(self, event_sequences, inputs,
                                     initial_state)
return self._config.encoder_decoder.evaluate_log_likelihood(
        event_sequences, softmax)
Evaluates the log likelihood of a batch of event sequences.

    Args:
      event_sequences: A list of event sequences, each of which is a Python
          list-like object. The list of event sequences should have length equal
          to `self._batch_size()`.
      inputs: A Python list of model inputs, with length equal to
          `self._batch_size()`.
      initial_state: A numpy array containing the initial RNN state, where
          `initial_state.shape[0]` is equal to `self._batch_size()`.

    Returns:
      A Python list containing the log likelihood of each sequence in
      `event_sequences`.
Evaluates the log likelihood of a batch of event sequences.
Evaluates the log likelihood of a batch of event sequences .
def _evaluate_batch_log_likelihood(self, event_sequences, inputs,
                                     initial_state):
    """Evaluates the log likelihood of a batch of event sequences.

    Args:
      event_sequences: A list of event sequences, each of which is a Python
          list-like object. The list of event sequences should have length equal
          to `self._batch_size()`.
      inputs: A Python list of model inputs, with length equal to
          `self._batch_size()`.
      initial_state: A numpy array containing the initial RNN state, where
          `initial_state.shape[0]` is equal to `self._batch_size()`.

    Returns:
      A Python list containing the log likelihood of each sequence in
      `event_sequences`.
    """
    graph_inputs = self._session.graph.get_collection('inputs')[0]
    graph_initial_state = self._session.graph.get_collection('initial_state')
    graph_softmax = self._session.graph.get_collection('softmax')[0]
    graph_temperature = self._session.graph.get_collection('temperature')

    feed_dict = {graph_inputs: inputs,
                 tuple(graph_initial_state): initial_state}
    # For backwards compatibility, we only try to pass temperature if the
    # placeholder exists in the graph.
    if graph_temperature:
      feed_dict[graph_temperature[0]] = 1.0
    softmax = self._session.run(graph_softmax, feed_dict)

    return self._config.encoder_decoder.evaluate_log_likelihood(
        event_sequences, softmax)
def _evaluate_batch_log_likelihood ( self , event_sequences , inputs , initial_state ) : graph_inputs = self . _session . graph . get_collection ( 'inputs' ) [ 0 ] graph_initial_state = self . _session . graph . get_collection ( 'initial_state' ) graph_softmax = self . _session . graph . get_collection ( 'softmax' ) [ 0 ] graph_temperature = self . _session . graph . get_collection ( 'temperature' ) feed_dict = { graph_inputs : inputs , tuple ( graph_initial_state ) : initial_state } # For backwards compatibility, we only try to pass temperature if the # placeholder exists in the graph. if graph_temperature : feed_dict [ graph_temperature [ 0 ] ] = 1.0 softmax = self . _session . run ( graph_softmax , feed_dict ) return self . _config . encoder_decoder . evaluate_log_likelihood ( event_sequences , softmax )