提交 082c302c 编写于 作者: W wanghaoshuang

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上级 0dd3919a
......@@ -493,3 +493,8 @@ swish
------
.. autofunction:: paddle.v2.fluid.layers.swish
:noindex:
greedy_ctc_error
------
.. autofunction:: paddle.v2.fluid.layers.greedy_ctc_error
:noindex:
......@@ -50,7 +50,7 @@ __all__ = [
'sequence_last_step',
'dropout',
'split',
'greedy_ctc_evaluator',
'greedy_ctc_error',
]
......@@ -1600,11 +1600,32 @@ def split(input, num_or_sections, dim=-1):
return outs
def greedy_ctc_evaluator(input, label, blank, normalized=False, name=None):
"""
def greedy_ctc_error(input, label, blank, normalized=False, name=None):
"""
This evaluator is to calculate sequence-to-sequence edit distance.
Args:
input(Variable): (LodTensor, default: LoDTensor<float>), the unscaled probabilities of variable-length sequences, which is a 2-D Tensor with LoD information. It's shape is [Lp, num_classes + 1], where Lp is the sum of all input sequences' length and num_classes is the true number of classes. (not including the blank label).
label(Variable): (LodTensor, default: LoDTensor<int>), the ground truth of variable-length sequence, which is a 2-D Tensor with LoD information. It is of the shape [Lg, 1], where Lg is th sum of all labels' length.
blank(int): the blank label index of Connectionist Temporal Classification (CTC) loss, which is in thehalf-opened interval [0, num_classes + 1).
normalized(bool): Indicated whether to normalize the edit distance by the length of reference string.
helper = LayerHelper("greedy_ctc_evalutor", **locals())
Returns:
Variable: sequence-to-sequence edit distance loss in shape [batch_size, 1].
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[8], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.greedy_ctc_error(input=x,label=y, blank=0)
"""
helper = LayerHelper("greedy_ctc_error", **locals())
# top 1 op
topk_out = helper.create_tmp_variable(dtype=input.dtype)
topk_indices = helper.create_tmp_variable(dtype="int64")
......@@ -1620,7 +1641,7 @@ def greedy_ctc_evaluator(input, label, blank, normalized=False, name=None):
helper.append_op(
type="ctc_align",
inputs={"Input": [topk_indices]},
outputs={"Out": [ctc_out]},
outputs={"Output": [ctc_out]},
attrs={"merge_repeated": True,
"blank": blank})
......
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