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体验新版 GitCode,发现更多精彩内容 >>
提交
da66891b
编写于
9月 07, 2017
作者:
C
Cao Ying
提交者:
GitHub
9月 07, 2017
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差异文件
Merge pull request #3928 from lcy-seso/refine_softmax_op
Refine names and doc of some operators.
上级
f9af3d25
5b4526fa
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
93 addition
and
73 deletion
+93
-73
paddle/operators/identity_op.cc
paddle/operators/identity_op.cc
+8
-5
paddle/operators/scale_op.cc
paddle/operators/scale_op.cc
+4
-2
paddle/operators/softmax_op.cc
paddle/operators/softmax_op.cc
+8
-7
paddle/operators/softmax_op.h
paddle/operators/softmax_op.h
+5
-5
python/paddle/v2/framework/op.py
python/paddle/v2/framework/op.py
+33
-26
python/paddle/v2/framework/tests/mnist.py
python/paddle/v2/framework/tests/mnist.py
+19
-16
python/paddle/v2/framework/tests/test_gradient_checker.py
python/paddle/v2/framework/tests/test_gradient_checker.py
+4
-4
python/paddle/v2/framework/tests/test_softmax_op.py
python/paddle/v2/framework/tests/test_softmax_op.py
+12
-8
未找到文件。
paddle/operators/identity_op.cc
浏览文件 @
da66891b
...
...
@@ -18,17 +18,20 @@
namespace
paddle
{
namespace
operators
{
//
identity is a alias of scale op. This is also a example for creating a alias
// operator.
//
The identity operator is an alias of the scale operator. This is also an
//
example for creating an alias for an existing
operator.
template
<
typename
AttrType
>
class
IdentityOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
IdentityOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"input tensor of identity op"
);
AddOutput
(
"Out"
,
"output tensor of identity op"
);
AddComment
(
"identity operator. Just a alias of scale op which scale = 1.0"
);
AddInput
(
"X"
,
"The input tensor of identity operator."
);
AddOutput
(
"Out"
,
"The output tensor of identity operator."
);
AddComment
(
R"DOC(
The identity operator is an alias of the scale operator
with the attribute scale fixed to 1.0.
)DOC"
);
}
};
...
...
paddle/operators/scale_op.cc
浏览文件 @
da66891b
...
...
@@ -44,11 +44,13 @@ class ScaleOpMaker : public framework::OpProtoAndCheckerMaker {
The equation is: Out = scale*X
)DOC"
);
AddAttr
<
AttrType
>
(
"scale"
,
"scale of scale operator."
).
SetDefault
(
1.0
);
AddAttr
<
AttrType
>
(
"scale"
,
"The scaling factor of the scale operator."
)
.
SetDefault
(
1.0
);
}
};
// Scale Op's gradient is scale op, too.
// The operator to calculate gradients of a scale operator is just the scale
// operator itself.
// Grad(Out=scale(X)) => Grad(X) = scale(Grad(Out))
template
<
typename
AttrType
>
class
ScaleGradOp
:
public
NetOp
{
...
...
paddle/operators/softmax_op.cc
浏览文件 @
da66891b
...
...
@@ -51,7 +51,7 @@ the other dimensions in the K-dimensional vector input. Then the ratio of the
exponential of the given dimension and the sum of exponential values of all
the other dimensions is the output of the softmax operator.
For each row `i` and each column `j` in X, we have:
For each row `i` and each column `j` in
input
X, we have:
Y[i, j] = exp(X[i, j]) / sum_j(exp(X[i, j]))
)DOC"
);
...
...
@@ -64,14 +64,15 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
.
InputVar
(
"Y"
)
!=
nullptr
,
"Input(Y) should not be null
"
);
PADDLE_ENFORCE
_NOT_NULL
(
ctx
.
InputVar
(
"Y"
),
"Input(Y) should be not null.
"
);
PADDLE_ENFORCE_NOT_NULL
(
ctx
.
