tensorflow_probability/examples/grammar_vae.py
def call(self, inputs):
    """Runs the model forward to return a stochastic encoding.

    Args:
      inputs: Tensor of shape [1, num_productions, num_production_rules]. It is
        a sequence of productions of length `num_productions`. Each production
        is a one-hot vector of length `num_production_rules`: it determines
        which production rule the production corresponds to.

    Returns:
      latent_code_posterior: A random variable capturing a sample from the
        variational distribution, of shape [1, self.latent_size].
    """
    net = self.encoder_net(tf.cast(inputs, tf.float32))
    return ed.MultivariateNormalDiag(
        loc=net[..., :self.latent_size],
        scale_diag=tf.nn.softplus(net[..., self.latent_size:]),
        name="latent_code_posterior")
def call(self, inputs):
    net = self.encoder_net(tf.cast(inputs, tf.float32))
    return ed.MultivariateNormalDiag(
        loc=net[..., :self.latent_size],
        scale_diag=tf.nn.softplus(net[..., self.latent_size:]),
        name="latent_code_posterior")
def call ( self , inputs ) : net = self . encoder_net ( tf . cast ( inputs , tf . float32 ) ) return ed . MultivariateNormalDiag ( loc = net [ ... , : self . latent_size ] , scale_diag = tf . nn . softplus ( net [ ... , self . latent_size : ] ) , name = "latent_code_posterior" )
Runs the model forward to return a stochastic encoding.

    Args:
      inputs: Tensor of shape [1, num_productions, num_production_rules]. It is
        a sequence of productions of length `num_productions`. Each production
        is a one-hot vector of length `num_production_rules`: it determines
        which production rule the production corresponds to.

    Returns:
      latent_code_posterior: A random variable capturing a sample from the
        variational distribution, of shape [1, self.latent_size].
Runs the model forward to return a stochastic encoding .