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体验新版 GitCode,发现更多精彩内容 >>
提交
b8f557f2
编写于
11月 09, 2017
作者:
D
Dong Zhihong
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
"add elementwise_add more type"
上级
e34e1293
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
194 addition
and
58 deletion
+194
-58
paddle/operators/accuracy_op.h
paddle/operators/accuracy_op.h
+2
-2
paddle/operators/elementwise_add_op.cc
paddle/operators/elementwise_add_op.cc
+8
-2
python/paddle/v2/framework/evaluator.py
python/paddle/v2/framework/evaluator.py
+151
-39
python/paddle/v2/framework/framework.py
python/paddle/v2/framework/framework.py
+1
-1
python/paddle/v2/framework/layers.py
python/paddle/v2/framework/layers.py
+8
-2
python/paddle/v2/framework/tests/test_accuracy_op.py
python/paddle/v2/framework/tests/test_accuracy_op.py
+2
-2
python/paddle/v2/framework/tests/test_recognize_digits_conv.py
...n/paddle/v2/framework/tests/test_recognize_digits_conv.py
+22
-10
未找到文件。
paddle/operators/accuracy_op.h
浏览文件 @
b8f557f2
...
@@ -45,9 +45,9 @@ class AccuracyKernel : public framework::OpKernel<T> {
...
@@ -45,9 +45,9 @@ class AccuracyKernel : public framework::OpKernel<T> {
auto
*
correct
=
ctx
.
Output
<
Tensor
>
(
"Correct"
);
auto
*
correct
=
ctx
.
Output
<
Tensor
>
(
"Correct"
);
auto
*
total
=
ctx
.
Output
<
Tensor
>
(
"Total"
);
auto
*
total
=
ctx
.
Output
<
Tensor
>
(
"Total"
);
float
*
correct_data
=
correct
->
mutable_data
<
float
>
(
ctx
.
GetPlace
());
int
*
correct_data
=
correct
->
mutable_data
<
int
>
(
ctx
.
GetPlace
());
int
*
accuracy_data
=
accuracy
->
mutable_data
<
int
>
(
ctx
.
GetPlace
());
int
*
total_data
=
total
->
mutable_data
<
int
>
(
ctx
.
GetPlace
());
int
*
total_data
=
total
->
mutable_data
<
int
>
(
ctx
.
GetPlace
());
float
*
accuracy_data
=
accuracy
->
mutable_data
<
float
>
(
ctx
.
GetPlace
());
const
int64_t
*
indices_data
=
indices
->
data
<
int64_t
>
();
const
int64_t
*
indices_data
=
indices
->
data
<
int64_t
>
();
const
int64_t
*
label_data
=
label
->
data
<
int64_t
>
();
const
int64_t
*
label_data
=
label
->
data
<
int64_t
>
();
...
...
paddle/operators/elementwise_add_op.cc
浏览文件 @
b8f557f2
...
@@ -34,7 +34,13 @@ REGISTER_OP(elementwise_add, ops::ElementwiseOp, ops::ElementwiseAddOpMaker,
...