InputVar
(
framework
::
GradVarName
(
"Y"
)),
"Input(Y@GRAD) should not be null"
);
PADDLE_ENFORCE
(
ctx
.
Input
<
Tensor
>
(
"Y"
)
->
dims
()
==
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
))
->
dims
(),
"the shape of Input(0) and Input(1) should be the same"
);
"Input(Y@GRAD) should be not null."
);
PADDLE_ENFORCE_EQ
(
ctx
.
Input
<
Tensor
>
(
"Y"
)
->
dims
(),
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Y"
))
->
dims
(),
"Input(Y) and its gradients should have a same shape."
);
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
))
->
Resize
(
ctx
.
Input
<
Tensor
>
(
"
Y
"
)
->
dims
());
->
Resize
(
ctx
.
Input
<
Tensor
>
(
"
X
"
)
->
dims
());
}
};
...
...
paddle/operators/softmax_op.h
浏览文件 @
da66891b
...
...
@@ -28,12 +28,12 @@ template <typename Place, typename T>
class
SoftmaxKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
input
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
output
=
context
.
Output
<
Tensor
>
(
"Y"
);
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
X
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
Y
=
context
.
Output
<
Tensor
>
(
"Y"
);
Y
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
logits
=
EigenMatrix
<
T
>::
From
(
*
input
);
auto
softmax
=
EigenMatrix
<
T
>::
From
(
*
output
);
auto
logits
=
EigenMatrix
<
T
>::
From
(
*
X
);
auto
softmax
=
EigenMatrix
<
T
>::
From
(
*
Y
);
const
int
kBatchDim
=
0
;
const
int
kClassDim
=
1
;
...
...
python/paddle/v2/framework/op.py
浏览文件 @
da66891b
...
...
@@ -4,8 +4,8 @@ import paddle.v2.framework.proto.framework_pb2 as framework_pb2
def
get_all_op_protos
():
"""
Get all registered op proto from Paddle
C++
:return:
list of OpProto
Get all registered op proto from Paddle
Paddle C++ end.
:return:
A list of registered OpProto.
"""
protostrs
=
core
.
get_all_op_protos
()
ret_values
=
[]
...
...
@@ -21,8 +21,8 @@ def is_str(s):
class
OpDescCreationMethod
(
object
):
"""
A Functor object to convert user input(use key word args) to OpDesc based on
OpProto.
Convert the user's input(only keyword arguments are supported) to OpDesc
based on the
OpProto.
:param op_proto: The OpProto object.
:type op_proto: op_proto_pb2.OpProto
...
...
@@ -30,17 +30,18 @@ class OpDescCreationMethod(object):
def
__init__
(
self
,
op_proto
):
if
not
isinstance
(
op_proto
,
framework_pb2
.
OpProto
):
raise
TypeError
(
"Argument should be OpProto"
)
raise
TypeError
(
"Type of op_proto should be OpProto in PaddlePaddle."
)
self
.
__op_proto__
=
op_proto
def
__call__
(
self
,
*
args
,
**
kwargs
):
"""
Convert user
input to OpDesc. Only key-word args are supported.
:return:
OpDesc based on user input
Convert user
's input to OpDesc. Only keyword arguments are supported.
:return:
The OpDesc based on user input.
:rtype: op_desc_pb2.OpDesc
"""
if
len
(
args
)
!=
0
:
raise
ValueError
(
"Only keyword arguments
is supported by Paddle
"
)
raise
ValueError
(
"Only keyword arguments
are supported.
"
)
op_desc
=
framework_pb2
.
OpDesc
()
for
input_parameter
in
self
.
__op_proto__
.
inputs
:
...
...
@@ -49,8 +50,9 @@ class OpDescCreationMethod(object):
input_arguments
=
[
input_arguments
]
if
not
input_parameter
.
duplicable
and
len
(
input_arguments
)
>
1
:
raise
ValueError
(
"Input %s only accepts one input, but give %d"
%
(
input_parameter
.
name
,
len
(
input_arguments
)))
raise
ValueError
(
"Input %s expects only one input, but %d are given."
