Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
book
提交
bc756eb0
B
book
项目概览
PaddlePaddle
/
book
通知
17
Star
4
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
40
列表
看板
标记
里程碑
合并请求
37
Wiki
5
Wiki
分析
仓库
DevOps
项目成员
Pages
B
book
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
40
Issue
40
列表
看板
标记
里程碑
合并请求
37
合并请求
37
Pages
分析
分析
仓库分析
DevOps
Wiki
5
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
提交
bc756eb0
编写于
12月 05, 2018
作者:
L
lujun
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
ses-1,fix review for pr-644,rever for excutor,test=develop
上级
20fbfb0c
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
45 addition
and
45 deletion
+45
-45
01.fit_a_line/README.cn.md
01.fit_a_line/README.cn.md
+16
-16
01.fit_a_line/index.cn.html
01.fit_a_line/index.cn.html
+16
-16
01.fit_a_line/train.py
01.fit_a_line/train.py
+13
-13
未找到文件。
01.fit_a_line/README.cn.md
浏览文件 @
bc756eb0
...
...
@@ -106,8 +106,6 @@ import numpy
import
math
import
sys
from
__future__
import
print_function
import
os
os
.
environ
[
'CPU_NUM'
]
=
'1'
```
我们通过uci_housing模块引入了数据集合
[
UCI Housing Data Set
](
https://archive.ics.uci.edu/ml/datasets/Housing
)
...
...
@@ -167,7 +165,7 @@ test_program = main_program.clone(for_test=True)
use_cuda
=
False
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
ParallelExecutor
(
use_cuda
,
main_program
=
main_program
)
exe
=
fluid
.
Executor
(
place
)
```
...
...
@@ -177,9 +175,9 @@ exe = fluid.ParallelExecutor(use_cuda, main_program=main_program)
# Plot data
from
paddle.utils.plot
import
Ploter
train_
title
=
"Train cost"
test_
title
=
"Test cost"
plot_
cost
=
Ploter
(
train_title
,
test_title
)
train_
prompt
=
"Train cost"
test_
prompt
=
"Test cost"
plot_
prompt
=
Ploter
(
train_prompt
,
test_prompt
)
```
...
...
@@ -190,11 +188,12 @@ plot_cost = Ploter(train_title, test_title)
num_epochs
=
100
# For training test cost
def
train_test
(
executor
,
reader
,
feeder
,
fetch_list
):
def
train_test
(
executor
,
program
,
reader
,
feeder
,
fetch_list
):
accumulated
=
1
*
[
0
]
count
=
0
for
data_test
in
reader
():
outs
=
executor
.
run
(
feed
=
feeder
.
feed
(
data_test
),
outs
=
executor
.
run
(
program
=
program
,
feed
=
feeder
.
feed
(
data_test
),
fetch_list
=
fetch_list
)
accumulated
=
[
x_c
[
0
]
+
x_c
[
1
][
0
]
for
x_c
in
zip
(
accumulated
,
outs
)]
count
+=
1
...
...
@@ -215,23 +214,24 @@ naive_exe = fluid.Executor(place)
naive_exe
.
run
(
startup_program
)
step
=
0
exe_test
=
fluid
.
ParallelExecutor
(
use_cuda
,
main_program
=
test_program
,
share_vars_from
=
exe
)
exe_test
=
fluid
.
Executor
(
place
)
# main train loop.
for
pass_id
in
range
(
num_epochs
):
for
data_train
in
train_reader
():
avg_loss_value
,
=
exe
.
run
(
feed
=
feeder
.
feed
(
data_train
),
fetch_list
=
[
avg_loss
.
name
])
avg_loss_value
,
=
exe
.
run
(
main_program
,
feed
=
feeder
.
feed
(
data_train
),
fetch_list
=
[
avg_loss
])
if
step
%
10
==
0
:
# record a train cost every 10 batches
plot_cost
.
append
(
train_
title
,
step
,
avg_loss_value
[
0
])
plot_cost
.
