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体验新版 GitCode,发现更多精彩内容 >>
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f314330c
编写于
9月 05, 2017
作者:
Q
qijun
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
refactor operator python test and add sum operator
上级
843a8b1e
变更
11
隐藏空白更改
内联
并排
Showing
11 changed file
with
513 addition
and
39 deletion
+513
-39
paddle/framework/operator.h
paddle/framework/operator.h
+23
-2
paddle/operators/sum_op.cc
paddle/operators/sum_op.cc
+69
-0
paddle/operators/sum_op.cu
paddle/operators/sum_op.cu
+18
-0
paddle/operators/sum_op.h
paddle/operators/sum_op.h
+65
-0
paddle/pybind/pybind.cc
paddle/pybind/pybind.cc
+9
-0
python/paddle/v2/framework/op.py
python/paddle/v2/framework/op.py
+8
-2
python/paddle/v2/framework/tests/CMakeLists.txt
python/paddle/v2/framework/tests/CMakeLists.txt
+1
-0
python/paddle/v2/framework/tests/op_test.py
python/paddle/v2/framework/tests/op_test.py
+271
-0
python/paddle/v2/framework/tests/test_cross_entropy_op.py
python/paddle/v2/framework/tests/test_cross_entropy_op.py
+11
-19
python/paddle/v2/framework/tests/test_sigmoid_op.py
python/paddle/v2/framework/tests/test_sigmoid_op.py
+12
-16
python/paddle/v2/framework/tests/test_sum_op.py
python/paddle/v2/framework/tests/test_sum_op.py
+26
-0
未找到文件。
paddle/framework/operator.h
浏览文件 @
f314330c
...
...
@@ -94,6 +94,27 @@ class OperatorBase {
const
VariableNameMap
&
Inputs
()
const
{
return
inputs_
;
}
const
VariableNameMap
&
Outputs
()
const
{
return
outputs_
;
}
const
std
::
vector
<
std
::
string
>
InputsNames
()
const
{
std
::
vector
<
std
::
string
>
result
;
for
(
auto
&
kv
:
inputs_
)
{
for
(
auto
&
name
:
kv
.
second
)
{
result
.
push_back
(
name
);
}
}
return
result
;
}
const
std
::
vector
<
std
::
string
>
OutputsNames
()
const
{
std
::
vector
<
std
::
string
>
result
;
for
(
auto
&
kv
:
outputs_
)
{
for
(
auto
&
name
:
kv
.
second
)
{
result
.
push_back
(
name
);
}
}
return
result
;
}
//! Get a input with argument's name described in `op_proto`
std
::
string
Input
(
const
std
::
string
&
name
)
const
;
//! Get a input which has multiple variables.
...
...
@@ -311,9 +332,9 @@ class InferShapeContext {
}
template
<
typename
T
>
std
::
vector
<
const
T
*>
MultiOutput
(
const
std
::
string
&
name
)
const
{
std
::
vector
<
T
*>
MultiOutput
(
const
std
::
string
&
name
)
const
{
auto
names
=
op_
.
Outputs
(
name
);
std
::
vector
<
const
T
*>
res
;
std
::
vector
<
T
*>
res
;
res
.
reserve
(
names
.
size
());
std
::
transform
(
names
.
begin
(),
names
.
end
(),
std
::
back_inserter
(
res
),
[
&
](
const
std
::
string
&
sub_name
)
{
...
...
paddle/operators/sum_op.cc
0 → 100644
浏览文件 @
f314330c
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/sum_op.h"
#include <vector>
namespace
paddle
{
namespace
operators
{
using
framework
::
Tensor
;
class
SumOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
auto
ins
=
ctx
.
MultiInput
<
framework
::
Tensor
>
(
"X"
);
auto
*
out
=
ctx
.
Output
<
framework
::
Tensor
>
(
"Out"
);
int
N
=
ins
.
size
();
PADDLE_ENFORCE_GT
(
N
,
1
,
"Input tensors count should > 1."
);
auto
dim_zero
=
ins
[
0
]
->
dims
();
out
->
Resize
(
dim_zero
);
}
};
class
SumOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
SumOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"the input tensors of sum operator."
).
AsDuplicable
();
AddOutput
(
"Out"
,
"the output tensor of sum operator."
);
AddComment
(
R"DOC(
Sum the input tensors.
)DOC"
);
}
};
class
SumGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
const
framework
::
InferShapeContext
&
ctx
)
const
override
{
auto
outputs
=
ctx
.
