Function ctc_loss_v2

tensorflow/python/ops/ctc_ops.py:642–741

Computes CTC (Connectionist Temporal Classification) loss. This op implements the CTC loss as presented in the article: [A. Graves, S. Fernandez, F. Gomez, J. Schmidhuber. Connectionist Temporal Classification: Labeling Unsegmented Sequence Data with Recurrent Neural Networks. ICML 2006, P

(labels,
                logits,
                label_length,
                logit_length,
                logits_time_major=True,
                unique=None,
                blank_index=None,
                name=None)

@tf_export("nn.ctc_loss", v1=["nn.ctc_loss_v2"])
def ctc_loss_v2(labels,
                logits,
                label_length,
                logit_length,
                logits_time_major=True,
                unique=None,
                blank_index=None,
                name=None):
  """Computes CTC (Connectionist Temporal Classification) loss.

  This op implements the CTC loss as presented in the article:

  [A. Graves, S. Fernandez, F. Gomez, J. Schmidhuber.
  Connectionist Temporal Classification: Labeling Unsegmented Sequence Data
  with Recurrent Neural Networks. ICML 2006, Pittsburgh, USA,
  pp. 369-376.](http://www.cs.toronto.edu/~graves/icml_2006.pdf)

  Notes:

  - Same as the "Classic CTC" in TensorFlow 1.x's tf.compat.v1.nn.ctc_loss
    setting of preprocess_collapse_repeated=False, ctc_merge_repeated=True
  - Labels may be supplied as either a dense, zero-padded tensor with a
    vector of label sequence lengths OR as a SparseTensor.
  - On TPU and GPU: Only dense padded labels are supported.
  - On CPU: Caller may use SparseTensor or dense padded labels but calling with
    a SparseTensor will be significantly faster.
  - Default blank label is 0 rather num_classes - 1, unless overridden by
    blank_index.

  Args:
    labels: tensor of shape [batch_size, max_label_seq_length] or SparseTensor
    logits: tensor of shape [frames, batch_size, num_labels], if
      logits_time_major == False, shape is [batch_size, frames, num_labels].
    label_length: tensor of shape [batch_size], None if labels is SparseTensor
      Length of reference label sequence in labels.
    logit_length: tensor of shape [batch_size] Length of input sequence in
      logits.
    logits_time_major: (optional) If True (default), logits is shaped [time,
      batch, logits]. If False, shape is [batch, time, logits]
    unique: (optional) Unique label indices as computed by
      ctc_unique_labels(labels).  If supplied, enable a faster, memory efficient
      implementation on TPU.
    blank_index: (optional) Set the class index to use for the blank label.
      Negative values will start from num_classes, ie, -1 will reproduce the
      ctc_loss behavior of using num_classes - 1 for the blank symbol. There is
      some memory/performance overhead to switching from the default of 0 as an
      additional shifted copy of the logits may be created.
    name: A name for this `Op`. Defaults to "ctc_loss_dense".

  Returns:
    loss: tensor of shape [batch_size], negative log probabilities.
  """
  if isinstance(labels, sparse_tensor.SparseTensor):
    if blank_index is None:
      raise ValueError(
          "blank_index must be given when using SparseTensor labels.")

    _ctc_use_cudnn = os.environ.get("TF_CUDNN_CTC_LOSS", "0")