dynamic_rnn_estimator.py 文件源码

def padding_mask(sequence_lengths, padded_length):
  """Creates a mask used for calculating losses with padded input.

  Args:
    sequence_lengths: A `Tensor` of shape `[batch_size]` containing the unpadded
      length of  each sequence.
    padded_length: A scalar `Tensor` indicating the length of the sequences
      after padding
  Returns:
    A boolean `Tensor` M of shape `[batch_size, padded_length]` where
    `M[i, j] == True` when `lengths[i] > j`.

  """
  range_tensor = math_ops.range(padded_length)
  return math_ops.less(array_ops.expand_dims(range_tensor, 0),
                       array_ops.expand_dims(sequence_lengths, 1))