MultiOutput
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
auto
dims
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
))
->
dims
();
for
(
auto
output
:
outputs
)
{
output
->
Resize
(
dims
);
}
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
sum
,
ops
::
SumOp
,
ops
::
SumOpMaker
,
sum_grad
,
ops
::
SumGradOp
);
REGISTER_OP_CPU_KERNEL
(
sum
,
ops
::
SumKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
sum_grad
,
ops
::
SumGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/sum_op.cu
0 → 100644
浏览文件 @
f314330c
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/sum_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
sum
,
ops
::
SumKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
sum_grad
,
ops
::
SumGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/sum_op.h
0 → 100644
浏览文件 @
f314330c
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
template
<
typename
T
,
int
MajorType
=
Eigen
::
RowMajor
,
typename
IndexType
=
Eigen
::
DenseIndex
>
using
EigenVector
=
framework
::
EigenVector
<
T
,
MajorType
,
IndexType
>
;
template
<
typename
Place
,
typename
T
>
class
SumKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
ins
=
context
.
MultiInput
<
Tensor
>
(
"X"
);
auto
*
out
=
context
.
Output
<
Tensor
>
(
"Out"
);
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
result
=
EigenVector
<
T
>::
Flatten
(
*
out
);
int
N
=
ins
.
size
();
auto
in
=
EigenVector
<
T
>::
Flatten
(
*
(
ins
[
0
]));
result
.
device
(
place
)
=
in
;
for
(
int
i
=
1
;
i
<
N
;
i
++
)
{
auto
in
=
EigenVector
<
T
>::
Flatten
(
*
(
ins
[
i
]));
result
.
device
(
place
)
=
result
+
in
;
}
}
};
template
<
typename
Place
,
typename
T
>
class
SumGradKernel
:
public
framework
::
OpKernel
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
input
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
outs
=
context
.
MultiOutput
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
for
(
auto
out
:
outs
)
{
out
->
mutable_data
<
T
>
(
context
.
GetPlace
());
}
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
auto
in
=
EigenVector
<
T
>::
Flatten
(
*
input
);
for
(
auto
out
:
outs
)
{
auto
result
=
EigenVector
<
T
>::
Flatten
(
*
out
);
result
.
device
(
place
)
=
in
;
}
}
};
}
// namespace operators
}
// namespace paddle
paddle/pybind/pybind.cc
浏览文件 @
f314330c
...
...
@@ -48,6 +48,7 @@ USE_NO_KERNEL_OP(identity);
USE_OP
(
minus
);
USE_CPU_ONLY_OP
(
gather
);
USE_CPU_ONLY_OP
(
scatter
);
USE_OP
(
sum
);
namespace
paddle
{
namespace
framework
{
...
...
@@ -213,7 +214,15 @@ All parameter, weight, gradient are variables in Paddle.
->
std
::
map
<
std
::
string
,
std
::
vector
<
std
::
string
>>
{
return
op
.
Outputs
();
})
.
def
(
"outputs_names"
,
[](
const
OperatorBase
&
op
)
->
std
::
vector
<
std
::
string
>
{
return
op
.
OutputsNames
();
})
.
def
(
"inputs"
,
[](
const
OperatorBase
&
op
)
{
return
op
.
Inputs
();
})
.
def
(
"inputs_names"
,
[](
const
OperatorBase
&
op
)
->
std
::
vector
<
std
::
string
>
{
return
op
.
InputsNames
();
})
.
def
(
"__str__"
,
&
OperatorBase
::
DebugString
)
.
def
(
"no_intermediate_outputs"
,
[](
const
OperatorBase
&
op
)
{
return
op
.
OutputVars
(
false
);
})
...
...
python/paddle/v2/framework/op.py
浏览文件 @
f314330c
...
...
@@ -133,8 +133,8 @@ def create_op_creation_method(op_proto):
return
OpInfo
(
method
=
__impl__
,
name
=
op_proto
.
type
,
inputs
=
[
var
.
name
for
var
in
op_proto
.
inputs
],
outputs
=
[
var
.
name
for
var
in
op_proto
.
outputs
],
inputs
=
[
(
var
.
name
,
var
.
duplicable
)
for
var
in
op_proto
.
inputs
],
outputs
=
[
(
var
.
name
,
var
.
duplicable
)
for
var
in
op_proto
.
outputs
],
attrs
=
[
attr
.
name
for
attr
in
op_proto
.
attrs
])
...
...
