Convolution3D | Convolution3D is an Avisynth filter that will apply | Machine Learning library

    def get_model(summary=False, backend='tf'):
        """ Return the Keras model of the network
        """
        model = Sequential()
        if backend == 'tf':
            input_shape=(256, 80, 60, 1) # l, h, w, c
        else:
            input_shape=(1, 256, 80, 60) # c, l, h, w
        model.add(Convolution3D(64, 3, 3, 3, activation='relu',
                                padding='same', name='conv1',
                                input_shape=input_shape))
        model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2),
                               padding='valid', name='pool1'))
        # 2nd layer group
        model.add(Convolution3D(128, 3, 3, 3, activation='relu',
                                padding='same', name='conv2'))
        model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
                               padding='valid', name='pool2'))
+ other layers as well

if __name__ == '__main__':
    model = get_model(summary=True,backend='tf')