tensorflow_probability/examples/disentangled_vae.py
def generate(self, batch_size, length, samples=1, fix_static=False,
               fix_dynamic=False):
    """Generate new sequences.

    Args:
      batch_size: Number of sequences to generate.
      length: Number of timesteps to generate for each sequence.
      samples: Number of samples to draw from the latent distributions.
      fix_static: Boolean for whether or not to share the same random
        sample of the static latent variable `f` from its prior across
        all examples.
      fix_dynamic: Boolean for whether or not to share the same random
        sample of the dynamic latent variable `z_{1:T}` from its prior
        across all examples.

    Returns:
      A batched Independent distribution wrapping a set of Normal
      distributions over the pixels of the generated sequences, where
      the Independent distribution has event shape [height, width,
      channels], batch shape [samples, batch_size, timesteps], and
      sample shape [sample_shape, samples, batch_size, timesteps,
      height, width, channels].
    """
    static_sample, _ = self.sample_static_prior(samples, batch_size, fix_static)
    dynamic_sample, _ = self.sample_dynamic_prior(samples, batch_size, length,
                                                  fix_dynamic)
    likelihood = self.decoder((dynamic_sample, static_sample))
    return likelihood
def generate(self, batch_size, length, samples=1, fix_static=False,
               fix_dynamic=False):
    static_sample, _ = self.sample_static_prior(samples, batch_size, fix_static)
    dynamic_sample, _ = self.sample_dynamic_prior(samples, batch_size, length,
                                                  fix_dynamic)
    likelihood = self.decoder((dynamic_sample, static_sample))
    return likelihood
def generate ( self , batch_size , length , samples = 1 , fix_static = False , fix_dynamic = False ) : static_sample , _ = self . sample_static_prior ( samples , batch_size , fix_static ) dynamic_sample , _ = self . sample_dynamic_prior ( samples , batch_size , length , fix_dynamic ) likelihood = self . decoder ( ( dynamic_sample , static_sample ) ) return likelihood
Generate new sequences.

    Args:
      batch_size: Number of sequences to generate.
      length: Number of timesteps to generate for each sequence.
      samples: Number of samples to draw from the latent distributions.
      fix_static: Boolean for whether or not to share the same random
        sample of the static latent variable `f` from its prior across
        all examples.
      fix_dynamic: Boolean for whether or not to share the same random
        sample of the dynamic latent variable `z_{1:T}` from its prior
        across all examples.

    Returns:
      A batched Independent distribution wrapping a set of Normal
      distributions over the pixels of the generated sequences, where
      the Independent distribution has event shape [height, width,
      channels], batch shape [samples, batch_size, timesteps], and
      sample shape [sample_shape, samples, batch_size, timesteps,
      height, width, channels].
Generate new sequences .