%
(
input_parameter
.
name
,
len
(
input_arguments
)))
ipt
=
op_desc
.
inputs
.
add
()
ipt
.
parameter
=
input_parameter
.
name
...
...
@@ -63,7 +65,7 @@ class OpDescCreationMethod(object):
if
not
output_parameter
.
duplicable
and
len
(
output_arguments
)
>
1
:
raise
ValueError
(
"Output %s
only accepts one output, but give %d
"
%
"Output %s
expects only one output, but %d are given.
"
%
(
output_parameter
.
name
,
len
(
output_arguments
)))
out
=
op_desc
.
outputs
.
add
()
...
...
@@ -100,15 +102,17 @@ class OpDescCreationMethod(object):
pair
.
first
=
p
[
0
]
pair
.
second
=
p
[
1
]
else
:
raise
NotImplementedError
(
"Not support attribute type "
+
str
(
attr
.
type
))
raise
NotImplementedError
(
"A not supported attribute type: %s."
%
(
str
(
attr
.
type
)))
return
op_desc
@
staticmethod
def
any_is_true
(
generator
):
"""
Reduce a bool array to one. If any of them is True, then return True.
Reduce a boolean array to a single boolean parameter. If any element in
the array is True, this function will return True, otherwise False.
"""
for
flag
in
generator
:
if
flag
:
...
...
@@ -127,7 +131,7 @@ class OpInfo(object):
def
create_op_creation_method
(
op_proto
):
"""
Generate op creation method for an OpProto
Generate op creation method for an OpProto
.
"""
method
=
OpDescCreationMethod
(
op_proto
)
...
...
@@ -146,20 +150,23 @@ def create_op_creation_method(op_proto):
class
OperatorFactory
(
object
):
def
__init__
(
self
):
self
.
op_methods
=
dict
()
for
op_proto
in
get_all_op_protos
():
method
=
create_op_creation_method
(
op_proto
)
self
.
op_methods
[
method
.
name
]
=
method
def
__call__
(
self
,
*
args
,
**
kwargs
):
if
'type'
in
kwargs
:
if
"type"
in
kwargs
:
if
len
(
args
)
!=
0
:
raise
ValueError
(
"All Paddle argument should be key-word "
"argument except type"
)
t
=
kwargs
.
pop
(
'type'
)
raise
ValueError
(
"Except the argument
\"
type
\"
,"
"all of the other arguments should be keyword arguments."
)
t
=
kwargs
.
pop
(
"type"
)
else
:
if
len
(
args
)
!=
1
:
raise
ValueError
(
"All Paddle argument should be key-word "
"argument except type"
)
raise
ValueError
(
"Except the argument
\"
type
\"
,"
"all of the other arguments should be keyword arguments."
)
t
=
args
[
0
]
return
self
.
get_op_info
(
t
).
method
(
**
kwargs
)
...
...
@@ -169,7 +176,7 @@ class OperatorFactory(object):
def
get_op_info
(
self
,
t
):
if
t
not
in
self
.
op_methods
:
raise
ValueError
(
"
operator %s is not registered"
,
t
)
raise
ValueError
(
"
The operator: %s is not registered."
%
t
)
return
self
.
op_methods
.
get
(
t
)
def
get_op_input_names
(
self
,
type
):
...
...
@@ -184,7 +191,7 @@ class OperatorFactory(object):
class
__RecurrentOp__
(
object
):
__proto__
=
None
type
=
'recurrent'
type
=
"recurrent"
def
__init__
(
self
):
# cache recurrent_op's proto
...
...
@@ -194,8 +201,8 @@ class __RecurrentOp__(object):
self
.
__proto__
=
op_proto
def
__call__
(
self
,
*
args
,
**
kwargs
):
if
self
.
type
not
in
args
and
'type'
not
in
kwargs
:
kwargs
[
'type'
]
=
self
.
type
if
self
.
type
not
in
args
and
"type"
not
in
kwargs
:
kwargs
[
"type"
]
=
self
.
type
# create proto
create_method
=
OpDescCreationMethod
(
self
.