@@ -34,7 +34,13 @@ REGISTER_OP(elementwise_add, ops::ElementwiseOp, ops::ElementwiseAddOpMaker,
elementwise_add_grad
,
ops
::
ElementwiseOpGrad
);
elementwise_add_grad
,
ops
::
ElementwiseOpGrad
);
REGISTER_OP_CPU_KERNEL
(
REGISTER_OP_CPU_KERNEL
(
elementwise_add
,
elementwise_add
,
ops
::
ElementwiseAddKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
ElementwiseAddKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
ElementwiseAddKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
,
ops
::
ElementwiseAddKernel
<
paddle
::
platform
::
CPUPlace
,
int
>
,
ops
::
ElementwiseAddKernel
<
paddle
::
platform
::
CPUPlace
,
int64_t
>
);
REGISTER_OP_CPU_KERNEL
(
REGISTER_OP_CPU_KERNEL
(
elementwise_add_grad
,
elementwise_add_grad
,
ops
::
ElementwiseAddGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
ops
::
ElementwiseAddGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
,
ops
::
ElementwiseAddGradKernel
<
paddle
::
platform
::
CPUPlace
,
double
>
,
ops
::
ElementwiseAddGradKernel
<
paddle
::
platform
::
CPUPlace
,
int
>
,
ops
::
ElementwiseAddGradKernel
<
paddle
::
platform
::
CPUPlace
,
int64_t
>
);
python/paddle/v2/framework/evaluator.py
浏览文件 @
b8f557f2
from
paddle.v2.framework.framework
import
Program
,
g_main_program
,
unique_name
from
paddle.v2.framework.framework
import
Program
,
g_main_program
,
unique_name
,
Variable
from
paddle.v2.framework.layer_helper
import
LayerHelper
import
paddle.v2.framework.core
as
core
import
paddle.v2.framework.core
as
core
def
_clone_var_in_block_
(
block
,
var
):
assert
isinstance
(
var
,
Variable
)
return
block
.
create_var
(
name
=
var
.
name
,
shape
=
var
.
shape
,
dtype
=
var
.
data_type
,
type
=
var
.
type
,
lod_level
=
var
.
lod_level
,
persistable
=
True
)
class
Evaluator
(
object
):
class
Evaluator
(
object
):
"""
"""
Evalutor Base class.
Evalutor Base class.
...
@@ -13,33 +23,49 @@ class Evaluator(object):
...
@@ -13,33 +23,49 @@ class Evaluator(object):
"""
"""
def
__init__
(
self
,
name
,
**
kwargs
):
def
__init__
(
self
,
name
,
**
kwargs
):
"""
init the global states
"""
self
.
_states
=
{}
self
.
_states
=
{}
if
kwargs
.
has_key
(
"program"
):
if
kwargs
.
has_key
(
"main_program"
):
self
.
_program
=
kwargs
.
get
(
"program"
)
self
.
_main_program
=
kwargs
.
get
(
"main_program"
)
else
:
self
.
_main_program
=
g_main_program
if
kwargs
.
has_key
(
"eval_program"
):
self
.
_eval_program
=
kwargs
.
get
(
"eval_program"
)
else
:
else
:
self
.
_program
=
g_main_program
self
.
_eval_program
=
Program
()
def
_update_ops
(
self
):
"""
append update ops to the global states
"""
raise
NotImplementedError
()
def
reset
(
self
,
executor
,
program
=
None
):
def
reset
(
self
,
executor
,
program
=
None
):
"""
"""
Clear metric states at the begin of each pass/user specified batch
Clear metric states at the begin of each pass/user specified batch
"""
"""
if
program
==
None
:
if
program
==
None
:
reset_program
=
Program
()
reset_program
=
Program
()
else
:
else
:
reset_program
=
program
reset_program
=
program
block
=
reset_program
.
global_block
()
block
=
reset_program
.
global_block
()
for
k
,
var
in
self
.
_states
.
iteritems
():
for
k
,
var
in
self
.
_states
.
iteritems
():
zeros
=
block
.
create_var
(
dtype
=
var
.
data_type
)
g_var
=
_clone_var_in_block_
(
block
,
var
)
zeros
=
block
.
create_var
(
dtype
=
"float32"
,
persistable
=
True
)
block
.
append_op
(
block
.
append_op
(
type
=
"fill_constant"
,
type
=
"fill_constant"
,
outputs
=
{
"Out"
:
[
zeros
]},
outputs
=
{
"Out"
:
[
zeros
]},
attrs
=
{
attrs
=
{
"shape"
:
var
.
shape
,
"shape"
:
g_var
.
shape
,
"value"
:
0
,
"value"
:
.
