def setUp(self):
super(GANHeadTest, self).setUp()
self.gan_head = head.gan_head(
generator_loss_fn=dummy_loss,
discriminator_loss_fn=dummy_loss,
generator_optimizer=training.GradientDescentOptimizer(1.0),
discriminator_optimizer=training.GradientDescentOptimizer(1.0))
self.assertTrue(isinstance(self.gan_head, head.GANHead))
def setUp(self):
super(GANHeadTest, self).setUp()
self.gan_head = head.gan_head(
generator_loss_fn=dummy_loss,
discriminator_loss_fn=dummy_loss,
generator_optimizer=training.GradientDescentOptimizer(1.0),
discriminator_optimizer=training.GradientDescentOptimizer(1.0))
self.assertTrue(isinstance(self.gan_head, head.GANHead))
def setUp(self):
super(GANHeadTest, self).setUp()
self.gan_head = head.gan_head(
generator_loss_fn=dummy_loss,
discriminator_loss_fn=dummy_loss,
generator_optimizer=training.GradientDescentOptimizer(1.0),
discriminator_optimizer=training.GradientDescentOptimizer(1.0),
get_eval_metric_ops_fn=self.get_metrics)
self.assertIsInstance(self.gan_head, head.GANHead)
def _model_fn(features, labels, mode):
gopt = (generator_optimizer() if callable(generator_optimizer) else
generator_optimizer)
dopt = (discriminator_optimizer() if callable(discriminator_optimizer)
else discriminator_optimizer)
gan_head = head_lib.gan_head(
generator_loss_fn, discriminator_loss_fn, gopt, dopt,
use_loss_summaries, get_hooks_fn=get_hooks_fn)
return _gan_model_fn(
features, labels, mode, generator_fn, discriminator_fn, gan_head,
add_summaries)
