models.py 文件源码

def create_discriminator(hr_images_fake, hr_images, cfg):
    n_layers = 3
    layers = []

    input = tf.concat([hr_images_fake, hr_images ], axis = 3)

    conv = slim.conv2d(input, cfg.ndf, [3,3], stride = 2, activation_fn = lrelu, scope = 'layers%d'%(0))
    layers.append(conv)

    for i in range(n_layers):
        out_channels = cfg.ndf*min(2**(i+1), 8)
        stride = 1 if i == n_layers -1 else 2
        conv = slim.conv2d(layers[-1], out_channels, [3,3], stride = stride, activation_fn = lrelu, scope = 'layers_%d'%(i+2))
        layers.append(conv)

    conv = slim.conv2d(layers[-1], 1, [3,3], stride = 1)
    output = tf.sigmoid(conv)
    return output