@@ -168,9 +168,15 @@ class OperatorFactory(object):
return
self
.
op_methods
.
get
(
t
)
def
get_op_input_names
(
self
,
type
):
return
map
(
lambda
x
:
x
[
0
],
self
.
get_op_info
(
type
).
inputs
)
def
get_op_inputs
(
self
,
type
):
return
self
.
get_op_info
(
type
).
inputs
def
get_op_output_names
(
self
,
type
):
return
map
(
lambda
x
:
x
[
0
],
self
.
get_op_info
(
type
).
outputs
)
def
get_op_outputs
(
self
,
type
):
return
self
.
get_op_info
(
type
).
outputs
def
get_op_attr_names
(
self
,
type
):
...
...
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @
f314330c
...
...
@@ -31,4 +31,5 @@ py_test(test_sgd_op SRCS test_sgd_op.py)
py_test
(
test_gradient_checker SRCS test_gradient_checker.py
)
py_test
(
test_lookup_table SRCS test_lookup_table.py
)
py_test
(
test_scale_and_identity_op SRCS test_scale_and_identity_op.py
)
py_test
(
test_sum_op SRCS test_sum_op.py
)
py_test
(
mnist SRCS mnist.py
)
python/paddle/v2/framework/tests/op_test.py
0 → 100644
浏览文件 @
f314330c
import
unittest
import
numpy
as
np
import
itertools
import
paddle.v2.framework.core
as
core
from
paddle.v2.framework.op
import
Operator
def
grad_var_name
(
var_name
):
return
var_name
+
"@GRAD"
def
remove_grad_var_name
(
var_name
):
return
var_name
[
0
:
-
5
]
def
create_op
(
scope
,
op_type
,
inputs
,
outputs
,
attrs
=
None
):
kwargs
=
dict
()
for
ins
in
Operator
.
get_op_inputs
(
op_type
):
in_name
=
ins
[
0
]
in_dup
=
ins
[
1
]
if
in_name
in
inputs
:
kwargs
[
in_name
]
=
[]
if
in_dup
:
sub_in
=
inputs
[
in_name
]
for
sub_in_name
in
sub_in
:
var
=
scope
.
new_var
(
sub_in_name
)
tensor
=
var
.
get_tensor
()
kwargs
[
in_name
].
append
(
sub_in_name
)
else
:
var
=
scope
.
new_var
(
in_name
)
tensor
=
var
.
get_tensor
()
kwargs
[
in_name
].
append
(
in_name
)
for
outs
in
Operator
.
get_op_outputs
(
op_type
):
out_name
=
outs
[
0
]
out_dup
=
outs
[
1
]
if
out_name
in
outputs
:
kwargs
[
out_name
]
=
[]
if
out_dup
:
sub_in
=
outputs
[
out_name
]
for
sun_in_name
in
sub_in
:
var
=
scope
.
new_var
(
sun_in_name
)
tensor
=
var
.
get_tensor
()
kwargs
[
out_name
].
append
(
sun_in_name
)
else
:
var
=
scope
.
new_var
(
out_name
)
tensor
=
var
.
get_tensor
()
kwargs
[
out_name
].
append
(
out_name
)
# for attr_name in Operator.get_op_attr_names(op_type):
# kwargs[attr_name] = attrs[attr_name]
return
Operator
(
op_type
,
**
kwargs
)
def
set_input
(
scope
,
op
,
inputs
,
place
):
for
ins
in
Operator
.
get_op_inputs
(
op
.
type
()):
in_name
=
ins
[
0
]
in_dup
=
ins
[
1
]
if
in_name
in
inputs
:
if
in_dup
:
sub_in
=
inputs
[
in_name
]
for
sub_in_name
in
sub_in
:
var
=
scope
.
find_var
(
sub_in_name
)
tensor
=
var
.
get_tensor
()
arr
=
sub_in
[
sub_in_name
]
tensor
.
set_dims
(
arr
.
shape
)
tensor
.
set
(
arr
,
place
)
else
:
var
=
scope
.
find_var
(
in_name
)
tensor
=
var
.
get_tensor
()
arr
=
inputs
[
in_name
]
tensor
.
set_dims
(
arr
.
shape
)
tensor
.
set
(
arr
,
place
)
def
set_output_grad
(
scope
,
op
,
outputs
,
place
):
for
outs
in
Operator
.
get_op_outputs
(
op
.
type
()):
out_name
=
outs
[
0
]
out_dup
=
outs
[
1
]
if
out_name
in
outputs
:
if
out_dup
:
sub_out
=
outputs
[
out_name
]
for
sub_out_name
in
sub_out
:
out_tensor
=
scope
.
