piiswrong commented on a change in pull request #7304: gluon bce & ctc losses
URL: https://github.com/apache/incubator-mxnet/pull/7304#discussion_r131713817
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File path: python/mxnet/gluon/loss.py
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@@ -239,3 +281,59 @@ def hybrid_forward(self, F, output, label,
sample_weight=None):
loss = label * (F.log(label+1e-8) - output)
loss = _apply_weighting(F, loss, self._weight, sample_weight)
return F.mean(loss, axis=self._batch_axis, exclude=True)
+
+class CTCLoss(Loss):
+ r"""Connectionist Temporal Classification Loss.
+
+ See `"Connectionist Temporal Classification: Labelling Unsegmented
+ Sequence Data with Recurrent Neural Networks"
+ <http://www.cs.toronto.edu/~graves/icml_2006.pdf>`_ paper for more
information.
+
+ The prediction output should be an activation vector without softmax, with
shape
+ according to the output_layout:
+ **TNC**: *(sequence_length, batch_size, alphabet_size + 1)*
+ **NTC**: *(batch_size, sequence_length, alphabet_size + 1)*
+ **out**: *(batch_size)*.
+
+ ``label`` is a tensor of integers between 1 and *alphabet_size*, with
shape according
+ to the batch_axis:
+ **batch_axis=0**: *(batch_size, label_sequence_length)*
+ **batch_axis=1**: *(label_sequence_length, batch_size)*
+
+ If a sequence of labels is shorter than *label_sequence_length*, use the
special
+ padding character 0 at the end of the sequence to conform it to the correct
+ length. For example, if *label_sequence_length* = 4, and one has two
sequences
+ of labels [2, 1] and [3, 2, 2], the resulting ```label``` tensor should be
+ padded to be::
+
+ [[2, 1, 0, 0], [3, 2, 2, 0]]
+
+
+ Parameters
+ ----------
+ output_layout : str, default 'NTC'
+ Layout of the output sequence activation vector.
+ weight : float or None
+ Global scalar weight for loss.
+ sample_weight : Symbol or None
+ Per sample weighting. Must be broadcastable to
+ the same shape as loss. For example, if loss has
+ shape (64, 10) and you want to weight each sample
+ in the batch, `sample_weight` should have shape (64, 1).
+ batch_axis : int, default 0
+ The axis in label that represents mini-batch.
+ """
+ def __init__(self, output_layout='NTC', weight=None, batch_axis=0,
**kwargs):
Review comment:
output_layout & label_layout
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