0
,
"data_type"
:
5
,
})
})
block
.
append_op
(
block
.
append_op
(
type
=
"scale"
,
inputs
=
{
"X"
:
zeros
},
outputs
=
{
"Out"
:
var
})
type
=
"scale"
,
inputs
=
{
"X"
:
zeros
},
outputs
=
{
"Out"
:
g_var
})
executor
.
run
(
reset_program
)
print
reset_program
executor
.
run
(
reset_program
,
fetch_list
=
self
.
_states
.
values
())
def
eval
(
self
,
executor
,
program
=
None
):
def
eval
(
self
,
executor
,
program
=
None
):
"""
"""
...
@@ -53,15 +79,16 @@ class Accuracy(Evaluator):
...
@@ -53,15 +79,16 @@ class Accuracy(Evaluator):
Accuracy need two state variable Total, Correct
Accuracy need two state variable Total, Correct
"""
"""
def
__init__
(
self
,
input
,
label
,
k
=
1
,
**
kwargs
):
def
__init__
(
self
,
*
args
,
**
kwargs
):
super
(
Accuracy
,
self
).
__init__
(
"accuracy"
,
**
kwargs
)
super
(
Accuracy
,
self
).
__init__
(
"accuracy"
,
**
kwargs
)
block
=
self
.
_program
.
global_block
()
# block = self._eval_program.global_block()
block
=
self
.
_main_program
.
global_block
()
g_total
=
block
.
create_var
(
g_total
=
block
.
create_var
(
name
=
unique_name
(
"Total"
),
name
=
unique_name
(
"Total"
),
persistable
=
True
,
persistable
=
True
,
dtype
=
"int64"
,
dtype
=
"int64"
,
shape
=
[
1
])
shape
=
[
1
])
g_correct
=
helper
.
create_global_variable
(
g_correct
=
block
.
create_var
(
name
=
unique_name
(
"Correct"
),
name
=
unique_name
(
"Correct"
),
persistable
=
True
,
persistable
=
True
,
dtype
=
"int64"
,
dtype
=
"int64"
,
...
@@ -69,6 +96,8 @@ class Accuracy(Evaluator):
...
@@ -69,6 +96,8 @@ class Accuracy(Evaluator):
self
.
_states
[
"Total"
]
=
g_total
self
.
_states
[
"Total"
]
=
g_total
self
.
_states
[
"Correct"
]
=
g_correct
self
.
_states
[
"Correct"
]
=
g_correct
def
_update_ops
(
self
,
input
,
label
,
k
=
1
,
**
kwargs
):
block
=
self
.
_main_program
.
global_block
()
topk_out
=
block
.
create_var
(
dtype
=
input
.
data_type
)
topk_out
=
block
.
create_var
(
dtype
=
input
.
data_type
)
topk_indices
=
block
.
create_var
(
dtype
=
"int64"
)
topk_indices
=
block
.
create_var
(
dtype
=
"int64"
)
block
.
append_op
(
block
.
append_op
(
...
@@ -77,8 +106,9 @@ class Accuracy(Evaluator):
...
@@ -77,8 +106,9 @@ class Accuracy(Evaluator):
outputs
=
{
"Out"
:
[
topk_out
],
outputs
=
{
"Out"
:
[
topk_out
],
"Indices"
:
[
topk_indices
]},
"Indices"
:
[
topk_indices
]},
attrs
=
{
"k"
:
k
})
attrs
=
{
"k"
:
k
})
acc_out_dtype
=
kwargs
.
get
(
"out_dtype"
,
"float32"
)
acc_out
=
block
.
create_var
(
dtype
=
kwargs
.
get
(
"out_dtype"
,
"float32"
))
acc_out
=
block
.
create_var
(
dtype
=
acc_out_dtype
)
correct
=
block
.
create_var
(
dtype
=
"int64"
,
persistable
=
True
)
total
=
block
.
create_var
(
dtype
=
"int64"
,
persistable
=
True
)
block
.
append_op
(
block
.
append_op
(
type
=
"accuracy"
,
type
=
"accuracy"
,
inputs
=
{
inputs
=
{
...
