tensorflow_probability/examples/latent_dirichlet_allocation_distributions.py
def make_decoder(num_topics, num_words):
  """Create the decoder function.

  Args:
    num_topics: The number of topics.
    num_words: The number of words.

  Returns:
    decoder: A `callable` mapping a `Tensor` of encodings to a
      `tfd.Distribution` instance over words.
  """
  topics_words_logits = tf.compat.v1.get_variable(
      "topics_words_logits",
      shape=[num_topics, num_words],
      initializer=tf.compat.v1.glorot_normal_initializer())
  topics_words = tf.nn.softmax(topics_words_logits, axis=-1)

  def decoder(topics):
    word_probs = tf.matmul(topics, topics_words)
    # The observations are bag of words and therefore not one-hot. However,
    # log_prob of OneHotCategorical computes the probability correctly in
    # this case.
    return tfd.OneHotCategorical(probs=word_probs,
                                 name="bag_of_words")

  return decoder, topics_words
def make_decoder(num_topics, num_words):
  """Create the decoder function.

  Args:
    num_topics: The number of topics.
    num_words: The number of words.

  Returns:
    decoder: A `callable` mapping a `Tensor` of encodings to a
      `tfd.Distribution` instance over words.
  """
  topics_words_logits = tf.compat.v1.get_variable(
      "topics_words_logits",
      shape=[num_topics, num_words],
      initializer=tf.compat.v1.glorot_normal_initializer())
  topics_words = tf.nn.softmax(topics_words_logits, axis=-1)

  def decoder(topics):
    word_probs = tf.matmul(topics, topics_words)
    # The observations are bag of words and therefore not one-hot. However,
    # log_prob of OneHotCategorical computes the probability correctly in
    # this case.
    return tfd.OneHotCategorical(probs=word_probs,
                                 name="bag_of_words")

  return decoder, topics_words
def make_decoder ( num_topics , num_words ) : topics_words_logits = tf . compat . v1 . get_variable ( "topics_words_logits" , shape = [ num_topics , num_words ] , initializer = tf . compat . v1 . glorot_normal_initializer ( ) ) topics_words = tf . nn . softmax ( topics_words_logits , axis = - 1 ) def decoder ( topics ) : word_probs = tf . matmul ( topics , topics_words ) # The observations are bag of words and therefore not one-hot. However, # log_prob of OneHotCategorical computes the probability correctly in # this case. return tfd . OneHotCategorical ( probs = word_probs , name = "bag_of_words" ) return decoder , topics_words
Create the decoder function.

  Args:
    num_topics: The number of topics.
    num_words: The number of words.

  Returns:
    decoder: A `callable` mapping a `Tensor` of encodings to a
      `tfd.Distribution` instance over words.
Create the decoder function .