A smaller style transfer network.

fast_style_transfer_small.py

@classmethod
def build(
        cls,
        image_size,
        alpha=1.0,
        input_tensor=None,
        checkpoint_file=None):
    """Build a Small Transfer Network Model using keras' functional API.
    This architecture removes some blocks of layers and reduces the size
    of convolutions to save on computation.
    Args:
        image_size - the size of the input and output image (H, W)
        alpha - a width parameter to scale the number of channels by
    Returns:
        model: a keras model object
    """
    x = keras.layers.Input(
        shape=(image_size[0], image_size[1], 3), tensor=input_tensor)
    out = cls._convolution(x, int(alpha * 32), 9, strides=1)
    out = cls._convolution(out, int(alpha * 32), 3, strides=2)
    out = cls._convolution(out, int(alpha * 32), 3, strides=2)
    out = cls._residual_block(out, int(alpha * 32))
    out = cls._residual_block(out, int(alpha * 32))
    out = cls._residual_block(out, int(alpha * 32))
    out = cls._upsample(out, int(alpha * 32), 3)
    out = cls._upsample(out, int(alpha * 32), 3)
    out = cls._convolution(out, 3, 9, relu=False, padding='same')
    # Restrict outputs of pixel values to -1 and 1.
    out = keras.layers.Activation('tanh')(out)
    # Deprocess the image into valid image data. Note we'll need to define
    # a custom layer for this in Core ML as well.
    out = layers.DeprocessStylizedImage()(out)
    model = keras.models.Model(inputs=x, outputs=out)