tf.nn.ctc_beam_search_decoder(inputs, sequence_length, beam_width=100, top_paths=1, merge_repeated=True)

tf.nn.ctc_beam_search_decoder(inputs, sequence_length, beam_width=100, top_paths=1, merge_repeated=True)

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.

Args:

• 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.

Returns:

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].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.

Defined in tensorflow/python/ops/ctc_ops.py.