def testInvertMutuallyConsistent(self):
dims = 4
with self.cached_session() as sess:
ma = Invert(
MaskedAutoregressiveFlow(validate_args=True,
**self._autoregressive_flow_kwargs))
dist = transformed_distribution_lib.TransformedDistribution(
distribution=normal_lib.Normal(loc=0., scale=1.),
bijector=ma,
event_shape=[dims],
validate_args=True)
self.run_test_sample_consistent_log_prob(sess_run_fn=sess.run,
dist=dist,
num_samples=int(1e5),
radius=1.,
center=0.,
rtol=0.02)
def testInvertMutuallyConsistent(self):
dims = 4
with self.test_session() as sess:
nvp = Invert(
RealNVP(num_masked=3,
validate_args=True,
**self._real_nvp_kwargs))
dist = transformed_distribution_lib.TransformedDistribution(
distribution=normal_lib.Normal(loc=0., scale=1.),
bijector=nvp,
event_shape=[dims],
validate_args=True)
self.run_test_sample_consistent_log_prob(sess_run_fn=sess.run,
dist=dist,
num_samples=int(1e5),
radius=1.,
center=0.,
rtol=0.02)
def testInvertMutuallyConsistent(self):
# BatchNorm bijector is only mutually consistent when training=False.
dims = 4
with self.cached_session() as sess:
layer = normalization.BatchNormalization(epsilon=0.)
batch_norm = Invert(
BatchNormalization(batchnorm_layer=layer, training=False))
dist = transformed_distribution_lib.TransformedDistribution(
distribution=normal_lib.Normal(loc=0., scale=1.),
bijector=batch_norm,
event_shape=[dims],
validate_args=True)
self.run_test_sample_consistent_log_prob(sess_run_fn=sess.run,
dist=dist,
num_samples=int(1e5),
radius=2.,
center=0.,
rtol=0.02)