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| from keras.models import Sequential | |
| from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, InputLayer, BatchNormalization, Dropout | |
| # build a sequential model | |
| model = Sequential() | |
| model.add(InputLayer(input_shape=(224, 224, 3))) | |
| # 1st conv block | |
| model.add(Conv2D(25, (5, 5), activation='relu', strides=(1, 1), padding='same')) | |
| model.add(MaxPool2D(pool_size=(2, 2), padding='same')) | |
| # 2nd conv block | |
| model.add(Conv2D(50, (5, 5), activation='relu', strides=(2, 2), padding='same')) | |
| model.add(MaxPool2D(pool_size=(2, 2), padding='same')) | |
| model.add(BatchNormalization()) | |
| # 3rd conv block | |
| model.add(Conv2D(70, (3, 3), activation='relu', strides=(2, 2), padding='same')) | |
| model.add(MaxPool2D(pool_size=(2, 2), padding='valid')) | |
| model.add(BatchNormalization()) | |
| # ANN block | |
| model.add(Flatten()) | |
| model.add(Dense(units=100, activation='relu')) | |
| model.add(Dense(units=100, activation='relu')) | |
| model.add(Dropout(0.25)) | |
| # output layer | |
| model.add(Dense(units=10, activation='softmax')) | |
| # compile model | |
| model.compile(loss='categorical_crossentropy', optimizer="adam", metrics=['accuracy']) | |
| # fit on data for 30 epochs | |
| model.fit_generator(train, epochs=30, validation_data=val) |
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