nets.py 文件源码

def build_gen( shape ) :
    def deconv2d( x, filters, shape=(4, 4) ) :
        '''
        Conv2DTransposed gives me checkerboard artifact...
        Select one of the 3.
        '''
        # Simpe Conv2DTranspose
        # Not good, compared to upsample + conv2d below.
        x= Conv2DTranspose( filters, shape, padding='same',
            strides=(2, 2), kernel_initializer=Args.kernel_initializer )(x)

        # simple and works
        #x = UpSampling2D( (2, 2) )( x )
        #x = Conv2D( filters, shape, padding='same' )( x )

        # Bilinear2x... Not sure if it is without bug, not tested yet.
        # Tend to make output blurry though
        #x = bilinear2x( x, filters )
        #x = Conv2D( filters, shape, padding='same' )( x )

        x = BatchNormalization(momentum=Args.bn_momentum)( x )
        x = LeakyReLU(alpha=Args.alpha_G)( x )
        return x

    # https://github.com/tdrussell/IllustrationGAN  z predictor...?
    # might help. Not sure.

    noise = Input( shape=Args.noise_shape )
    x = noise
    # 1x1x256
    # noise is not useful for generating images.

    x= Conv2DTranspose( 512, (4, 4),
        kernel_initializer=Args.kernel_initializer )(x)
    x = BatchNormalization(momentum=Args.bn_momentum)( x )
    x = LeakyReLU(alpha=Args.alpha_G)( x )
    # 4x4
    x = deconv2d( x, 256 )
    # 8x8
    x = deconv2d( x, 128 )
    # 16x16
    x = deconv2d( x, 64 )
    # 32x32

    # Extra layer
    x = Conv2D( 64, (3, 3), padding='same',
        kernel_initializer=Args.kernel_initializer )( x )
    x = BatchNormalization(momentum=Args.bn_momentum)( x )
    x = LeakyReLU(alpha=Args.alpha_G)( x )
    # 32x32

    x= Conv2DTranspose( 3, (4, 4), padding='same', activation='tanh',
        strides=(2, 2), kernel_initializer=Args.kernel_initializer )(x)
    # 64x64

    return models.Model( inputs=noise, outputs=x )