dynamic_rnn_estimator.py 文件源码

def _single_value_predictions(
    activations, sequence_length, target_column, predict_probabilities):
  """Maps `activations` from the RNN to predictions for single value models.

  If `predict_probabilities` is `False`, this function returns a `dict`
  containing single entry with key `PREDICTIONS_KEY`. If `predict_probabilities`
  is `True`, it will contain a second entry with key `PROBABILITIES_KEY`. The
  value of this entry is a `Tensor` of probabilities with shape
  `[batch_size, num_classes]`.

  Args:
    activations: Output from an RNN. Should have dtype `float32` and shape
      `[batch_size, padded_length, ?]`.
    sequence_length: A `Tensor` with shape `[batch_size]` and dtype `int32`
      containing the length of each sequence in the batch. If `None`, sequences
      are assumed to be unpadded.
    target_column: An initialized `TargetColumn`, calculate predictions.
    predict_probabilities: A Python boolean, indicating whether probabilities
      should be returned. Should only be set to `True` for
      classification/logistic regression problems.
  Returns:
    A `dict` mapping strings to `Tensors`.
  """
  with ops.name_scope('SingleValuePrediction'):
    last_activations = select_last_activations(activations, sequence_length)
    if predict_probabilities:
      probabilities = target_column.logits_to_predictions(
          last_activations, proba=True)
      prediction_dict = {
          RNNKeys.PROBABILITIES_KEY: probabilities,
          RNNKeys.PREDICTIONS_KEY: math_ops.argmax(probabilities, 1)}
    else:
      predictions = target_column.logits_to_predictions(
          last_activations, proba=False)
      prediction_dict = {RNNKeys.PREDICTIONS_KEY: predictions}
    return prediction_dict