tf.nn.ctc_greedy_decoder( inputs, sequence_length, merge_repeated=True )
Defined in tensorflow/python/ops/ctc_ops.py
.
See the guide: Neural Network > Connectionist Temporal Classification (CTC)
Performs greedy decoding on the logits given in input (best path).
Note: Regardless of the value of merge_repeated, if the maximum index of a given time and batch corresponds to the blank index (num_classes - 1)
, no new element is emitted.
If merge_repeated
is True
, merge repeated classes in output. This means that if consecutive logits' maximum indices are the same, only the first of these is emitted. The sequence A B B * B * B
(where '*' is the blank label) becomes
A B B B
if merge_repeated=True
.A B B B B
if merge_repeated=False
.inputs
: 3-D float
Tensor
sized [max_time, batch_size, num_classes]
. The logits.sequence_length
: 1-D int32
vector containing sequence lengths, having size [batch_size]
.merge_repeated
: Boolean. Default: True.A tuple (decoded, neg_sum_logits)
where decoded
: A single-element list. decoded[0]
is an SparseTensor
containing the decoded outputs s.t.:
decoded.indices
: Indices matrix (total_decoded_outputs, 2)
. The rows store: [batch, time]
.
decoded.values
: Values vector, size (total_decoded_outputs)
. The vector stores the decoded classes.
decoded.dense_shape
: Shape vector, size (2)
. The shape values are: [batch_size, max_decoded_length]
neg_sum_logits
: A float
matrix (batch_size x 1)
containing, for the sequence found, the negative of the sum of the greatest logit at each timeframe.
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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_greedy_decoder