append
(
train_
prompt
,
step
,
avg_loss_value
[
0
])
plot_cost
.
plot
()
if
step
%
100
==
0
:
# record a test cost every 100 batches
test_metics
=
train_test
(
executor
=
exe_test
,
program
=
test_program
,
reader
=
test_reader
,
fetch_list
=
[
avg_loss
.
name
],
feeder
=
feeder
)
plot_cost
.
append
(
test_
title
,
step
,
test_metics
[
0
])
plot_cost
.
append
(
test_
prompt
,
step
,
test_metics
[
0
])
plot_cost
.
plot
()
# If the accuracy is good enough, we can stop the training.
if
test_metics
[
0
]
<
10.0
:
...
...
@@ -244,7 +244,7 @@ for pass_id in range(num_epochs):
if
params_dirname
is
not
None
:
# We can save the trained parameters for the inferences later
fluid
.
io
.
save_inference_model
(
params_dirname
,
[
'x'
],
[
y_predict
],
naive_
exe
)
[
y_predict
],
exe
)
```
## 预测
...
...
01.fit_a_line/index.cn.html
浏览文件 @
bc756eb0
...
...
@@ -148,8 +148,6 @@ import numpy
import math
import sys
from __future__ import print_function
import os
os.environ['CPU_NUM'] = '1'
```
我们通过uci_housing模块引入了数据集合[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing)
...
...
@@ -209,7 +207,7 @@ test_program = main_program.clone(for_test=True)
use_cuda = False
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.
ParallelExecutor(use_cuda, main_program=main_program
)
exe = fluid.
Executor(place
)
```
...
...
@@ -219,9 +217,9 @@ exe = fluid.ParallelExecutor(use_cuda, main_program=main_program)
# Plot data
from paddle.utils.plot import Ploter
train_
title
= "Train cost"
test_
title
= "Test cost"
plot_
cost = Ploter(train_title, test_title
)
train_
prompt
= "Train cost"
test_
prompt
= "Test cost"
plot_
prompt = Ploter(train_prompt, test_prompt
)
```
...
...
@@ -232,11 +230,12 @@ plot_cost = Ploter(train_title, test_title)
num_epochs = 100
# For training test cost
def train_test(executor, reader, feeder, fetch_list):
def train_test(executor,
program,
reader, feeder, fetch_list):
accumulated = 1 * [0]
count = 0
for data_test in reader():
outs = executor.run(feed=feeder.feed(data_test),
outs = executor.run(program=program,
feed=feeder.feed(data_test),
fetch_list=fetch_list)
accumulated = [x_c[0] + x_c[1][0] for x_c in zip(accumulated, outs)]
count += 1
...
...
@@ -257,23 +256,24 @@ naive_exe = fluid.Executor(place)
naive_exe.run(startup_program)
step = 0
exe_test = fluid.ParallelExecutor(use_cuda,
main_program=test_program,
share_vars_from=exe)
exe_test = fluid.Executor(place)
# main train loop.
for pass_id in range(num_epochs):
for data_train in train_reader():
avg_loss_value, = exe.run(feed=feeder.feed(data_train),
fetch_list=[avg_loss.name])
avg_loss_value, = exe.run(main_program,
feed=feeder.feed(data_train),
fetch_list=[avg_loss])
if step % 10 == 0: # record a train cost every 10 batches
plot_cost.append(train_
title
, step, avg_loss_value[0])
plot_cost.append(train_
prompt
, step, avg_loss_value[0])
plot_cost.plot()
if step % 100 == 0: # record a test cost every 100 batches
test_metics = train_test(executor=exe_test,
program=test_program,
reader=test_reader,
fetch_list=[avg_loss.name],
feeder=feeder)
plot_cost.append(test_
title
, step, test_metics[0])
plot_cost.append(test_
prompt
, step, test_metics[0])
plot_cost.plot()
# If the accuracy is good enough, we can stop the training.
if test_metics[0]
<
10.0
:
...
