def __call__(self, shape, dtype=None):
if not self.built:
self.build(shape, dtype)
return generated_random_variables.Independent(
generated_random_variables.Normal(
loc=self.mean, scale=self.stddev).distribution,
reinterpreted_batch_ndims=len(shape))
def __call__(self, shape, dtype=None):
if not self.built:
self.build(shape, dtype)
return generated_random_variables.Independent(
generated_random_variables.HalfCauchy(
loc=self.loc, scale=self.scale).distribution,
reinterpreted_batch_ndims=len(shape))
def call(self, inputs):
if self.conditional_inputs is None and self.conditional_outputs is None:
covariance_matrix = self.covariance_fn(inputs, inputs)
# Tile locations so output has shape [units, batch_size]. Covariance will
# broadcast to [units, batch_size, batch_size], and we perform
# shape manipulations to get a random variable over [batch_size, units].
loc = self.mean_fn(inputs)
loc = tf.tile(loc[tf.newaxis], [self.units] + [1] * len(loc.shape))
else:
knn = self.covariance_fn(inputs, inputs)
knm = self.covariance_fn(inputs, self.conditional_inputs)
kmm = self.covariance_fn(self.conditional_inputs, self.conditional_inputs)
kmm = tf.linalg.set_diag(
kmm, tf.linalg.diag_part(kmm) + tf.keras.backend.epsilon())
kmm_tril = tf.linalg.cholesky(kmm)
kmm_tril_operator = tf.linalg.LinearOperatorLowerTriangular(kmm_tril)
knm_operator = tf.linalg.LinearOperatorFullMatrix(knm)
# TODO(trandustin): Vectorize linear algebra for multiple outputs. For
# now, we do each separately and stack to obtain a locations Tensor of
# shape [units, batch_size].
loc = []
for conditional_outputs_unit in tf.unstack(self.conditional_outputs,
axis=-1):
center = conditional_outputs_unit - self.mean_fn(
self.conditional_inputs)
loc_unit = knm_operator.matvec(
kmm_tril_operator.solvevec(kmm_tril_operator.solvevec(center),
adjoint=True))
loc.append(loc_unit)
loc = tf.stack(loc) + self.mean_fn(inputs)[tf.newaxis]
covariance_matrix = knn
covariance_matrix -= knm_operator.matmul(
kmm_tril_operator.solve(
kmm_tril_operator.solve(knm, adjoint_arg=True), adjoint=True))
covariance_matrix = tf.linalg.set_diag(
covariance_matrix,
tf.linalg.diag_part(covariance_matrix) + tf.keras.backend.epsilon())
# Form a multivariate normal random variable with batch_shape units and
# event_shape batch_size. Then make it be independent across the units
# dimension. Then transpose its dimensions so it is [batch_size, units].
random_variable = (
generated_random_variables.MultivariateNormalFullCovariance(
loc=loc, covariance_matrix=covariance_matrix))
random_variable = generated_random_variables.Independent(
random_variable.distribution, reinterpreted_batch_ndims=1)
bijector = tfp.bijectors.Inline(
forward_fn=lambda x: tf.transpose(x, perm=[1, 0]),
inverse_fn=lambda y: tf.transpose(y, perm=[1, 0]),
forward_event_shape_fn=lambda input_shape: input_shape[::-1],
forward_event_shape_tensor_fn=lambda input_shape: input_shape[::-1],
inverse_log_det_jacobian_fn=lambda y: tf.cast(0, y.dtype),
forward_min_event_ndims=2)
random_variable = generated_random_variables.TransformedDistribution(
random_variable.distribution, bijector=bijector)
return random_variable
def __call__(self, shape, dtype=None):
if not self.built:
self.build(shape, dtype)
loc = self.loc
if self.loc_constraint:
loc = self.loc_constraint(loc)
return generated_random_variables.Independent(
generated_random_variables.Deterministic(loc=loc).distribution,
reinterpreted_batch_ndims=len(shape))
def __call__(self, x):
"""Computes regularization given an ed.Normal random variable as input."""
if not isinstance(x, random_variable.RandomVariable):
raise ValueError('Input must be an ed.RandomVariable.')
prior = generated_random_variables.Independent(
generated_random_variables.Normal(loc=x.distribution.mean(),
scale=self.stddev).distribution,
reinterpreted_batch_ndims=len(x.distribution.event_shape))
regularization = x.distribution.kl_divergence(prior.distribution)
return self.scale_factor * regularization
def __call__(self, x):
"""Computes regularization using an unbiased Monte Carlo estimate."""
prior = generated_random_variables.Independent(
generated_random_variables.HalfCauchy(
loc=tf.broadcast_to(self.loc, x.distribution.event_shape),
scale=tf.broadcast_to(self.scale, x.distribution.event_shape)
).distribution,
reinterpreted_batch_ndims=len(x.distribution.event_shape))
negative_entropy = x.distribution.log_prob(x)
cross_entropy = -prior.distribution.log_prob(x)
return self.scale_factor * (negative_entropy + cross_entropy)
def __call__(self, shape, dtype=None):
if not self.built:
self.build(shape, dtype)
loc = self.loc
if self.loc_constraint:
loc = self.loc_constraint(loc)
scale = self.scale
if self.scale_constraint:
scale = self.scale_constraint(scale)
return generated_random_variables.Independent(
generated_random_variables.LogNormal(loc=loc, scale=scale).distribution,
reinterpreted_batch_ndims=len(shape))
def call(self, inputs):
"""Computes regularization given an input ed.RandomVariable."""
if not isinstance(inputs, random_variable.RandomVariable):
raise ValueError('Input must be an ed.RandomVariable.')
stddev = self.stddev
if self.stddev_constraint:
stddev = self.stddev_constraint(stddev)
prior = generated_random_variables.Independent(
generated_random_variables.Normal(
loc=self.mean, scale=stddev).distribution,
reinterpreted_batch_ndims=len(inputs.distribution.event_shape))
regularization = inputs.distribution.kl_divergence(prior.distribution)
return self.scale_factor * regularization
def __call__(self, shape, dtype=None):
if not self.built:
self.build(shape, dtype)
loc = self.loc
if self.loc_constraint:
loc = self.loc_constraint(loc)
return generated_random_variables.Independent(
generated_random_variables.MixtureSameFamily(
mixture_distribution=generated_random_variables.Categorical(
probs=tf.broadcast_to([[1 / self.num_components] *
self.num_components],
list(shape) +
[self.num_components])).distribution,
components_distribution=generated_random_variables.
Deterministic(loc=loc).distribution).distribution,
reinterpreted_batch_ndims=len(shape))
