def test_create(self):
with self.test_session():
gan = mock_gan()
loss = WassersteinLoss(gan, loss_config)
d_loss, g_loss = loss.create()
d_shape = gan.ops.shape(d_loss)
g_shape = gan.ops.shape(g_loss)
self.assertEqual(sum(d_shape), 0)
self.assertEqual(sum(g_shape), 0)
def _create(self, d_real, d_fake):
config = self.config
alpha = config.alpha
beta = config.beta
wgan_loss_d, wgan_loss_g = WassersteinLoss._create(self, d_real, d_fake)
lsgan_loss_d, lsgan_loss_g = LeastSquaresLoss._create(self, d_real, d_fake)
standard_loss_d, standard_loss_g = StandardLoss._create(self, d_real, d_fake)
total = min(alpha + beta,1)
d_loss = wgan_loss_d*alpha + lsgan_loss_d*beta + (1-total)*standard_loss_d
g_loss = wgan_loss_g*alpha + lsgan_loss_g*beta + (1-total)*standard_loss_g
return [d_loss, g_loss]
def test_config(self):
with self.test_session():
loss = WassersteinLoss(hg.GAN(), loss_config)
self.assertTrue(loss.config.test)