...
@@ -286,7 +286,7 @@ for pass_id in range(num_epochs):
if
params_dirname
is
not
None:
#
We
can
save
the
trained
parameters
for
the
inferences
later
fluid.io.save_inference_model
(
params_dirname
,
['
x
'],
[
y_predict
],
naive_
exe
)
[
y_predict
],
exe
)
```
##
预测
...
...
01.fit_a_line/train.py
浏览文件 @
bc756eb0
...
...
@@ -19,16 +19,15 @@ import paddle.fluid as fluid
import
numpy
import
math
import
sys
import
os
os
.
environ
[
'CPU_NUM'
]
=
'1'
# For training test cost
def
train_test
(
executor
,
reader
,
feeder
,
fetch_list
):
def
train_test
(
executor
,
program
,
reader
,
feeder
,
fetch_list
):
accumulated
=
1
*
[
0
]
count
=
0
for
data_test
in
reader
():
outs
=
executor
.
run
(
feed
=
feeder
.
feed
(
data_test
),
fetch_list
=
fetch_list
)
outs
=
executor
.
run
(
program
=
program
,
feed
=
feeder
.
feed
(
data_test
),
fetch_list
=
fetch_list
)
accumulated
=
[
x_c
[
0
]
+
x_c
[
1
][
0
]
for
x_c
in
zip
(
accumulated
,
outs
)]
count
+=
1
return
[
x_d
/
count
for
x_d
in
accumulated
]
...
...
@@ -62,28 +61,28 @@ def main():
# can use CPU or GPU
use_cuda
=
False
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
ParallelExecutor
(
use_cuda
,
main_program
=
main_program
)
exe
=
fluid
.
Executor
(
place
)
# Specify the directory to save the parameters
params_dirname
=
"fit_a_line.inference.model"
num_epochs
=
2
00
num_epochs
=
1
00
# main train loop.
feeder
=
fluid
.
DataFeeder
(
place
=
place
,
feed_list
=
[
x
,
y
])
naive_exe
=
fluid
.
Executor
(
place
)
naive_exe
.
run
(
startup_program
)
exe
.
run
(
startup_program
)
train_prompt
=
"Train cost"
test_prompt
=
"Test cost"
step
=
0
exe_test
=
fluid
.
ParallelExecutor
(
use_cuda
,
main_program
=
test_program
,
share_vars_from
=
exe
)
exe_test
=
fluid
.
Executor
(
place
)
for
pass_id
in
range
(
num_epochs
):
for
data_train
in
train_reader
():
avg_loss_value
,
=
exe
.
run
(
feed
=
feeder
.
feed
(
data_train
),
fetch_list
=
[
avg_loss
.
name
])
main_program
,
feed
=
feeder
.
feed
(
data_train
),
fetch_list
=
[
avg_loss
])
if
step
%
10
==
0
:
# record a train cost every 10 batches
print
(
"%s, Step %d, Cost %f"
%
(
train_prompt
,
step
,
avg_loss_value
[
0
]))
...
...
@@ -91,8 +90,9 @@ def main():
if
step
%
100
==
0
:
# record a test cost every 100 batches
test_metics
=
train_test
(
executor
=
exe_test
,
program
=
test_program
,
reader
=
test_reader
,
fetch_list
=
[
avg_loss
.
name
],
fetch_list
=
[
avg_loss
],
feeder
=
feeder
)
print
(
"%s, Step %d, Cost %f"
%
(
test_prompt
,
step
,
test_metics
[
0
]))
...
...
@@ -107,7 +107,7 @@ def main():
if
params_dirname
is
not
None
:
# We can save the trained parameters for the inferences later
fluid
.
io
.
save_inference_model
(
params_dirname
,
[
'x'
],
[
y_predict
],
naive_
exe
)
exe
)
infer_exe
=
fluid
.
Executor
(
place
)
inference_scope
=
fluid
.
core
.
Scope
()
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录