{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"!git clone https://github.com/krzysztofspalinski/deep-learning-methods-project-2.git\n!mv deep-learning-methods-project-2 src","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.datasets import cifar10\n\n(x_train,y_train),(x_test,y_test) = cifar10.load_data() \n\nx_train = x_train / 255\nx_test = x_test / 255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.utils.np_utils import to_categorical   \n\ny_train = to_categorical(y_train, num_classes=10)\ny_test = to_categorical(y_test, num_classes=10)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import os\nimport pandas as pd\nimport numpy as np\n\nimport tensorflow as tf\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras import regularizers\n\nfrom src.scripts.residuallayer import ResnetIdentityBlock","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# image_generator = ImageDataGenerator(rotation_range=30,\n#                                      width_shift_range=0.05,\n#                                      height_shift_range=0.05,\n#                                      zoom_range=0.1,\n#                                      horizontal_flip=True,\n#                                      brightness_range=(0.9, 1.1))\n\nimage_generator = ImageDataGenerator(\n        featurewise_center=False,\n        samplewise_center=False,\n        featurewise_std_normalization=False,\n        samplewise_std_normalization=False,\n        zca_whitening=False,\n        zca_epsilon=1e-06,\n        rotation_range=5,\n        width_shift_range=0.1,\n        height_shift_range=0.1,\n        shear_range=0.,\n        zoom_range=0.,\n        channel_shift_range=0.,\n        fill_mode='nearest',\n        cval=0.,\n        horizontal_flip=True,\n        vertical_flip=False,\n        rescale=None,\n        preprocessing_function=None,\n        data_format=None,\n        validation_split=0)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"image_generator.fit(x_train)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"NUM_CLASSES = 10\nINPUT_SHAPE = (32, 32, 3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = tf.keras.Sequential()\nmodel.add(tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=INPUT_SHAPE, kernel_regularizer=regularizers.l2(0.0005)))\nmodel.add(tf.keras.layers.BatchNormalization())\nmodel.add(tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu', kernel_regularizer=regularizers.l2(0.001)))\nmodel.add(tf.keras.layers.BatchNormalization())\nmodel.add(tf.keras.layers.MaxPooling2D((2, 2)))\n\nmodel.add(tf.keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu', input_shape=INPUT_SHAPE, kernel_regularizer=regularizers.l2(0.001)))\nmodel.add(tf.keras.layers.BatchNormalization())\nmodel.add(tf.keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu', kernel_regularizer=regularizers.l2(0.0005)))\nmodel.add(tf.keras.layers.BatchNormalization())\nmodel.add(tf.keras.layers.MaxPooling2D((2, 2)))\n\nmodel.add(tf.keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu', kernel_regularizer=regularizers.l2(0.0005)))\nmodel.add(tf.keras.layers.BatchNormalization())\nmodel.add(tf.keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu', kernel_regularizer=regularizers.l2(0.0005)))\nmodel.add(tf.keras.layers.BatchNormalization())\nmodel.add(tf.keras.layers.MaxPooling2D((2, 2)))\n\nmodel.add(tf.keras.layers.Conv2D(256, (2, 2), padding='same', activation='relu', kernel_regularizer=regularizers.l2(0.0005)))\nmodel.add(tf.keras.layers.BatchNormalization())\nmodel.add(tf.keras.layers.Conv2D(256, (2, 2), padding='same', activation='relu', kernel_regularizer=regularizers.l2(0.0005)))\nmodel.add(tf.keras.layers.BatchNormalization())\nmodel.add(tf.keras.layers.MaxPooling2D((2, 2)))\n\nmodel.add(tf.keras.layers.Conv2D(512, (2, 2), padding='same', activation='relu', kernel_regularizer=regularizers.l2(0.0005)))\nmodel.add(tf.keras.layers.BatchNormalization())\nmodel.add(tf.keras.layers.Conv2D(512, (2, 2), padding='same', activation='relu', kernel_regularizer=regularizers.l2(0.0005)))\nmodel.add(tf.keras.layers.BatchNormalization())\nmodel.add(tf.keras.layers.MaxPooling2D((2, 2)))\n\nmodel.add(tf.keras.layers.Flatten())\nmodel.add(tf.keras.layers.Dropout(0.4))\n\nmodel.add(tf.keras.layers.Dense(128, activation='relu', kernel_regularizer=regularizers.l2(0.0005)))\nmodel.add(tf.keras.layers.Dense(NUM_CLASSES, activation='softmax'))\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='adam',\n              loss='categorical_crossentropy',\n              metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit_generator(image_generator.flow(x_train, y_train, batch_size=128),\n                    validation_data=(x_test, y_test),\n                    epochs=50)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}