@@ -92,39 +122,121 @@ class Accuracy(Evaluator):
...
@@ -92,39 +122,121 @@ class Accuracy(Evaluator):
"Total"
:
[
total
],
"Total"
:
[
total
],
})
})
# block = self._eval_program.global_block()
# e_correct = _clone_var_in_block_(block, correct)
# e_total = _clone_var_in_block_(block, total)
# block.append_op(
# type="sum",
# inputs={"X": [self._states["Total"], total]},
# outputs={"Out": [self._states["Total"]]})
block
.
append_op
(
type
=
"cast"
,
inputs
=
{
"X"
:
[
self
.
_states
[
"Total"
]]},
outputs
=
{
"Out"
:
[
self
.
_states
[
"Total"
]]},
attrs
=
{
"in_data_type"
:
5
,
"out_data_type"
:
2
,
})
block
.
append_op
(
type
=
"cast"
,
inputs
=
{
"X"
:
[
self
.
_states
[
"Correct"
]]},
outputs
=
{
"Out"
:
[
self
.
_states
[
"Correct"
]]},
attrs
=
{
"in_data_type"
:
5
,
"out_data_type"
:
2
,
})
block
.
append_op
(
block
.
append_op
(
type
=
"sum"
,
type
=
"elementwise_add"
,
inputs
=
{
"X"
:
[
g_total
,
total
]},
inputs
=
{
"X"
:
[
self
.
_states
[
"Total"
]],
outputs
=
{
"Out"
:
[
g_total
]})
"Y"
:
[
total
]},
outputs
=
{
"Out"
:
[
self
.
_states
[
"Total"
]]})
block
.
append_op
(
block
.
append_op
(
type
=
"sum"
,
type
=
"elementwise_add"
,
inputs
=
{
"X"
:
[
g_correct
,
correct
]},
inputs
=
{
"X"
:
[
self
.
_states
[
"Correct"
]],
outputs
=
{
"Out"
:
[
g_total
]})
"Y"
:
[
correct
]},
outputs
=
{
"Out"
:
[
self
.
_states
[
"Correct"
]]})
# g_total = self._states["Total"]
# print g_total
# print total
# print "*" * 100
# print g_total.block.program == total.block.program
# g_total = _clone_var_in_block_(block, self._states["Total"])
# e_total = _clone_var_in_block_(block, total)
# block.append_op(
# type="sum",
# inputs={"X": [g_total, e_total]},
# outputs={"Out": [g_total]})
# block.append_op(
# type="sum",
# inputs={"X": [self._states["Correct"], correct]},
# outputs={"Out": [self._states["Correct"]]})
# print self._main_program
return
acc_out
return
acc_out
def
eval
(
self
,
executor
,
program
=
None
):
def
eval
(
self
,
executor
):
if
program
==
None
:
block
=
self
.
_eval_program
.
global_block
()
eval_program
=
Program
()
eval_out
=
block
.
create_var
(
dtype
=
self
.
_states
[
"Total"
].
data_type
)
else
:
e_correct
=
_clone_var_in_block_
(
block
,
correct
)
eval_program
=
program
e_total
=
_clone_var_in_block_
(
block
,
total
)
block
=
eval_program
.
global_block
()
# block.append_op(
eval_out
=
block
.
create_var
(
dtype
=
self
.
_helper
.
input_dtype
())
# type="elementwise_div",
# inputs={"X": self._states["Total"],
# "Y": self._states["Correct"]},
# outputs={"Out": eval_out})
block
.
append_op
(
block
.
append_op
(
type
=
"elementwise_div"
,
type
=
"elementwise_div"
,
inputs
=
{
"X"
:
self
.
_states
[
"Total"
]
,
inputs
=
{
"X"
:
e_total
,
"Y"
:
self
.
