1, \n    height_shift_range=0.1, shear_range=0.15, zoom_range=0.1, \n    channel_shift_range=10., horizontal_flip=True)\n\ntrain_batches = train_gen.flow_from_directory(directory=train_path, target_size=(224,224),\n    classes=['dog', 'cat'], batch_size=32)\n\nvalid_batches = ImageDataGenerator().flow_from_directory(directory=valid_path, target_size=(224,224), \n    classes=['dog', 'cat'], batch_size=32)\ntest_batches = ImageDataGenerator().flow_from_directory(directory=test_path, target_size=(224,224),\n    classes=['dog', 'cat'], batch_size=32,shuffle=False)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"# learning rate schedule\ndef step_decay(epoch):\n\tinitial_lrate = 0.001\n\tdrop = 0.5\n\tepochs_drop = 1.0\n\tlrate = initial_lrate * math.pow(drop, math.floor((1+epoch)/epochs_drop))\n\treturn lrate","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"# Add all the call backs for Keras\nlr_print_callback = LambdaCallback(\n    on_epoch_begin=lambda epoch,logs: print(\"LearningRate of %e\",model.optimizer.lr))\n\nearlyStopping = EarlyStopping(\n    monitor='val_loss',\n    min_delta=0,\n    verbose=0,\n    mode='auto',\n    baseline=None,\n\tpatience=10,\n    restore_best_weights=True\n)\n\nreduce_lr_loss = ReduceLROnPlateau(\n    monitor='val_loss', \n\tfactor=0.1, \n    patience=7, \n\tverbose=1, \n    min_delta=1e-4,\n\tmode='auto')\n\t\nreduce_lr_loss = ReduceLROnPlateau(\n    monitor='val_loss', \n\tfactor=0.1, \n    patience=7, \n\tverbose=1, \n    min_delta=1e-4,\n\tmode='auto')\n\nmcp_save       = ModelCheckpoint(\n    'cat_dog_ckpt.h5', \n\tsave_best_only=True, \n\tmonitor='val_loss', \n\tmode='auto'\n)\nlrate_sched = LearningRateScheduler(step_decay)\n\ncsv_log = CSVLogger('train.csv', separator=',', append=False)\n","execution_count":null,"outputs":[]},{"metadata":{"scrolled":true,"trusted":false},"cell_type":"code","source":"# plots images with labels within jupyter notebook\ndef plots(ims, figsize=(12,6), rows=1, interp=False, titles=None):\n    if type(ims[0]) is np.ndarray:\n        ims = np.array(ims).astype(np.uint8)\n        if (ims.shape[-1] != 3):\n            ims = ims.transpose((0,2,3,1))\n    f = plt.figure(figsize=figsize)\n    plt.style.use('ggplot')\n    cols = len(ims)/rows\n    #cols = 8\n    for i in range(len(ims)):\n    #for i in range(10):\n        sp = f.add_subplot(rows, cols, i+1)\n        sp.axis('Off')\n        if titles is not None:\n            sp.set_title(titles[i], fontsize=16)\n        plt.imshow(ims[i], interpolation=None if interp else 'none')\n\n#test_imgs, labels = next(test_batches)\n#test_imgs, labels = next(train_gen)\n\n#plots(aug_images, figsize=(20,7), rows=2)\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"vgg19_model = VGG19()\n#vgg16_model = VGG16() \nmodel_vgg19 = Sequential() \nfor layer in vgg19_model.layers[:-1]: \n    model_vgg19.add(layer) \nfor layer in model_vgg19.layers: \n    layer.trainable = False \n    \nmodel_vgg19.add(Dense(2, activation='sigmoid'))\n\n\nmcp_save       = ModelCheckpoint(\n    'cat_dog_ckpt_vgg19.h5', \n\tsave_best_only=True, \n\tmonitor='val_loss', \n\tmode='auto'\n)\n#print(\"Running the Training of Model from Scratch\") \n#history = model_vgg16.fit_generator(generator=train_batches, steps_per_epoch=len(train_batches),\n#                                    validation_data=valid_batches, validation_steps=len(valid_batches), \n#                                    epochs=50, verbose=1)\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"if os.path.isfile('cat_dog_ckpt_vgg19.h5'):\n    #This code is implemented to load the partly trained model which was stopped due to some reason\n    print(\"Running the Partially Trained Model\")\n    partially_trained_model = load_model('cat_dog_ckpt_vgg19.h5')\n    history = partially_trained_model.fit_generator(generator=train_batches, steps_per_epoch=len(train_batches),\n                                 validation_data=valid_batches, validation_steps=len(valid_batches), \n                                    epochs=50,callbacks=[mcp_save], verbose=1)\nelse:\n    