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d0355ba0
编写于
3月 17, 2017
作者:
L
Luo Tao
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
rename regression_cost to mse_cost
上级
7a59eea0
变更
14
隐藏空白更改
内联
并排
Showing
14 changed file
with
23 addition
and
23 deletion
+23
-23
fit_a_line/README.en.ipynb
fit_a_line/README.en.ipynb
+1
-1
fit_a_line/README.en.md
fit_a_line/README.en.md
+1
-1
fit_a_line/README.ipynb
fit_a_line/README.ipynb
+1
-1
fit_a_line/README.md
fit_a_line/README.md
+1
-1
fit_a_line/index.en.html
fit_a_line/index.en.html
+1
-1
fit_a_line/index.html
fit_a_line/index.html
+1
-1
fit_a_line/train.py
fit_a_line/train.py
+1
-1
recommender_system/README.en.ipynb
recommender_system/README.en.ipynb
+2
-2
recommender_system/README.en.md
recommender_system/README.en.md
+2
-2
recommender_system/README.ipynb
recommender_system/README.ipynb
+3
-3
recommender_system/README.md
recommender_system/README.md
+3
-3
recommender_system/index.en.html
recommender_system/index.en.html
+2
-2
recommender_system/index.html
recommender_system/index.html
+3
-3
recommender_system/train.py
recommender_system/train.py
+1
-1
未找到文件。
fit_a_line/README.en.ipynb
浏览文件 @
d0355ba0
...
...
@@ -189,7 +189,7 @@
" size=1,\n",
" act=paddle.activation.Linear())\n",
"y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))\n",
"cost = paddle.layer.
regression
_cost(input=y_predict, label=y)\n"
"cost = paddle.layer.
mse
_cost(input=y_predict, label=y)\n"
],
"outputs": [
{
...
...
fit_a_line/README.en.md
浏览文件 @
d0355ba0
...
...
@@ -132,7 +132,7 @@ y_predict = paddle.layer.fc(input=x,
size
=
1
,
act
=
paddle
.
activation
.
Linear
())
y
=
paddle
.
layer
.
data
(
name
=
'y'
,
type
=
paddle
.
data_type
.
dense_vector
(
1
))
cost
=
paddle
.
layer
.
regression
_cost
(
input
=
y_predict
,
label
=
y
)
cost
=
paddle
.
layer
.
mse
_cost
(
input
=
y_predict
,
label
=
y
)
```
### Create Parameters
...
...
fit_a_line/README.ipynb
浏览文件 @
d0355ba0
...
...
@@ -183,7 +183,7 @@
" size=1,\n",
" act=paddle.activation.Linear())\n",
"y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))\n",
"cost = paddle.layer.
regression
_cost(input=y_predict, label=y)\n"
"cost = paddle.layer.
mse
_cost(input=y_predict, label=y)\n"
],
"outputs": [
{
...
...
fit_a_line/README.md
浏览文件 @
d0355ba0
...
...
@@ -126,7 +126,7 @@ y_predict = paddle.layer.fc(input=x,
size
=
1
,
act
=
paddle
.
activation
.
Linear
())
y
=
paddle
.
layer
.
data
(
name
=
'y'
,
type
=
paddle
.
data_type
.
dense_vector
(
1
))
cost
=
paddle
.
layer
.
regression
_cost
(
input
=
y_predict
,
label
=
y
)
cost
=
paddle
.
layer
.
mse
_cost
(
input
=
y_predict
,
label
=
y
)
```
### 创建参数
...
...
fit_a_line/index.en.html
浏览文件 @
d0355ba0
...
...
@@ -174,7 +174,7 @@ y_predict = paddle.layer.fc(input=x,
size=1,
act=paddle.activation.Linear())
y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
cost = paddle.layer.
regression
_cost(input=y_predict, label=y)
cost = paddle.layer.
mse
_cost(input=y_predict, label=y)
```
### Create Parameters
...
...
fit_a_line/index.html
浏览文件 @
d0355ba0
...
...
@@ -168,7 +168,7 @@ y_predict = paddle.layer.fc(input=x,
size=1,
act=paddle.activation.Linear())
y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
cost = paddle.layer.
regression
_cost(input=y_predict, label=y)
cost = paddle.layer.
mse
_cost(input=y_predict, label=y)
```
### 创建参数
...
