def add_regularization_loss_summaries(gan_model): """Adds summaries for a regularization losses.. Args: gan_model: A GANModel tuple. """ if gan_model.generator_scope: summary.scalar( 'generator_regularization_loss', loss_util.get_regularization_loss(gan_model.generator_scope.name)) if gan_model.discriminator_scope: summary.scalar( 'discriminator_regularization_loss', loss_util.get_regularization_loss(gan_model.discriminator_scope.name))
def testGetRegularizationLoss(self): # Empty regularization collection should evaluate to 0.0. with self.test_session(): self.assertEqual(0.0, util.get_regularization_loss().eval()) # Loss should sum. ops.add_to_collection( ops.GraphKeys.REGULARIZATION_LOSSES, constant_op.constant(2.0)) ops.add_to_collection( ops.GraphKeys.REGULARIZATION_LOSSES, constant_op.constant(3.0)) with self.test_session(): self.assertEqual(5.0, util.get_regularization_loss().eval()) # Check scope capture mechanism. with ops.name_scope('scope1'): ops.add_to_collection( ops.GraphKeys.REGULARIZATION_LOSSES, constant_op.constant(-1.0)) with self.test_session(): self.assertEqual(-1.0, util.get_regularization_loss('scope1').eval())
def testGetRegularizationLoss(self): # Empty regularization collection should evaluate to 0.0. with self.cached_session(): self.assertEqual(0.0, util.get_regularization_loss().eval()) # Loss should sum. ops.add_to_collection( ops.GraphKeys.REGULARIZATION_LOSSES, constant_op.constant(2.0)) ops.add_to_collection( ops.GraphKeys.REGULARIZATION_LOSSES, constant_op.constant(3.0)) with self.cached_session(): self.assertEqual(5.0, util.get_regularization_loss().eval()) # Check scope capture mechanism. with ops.name_scope('scope1'): ops.add_to_collection( ops.GraphKeys.REGULARIZATION_LOSSES, constant_op.constant(-1.0)) with self.cached_session(): self.assertEqual(-1.0, util.get_regularization_loss('scope1').eval())
def add_regularization_loss_summaries(gan_model): """Adds summaries for a regularization losses.. Args: gan_model: A GANModel tuple. """ if isinstance(gan_model, namedtuples.CycleGANModel): with ops.name_scope('cyclegan_x2y_regularization_loss_summaries'): add_regularization_loss_summaries(gan_model.model_x2y) with ops.name_scope('cyclegan_y2x_regularization_loss_summaries'): add_regularization_loss_summaries(gan_model.model_y2x) return if gan_model.generator_scope: summary.scalar( 'generator_regularization_loss', loss_util.get_regularization_loss(gan_model.generator_scope.name)) if gan_model.discriminator_scope: summary.scalar( 'discriminator_regularization_loss', loss_util.get_regularization_loss(gan_model.discriminator_scope.name))