tf.nn.ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True)

tf.nn.ctc_greedy_decoder(inputs, sequence_length, merge_repeated=True)

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 if merge_repeated=True.
• A B B B B B if merge_repeated=False.

Args:

• inputs: 3-D float Tensor sized [max_time x batch_size x num_classes]. The logits.
• sequence_length: 1-D int32 vector containing sequence lengths, having size [batch_size].
• merge_repeated: Boolean. Default: True.

Returns:

A tuple (decoded, log_probabilities) where decoded: A single-element list. decoded[0] is an SparseTensor containing the decoded outputs s.t.:
decoded.indices: Indices matrix (total_decoded_outputs x 2). The rows store: [batch, time].
decoded.values: Values vector, size (total_decoded_outputs). The vector stores the decoded classes.
decoded.shape: Shape vector, size (2). The shape values are: [batch_size, max_decoded_length] log_probability: A float matrix (batch_size x 1) containing sequence log-probabilities.

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