...
fit_a_line/train.py
浏览文件 @
d0355ba0
...
...
@@ -10,7 +10,7 @@ def main():
x
=
paddle
.
layer
.
data
(
name
=
'x'
,
type
=
paddle
.
data_type
.
dense_vector
(
13
))
y_predict
=
paddle
.
layer
.
fc
(
input
=
x
,
size
=
1
,
act
=
paddle
.
activation
.
Linear
())
y
=
paddle
.
layer
.
data
(
name
=
'y'
,
type
=
paddle
.
data_type
.
dense_vector
(
1
))
cost
=
paddle
.
layer
.
regression
_cost
(
input
=
y_predict
,
label
=
y
)
cost
=
paddle
.
layer
.
mse
_cost
(
input
=
y_predict
,
label
=
y
)
# create parameters
parameters
=
paddle
.
parameters
.
create
(
cost
)
...
...
recommender_system/README.en.ipynb
浏览文件 @
d0355ba0
...
...
@@ -449,7 +449,7 @@
},
"source": [
"inference = paddle.layer.cos_sim(a=usr_combined_features, b=mov_combined_features, size=1, scale=5)\n",
"cost = paddle.layer.
regression
_cost(\n",
"cost = paddle.layer.
mse
_cost(\n",
" input=inference,\n",
" label=paddle.layer.data(\n",
" name='score', type=paddle.data_type.dense_vector(1)))\n"
...
...
@@ -536,7 +536,7 @@
"\n",
"```text\n",
"[INFO 2017-03-06 17:12:13,378 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]\n",
"[INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__
regression
_cost_0__]\n",
"[INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__
mse
_cost_0__]\n",
"```\n",
"\n",
"### Training\n",
...
...
recommender_system/README.en.md
浏览文件 @
d0355ba0
...
...
@@ -260,7 +260,7 @@ Finally, we can use cosine similarity to calculate the similarity between user c
```
python
inference
=
paddle
.
layer
.
cos_sim
(
a
=
usr_combined_features
,
b
=
mov_combined_features
,
size
=
1
,
scale
=
5
)
cost
=
paddle
.
layer
.
regression
_cost
(
cost
=
paddle
.
layer
.
mse
_cost
(
input
=
inference
,
label
=
paddle
.
layer
.
data
(
name
=
'score'
,
type
=
paddle
.
data_type
.
dense_vector
(
1
)))
...
...
@@ -288,7 +288,7 @@ trainer = paddle.trainer.SGD(cost=cost, parameters=parameters,
```
text
[INFO 2017-03-06 17:12:13,378 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]
[INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__
regression
_cost_0__]
[INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__
mse
_cost_0__]
```
### Training
...
...
recommender_system/README.ipynb
浏览文件 @
d0355ba0
...
...
@@ -479,7 +479,7 @@
"editable": true
},
"source": [
"cost = paddle.layer.
regression
_cost(\n",
"cost = paddle.layer.
mse
_cost(\n",
" input=inference,\n",
" label=paddle.layer.data(\n",
" name='score', type=paddle.data_type.dense_vector(1)))\n"
...
...
@@ -535,7 +535,7 @@
"source": [
"\n",
" [INFO 2017-03-06 17:12:13,284 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]\n",
" [INFO 2017-03-06 17:12:13,287 networks.py:1478] The output order is [__
regression
_cost_0__]\n",
" [INFO 2017-03-06 17:12:13,287 networks.py:1478] The output order is [__
mse
_cost_0__]\n",
"\n",
"\n",
"`parameters`是模型的所有参数集合。他是一个python的dict。我们可以查看到这个网络中的所有参数名称。因为之前定义模型的时候,我们没有指定参数名称,这里参数名称是自动生成的。当然,我们也可以指定每一个参数名称,方便日后维护。\n",
...
...
@@ -603,7 +603,7 @@
"source": [
"\n",
" [INFO 2017-03-06 17:12:13,378 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]\n",
" [INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__
regression
_cost_0__]\n",
" [INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__
mse
_cost_0__]\n",
"\n",
"\n",
"### 训练\n",
...
...
recommender_system/README.md
浏览文件 @
d0355ba0
...
...
