tf.compat.v1.nn.ctc_loss

Computes the CTC (Connectionist Temporal Classification) Loss.

tf.compat.v1.nn.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
)

This op implements the CTC loss as presented in (Graves et al., 2006).

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'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.

• preprocess_collapse_repeated=True, ctc_merge_repeated=False

  Never learns to output repeated classes, as they are collapsed in the input labels before training.

• preprocess_collapse_repeated=False, ctc_merge_repeated=False

  Outputs repeated classes with blanks in between, but generally does not require the decoder to collapse/merge repeated classes.

• preprocess_collapse_repeated=True, ctc_merge_repeated=True

  Untested. Very likely will not learn to output repeated classes.

The ignore_longer_outputs_than_inputs option allows to specify the behavior of the CTCLoss when dealing with sequences that have longer outputs than inputs. If true, the CTCLoss will simply return zero gradient for those items, otherwise an InvalidArgument error is returned, stopping training.

Args
labels	An int32 SparseTensor. labels.indices[i, :] == [b, t] means labels.values[i] stores the id for (batch b, time t). labels.values[i] must take on values in [0, num_labels). See core/ops/ctc_ops.cc for more details.
inputs	3-D float Tensor. If time_major == False, this will be a Tensor shaped: [batch_size, max_time, num_classes]. If time_major == True (default), this will be a Tensor shaped: [max_time, batch_size, num_classes]. The logits.
sequence_length	1-D int32 vector, size [batch_size]. The sequence lengths.
preprocess_collapse_repeated	Boolean. Default: False. If True, repeated labels are collapsed prior to the CTC calculation.
ctc_merge_repeated	Boolean. Default: True.
ignore_longer_outputs_than_inputs	Boolean. Default: False. If True, sequences with longer outputs than inputs will be ignored.
time_major	The shape format of the inputs Tensors. If True, these Tensors must be shaped [max_time, batch_size, num_classes]. If False, these Tensors must be shaped [batch_size, max_time, num_classes]. Using time_major = True (default) is a bit more efficient because it avoids transposes at the beginning of the ctc_loss calculation. However, most TensorFlow data is batch-major, so by this function also accepts inputs in batch-major form.
logits	Alias for inputs.

Returns
A 1-D float Tensor, size [batch], containing the negative log probabilities.

Raises
TypeError	if labels is not a SparseTensor.

References:

Connectionist Temporal Classification - Labeling Unsegmented Sequence Data with Recurrent Neural Networks: Graves et al., 2006 (pdf)