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
wmsofts
Paddle
提交
316eb3e9
P
Paddle
项目概览
wmsofts
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
0
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
0
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
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
0
Issue
0
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
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
体验新版 GitCode,发现更多精彩内容 >>
提交
316eb3e9
编写于
6月 15, 2018
作者:
Y
Yibing Liu
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add doc for layers.auc
上级
cafdeb0a
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
37 addition
and
0 deletion
+37
-0
python/paddle/fluid/layers/metric.py
python/paddle/fluid/layers/metric.py
+37
-0
未找到文件。
python/paddle/fluid/layers/metric.py
浏览文件 @
316eb3e9
...
...
@@ -53,6 +53,43 @@ def accuracy(input, label, k=1, correct=None, total=None):
def
auc
(
input
,
label
,
curve
=
'ROC'
,
num_thresholds
=
200
):
"""
**Area Under The Curve (AUC) Layer**
This implementation computes the AUC according to forward output and label.
It is used very widely in binary classification evaluation.
As a note: If input label contains values other than 0 and 1, it will be
cast to bool. You can find the relevant definitions `here
<https://en.wikipedia.org/wiki/Receiver_operating_characteristic
#Area_under_the_curve>`_.
There are two types of possible curves:
1. ROC: Receiver operating characteristic
2. PR: Precision Recall
Args:
input(Variable): A floating-point 2D Variable, values are in the range
[0, 1]. Each row is sorted in descending order. This
input should be the output of topk. Typically, this
Variable indicates the probability of each label.
label(Variable): A 2D int Variable indicating the label of the training
data. The height is batch size and width is always 1.
curve(str): Curve type, can be 'ROC' or 'PR'. Default 'ROC'.
num_thresholds(int): The number of thresholds to use when discretizing
the roc curve. Default 200.
Returns:
Variable: A scalar representing the current AUC.
Examples:
.. code-block:: python
# network is a binary classification model and label the ground truth
prediction = network(image, is_infer=True)
auc_out=fluid.layers.auc(input=prediction, label=label)
"""
warnings
.
warn
(
"This interface not recommended, fluid.layers.auc compute the auc at every minibatch,
\
but can not aggregate them and get the pass AUC, because pass
\
...
...
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