find_var
(
sub_out_name
).
get_tensor
()
grad_tensor
=
scope
.
new_var
(
grad_var_name
(
sub_out_name
)).
get_tensor
()
grad_tensor
.
set_dims
(
out_tensor
.
shape
())
data
=
np
.
ones
(
out_tensor
.
shape
(),
dtype
=
np
.
float32
)
grad_tensor
.
set
(
data
,
place
)
else
:
out_tensor
=
scope
.
find_var
(
out_name
).
get_tensor
()
grad_tensor
=
scope
.
new_var
(
grad_var_name
(
out_name
)).
get_tensor
(
)
grad_tensor
.
set_dims
(
out_tensor
.
shape
())
data
=
np
.
ones
(
out_tensor
.
shape
(),
dtype
=
np
.
float32
)
grad_tensor
.
set
(
data
,
place
)
def
get_numeric_gradient
(
scope
,
op
,
inputs
,
input_to_check
,
output_name
,
delta
=
0.005
,
in_place
=
False
):
set_input
(
scope
,
op
,
inputs
,
core
.
CPUPlace
())
op
.
infer_shape
(
scope
)
tensor_to_check
=
scope
.
find_var
(
input_to_check
).
get_tensor
()
def
product
(
dim
):
return
reduce
(
lambda
a
,
b
:
a
*
b
,
dim
,
1
)
ctx
=
core
.
DeviceContext
.
create
(
core
.
CPUPlace
())
def
get_output
():
op
.
run
(
scope
,
ctx
)
return
np
.
array
(
scope
.
find_var
(
output_name
).
get_tensor
()).
sum
()
tensor_to_check
=
scope
.
find_var
(
input_to_check
).
get_tensor
()
tensor_size
=
product
(
tensor_to_check
.
get_dims
())
gradient_flat
=
np
.
zeros
(
shape
=
(
tensor_size
,
),
dtype
=
'float32'
)
# we only compute gradient of one element each time.
# we use a for loop to compute the gradient of every element.
for
i
in
xrange
(
tensor_size
):
if
in_place
:
set_input
(
op
,
inputs
,
core
.
CPUPlace
())
# get one input element throw it's index i.
origin
=
tensor_to_check
.
get_float_element
(
i
)
# add delta to it, run op and then get the sum of the result tensor.
x_pos
=
origin
+
delta
tensor_to_check
.
set_float_element
(
i
,
x_pos
)
y_pos
=
get_output
()
if
in_place
:
set_input
(
op
,
inputs
,
core
.
CPUPlace
())
x_neg
=
origin
-
delta
tensor_to_check
.
set_float_element
(
i
,
x_neg
)
y_neg
=
get_output
()
tensor_to_check
.
set_float_element
(
i
,
origin
)
gradient_flat
[
i
]
=
(
y_pos
-
y_neg
)
/
delta
/
2
return
gradient_flat
.
reshape
(
tensor_to_check
.
get_dims
())
def
get_backward_op
(
scope
,
op
,
no_grad_set
):
backward_op
=
core
.
Operator
.
backward
(
op
,
no_grad_set
)
for
input
in
backward_op
.
inputs_names
():
var
=
scope
.
new_var
(
input
)
var
.
get_tensor
()
for
output
in
backward_op
.
outputs_names
():
var
=
scope
.
new_var
(
output
)
var
.
get_tensor
()
return
backward_op
def
get_gradient
(
scope
,
op
,
inputs
,
outputs
,
grad_name
,
place
,
no_grad_set
=
None
):
ctx
=
core
.
DeviceContext
.
create
(
place
)
set_input
(
scope
,
op
,
inputs
,
place
)
op
.
infer_shape
(
scope
)
op
.
run
(
scope
,
ctx
)
if
no_grad_set
is
None
:
no_grad_set
=
set
()
backward_op
=
get_backward_op
(
scope
,
op
,
no_grad_set
)
set_output_grad
(
scope
,
op
,
outputs
,
place
)
backward_op
.
infer_shape
(
scope
)
backward_op
.
run
(
scope
,
ctx
)
out
=
np
.
array
(
scope
.
find_var
(
grad_name
).
get_tensor
())
return
out
class
OpTest
(
unittest
.
TestCase
):
def
check_output
(
self
,
place
):
self
.
scope
=
core
.