__proto__
)
proto
=
create_method
(
*
args
,
**
kwargs
)
...
...
@@ -203,5 +210,5 @@ class __RecurrentOp__(object):
return
core
.
RecurrentOp
.
create
(
proto
.
SerializeToString
())
Operator
=
OperatorFactory
()
#
D
efault global factory
Operator
=
OperatorFactory
()
#
The d
efault global factory
RecurrentOp
=
__RecurrentOp__
()
python/paddle/v2/framework/tests/mnist.py
浏览文件 @
da66891b
...
...
@@ -38,9 +38,9 @@ def feed_data(name, data):
assert
isinstance
(
data
,
numpy
.
ndarray
)
tensor
=
scope
.
find_var
(
name
).
get_tensor
()
tensor
.
set_dims
(
data
.
shape
)
if
data
.
dtype
==
numpy
.
dtype
(
'int32'
):
if
data
.
dtype
==
numpy
.
dtype
(
"int32"
):
tensor
.
alloc_int
(
place
)
elif
data
.
dtype
==
numpy
.
dtype
(
'float32'
):
elif
data
.
dtype
==
numpy
.
dtype
(
"float32"
):
tensor
.
alloc_float
(
place
)
else
:
raise
ValueError
(
"data type not supported"
)
...
...
@@ -74,22 +74,25 @@ def init_param(net, param_name, dims):
# fc_layer
def
fc_layer
(
net
,
input
,
size
,
act
=
"softmax"
,
bias
=
True
,
param
=
None
,
name
=
None
):
"""
Add a fc layer to net
The fully connected layer.
:param input:
input variable nam
e.
:param input:
The name of input variabl
e.
:type input: str
:param size: fully connected layer size.
:param act: activation name
:param param: parameter attribute, used for initialize parameters.
:param bias: bias attribute. False will not have a bias.
:param name: the name of fc layer. If not set, model will generate a
readable name
:return: output variable name.
:param size: The size of fully connected layer.
:param act: The name of activation.
:param param: The attribute of learnable parameter which can be used to
modify initialization mean and std of the parameter.
:param bias: The attribute of bias. If set False, this layer does not have
a bias.
:param name: The name of this layer. If it is not set explictly, a name
will be generated automatically.
:return: The name of the output variable.
"""
if
name
is
None
:
name
=
'fc_%d'
%
uniq_id
()
name
=
"fc_%d"
%
uniq_id
()
if
not
isinstance
(
name
,
str
):
raise
ValueError
(
"
name should be string
"
)
raise
ValueError
(
"
The name of a layer should be a string.
"
)
input_dims
=
scope
.
find_var
(
input
).
get_tensor
().
get_dims
()
...
...
@@ -123,7 +126,7 @@ def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None):
def
cross_entropy_layer
(
net
,
input
,
label
):
cost_name
=
'cross_entropy_%d'
%
uniq_id
()
cost_name
=
"cross_entropy_%d"
%
uniq_id
()
cross_entropy_op
=
Operator
(
"onehot_cross_entropy"
,
X
=
input
,
label
=
label
,
Y
=
cost_name
)
net
.
append_op
(
cross_entropy_op
)
...
...
@@ -177,8 +180,8 @@ def error_rate(predict, label):
return
error_num
/
float
(
len
(
label
))
images
=
data_layer
(
name
=
'pixel'
,
dims
=
[
BATCH_SIZE
,
784
])
labels
=
data_layer
(
name
=
'label'
,
dims
=
[
BATCH_SIZE
])
images
=
data_layer
(
name
=
"pixel"
,
dims
=
[
BATCH_SIZE
,
784
])
labels
=
data_layer
(
name
=
"label"
,
dims
=
[
BATCH_SIZE
])
fc1
=
fc_layer
(
net
=
forward_net
,
input
=
images
,
size
=
100
,
act
=
"sigmoid"
)
fc2
=
fc_layer
(
net
=
forward_net
,
input
=
fc1
,
size
=
100
,
act
=
"sigmoid"
)
predict
=
fc_layer
(
net
=
forward_net
,
input
=
fc2
,
size
=
10
,
act
=
"softmax"
)
...
