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e56fd438
编写于
3月 05, 2019
作者:
D
dengkaipeng
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电子邮件补丁
差异文件
fix statement. test=develop
上级
0c8351e8
变更
2
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2 changed file
with
14 addition
and
12 deletion
+14
-12
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-1
paddle/fluid/operators/kldiv_loss_op.cc
paddle/fluid/operators/kldiv_loss_op.cc
+13
-11
未找到文件。
paddle/fluid/API.spec
浏览文件 @
e56fd438
...
...
@@ -220,7 +220,7 @@ paddle.fluid.layers.py_func (ArgSpec(args=['func', 'x', 'out', 'backward_func',
paddle.fluid.layers.psroi_pool (ArgSpec(args=['input', 'rois', 'output_channels', 'spatial_scale', 'pooled_height', 'pooled_width', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1546136806fef5c08f6918544bd9151d'))
paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)), ('document', '2f6ff96864054a31aa4bb659c6722c99'))
paddle.fluid.layers.huber_loss (ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None), ('document', '431a4301c35032166ec029f7432c80a7'))
paddle.fluid.layers.kldiv_loss (ArgSpec(args=['x', 'target', 'reduction', 'name'], varargs=None, keywords=None, defaults=('mean', None)), ('document', '7
4112f07e2329448f9f583cabd9d681e
'))
paddle.fluid.layers.kldiv_loss (ArgSpec(args=['x', 'target', 'reduction', 'name'], varargs=None, keywords=None, defaults=('mean', None)), ('document', '7
76d536cac47c89073abc7ee524d5aec
'))
paddle.fluid.layers.tree_conv (ArgSpec(args=['nodes_vector', 'edge_set', 'output_size', 'num_filters', 'max_depth', 'act', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1, 2, 'tanh', None, None, None)), ('document', '34ea12ac9f10a65dccbc50100d12e607'))
paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '33bbd42027d872b3818b3d64ec52e139'))
paddle.fluid.layers.open_files (ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None)), ('document', 'b1ae2e1cc0750e58726374061ea90ecc'))
...
...
paddle/fluid/operators/kldiv_loss_op.cc
浏览文件 @
e56fd438
...
...
@@ -65,11 +65,11 @@ class KLDivLossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void
Make
()
override
{
AddInput
(
"X"
,
"The input tensor of KL divergence loss operator
,
"
"This is a tensor with shape of [N, *], where N is the"
"The input tensor of KL divergence loss operator
.
"
"This is a tensor with shape of [N, *], where N is the
"
"batch size, * means any number of additional dimensions."
);
AddInput
(
"Target"
,
"The tensor of KL divergence loss operator
,
"
"The tensor of KL divergence loss operator
.
"
"This is a tensor with shape of Input(X)."
);
AddOutput
(
"Loss"
,
...
...
@@ -82,7 +82,7 @@ class KLDivLossOpMaker : public framework::OpProtoAndCheckerMaker {
"The reduction type to apply to the output, available types "
"are 'none' | 'batchmean' | 'mean' | 'sum', 'none' for no "
"reduction, 'batchmean' for the sum of output divided by "
"batch size, 'mean' for the average valu
d
of all output, "
"batch size, 'mean' for the average valu
e
of all output, "
"'sum' for the sum of the output."
)
.
SetDefault
(
"mean"
);
...
...
@@ -90,21 +90,23 @@ class KLDivLossOpMaker : public framework::OpProtoAndCheckerMaker {
This operator calculates the Kullback-Leibler divergence loss
between Input(X) and Input(Target).
KL divergence loss
calculates
as follows:
KL divergence loss
is calculated
as follows:
$$l(x, y) = y * (\log y - x)$$
$$l(x, y) = y * (\log(y) - x)$$
While :math:`x` is Input(X) and :math:`y` is Input(Target).
While :attr:`reduction` is :attr:`none`, output loss is in
same shape with
Input(X), loss in each point is calculated
seperately and no reduction applied.
the same shape as
Input(X), loss in each point is calculated
seperately and no reduction
is
applied.
While :attr:`reduction` is :attr:`mean`, output loss i
n
in
While :attr:`reduction` is :attr:`mean`, output loss i
s
in
shape of [1] and loss value is the mean value of all losses.
While :attr:`reduction` is :attr:`sum`, output loss i
n
in
While :attr:`reduction` is :attr:`sum`, output loss i
s
in
shape of [1] and loss value is the sum value of all losses.
While :attr:`reduction` is :attr:`batchmean`, output loss i
n
While :attr:`reduction` is :attr:`batchmean`, output loss i
s
in shape of [1] and loss value is the sum value of all losses
divided by batch size.
...
...
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