_states
[
"Correct"
]
},
"Y"
:
e_correct
},
outputs
=
{
"Out"
:
eval_out
})
outputs
=
{
"Out"
:
eval_out
})
return
executor
.
run
(
eval_program
,
fetch_list
=
[
eval_out
])
return
executor
.
run
(
self
.
_
eval_program
,
fetch_list
=
[
eval_out
])
# Demo for composing low level op to compute the F1 metric
# Demo for composing low level ops to compute the F1 metric
class
F1
(
Evaluator
):
class
FScore
(
Evaluator
):
def
__init__
(
self
,
input
,
label
,
**
kwargs
):
def
__init__
(
self
,
input
,
label
,
beta
=
1.0
,
**
kwargs
):
super
(
F1
,
self
).
__init__
(
"F1"
,
**
kwargs
)
super
(
F1
,
self
).
__init__
(
"FScore"
,
**
kwargs
)
g_tp
=
helper
.
create_global_variable
(
block
=
self
.
_program
.
global_block
()
g_tp
=
block
.
create_var
(
name
=
unique_name
(
"Tp"
),
persistable
=
True
,
dtype
=
"int64"
,
shape
=
[
1
])
name
=
unique_name
(
"Tp"
),
persistable
=
True
,
dtype
=
"int64"
,
shape
=
[
1
])
g_fp
=
helper
.
create_global_variable
(
g_fn
=
block
.
create_var
(
name
=
unique_name
(
"Fn"
),
persistable
=
True
,
dtype
=
"int64"
,
shape
=
[
1
])
g_fp
=
block
.
create_var
(
name
=
unique_name
(
"Fp"
),
persistable
=
True
,
dtype
=
"int64"
,
shape
=
[
1
])
name
=
unique_name
(
"Fp"
),
persistable
=
True
,
dtype
=
"int64"
,
shape
=
[
1
])
self
.
_states
[
"Tp"
]
=
g_tp
self
.
_states
[
"Tp"
]
=
g_tp
self
.
_states
[
"Fp"
]
=
g_fp
self
.
_states
[
"Fp"
]
=
g_fp
self
.
_states
[
"Fn"
]
=
g_fn
def
_update_ops
(
self
):
block
=
self
.
_program
.
global_block
()
equal_out
=
block
.
create_var
()
block
.
append_op
(
type
=
"equal"
,
inputs
=
{
"X"
:
[
input
],
"Y"
:
[
label
]},
outputs
=
{
"Out"
:
equal_out
})
positive
=
block
.
create_var
()
block
.
append_op
(
type
=
"sequence_pool"
,
inputs
=
{
"X"
:
[
equal_out
]},
outputs
=
{
"Out"
:
positive
},
attrs
=
{
"pooltype"
:
"SUM"
})
batch
=
block
.
create_var
(
name
=
feed_var_name
,
type
=
core
.
VarDesc
.
VarType
.
FEED_MINIBATCH
,
persistable
=
True
)
# def register():
accuracy
=
Accuracy
# def accuracy(*args, **kwargs):
# acc = Accuracy(**kwargs)
# return acc._update_ops(*args, **kwargs)
python/paddle/v2/framework/framework.py
浏览文件 @
b8f557f2
...
@@ -550,7 +550,7 @@ class Parameter(Variable):
...
@@ -550,7 +550,7 @@ class Parameter(Variable):
raise
ValueError
(
"Parameter shape should not be related with "
raise
ValueError
(
"Parameter shape should not be related with "
"batch-size"
)
"batch-size"
)
super
(
Parameter
,
self
)
.
__init__
(
Variable
.
__init__
(
self
,
block
,
persistable
=
True
,
shape
=
shape
,
dtype
=
dtype
,
**
kwargs
)
self
,
block
,
persistable
=
True
,
shape
=
shape
,
dtype
=
dtype
,
**
kwargs
)
self
.
trainable
=
kwargs
.
get
(
'trainable'
,
True
)
self
.
trainable
=
kwargs
.
get
(
'trainable'
,
True
)
...
