tf.nn.ctc_beam_search_decoder

Performs beam search decoding on the logits given in input.

Compat aliases for migration

See Migration guide for more details.

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

Args
`inputs`	3-D `float` `Tensor`, size `[max_time, batch_size, 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).

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], 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, top_paths]` containing sequence log-probabilities.

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], 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, top_paths] containing sequence log-probabilities.