How to have parallel convolutional layers in keras?

Question

I am a little new to neural networks and keras. I have some images with size 6*7 and the size of the filter is 15. I want to have several filters and train a convolutional layer separately on each and then combine them. I have looked at one example here:

model = Sequential()
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
                    border_mode='valid',
                    input_shape=input_shape))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.25))
model.add(Flatten(input_shape=input_shape))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('tanh'))

This model works with one filter. Can anybody give me some hints on how to modify the model to work with parallel convolutional layers.

Thanks

Answer 1

Here is an example of designing a network of parallel convolution and sub sampling layers in keras version 2. I hope this resolves your problem.

rows, cols = 100, 15
def create_convnet(img_path='network_image.png'):
    input_shape = Input(shape=(rows, cols, 1))

    tower_1 = Conv2D(20, (100, 5), padding='same', activation='relu')(input_shape)
    tower_1 = MaxPooling2D((1, 11), strides=(1, 1), padding='same')(tower_1)

    tower_2 = Conv2D(20, (100, 7), padding='same', activation='relu')(input_shape)
    tower_2 = MaxPooling2D((1, 9), strides=(1, 1), padding='same')(tower_2)

    tower_3 = Conv2D(20, (100, 10), padding='same', activation='relu')(input_shape)
    tower_3 = MaxPooling2D((1, 6), strides=(1, 1), padding='same')(tower_3)

    merged = keras.layers.concatenate([tower_1, tower_2, tower_3], axis=1)
    merged = Flatten()(merged)

    out = Dense(200, activation='relu')(merged)
    out = Dense(num_classes, activation='softmax')(out)

    model = Model(input_shape, out)
    plot_model(model, to_file=img_path)
    return model

The image of this network will look like