(self, event_sequences, inputs, initial_state,
                               temperature)
return final_state, loglik[:, 0]
Extends a batch of event sequences by a single step each.

    This method modifies the event sequences in place.

    Args:
      event_sequences: A list of PianorollSequences. 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-NADE state, where
          `initial_state.shape[0]` is equal to `self._batch_size()`.
      temperature: Unused.

    Returns:
      final_state: The final RNN-NADE state, the same size as `initial_state`.
      loglik: The log-likelihood of the sampled value for each event
          sequence, a 1-D numpy array of length
          `self._batch_size()`. If `inputs` is a full-length inputs batch, the
          log-likelihood of each entire sequence up to and including the
          generated step will be computed and returned.
Extends a batch of event sequences by a single step each.
Extends a batch of event sequences by a single step each .
def _generate_step_for_batch(self, event_sequences, inputs, initial_state,
                               temperature):
    """Extends a batch of event sequences by a single step each.

    This method modifies the event sequences in place.

    Args:
      event_sequences: A list of PianorollSequences. 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-NADE state, where
          `initial_state.shape[0]` is equal to `self._batch_size()`.
      temperature: Unused.

    Returns:
      final_state: The final RNN-NADE state, the same size as `initial_state`.
      loglik: The log-likelihood of the sampled value for each event
          sequence, a 1-D numpy array of length
          `self._batch_size()`. If `inputs` is a full-length inputs batch, the
          log-likelihood of each entire sequence up to and including the
          generated step will be computed and returned.
    """
    pianoroll_sequences = event_sequences
    assert len(pianoroll_sequences) == self._batch_size()

    graph_inputs = self._session.graph.get_collection('inputs')[0]
    graph_initial_state = tuple(
        self._session.graph.get_collection('initial_state'))
    graph_final_state = tuple(
        self._session.graph.get_collection('final_state'))
    graph_sample = self._session.graph.get_collection('sample')[0]
    graph_log_prob = self._session.graph.get_collection('log_prob')[0]

    sample, loglik, final_state = self._session.run(
        [graph_sample, graph_log_prob, graph_final_state],
        {
            graph_inputs: inputs,
            graph_initial_state: initial_state,
        })

    self._config.encoder_decoder.extend_event_sequences(
        pianoroll_sequences, sample)

    return final_state, loglik[:, 0]
def _generate_step_for_batch ( self , event_sequences , inputs , initial_state , temperature ) : pianoroll_sequences = event_sequences assert len ( pianoroll_sequences ) == self . _batch_size ( ) graph_inputs = self . _session . graph . get_collection ( 'inputs' ) [ 0 ] graph_initial_state = tuple ( self . _session . graph . get_collection ( 'initial_state' ) ) graph_final_state = tuple ( self . _session . graph . get_collection ( 'final_state' ) ) graph_sample = self . _session . graph . get_collection ( 'sample' ) [ 0 ] graph_log_prob = self . _session . graph . get_collection ( 'log_prob' ) [ 0 ] sample , loglik , final_state = self . _session . run ( [ graph_sample , graph_log_prob , graph_final_state ] , { graph_inputs : inputs , graph_initial_state : initial_state , } ) self . _config . encoder_decoder . extend_event_sequences ( pianoroll_sequences , sample ) return final_state , loglik [ : , 0 ]