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),
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.contrib.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):
"""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),
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 = contrib_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