print(\"Running the Training of Model from Scratch\")\n    model_vgg19.compile(loss='binary_crossentropy',optimizer=Adam(lr=0.0001),metrics=['accuracy'])\n    history = model_vgg19.fit_generator(generator=train_batches, steps_per_epoch=len(train_batches),\n                                 validation_data=valid_batches, validation_steps=len(valid_batches), \n                                    epochs=50,callbacks=[mcp_save], verbose=1)\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"![accuracy_vgg19.png](attachment:accuracy_vgg19.png)","attachments":{"accuracy_vgg19.png":{"image/png":"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"}}},{"metadata":{"trusted":false},"cell_type":"code","source":"acc = history.history['accuracy'] \nval_acc = history.history['val_accuracy'] \nloss = history.history['loss'] \nval_loss = history.history['val_loss']\n\nepochs = range(1, len(acc) + 1)\n\nplt.plot(epochs, acc, 'bo', label='Training acc')\nplt.plot(epochs, val_acc, 'b', label='Validation acc') \nplt.title('Training and validation accuracy') \nplt.legend() \nplt.figure() \nplt.plot(epochs, loss, 'bo', label='Training loss') \nplt.plot(epochs, val_loss, 'b', label='Validation loss') \nplt.title('Training and validation loss') \nplt.savefig('accuracy.png')\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"predictions = model_vgg16.predict_generator(generator=test_batches, steps=len(test_batches), verbose=0)\npred_label = np.argmax(predictions,axis=1)\nclasses = np.argmax(predictions, axis=1)\ncm = confusion_matrix(test_batches.labels,pred_label)\nf,ax = plt.subplots(figsize=(4, 4))\nsns.heatmap(cm, annot=True, linewidths=0.01,cmap=\"Greens\",linecolor=\"gray\", fmt= '.1f',ax=ax)\nplt.xlabel(\"Predicted Label\")\nplt.ylabel(\"True Label\")\nplt.title(\"Confusion Matrix\")\nplt.show()\nplt.savefig('confusion_matrix.png')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"![cm_vgg19.png](attachment:cm_vgg19.png)","attachments":{"cm_vgg19.png":{"image/png":"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"}}},{"metadata":{"trusted":false},"cell_type":"code","source":"from sklearn.metrics import precision_score\nfrom sklearn.metrics import recall_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import roc_curve\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn import metrics\ntn, fp, fn, tp = cm.ravel()\naccuracy = (tn + tp)/(tn + tp + fn +fp)\nprecision = precision_score(test_batches.labels, pred_label, average='binary')\nrecall = recall_score(test_batches.labels, pred_label,average='binary')\nf1_score = f1_score(test_batches.labels, pred_label, average='binary')\nscore = metrics.accuracy_score(test_batches.labels, pred_label)\nlog_score = metrics.log_loss(pred_label, predictions)","execution_count":null,"outputs":[]},{"metadata":{"trusted":false},"cell_type":"code","source":"print(\"Precision \",precision*100)\nprint(\"Recall \",recall*100)\nprint(\"F1 Score \",recall*100)\nprint(\"Accuracy of the model\",accuracy*100)\nprint(\"Accuracy score: {}\".format(score))\nprint(\"Log loss score: {}\".format(log_score))","execution_count":null,"outputs":[]},{"metadata":{"scrolled":true,"trusted":false},"cell_type":"code","source":"from sklearn.metrics import roc_curve, auc\ndef plot_roc(pred,y):\n    fpr, tpr, _ = roc_curve(y, pred)\n    roc_auc = auc(fpr, tpr)\n\n    plt.figure()\n    plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n    plt.plot([0, 1], [0, 1], 'k--')\n    plt.xlim([0.0, 1.0])\n    plt.ylim([0.0, 1.05])\n    plt.xlabel('False Positive Rate')\n    plt.ylabel('True Positive Rate')\n    plt.title('Receiver Operating Characteristic (ROC)')\n    plt.legend(loc=\"lower right\")\n    plt.show()\nplot_roc(pred_label,test_batches.labels)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"![roc_vgg19.png](attachment:roc_vgg19.png)","attachments":{"roc_vgg19.png":{"image/png":"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"}}}],"metadata":{"kernelspec":{"display_name":"Python 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