tf.nn.ctc_greedy_decoder | TensorFlow v2.16.1 (original) (raw)

Performs greedy decoding on the logits given in input (best path).

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Compat aliases for migration

tf.compat.v1.nn.ctc_greedy_decoder

tf.nn.ctc_greedy_decoder(
    inputs, sequence_length, merge_repeated=True, blank_index=None
)

Given a tensor as inputs, the blank_index parameter defines the class index of the blank symbol.

For example:

If blank_index is equal to 1:

inf = float("inf") logits = tf.constant([[[ 0., -inf, -inf], [ -2.3, -inf, -0.1]], [[ -inf, -0.5, -inf], [ -inf, -inf, -0.1]], [[ -inf, -inf, -inf], [ -0.1, -inf, -2.3]]]) seq_lens = tf.constant([2, 3]) outputs = tf.nn.ctc_greedy_decoder( logits, seq_lens, blank_index=1)

Notes:

Unlike ctc_beam_search_decoder, ctc_greedy_decoder considers blanks as regular elements when computing the probability of a sequence.
Default blank_index is (num_classes - 1), unless overriden.

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.

Args
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.
blank_index	(Optional). Default: num_classes - 1. Define the class index to use for the blank label. Negative values will start from num_classes, ie, -1 will reproduce the ctc_greedy_decoder behavior of using num_classes - 1 for the blank symbol, which corresponds to the default.

Returns
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.