Scope
()
self
.
op
=
create_op
(
self
.
scope
,
self
.
op_type
,
self
.
inputs
,
self
.
outputs
)
if
isinstance
(
place
,
core
.
GPUPlace
)
and
not
self
.
op
.
support_gpu
():
return
set_input
(
self
.
scope
,
self
.
op
,
self
.
inputs
,
place
)
self
.
op
.
infer_shape
(
self
.
scope
)
ctx
=
core
.
DeviceContext
.
create
(
place
)
self
.
op
.
run
(
self
.
scope
,
ctx
)
for
outs
in
Operator
.
get_op_outputs
(
self
.
op
.
type
()):
out_name
=
outs
[
0
]
out_dup
=
outs
[
1
]
if
out_dup
:
sub_out
=
self
.
outputs
[
out_name
]
for
sub_out_name
in
sub_out
:
actual
=
np
.
array
(
self
.
scope
.
find_var
(
sub_out_name
).
get_tensor
())
expect
=
sub_out
[
sub_out_name
]
self
.
assertTrue
(
np
.
allclose
(
actual
,
expect
,
atol
=
1e-05
),
"output name: "
+
out_name
+
"has diff"
)
else
:
actual
=
np
.
array
(
self
.
scope
.
find_var
(
out_name
).
get_tensor
())
expect
=
self
.
outputs
[
out_name
]
self
.
assertTrue
(
np
.
allclose
(
actual
,
expect
,
atol
=
1e-05
),
"output name: "
+
out_name
+
"has diff"
)
def
__assert_is_close
(
self
,
numeric_grads
,
analytic_grads
,
names
,
max_relative_error
,
msg_prefix
):
for
a
,
b
,
name
in
itertools
.
izip
(
numeric_grads
,
analytic_grads
,
names
):
abs_a
=
np
.
abs
(
a
)
abs_a
[
abs_a
<
1e-3
]
=
1
diff_mat
=
np
.
abs
(
a
-
b
)
/
abs_a
max_diff
=
np
.
max
(
diff_mat
)
def
err_msg
():
offset
=
np
.
argmax
(
diff_mat
>
max_relative_error
)
return
"%s Variable %s max gradient diff %f over limit %f, the first "
\
"error element is %d"
%
(
msg_prefix
,
name
,
max_diff
,
max_relative_error
,
offset
)
self
.
assertLessEqual
(
max_diff
,
max_relative_error
,
err_msg
())
def
check_grad
(
self
,
inputs_to_check
,
output_name
,
no_grad_set
=
None
,
in_place
=
False
,
max_relative_error
=
0.005
):
self
.
scope
=
core
.
Scope
()
self
.
op
=
create_op
(
self
.
scope
,
self
.
op_type
,
self
.
inputs
,
self
.
outputs
)
if
no_grad_set
is
None
:
no_grad_set
=
set
()
numeric_grads
=
[
get_numeric_gradient
(
self
.
scope
,
self
.
op
,
self
.
inputs
,
input_to_check
,
output_name
,
in_place
=
in_place
)
for
input_to_check
in
inputs_to_check
]
grad_names
=
[
grad_var_name
(
input_to_check
)
for
input_to_check
in
inputs_to_check
]
places
=
[
core
.
CPUPlace
()]
if
core
.
is_compile_gpu
()
and
op
.
support_gpu
():
places
.
append
(
core
.
GPUPlace
(
0
))
for
place
in
places
:
analytic_grads
=
[
get_gradient
(
self
.
scope
,
self
.
op
,
self
.
inputs
,
self
.
outputs
,
grad_name
,
place
,
no_grad_set
)
for
grad_name
in
grad_names
]
self
.
__assert_is_close
(
numeric_grads
,
analytic_grads
,
grad_names
,
max_relative_error
,
"Gradient Check On %s"
%
str
(
place
))
python/paddle/v2/framework/tests/test_cross_entropy_op.py
浏览文件 @
f314330c
import
unittest
import
numpy
from
op_test
_util
import
OpTestMeta
from
gradient_checker
import
GradientChecker
,
create_op
from
op_test
import
OpTest
import
paddle.v2.framework.core
as
core
class
TestCrossEntropy
(
unittest
.
