tf.compat.v1.nn.crelu

Performs beam search decoding on the logits given in input.

tf.compat.v1.nn.ctc_beam_search_decoder(
    inputs, sequence_length, beam_width=100, top_paths=1, merge_repeated=True
)

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 sequence is A B B * B * B (where '*' is the blank label), the return value is:

A B if merge_repeated = True.
A 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].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.

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].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.

TensorFlow

tf.compat / v1 / v1.nn.crelu

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tf.compat / v1 / v1.nn.crelu

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