Method g_forward

unsupervised_class3/dcgan_tf.py:375–404

(self, Z, reuse=None, is_training=True)

  def g_forward(self, Z, reuse=None, is_training=True):
    # dense layers
    output = Z
    for layer in self.g_denselayers:
      output = layer.forward(output, reuse, is_training)

    # project and reshape
    output = tf.reshape(
      output,
      [-1, self.g_dims[0], self.g_dims[0], self.g_sizes['projection']],
    )

    # apply batch norm
    if self.g_sizes['bn_after_project']:
      output = tf.contrib.layers.batch_norm(
        output,
        decay=0.9, 
        updates_collections=None,
        epsilon=1e-5,
        scale=True,
        is_training=is_training,
        reuse=reuse,
        scope='bn_after_project'
      )

    # pass through fs-conv layers
    for layer in self.g_convlayers:
      output = layer.forward(output, reuse, is_training)

    return output


  def fit(self, X):