...
python/paddle/v2/framework/layers.py
浏览文件 @
b8f557f2
...
@@ -263,7 +263,9 @@ def accuracy(input, label, k=1, **kwargs):
...
@@ -263,7 +263,9 @@ def accuracy(input, label, k=1, **kwargs):
"Indices"
:
[
topk_indices
]},
"Indices"
:
[
topk_indices
]},
attrs
=
{
"k"
:
k
})
attrs
=
{
"k"
:
k
})
acc_out_dtype
=
kwargs
.
get
(
"out_dtype"
,
"float32"
)
acc_out_dtype
=
kwargs
.
get
(
"out_dtype"
,
"float32"
)
acc_out
=
helper
.
create_tmp_variable
(
dtype
=
acc_out_dtype
)
acc_out
=
helper
.
create_tmp_variable
(
dtype
=
"float32"
)
correct
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
total
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
helper
.
append_op
(
helper
.
append_op
(
type
=
"accuracy"
,
type
=
"accuracy"
,
inputs
=
{
inputs
=
{
...
@@ -271,7 +273,11 @@ def accuracy(input, label, k=1, **kwargs):
...
@@ -271,7 +273,11 @@ def accuracy(input, label, k=1, **kwargs):
"Indices"
:
[
topk_indices
],
"Indices"
:
[
topk_indices
],
"Label"
:
[
label
]
"Label"
:
[
label
]
},
},
outputs
=
{
"Accuracy"
:
[
acc_out
]})
outputs
=
{
"Accuracy"
:
[
acc_out
],
"Correct"
:
[
correct
],
"Total"
:
[
total
],
})
return
acc_out
return
acc_out
...
...
python/paddle/v2/framework/tests/test_accuracy_op.py
浏览文件 @
b8f557f2
...
@@ -19,7 +19,8 @@ class TestAccuracyOp(OpTest):
...
@@ -19,7 +19,8 @@ class TestAccuracyOp(OpTest):
break
break
self
.
outputs
=
{
self
.
outputs
=
{
'Accuracy'
:
np
.
array
([
num_correct
/
float
(
n
)]).
astype
(
"float32"
),
'Accuracy'
:
np
.
array
([
num_correct
/
float
(
n
)]).
astype
(
"float32"
),
'Correct'
:
np
.
array
([
num_correct
]).
astype
(
"int32"
)
'Correct'
:
np
.
array
([
num_correct
]).
astype
(
"int32"
),
'Total'
:
np
.
array
([
n
]).
astype
(
"int32"
)
}
}
def
test_check_output
(
self
):
def
test_check_output
(
self
):
...
@@ -27,5 +28,4 @@ class TestAccuracyOp(OpTest):
...
@@ -27,5 +28,4 @@ class TestAccuracyOp(OpTest):
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
exit
(
0
)
unittest
.
main
()
unittest
.
main
()
python/paddle/v2/framework/tests/test_recognize_digits_conv.py
浏览文件 @
b8f557f2
...
@@ -3,6 +3,7 @@ import paddle.v2.framework.layers as layers
...
@@ -3,6 +3,7 @@ import paddle.v2.framework.layers as layers
import
paddle.v2.framework.nets
as
nets
import
paddle.v2.framework.nets
as
nets
import
paddle.v2.framework.core
as
core
import
paddle.v2.framework.core
as
core
import
paddle.v2.framework.optimizer
as
optimizer
import
paddle.v2.framework.optimizer
as
optimizer
import
paddle.v2.framework.evaluator
as
evaluator
from
paddle.v2.framework.framework
import
Program
,
g_main_program
from
paddle.v2.framework.framework
import
Program
,
g_main_program
from
paddle.v2.framework.executor
import
Executor
from
paddle.v2.framework.executor
import
Executor
...
@@ -54,17 +55,24 @@ cost = layers.cross_entropy(
...
