tf.raw_ops.CTCLossV2

Calculates the CTC Loss (log probability) for each batch entry. Also calculates

tf.raw_ops.CTCLossV2(
    inputs, labels_indices, labels_values, sequence_length,
    preprocess_collapse_repeated=False, ctc_merge_repeated=True,
    ignore_longer_outputs_than_inputs=False, name=None
)

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

Args:

  • inputs: A Tensor of type float32. 3-D, shape: (max_time x batch_size x num_classes), the logits. Default blank label is 0 rather num_classes - 1.
  • labels_indices: A Tensor of type int64. The indices of a SparseTensor<int32, 2>. labels_indices(i, :) == [b, t] means labels_values(i) stores the id for (batch b, time t).
  • labels_values: A Tensor of type int32. The values (labels) associated with the given batch and time.
  • sequence_length: A Tensor of type int32. A vector containing sequence lengths (batch).
  • preprocess_collapse_repeated: An optional bool. Defaults to False. Scalar, if true then repeated labels are collapsed prior to the CTC calculation.
  • ctc_merge_repeated: An optional bool. Defaults to True. Scalar. If set to false, during CTC calculation repeated non-blank labels will not be merged and are interpreted as individual labels. This is a simplified version of CTC.
  • ignore_longer_outputs_than_inputs: An optional bool. Defaults to False. Scalar. If set to true, during CTC calculation, items that have longer output sequences than input sequences are skipped: they don't contribute to the loss term and have zero-gradient.
  • name: A name for the operation (optional).

Returns:

A tuple of Tensor objects (loss, gradient).

  • loss: A Tensor of type float32.
  • gradient: A Tensor of type float32.