def test_stable_global_norm_avoids_overflow(self):
tensors = [array_ops.ones([4]), array_ops.ones([4, 4]) * 1e19, None]
gnorm_is_inf = math_ops.is_inf(clip_ops.global_norm(tensors))
stable_gnorm_is_inf = math_ops.is_inf(
tfgan_losses._numerically_stable_global_norm(tensors))
with self.test_session(use_gpu=True):
self.assertTrue(gnorm_is_inf.eval())
self.assertFalse(stable_gnorm_is_inf.eval())
def test_stable_global_norm_avoids_overflow(self):
tensors = [array_ops.ones([4]), array_ops.ones([4, 4]) * 1e19, None]
gnorm_is_inf = math_ops.is_inf(clip_ops.global_norm(tensors))
stable_gnorm_is_inf = math_ops.is_inf(
tfgan_losses._numerically_stable_global_norm(tensors))
with self.test_session(use_gpu=True):
self.assertTrue(gnorm_is_inf.eval())
self.assertFalse(stable_gnorm_is_inf.eval())
def test_stable_global_norm_unchanged(self):
"""Test that preconditioning doesn't change global norm value."""
random_seed.set_random_seed(1234)
tensors = [random_ops.random_uniform([3]*i, -10.0, 10.0) for i in range(6)]
gnorm = clip_ops.global_norm(tensors)
precond_gnorm = tfgan_losses._numerically_stable_global_norm(tensors)
with self.test_session(use_gpu=True) as sess:
for _ in range(10): # spot check closeness on more than one sample.
gnorm_np, precond_gnorm_np = sess.run([gnorm, precond_gnorm])
self.assertNear(gnorm_np, precond_gnorm_np, 1e-5)
def test_stable_global_norm_unchanged(self):
"""Test that preconditioning doesn't change global norm value."""
random_seed.set_random_seed(1234)
tensors = [random_ops.random_uniform([3]*i, -10.0, 10.0) for i in range(6)]
gnorm = clip_ops.global_norm(tensors)
precond_gnorm = tfgan_losses._numerically_stable_global_norm(tensors)
with self.test_session(use_gpu=True) as sess:
for _ in range(10): # spot check closeness on more than one sample.
gnorm_np, precond_gnorm_np = sess.run([gnorm, precond_gnorm])
self.assertNear(gnorm_np, precond_gnorm_np, 1e-5)
