cnn_model for hyperas.py

cnn_model.py

def create_model(X_train,y_train,X_test,y_test):
    # Initialising the CNN
    model = Sequential()

    # 1 - Convolution
    model.add(Conv2D(64,(3,3), padding='same', input_shape=(48, 48,1)))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout({{uniform(0,1)}}))

    # 2nd Convolution layer
    model.add(Conv2D(128,(5,5), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout({{uniform(0,1)}}))

    # 3rd Convolution layer
    model.add(Conv2D(512,(3,3), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
        
    #1st Max Pool Layer
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout({{uniform(0,1)}}))


    # 4th Convolution layer
    model.add(Conv2D(512,(3,3), padding='same'))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    
    # 5h Convolution layer
    if {{choice(['four', 'five'])}} == 'five':
        model.add(Conv2D(512,(3,3), padding='same'))
        model.add(BatchNormalization())
        model.add(Activation('relu'))
        
    #2nd Max Pool Layer
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout({{uniform(0,1)}}))
    
    # Flattening
    model.add(Flatten())

    # Fully connected layer 1st layer
    model.add(Dense({{choice([256, 512, 1024])}}))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout({{uniform(0,1)}}))

    # Fully connected layer 2nd layer
    model.add(Dense({{choice([512, 1024])}}))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(Dropout({{uniform(0,1)}}))

    model.add(Dense(4, activation='softmax'))

    model.compile(optimizer={{choice(['rmsprop', 'adam', 'sgd'])}}, loss='categorical_crossentropy', metrics=['accuracy'])
    result = model.fit(X_train, y_train,
                batch_size={{choice([64, 128])}},
                epochs=10,
                verbose=2,
                validation_data=(X_test, y_test))

    validation_acc = np.amax(result.history['val_acc']) 
    print('Best validation acc of epoch:', validation_acc)
    return {'loss': -validation_acc, 'status': STATUS_OK, 'model': model}