def call(self, inputs):
net = self.layer(inputs)
logits, loc, unconstrained_scale = tf.split(net, 3, axis=-1)
scale = tf.nn.softplus(
unconstrained_scale) + tf.keras.backend.epsilon()
return generated_random_variables.MixtureSameFamily(
mixture_distribution=generated_random_variables.Categorical(
logits=logits).distribution,
components_distribution=generated_random_variables.Logistic(
loc=loc, scale=scale).distribution)
def call(self, inputs):
net = self.layer(inputs)
logits, loc, scale = tf.split(net, 3, axis=-1)
if self.logits_constraint:
logits = self.logits_constraint(logits)
if self.loc_constraint:
loc = self.loc_constraint(loc)
if self.scale_constraint:
scale = self.scale_constraint(scale)
return generated_random_variables.MixtureSameFamily(
mixture_distribution=generated_random_variables.Categorical(
logits=logits).distribution,
components_distribution=generated_random_variables.Logistic(
loc=loc, scale=scale).distribution)
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))