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. |
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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/raw_ops/CTCLossV2