TestCase
):
__metaclass__
=
OpTestMeta
class
TestCrossEntropy
(
OpTest
):
def
setUp
(
self
):
self
.
type
=
"onehot_cross_entropy"
batch_size
=
30
class_num
=
10
self
.
op_
type
=
"onehot_cross_entropy"
batch_size
=
4
class_num
=
4
X
=
numpy
.
random
.
random
((
batch_size
,
class_num
)).
astype
(
"float32"
)
label
=
5
*
numpy
.
ones
(
batch_size
).
astype
(
"int32"
)
label
=
(
class_num
/
2
)
*
numpy
.
ones
(
batch_size
).
astype
(
"int32"
)
self
.
inputs
=
{
'X'
:
X
,
'label'
:
label
}
Y
=
[]
for
i
in
range
(
0
,
batch_size
):
Y
.
append
(
-
numpy
.
log
(
X
[
i
][
label
[
i
]]))
self
.
outputs
=
{
'Y'
:
numpy
.
array
(
Y
).
astype
(
"float32"
)}
def
test_check_output
(
self
):
self
.
check_output
(
core
.
CPUPlace
())
self
.
check_output
(
core
.
GPUPlace
(
0
))
class
CrossEntropyGradOpTest
(
GradientChecker
):
def
test_check_grad
(
self
):
op
=
create_op
(
"onehot_cross_entropy"
)
batch_size
=
30
class_num
=
10
inputs
=
{
"X"
:
numpy
.
random
.
uniform
(
0.1
,
1.0
,
[
batch_size
,
class_num
]).
astype
(
"float32"
),
"label"
:
(
class_num
/
2
)
*
numpy
.
ones
(
batch_size
).
astype
(
"int32"
)
}
self
.
check_grad
(
op
,
inputs
,
set
(
"X"
),
"Y"
)
self
.
check_grad
([
"X"
],
"Y"
)
if
__name__
==
"__main__"
:
...
...
python/paddle/v2/framework/tests/test_sigmoid_op.py
浏览文件 @
f314330c
import
unittest
import
numpy
as
np
from
op_test
_util
import
OpTestMeta
from
gradient_checker
import
GradientChecker
,
create_op
from
op_test
import
OpTest
import
paddle.v2.framework.core
as
core
class
TestSigmoidOp
(
unittest
.
TestCase
):
__metaclass__
=
OpTestMeta
class
TestSigmoid
(
OpTest
):
def
setUp
(
self
):
self
.
type
=
"sigmoid"
self
.
inputs
=
{
'X'
:
np
.
random
.
random
((
15
,
31
)).
astype
(
"float32"
)}
self
.
op_type
=
"sigmoid"
self
.
inputs
=
{
'X'
:
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)
}
self
.
outputs
=
{
'Y'
:
1
/
(
1
+
np
.
exp
(
-
self
.
inputs
[
'X'
]))}
def
test_check_output
(
self
):
self
.
check_output
(
core
.
CPUPlace
())
self
.
check_output
(
core
.
GPUPlace
(
0
))
class
TestSigmoidGradOp
(
GradientChecker
):
def
test_grad
(
self
):
op
=
create_op
(
"sigmoid"
)
inputs
=
{
"X"
:
np
.
random
.
uniform
(
0.1
,
1
,
[
11
,
17
]).
astype
(
"float32"
)}
# compare gpu and cpu results for backward op.
# this test will be skiped if only compiling CPU version.
self
.
compare_grad
(
op
,
inputs
)
# check gradients
self
.
check_grad
(
op
,
inputs
,
set
(
"X"
),
"Y"
,
max_relative_error
=
0.007
)
def
test_check_grad
(
self
):
self
.
check_grad
([
"X"
],
"Y"
,
max_relative_error
=
0.007
)
if
__name__
==
'__main__'
:
...
...
python/paddle/v2/framework/tests/test_sum_op.py
0 → 100644
浏览文件 @
f314330c
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
import
paddle.v2.framework.core
as
core
class
TestSumOp
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
"sum"
x0
=
np
.
random
.
random
((
3
,
4
)).
astype
(
'float32'
)
x1
=
np
.
random
.
random
((
3
,
4
)).
astype
(
'float32'
)
x2
=
np
.
random
.
random
((
3
,
4
)).
astype
(
'float32'
)
self
.
inputs
=
{
"X"
:
{
"x0"
:
x0
,
"x1"
:
x1
,
"x2"
:
x2
}}
y
=
x0
+
x1
+
x2
self
.
outputs
=
{
'Out'
:
y
}
def
test_check_output
(
self
):
self
.
check_output
(
core
.
CPUPlace
())
self
.
check_output
(
core
.
GPUPlace
(
0
))
def
test_check_grad
(
self
):
self
.
check_grad
([
"x0"
],
"Out"
)
if
__name__
==
'__main__'
:
unittest
.
main
()
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