tf.nn.ctc_beam_search_decoder( inputs, sequence_length, beam_width=100, top_paths=1, merge_repeated=True )
Defined in tensorflow/python/ops/ctc_ops.py
.
See the guide: Neural Network > Connectionist Temporal Classification (CTC)
Performs beam search decoding on the logits given in input.
Note The ctc_greedy_decoder
is a special case of the ctc_beam_search_decoder
with top_paths=1
and beam_width=1
(but that decoder is faster for this special case).
If merge_repeated
is True
, merge repeated classes in the output beams. This means that if consecutive entries in a beam are the same, only the first of these is emitted. That is, when the top path is A B B B B
, the return value is:
A B
if merge_repeated = True
.A B B B B
if merge_repeated = False
.inputs
: 3-D float
Tensor
, size [max_time x batch_size x num_classes]
. The logits.sequence_length
: 1-D int32
vector containing sequence lengths, having size [batch_size]
.beam_width
: An int scalar >= 0 (beam search beam width).top_paths
: An int scalar >= 0, <= beam_width (controls output size).merge_repeated
: Boolean. Default: True.A tuple (decoded, log_probabilities)
where decoded
: A list of length top_paths, where decoded[j]
is a SparseTensor
containing the decoded outputs:
decoded[j].indices
: Indices matrix (total_decoded_outputs[j] x 2)
The rows store: [batch, time].
decoded[j].values
: Values vector, size (total_decoded_outputs[j])
. The vector stores the decoded classes for beam j.
decoded[j].dense_shape
: Shape vector, size (2)
. The shape values are: [batch_size, max_decoded_length[j]]
. log_probability
: A float
matrix (batch_size x top_paths)
containing sequence log-probabilities.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/ctc_beam_search_decoder