images.py 文件源码

def linear_autoencoder_discriminator(
    x, output_dim, hidden_sizes, encoding_dim,
    scope='Discriminator', reuse=False, pretrained=None):
  with tf.variable_scope(scope, reuse=reuse):
    # Encoder.
    for hsz in hidden_sizes:
        x = tf.nn.elu(layers.linear(x, hsz))
    encoding = x = layers.linear(x, encoding_dim)
    # Decoder.
    for hsz in reversed(hidden_sizes):
        x = tf.nn.elu(layers.linear(x, hsz))
    decoding = layers.linear(x, output_dim * output_dim)
    if pretrained is not None:
      tf.contrib.framework.init_from_checkpoint(
        pretrained, {'Discriminator/': 'Discriminator/'})
    return decoding, None