8382_Ershov_Mikhail_lb5

8382/Ershov/lb/5/main.py

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+import matplotlib.pyplot as plt
+from tensorflow.keras.datasets import cifar10
+from tensorflow.keras.models import Model
+from tensorflow.keras.layers import Input, Convolution2D, MaxPooling2D, Dense, Dropout, Flatten
+from tensorflow.keras import utils
+from tensorflow.keras.utils import to_categorical
+# from tensorflow.contrib.keras.python.keras.utils import np_utils
+import numpy as np
+
+batch_size = 128
+num_epochs = 20
+kernel_size = 3
+pool_size = 2
+conv_depth_1 = 32
+conv_depth_2 = 64
+drop_prob_1 = 0.25
+drop_prob_2 = 0.5
+hidden_size = 512
+
+(X_train, y_train), (X_test, y_test) = cifar10.load_data()
+num_train, depth, height, width = X_train.shape
+num_test = X_test.shape[0]
+num_classes = np.unique(y_train).shape[0]
+X_train = X_train.astype('float32')
+X_test = X_test.astype('float32')
+X_train /= np.max(X_train)
+X_test /= np.max(X_train)
+Y_train = to_categorical(y_train, num_classes)
+Y_test = to_categorical(y_test, num_classes)
+
+
+inp = Input(shape=(depth, height, width))
+
+conv_1 = Convolution2D(conv_depth_1, (kernel_size, kernel_size), padding='same', activation='relu')(inp)
+conv_2 = Convolution2D(conv_depth_1, (kernel_size, kernel_size), padding='same', activation='relu')(conv_1)
+pool_1 = MaxPooling2D(pool_size=(pool_size, pool_size))(conv_2)
+drop_1 = Dropout(drop_prob_1)(pool_1)
+
+conv_3 = Convolution2D(conv_depth_2, (kernel_size, kernel_size), padding='same', activation='relu')(drop_1)
+conv_4 = Convolution2D(conv_depth_2, (kernel_size, kernel_size), padding='same', activation='relu')(conv_3)
+pool_2 = MaxPooling2D(pool_size=(pool_size, pool_size))(conv_4)
+drop_2 = Dropout(drop_prob_1)(pool_2)
+
+flat = Flatten()(drop_2)
+hidden = Dense(hidden_size, activation='relu')(flat)
+drop_3 = Dropout(drop_prob_2)(hidden)
+out = Dense(num_classes, activation='softmax')(drop_3)
+
+
+model = Model(inputs=inp, outputs=out)
+model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+
+history = model.fit(X_train, Y_train, batch_size=batch_size, epochs=num_epochs, verbose=1, validation_split=0.1)
+history_dict = history.history
+model.evaluate(X_test, Y_test, verbose=1)
+
+
+loss_values = history_dict['loss']
+val_loss_values = history_dict['val_loss']
+epochs = range(1, len(loss_values) + 1)
+plt.plot(epochs, loss_values, 'bo', label='Training loss')
+plt.plot(epochs, val_loss_values, 'g', label='Validation loss')
+plt.title('Training and validation loss')
+plt.xlabel('Epochs')
+plt.ylabel('Loss')
+plt.legend()
+plt.show()
+
+
+plt.clf()
+acc_values = history_dict['accuracy']
+val_acc_values = history_dict['val_accuracy']
+plt.plot(epochs, acc_values, 'bo', label='Training acc')
+plt.plot(epochs, val_acc_values, 'g', label='Validation acc')
+plt.title('Training and validation accuracy')
+plt.xlabel('Epochs')
+plt.ylabel('Accuracy')
+plt.legend()
+plt.show()