seq2seq_model.py 文件源码

def _create_predictions(self, decoder_output, features, labels, losses=None):
    """Creates the dictionary of predictions that is returned by the model.
    """
    predictions = {}

    # Add features and, if available, labels to predictions
    predictions.update(_flatten_dict({"features": features}))
    if labels is not None:
      predictions.update(_flatten_dict({"labels": labels}))

    if losses is not None:
      predictions["losses"] = _transpose_batch_time(losses)

    # Decoders returns output in time-major form [T, B, ...]
    # Here we transpose everything back to batch-major for the user
    output_dict = collections.OrderedDict(
        zip(decoder_output._fields, decoder_output))
    decoder_output_flat = _flatten_dict(output_dict)

    decoder_output_flat = {
        k: _transpose_batch_time(v)
        for k, v in decoder_output_flat.items()
    }
    predictions.update(decoder_output_flat)

    # If we predict the ids also map them back into the vocab and process them
    if "predicted_ids" in predictions.keys():
      vocab_tables = graph_utils.get_dict_from_collection("vocab_tables")
      target_id_to_vocab = vocab_tables["target_id_to_vocab"]
      predicted_tokens = target_id_to_vocab.lookup(
          tf.to_int64(predictions["predicted_ids"]))
      # Raw predicted tokens
      predictions["predicted_tokens"] = predicted_tokens

    return predictions