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体验新版 GitCode,发现更多精彩内容 >>
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c678924e
编写于
1月 16, 2018
作者:
Y
Yan Chunwei
提交者:
GitHub
1月 16, 2018
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电子邮件补丁
差异文件
Feature/add scratch demo (#150)
上级
ec572c0d
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
28 addition
and
17 deletion
+28
-17
demo/mxnet/mxnet_demo.py
demo/mxnet/mxnet_demo.py
+2
-2
demo/paddle/cifar10_image_classification_vgg.py
demo/paddle/cifar10_image_classification_vgg.py
+4
-3
demo/vdl_scratch.py
demo/vdl_scratch.py
+22
-12
未找到文件。
demo/mxnet/mxnet_demo.py
浏览文件 @
c678924e
import
mxnet
as
mx
import
logging
import
mxnet
as
mx
# Here we import LogWriter so that we can write log data while MXNet is training
from
visualdl
import
LogWriter
...
...
@@ -45,7 +46,6 @@ def add_scalar():
# Start to build CNN in MXNet, train MNIST dataset. For more info, check MXNet's official website:
# https://mxnet.incubator.apache.org/tutorials/python/mnist.html
import
logging
logging
.
getLogger
().
setLevel
(
logging
.
DEBUG
)
# logging to stdout
train_iter
=
mx
.
io
.
NDArrayIter
(
mnist
[
'train_data'
],
mnist
[
'train_label'
],
batch_size
,
shuffle
=
True
)
...
...
demo/paddle/cifar10_image_classification_vgg.py
浏览文件 @
c678924e
...
...
@@ -2,13 +2,14 @@ from __future__ import print_function
import
sys
import
numpy
as
np
from
visualdl
import
LogWriter
import
paddle.v2
as
paddle
import
paddle.v2.fluid
as
fluid
import
paddle.v2.fluid.framework
as
framework
from
paddle.v2.fluid.param_attr
import
ParamAttr
from
paddle.v2.fluid.initializer
import
NormalInitializer
from
visualdl
import
LogWriter
import
numpy
as
np
from
paddle.v2.fluid.param_attr
import
ParamAttr
logdir
=
"./tmp"
logwriter
=
LogWriter
(
logdir
,
sync_cycle
=
10
)
...
...
demo/vdl_scratch.py
浏览文件 @
c678924e
#!/user/bin/env python
import
math
import
os
from
visualdl
import
LogWriter
,
ROOT
import
random
import
subprocess
from
scipy.stats
import
norm
import
numpy
as
np
import
random
from
PIL
import
Image
from
scipy.stats
import
norm
from
visualdl
import
ROOT
,
LogWriter
logdir
=
'./scratch_log'
...
...
@@ -19,22 +21,29 @@ with logw.mode('test') as logger:
scalar1
=
logger
.
scalar
(
"scratch/scalar"
)
# add scalar records.
for
step
in
range
(
200
):
scalar0
.
add_record
(
step
,
step
*
1.
/
200
)
scalar1
.
add_record
(
step
,
1.
-
step
*
1.
/
200
)
last_record0
=
0.
last_record1
=
0.
for
step
in
range
(
1
,
100
):
last_record0
+=
0.1
*
(
random
.
random
()
-
0.3
)
last_record1
+=
0.1
*
(
random
.
random
()
-
0.7
)
scalar0
.
add_record
(
step
,
last_record0
)
scalar1
.
add_record
(
step
,
last_record1
)
# create histogram
with
logw
.
mode
(
'train'
)
as
logger
:
histogram
=
logger
.
histogram
(
"scratch/histogram"
,
num_buckets
=
1
00
)
for
step
in
range
(
100
):
histogram
=
logger
.
histogram
(
"scratch/histogram"
,
num_buckets
=
2
00
)
for
step
in
range
(
1
,
1
00
):
histogram
.
add_record
(
step
,
np
.
random
.
normal
(
0.1
+
step
*
0.01
,
size
=
1000
))
np
.
random
.
normal
(
0.1
+
step
*
0.001
,
200.
/
(
100
+
step
),
size
=
1000
))
# create image
with
logw
.
mode
(
"train"
)
as
logger
:
image
=
logger
.
image
(
"scratch/dog"
,
4
,
1
)
# randomly sample 4 images one pass
dog_jpg
=
Image
.
open
(
os
.
path
.
join
(
ROOT
,
'python/dog.jpg'
))
dog_jpg
=
dog_jpg
.
resize
(
np
.
array
(
dog_jpg
.
size
)
/
2
)
shape
=
[
dog_jpg
.
size
[
1
],
dog_jpg
.
size
[
0
],
3
]
for
pass_
in
xrange
(
4
):
...
...
@@ -47,12 +56,13 @@ with logw.mode("train") as logger:
right_x
=
left_x
+
target_shape
[
1
]
right_y
=
left_y
+
target_shape
[
0
]
idx
=
image
.
is_sample_taken
()
# a more efficient way to sample images is
# a more efficient way to sample images
idx
=
image
.
is_sample_taken
()
# check whether this image will be taken by reservoir sampling
if
idx
>=
0
:
data
=
np
.
array
(
dog_jpg
.
crop
((
left_x
,
left_y
,
right_x
,
right_y
))).
flatten
()
# add this image to log
image
.
set_sample
(
idx
,
target_shape
,
data
)
# you can also just write followig codes, it is more clear, but need to
# process image even if it will not be sampled.
...
...
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