def _real_nvp_kwargs(self):
return {
"shift_and_log_scale_fn":
tfb.real_nvp_default_template(hidden_layers=[3], shift_only=False),
"is_constant_jacobian":
False,
}
def _real_nvp_kwargs(self):
return {
"shift_and_log_scale_fn":
tfb.real_nvp_default_template(hidden_layers=[3], shift_only=False),
"is_constant_jacobian":
False,
}
def _real_nvp_kwargs(self):
return {
'shift_and_log_scale_fn':
tfb.real_nvp_default_template(hidden_layers=[2], shift_only=True),
'is_constant_jacobian':
True,
}
def testBijectorWithReverseMask(self):
flat_x_ = np.random.normal(0., 1., 8).astype(np.float32)
batched_x_ = np.random.normal(0., 1., (3, 8)).astype(np.float32)
num_masked = -5
for x_ in [flat_x_, batched_x_]:
flip_nvp = tfb.RealNVP(
num_masked=num_masked,
validate_args=True,
shift_and_log_scale_fn=tfb.real_nvp_default_template(
hidden_layers=[3], shift_only=False),
is_constant_jacobian=False)
_, x2_ = np.split(x_, [8 - abs(num_masked)], axis=-1)
x = tf.constant(x_)
# Check latter half is the same after passing thru reversed mask RealNVP.
forward_x = flip_nvp.forward(x)
_, forward_x2 = tf.split(forward_x, [8 - abs(num_masked),
abs(num_masked)], axis=-1)
self.evaluate(tf1.global_variables_initializer())
forward_x2_ = self.evaluate(forward_x2)
self.assertAllClose(forward_x2_, x2_, rtol=1e-4, atol=0.)
def testBijectorWithReverseMask(self, num_masked, fraction_masked):
input_depth = 8
flat_x_ = np.random.normal(0., 1., input_depth).astype(np.float32)
batched_x_ = np.random.normal(0., 1.,
(3, input_depth)).astype(np.float32)
for x_ in [flat_x_, batched_x_]:
flip_nvp = tfb.RealNVP(
num_masked=num_masked,
fraction_masked=fraction_masked,
validate_args=True,
shift_and_log_scale_fn=tfb.real_nvp_default_template(
hidden_layers=[3], shift_only=False),
is_constant_jacobian=False)
x = tf.constant(x_)
forward_x = flip_nvp.forward(x)
expected_num_masked = (num_masked if num_masked is not None else
np.floor(input_depth * fraction_masked))
self.assertEqual(flip_nvp._masked_size, expected_num_masked)
_, x2_ = np.split(x_, [input_depth - abs(flip_nvp._masked_size)],
axis=-1) # pylint: disable=unbalanced-tuple-unpacking
# Check latter half is the same after passing thru reversed mask RealNVP.
_, forward_x2 = tf.split(forward_x, [
input_depth - abs(flip_nvp._masked_size),
abs(flip_nvp._masked_size)
],
axis=-1)
self.evaluate(tf1.global_variables_initializer())
forward_x2_ = self.evaluate(forward_x2)
self.assertAllClose(forward_x2_, x2_, rtol=1e-4, atol=0.)
