(images, **kwargs)
return logits, end_points
Builds discriminator network.
Builds discriminator network.
Builds discriminator network .
def discriminator_fn_specgram(images, **kwargs):
  """Builds discriminator network."""
  shape = images.shape
  normalizer = data_normalizer.registry[kwargs['data_normalizer']](kwargs)
  images = normalizer.normalize_op(images)
  images.set_shape(shape)
  logits, end_points = networks.discriminator(
      images,
      kwargs['progress'],
      lambda block_id: _num_filters_fn(block_id, **kwargs),  # pylint:disable=unnecessary-lambda
      kwargs['resolution_schedule'],
      num_blocks=kwargs['num_blocks'],
      kernel_size=kwargs['kernel_size'],
      simple_arch=kwargs['simple_arch'])
  with tf.variable_scope('discriminator_cond'):
    x = tf.layers.flatten(end_points['last_conv'])
    end_points['classification_logits'] = layers.custom_dense(
        x=x, units=kwargs['num_tokens'], scope='classification_logits')
  return logits, end_points
def discriminator_fn_specgram ( images , * * kwargs ) : shape = images . shape normalizer = data_normalizer . registry [ kwargs [ 'data_normalizer' ] ] ( kwargs ) images = normalizer . normalize_op ( images ) images . set_shape ( shape ) logits , end_points = networks . discriminator ( images , kwargs [ 'progress' ] , lambda block_id : _num_filters_fn ( block_id , * * kwargs ) , # pylint:disable=unnecessary-lambda kwargs [ 'resolution_schedule' ] , num_blocks = kwargs [ 'num_blocks' ] , kernel_size = kwargs [ 'kernel_size' ] , simple_arch = kwargs [ 'simple_arch' ] ) with tf . variable_scope ( 'discriminator_cond' ) : x = tf . layers . flatten ( end_points [ 'last_conv' ] ) end_points [ 'classification_logits' ] = layers . custom_dense ( x = x , units = kwargs [ 'num_tokens' ] , scope = 'classification_logits' ) return logits , end_points