Function ctc_loss

tensorflow/python/ops/ctc_ops.py:49–162

Computes the 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 200

(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)

# pylint: disable=protected-access, invalid-name
@tf_export(v1=["nn.ctc_loss"])
def ctc_loss(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):
  """Computes the 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)

  Input requirements:

  ```
  sequence_length(b) <= time for all b

  max(labels.indices(labels.indices[:, 1] == b, 2))
    <= sequence_length(b) for all b.
  ```

  Notes:

  This class performs the softmax operation for you, so inputs should
  be e.g. linear projections of outputs by an LSTM.

  The `inputs` Tensor&#x27;s innermost dimension size, `num_classes`, represents
  `num_labels + 1` classes, where num_labels is the number of true labels, and
  the largest value `(num_classes - 1)` is reserved for the blank label.

  For example, for a vocabulary containing 3 labels `[a, b, c]`,
  `num_classes = 4` and the labels indexing is `{a: 0, b: 1, c: 2, blank: 3}`.

  Regarding the arguments `preprocess_collapse_repeated` and
  `ctc_merge_repeated`:

  If `preprocess_collapse_repeated` is True, then a preprocessing step runs
  before loss calculation, wherein repeated labels passed to the loss
  are merged into single labels.  This is useful if the training labels come
  from, e.g., forced alignments and therefore have unnecessary repetitions.

  If `ctc_merge_repeated` is set False, then deep within the CTC calculation,
  repeated non-blank labels will not be merged and are interpreted
  as individual labels.  This is a simplified (non-standard) version of CTC.

  Here is a table of the (roughly) expected first order behavior:

  * `preprocess_collapse_repeated=False`, `ctc_merge_repeated=True`

    Classical CTC behavior: Outputs true repeated classes with blanks in
    between, and can also output repeated classes with no blanks in
    between that need to be collapsed by the decoder.