(self, input_sequence, output_sequence, sequence_length,
           control_sequence=None)
return list(metrics_to_updates.values())
Evaluate on the given sequences, returning metric update ops.

    Args:
      input_sequence: The sequence to be fed to the encoder.
      output_sequence: The sequence expected from the decoder.
      sequence_length: The length of the given sequences (which must be
        identical).
      control_sequence: (Optional) sequence on which to condition the decoder.

    Returns:
      metric_update_ops: tf.metrics update ops.
Evaluate on the given sequences, returning metric update ops.
Evaluate on the given sequences returning metric update ops .
def eval(self, input_sequence, output_sequence, sequence_length,
           control_sequence=None):
    """Evaluate on the given sequences, returning metric update ops.

    Args:
      input_sequence: The sequence to be fed to the encoder.
      output_sequence: The sequence expected from the decoder.
      sequence_length: The length of the given sequences (which must be
        identical).
      control_sequence: (Optional) sequence on which to condition the decoder.

    Returns:
      metric_update_ops: tf.metrics update ops.
    """
    metric_map, scalars_to_summarize = self._compute_model_loss(
        input_sequence, output_sequence, sequence_length, control_sequence)

    for n, t in scalars_to_summarize.items():
      metric_map[n] = tf.metrics.mean(t)

    metrics_to_values, metrics_to_updates = (
        tf_slim.metrics.aggregate_metric_map(metric_map))

    for metric_name, metric_value in metrics_to_values.items():
      tf.summary.scalar(metric_name, metric_value)

    return list(metrics_to_updates.values())
def eval ( self , input_sequence , output_sequence , sequence_length , control_sequence = None ) : metric_map , scalars_to_summarize = self . _compute_model_loss ( input_sequence , output_sequence , sequence_length , control_sequence ) for n , t in scalars_to_summarize . items ( ) : metric_map [ n ] = tf . metrics . mean ( t ) metrics_to_values , metrics_to_updates = ( tf_slim . metrics . aggregate_metric_map ( metric_map ) ) for metric_name , metric_value in metrics_to_values . items ( ) : tf . summary . scalar ( metric_name , metric_value ) return list ( metrics_to_updates . values ( ) )