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体验新版 GitCode,发现更多精彩内容 >>
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1680903d
编写于
10月 20, 2017
作者:
Q
QI JUN
提交者:
GitHub
10月 20, 2017
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差异文件
Merge pull request #4956 from QiJune/test_book2
add book02.recognize_digits mlp train test
上级
05ece848
8278d97e
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
84 addition
and
1 deletion
+84
-1
python/paddle/v2/framework/tests/test_cross_entropy_op.py
python/paddle/v2/framework/tests/test_cross_entropy_op.py
+1
-1
python/paddle/v2/framework/tests/test_recognize_digits_mlp.py
...on/paddle/v2/framework/tests/test_recognize_digits_mlp.py
+83
-0
未找到文件。
python/paddle/v2/framework/tests/test_cross_entropy_op.py
浏览文件 @
1680903d
...
...
@@ -21,7 +21,7 @@ class TestCrossEntropyOp1(OpTest):
self
.
inputs
=
{
"X"
:
X
,
"Label"
:
label
}
self
.
outputs
=
{
"Y"
:
cross_entropy
}
self
.
attrs
=
{
"soft
L
abel"
:
False
}
self
.
attrs
=
{
"soft
_l
abel"
:
False
}
def
test_check_output
(
self
):
self
.
check_output
()
...
...
python/paddle/v2/framework/tests/test_recognize_digits_mlp.py
0 → 100644
浏览文件 @
1680903d
import
paddle.v2
as
paddle
import
paddle.v2.framework.layers
as
layers
import
paddle.v2.framework.core
as
core
import
paddle.v2.framework.optimizer
as
optimizer
from
paddle.v2.framework.framework
import
Program
,
g_program
from
paddle.v2.framework.executor
import
Executor
import
numpy
as
np
init_program
=
Program
()
program
=
Program
()
image
=
layers
.
data
(
name
=
'x'
,
shape
=
[
784
],
data_type
=
'float32'
,
program
=
program
,
init_program
=
init_program
)
hidden1
=
layers
.
fc
(
input
=
image
,
size
=
128
,
act
=
'relu'
,
program
=
program
,
init_program
=
init_program
)
hidden2
=
layers
.
fc
(
input
=
hidden1
,
size
=
64
,
act
=
'relu'
,
program
=
program
,
init_program
=
init_program
)
predict
=
layers
.
fc
(
input
=
hidden2
,
size
=
10
,
act
=
'softmax'
,
program
=
program
,
init_program
=
init_program
)
label
=
layers
.
data
(
name
=
'y'
,
shape
=
[
1
],
data_type
=
'int32'
,
program
=
program
,
init_program
=
init_program
)
cost
=
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
,
program
=
program
,
init_program
=
init_program
)
avg_cost
=
layers
.
mean
(
x
=
cost
,
program
=
program
,
init_program
=
init_program
)
sgd_optimizer
=
optimizer
.
SGDOptimizer
(
learning_rate
=
0.001
)
opts
=
sgd_optimizer
.
minimize
(
avg_cost
)
BATCH_SIZE
=
128
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
mnist
.
train
(),
buf_size
=
8192
),
batch_size
=
BATCH_SIZE
)
place
=
core
.
CPUPlace
()
exe
=
Executor
(
place
)
exe
.
run
(
init_program
,
feed
=
{},
fetch_list
=
[])
PASS_NUM
=
100
for
pass_id
in
range
(
PASS_NUM
):
for
data
in
train_reader
():
x_data
=
np
.
array
(
map
(
lambda
x
:
x
[
0
],
data
)).
astype
(
"float32"
)
y_data
=
np
.
array
(
map
(
lambda
x
:
x
[
1
],
data
)).
astype
(
"int32"
)
y_data
=
np
.
expand_dims
(
y_data
,
axis
=
1
)
tensor_x
=
core
.
LoDTensor
()
tensor_x
.
set
(
x_data
,
place
)
tensor_y
=
core
.
LoDTensor
()
tensor_y
.
set
(
y_data
,
place
)
outs
=
exe
.
run
(
program
,
feed
=
{
'x'
:
tensor_x
,
'y'
:
tensor_y
},
fetch_list
=
[
avg_cost
])
out
=
np
.
array
(
outs
[
0
])
if
out
[
0
]
<
5.0
:
exit
(
0
)
# if avg cost less than 5.0, we think our code is good.
exit
(
1
)
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