get_model.py

# Arda Mavi

import os
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D

def save_model(model):
    if not os.path.exists('Data/Model/'):
        os.makedirs('Data/Model/')
    model_json = model.to_json()
    with open("Data/Model/model.json", "w") as model_file:
        model_file.write(model_json)
    # serialize weights to HDF5
    model.save_weights("Data/Model/weights.h5")
    print('Model and weights saved')
    return

def get_model(num_classes=2):
    model = Sequential()

    model.add(Conv2D(32, (3, 3), input_shape=(64, 64, 3)))
    model.add(Activation('relu'))

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(32, (3, 3)))
    model.add(Activation('relu'))

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(64, (3, 3)))
    model.add(Activation('relu'))

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Flatten())
    model.add(Dense(64))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])

    return model

if __name__ == '__main__':
    save_model(get_model())