Function _ctc_loss_impl

tensorflow/python/ops/ctc_ops.py:164–204

(labels,
                   inputs=None,
                   sequence_length=None,
                   preprocess_collapse_repeated=False,
                   ctc_merge_repeated=True,
                   ignore_longer_outputs_than_inputs=False,
                   time_major=True,
                   logits=None,
                   use_cudnn=False)

                        use_cudnn=False)

def _ctc_loss_impl(labels,
                   inputs=None,
                   sequence_length=None,
                   preprocess_collapse_repeated=False,
                   ctc_merge_repeated=True,
                   ignore_longer_outputs_than_inputs=False,
                   time_major=True,
                   logits=None,
                   use_cudnn=False):
  # Helper function of ctc_loss with one additional param:
  # use_cudnn: A bool to enable cuDNN CTC loss operation. If true, the blank
  #   index has to be 0.

  # The second, third, etc output tensors contain the gradients.  We use it in
  # _CTCLossGrad() below.
  if not isinstance(labels, sparse_tensor.SparseTensor):
    raise TypeError("Expected labels (first argument) to be a SparseTensor")

  # For internal calculations, we transpose to [time, batch, num_classes]
  inputs = deprecation.deprecated_argument_lookup("logits", logits, "inputs",
                                                  inputs)
  if not time_major:
    inputs = array_ops.transpose(inputs, [1, 0, 2])  # (B,T,N) => (T,B,N)

  # gen_ctc_ops.ctc_loss_v2 differs from gen_ctc_ops.ctc_loss. v2 assumes the
  # blank index to be 0, but v1 views it as the last index.
  if use_cudnn:
    ctc_loss_func = gen_ctc_ops.ctc_loss_v2
  else:
    ctc_loss_func = gen_ctc_ops.ctc_loss

  loss, _ = ctc_loss_func(
      inputs,
      labels.indices,
      labels.values,
      sequence_length,
      preprocess_collapse_repeated=preprocess_collapse_repeated,
      ctc_merge_repeated=ctc_merge_repeated,
      ignore_longer_outputs_than_inputs=ignore_longer_outputs_than_inputs)

  return loss

# pylint: disable=unused-argument
def _CTCLossGradImpl(op, grad_loss, _):