...
python/paddle/v2/framework/tests/test_gradient_checker.py
浏览文件 @
da66891b
...
...
@@ -7,11 +7,11 @@ from gradient_checker import get_numeric_gradient
class
GetNumericGradientTest
(
unittest
.
TestCase
):
def
test_add_op
(
self
):
add_op
=
Operator
(
'add'
,
X
=
"X"
,
Y
=
"Y"
,
Out
=
"Z"
)
add_op
=
Operator
(
"add"
,
X
=
"X"
,
Y
=
"Y"
,
Out
=
"Z"
)
x
=
numpy
.
random
.
random
((
10
,
1
)).
astype
(
"float32"
)
y
=
numpy
.
random
.
random
((
10
,
1
)).
astype
(
"float32"
)
arr
=
get_numeric_gradient
(
add_op
,
{
'X'
:
x
,
"Y"
:
y
},
'Z'
,
'X'
)
arr
=
get_numeric_gradient
(
add_op
,
{
"X"
:
x
,
"Y"
:
y
},
"Z"
,
"X"
)
self
.
assertAlmostEqual
(
arr
.
mean
(),
1.0
,
delta
=
1e-4
)
def
test_softmax_op
(
self
):
...
...
@@ -35,9 +35,9 @@ class GetNumericGradientTest(unittest.TestCase):
dY
=
numpy
.
ones
(
Y
.
shape
)
dX
=
label_softmax_grad
(
Y
,
dY
)
arr
=
get_numeric_gradient
(
softmax_op
,
{
"X"
:
X
},
'Y'
,
'X'
)
arr
=
get_numeric_gradient
(
softmax_op
,
{
"X"
:
X
},
"Y"
,
"X"
)
numpy
.
testing
.
assert_almost_equal
(
arr
,
dX
,
decimal
=
1e-2
)
if
__name__
==
'__main__'
:
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/v2/framework/tests/test_softmax_op.py
浏览文件 @
da66891b
...
...
@@ -18,18 +18,22 @@ class TestSoftmaxOp(unittest.TestCase):
def
setUp
(
self
):
self
.
type
=
"softmax"
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
32
,
10
0
)).
astype
(
"float32"
)}
self
.
inputs
=
{
"X"
:
np
.
random
.
random
((
10
,
1
0
)).
astype
(
"float32"
)}
self
.
outputs
=
{
'Y'
:
np
.
apply_along_axis
(
stable_softmax
,
1
,
self
.
inputs
[
'X'
])
"Y"
:
np
.
apply_along_axis
(
stable_softmax
,
1
,
self
.
inputs
[
"X"
])
}
class
SoftmaxGradOpTest
(
GradientChecker
):
def
test_softmax
(
self
):
op
=
create_op
(
"softmax"
)
inputs
=
{
"X"
:
np
.
random
.
uniform
(
0.1
,
1
,
[
10
,
10
]).
astype
(
"float32"
)}
self
.
check_grad
(
op
,
inputs
,
set
(
"X"
),
"Y"
)
class
TestSoftmaxGradOp
(
GradientChecker
):
def
setUp
(
self
):
self
.
op
=
create_op
(
"softmax"
)
self
.
inputs
=
{
"X"
:
np
.
random
.
uniform
(
0.1
,
1
,
[
10
,
10
]).
astype
(
"float32"
)
}
def
test_softmax_grad
(
self
):
self
.
check_grad
(
self
.
op
,
self
.
inputs
,
[
"X"
],
"Y"
)
if
__name__
==
'__main__'
:
if
__name__
==
"__main__"
:
unittest
.
main
()
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