@@ -268,7 +268,7 @@ inference = paddle.layer.cos_sim(a=usr_combined_features, b=mov_combined_feature
```
python
cost
=
paddle
.
layer
.
regression
_cost
(
cost
=
paddle
.
layer
.
mse
_cost
(
input
=
inference
,
label
=
paddle
.
layer
.
data
(
name
=
'score'
,
type
=
paddle
.
data_type
.
dense_vector
(
1
)))
...
...
@@ -287,7 +287,7 @@ parameters = paddle.parameters.create(cost)
```
[INFO 2017-03-06 17:12:13,284 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]
[INFO 2017-03-06 17:12:13,287 networks.py:1478] The output order is [__
regression
_cost_0__]
[INFO 2017-03-06 17:12:13,287 networks.py:1478] The output order is [__
mse
_cost_0__]
`parameters`
是模型的所有参数集合。他是一个python的dict。我们可以查看到这个网络中的所有参数名称。因为之前定义模型的时候,我们没有指定参数名称,这里参数名称是自动生成的。当然,我们也可以指定每一个参数名称,方便日后维护。
...
...
@@ -311,7 +311,7 @@ trainer = paddle.trainer.SGD(cost=cost, parameters=parameters,
```
[INFO 2017-03-06 17:12:13,378 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]
[INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__
regression
_cost_0__]
[INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__
mse
_cost_0__]
### 训练
...
...
recommender_system/index.en.html
浏览文件 @
d0355ba0
...
...
@@ -302,7 +302,7 @@ Finally, we can use cosine similarity to calculate the similarity between user c
```python
inference = paddle.layer.cos_sim(a=usr_combined_features, b=mov_combined_features, size=1, scale=5)
cost = paddle.layer.
regression
_cost(
cost = paddle.layer.
mse
_cost(
input=inference,
label=paddle.layer.data(
name='score', type=paddle.data_type.dense_vector(1)))
...
...
@@ -330,7 +330,7 @@ trainer = paddle.trainer.SGD(cost=cost, parameters=parameters,
```text
[INFO 2017-03-06 17:12:13,378 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]
[INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__
regression
_cost_0__]
[INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__
mse
_cost_0__]
```
### Training
...
...
recommender_system/index.html
浏览文件 @
d0355ba0
...
...
@@ -310,7 +310,7 @@ inference = paddle.layer.cos_sim(a=usr_combined_features, b=mov_combined_feature
```python
cost = paddle.layer.
regression
_cost(
cost = paddle.layer.
mse
_cost(
input=inference,
label=paddle.layer.data(
name='score', type=paddle.data_type.dense_vector(1)))
...
...
@@ -329,7 +329,7 @@ parameters = paddle.parameters.create(cost)
```
[INFO 2017-03-06 17:12:13,284 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]
[INFO 2017-03-06 17:12:13,287 networks.py:1478] The output order is [__
regression
_cost_0__]
[INFO 2017-03-06 17:12:13,287 networks.py:1478] The output order is [__
mse
_cost_0__]
`parameters`是模型的所有参数集合。他是一个python的dict。我们可以查看到这个网络中的所有参数名称。因为之前定义模型的时候,我们没有指定参数名称,这里参数名称是自动生成的。当然,我们也可以指定每一个参数名称,方便日后维护。
...
...
@@ -353,7 +353,7 @@ trainer = paddle.trainer.SGD(cost=cost, parameters=parameters,
```
[INFO 2017-03-06 17:12:13,378 networks.py:1472] The input order is [user_id, gender_id, age_id, job_id, movie_id, category_id, movie_title, score]
[INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__
regression
_cost_0__]
[INFO 2017-03-06 17:12:13,379 networks.py:1478] The output order is [__
mse
_cost_0__]
### 训练
...
...
recommender_system/train.py
浏览文件 @
d0355ba0
...
...
@@ -61,7 +61,7 @@ def main():
inference
=
paddle
.
layer
.
cos_sim
(
a
=
usr_combined_features
,
b
=
mov_combined_features
,
size
=
1
,
scale
=
5
)
cost
=
paddle
.
layer
.
regression
_cost
(
cost
=
paddle
.
layer
.
mse
_cost
(
input
=
inference
,
label
=
paddle
.
layer
.
data
(
name
=
'score'
,
type
=
paddle
.
data_type
.
dense_vector
(
1
)))
...
...
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