TensorFlow 1 version | View source on GitHub |
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:
- Regardless of the value of
merge_repeated
, if an index of a given time and batch corresponds to theblank_index
, no new element is emitted. - 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
.
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.:
|
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.
|