tensorflow_probability/examples/latent_dirichlet_allocation_distributions.py
def make_prior(num_topics, initial_value):
  """Create the prior distribution.

  Args:
    num_topics: Number of topics.
    initial_value: The starting value for the prior parameters.

  Returns:
    prior: A `callable` that returns a `tf.distribution.Distribution`
        instance, the prior distribution.
    prior_variables: A `list` of `Variable` objects, the trainable parameters
        of the prior.
  """
  def _softplus_inverse(x):
    return np.log(np.expm1(x))

  logit_concentration = tf.compat.v1.get_variable(
      "logit_concentration",
      shape=[1, num_topics],
      initializer=tf.compat.v1.initializers.constant(
          _softplus_inverse(initial_value)))
  concentration = _clip_dirichlet_parameters(
      tf.nn.softplus(logit_concentration))

  def prior():
    return tfd.Dirichlet(concentration=concentration,
                         name="topics_prior")

  prior_variables = [logit_concentration]

  return prior, prior_variables
def make_prior(num_topics, initial_value):
  """Create the prior distribution.

  Args:
    num_topics: Number of topics.
    initial_value: The starting value for the prior parameters.

  Returns:
    prior: A `callable` that returns a `tf.distribution.Distribution`
        instance, the prior distribution.
    prior_variables: A `list` of `Variable` objects, the trainable parameters
        of the prior.
  """
  def _softplus_inverse(x):
    return np.log(np.expm1(x))

  logit_concentration = tf.compat.v1.get_variable(
      "logit_concentration",
      shape=[1, num_topics],
      initializer=tf.compat.v1.initializers.constant(
          _softplus_inverse(initial_value)))
  concentration = _clip_dirichlet_parameters(
      tf.nn.softplus(logit_concentration))

  def prior():
    return tfd.Dirichlet(concentration=concentration,
                         name="topics_prior")

  prior_variables = [logit_concentration]

  return prior, prior_variables
Create the prior distribution.

  Args:
    num_topics: Number of topics.
    initial_value: The starting value for the prior parameters.

  Returns:
    prior: A `callable` that returns a `tf.distribution.Distribution`
        instance, the prior distribution.
    prior_variables: A `list` of `Variable` objects, the trainable parameters
        of the prior.