@@ -54,17 +55,24 @@ cost = layers.cross_entropy(
main_program
=
main_program
,
main_program
=
main_program
,
startup_program
=
startup_program
)
startup_program
=
startup_program
)
avg_cost
=
layers
.
mean
(
x
=
cost
,
main_program
=
main_program
)
avg_cost
=
layers
.
mean
(
x
=
cost
,
main_program
=
main_program
)
accuracy
=
layers
.
accuracy
(
# accuracy = layers.accuracy(
input
=
predict
,
# input=predict,
label
=
label
,
# label=label,
main_program
=
main_program
,
# main_program=main_program,
startup_program
=
startup_program
)
# startup_program=startup_program)
# optimizer = optimizer.MomentumOptimizer(learning_rate=0.1 / 128.0,
# optimizer = optimizer.MomentumOptimizer(learning_rate=0.1 / 128.0,
# momentum=0.9)
# momentum=0.9)
optimizer
=
optimizer
.
AdamOptimizer
(
learning_rate
=
0.01
,
beta1
=
0.9
,
beta2
=
0.999
)
optimizer
=
optimizer
.
AdamOptimizer
(
learning_rate
=
0.01
,
beta1
=
0.9
,
beta2
=
0.999
)
opts
=
optimizer
.
minimize
(
avg_cost
,
startup_program
)
opts
=
optimizer
.
minimize
(
avg_cost
,
startup_program
)
accuracy
=
evaluator
.
accuracy
(
input
=
predict
,
label
=
label
,
main_program
=
main_program
,
startup_program
=
startup_program
)
acc_out
=
accuracy
.
_update_ops
(
input
=
predict
,
label
=
label
,
main_program
=
main_program
)
BATCH_SIZE
=
50
BATCH_SIZE
=
50
PASS_NUM
=
3
PASS_NUM
=
3
train_reader
=
paddle
.
batch
(
train_reader
=
paddle
.
batch
(
...
@@ -79,6 +87,7 @@ exe.run(startup_program, feed={}, fetch_list=[])
...
@@ -79,6 +87,7 @@ exe.run(startup_program, feed={}, fetch_list=[])
for
pass_id
in
range
(
PASS_NUM
):
for
pass_id
in
range
(
PASS_NUM
):
count
=
0
count
=
0
accuracy
.
reset
(
exe
)
for
data
in
train_reader
():
for
data
in
train_reader
():
img_data
=
np
.
array
(
map
(
lambda
x
:
x
[
0
].
reshape
([
1
,
28
,
28
]),
img_data
=
np
.
array
(
map
(
lambda
x
:
x
[
0
].
reshape
([
1
,
28
,
28
]),
data
)).
astype
(
"float32"
)
data
)).
astype
(
"float32"
)
...
@@ -93,11 +102,14 @@ for pass_id in range(PASS_NUM):
...
@@ -93,11 +102,14 @@ for pass_id in range(PASS_NUM):
outs
=
exe
.
run
(
main_program
,
outs
=
exe
.
run
(
main_program
,
feed
=
{
"pixel"
:
tensor_img
,
feed
=
{
"pixel"
:
tensor_img
,
"label"
:
tensor_y
},
"label"
:
tensor_y
},
fetch_list
=
[
avg_cost
,
acc
uracy
])
fetch_list
=
[
avg_cost
,
acc
_out
])
loss
=
np
.
array
(
outs
[
0
])
loss
=
np
.
array
(
outs
[
0
])
acc
=
np
.
array
(
outs
[
1
])
acc
=
np
.
array
(
outs
[
1
])
# pass_acc = accuracy.eval(exe)
# print pass_acc
print
loss
,
acc
if
loss
<
10.0
and
acc
>
0.9
:
#
if loss < 10.0 and acc > 0.9:
# if avg cost less than 10.0 and accuracy is larger than 0.9, we think our code is good.
#
# if avg cost less than 10.0 and accuracy is larger than 0.9, we think our code is good.
exit
(
0
)
#
exit(0)
exit
(
1
)
exit
(
1
)
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