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Performs greedy decoding on the logits given in input (best path).
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
ifmerge_repeated=True
.A B B B B
ifmerge_repeated=False
.