def cycle_consistency_loss(cyclegan_model, scope=None, add_summaries=False):
"""Defines the cycle consistency loss.
Uses `cycle_consistency_loss` to compute the cycle consistency loss for a
`cyclegan_model`.
Args:
cyclegan_model: A `CycleGANModel` namedtuple.
scope: The scope for the operations performed in computing the loss.
Defaults to None.
add_summaries: Whether or not to add detailed summaries for the loss.
Defaults to False.
Returns:
A scalar `Tensor` of cycle consistency loss.
Raises:
ValueError: If `cyclegan_model` is not a `CycleGANModel` namedtuple.
"""
if not isinstance(cyclegan_model, namedtuples.CycleGANModel):
raise ValueError(
'`cyclegan_model` must be a `CycleGANModel`. Instead, was %s.' %
type(cyclegan_model))
return losses_impl.cycle_consistency_loss(
cyclegan_model.model_x2y.generator_inputs, cyclegan_model.reconstructed_x,
cyclegan_model.model_y2x.generator_inputs, cyclegan_model.reconstructed_y,
scope, add_summaries)
def cycle_consistency_loss(cyclegan_model, scope=None, add_summaries=False):
"""Defines the cycle consistency loss.
Uses `cycle_consistency_loss` to compute the cycle consistency loss for a
`cyclegan_model`.
Args:
cyclegan_model: A `CycleGANModel` namedtuple.
scope: The scope for the operations performed in computing the loss.
Defaults to None.
add_summaries: Whether or not to add detailed summaries for the loss.
Defaults to False.
Returns:
A scalar `Tensor` of cycle consistency loss.
Raises:
ValueError: If `cyclegan_model` is not a `CycleGANModel` namedtuple.
"""
if not isinstance(cyclegan_model, namedtuples.CycleGANModel):
raise ValueError(
'`cyclegan_model` must be a `CycleGANModel`. Instead, was %s.' %
type(cyclegan_model))
return losses_impl.cycle_consistency_loss(
cyclegan_model.model_x2y.generator_inputs, cyclegan_model.reconstructed_x,
cyclegan_model.model_y2x.generator_inputs, cyclegan_model.reconstructed_y,
scope, add_summaries)
def test_correct_loss(self):
loss = tfgan_losses.cycle_consistency_loss(
self._data_x, self._reconstructed_data_x, self._data_y,
self._reconstructed_data_y)
with self.test_session(use_gpu=True):
variables.global_variables_initializer().run()
self.assertNear(5.25, loss.eval(), 1e-5)
def test_correct_loss(self):
loss = tfgan_losses.cycle_consistency_loss(self._data_x,
self._reconstructed_data_x,
self._data_y,
self._reconstructed_data_y)
with self.test_session(use_gpu=True):
variables.global_variables_initializer().run()
self.assertNear(5.25, loss.eval(), 1e-5)