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体验新版 GitCode,发现更多精彩内容 >>
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68811bcb
编写于
6月 15, 2018
作者:
Y
Yibing Liu
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Format the doc of layers.auc
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python/paddle/fluid/layers/metric.py
python/paddle/fluid/layers/metric.py
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python/paddle/fluid/layers/metric.py
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@@ -59,14 +59,14 @@ def auc(input, label, curve='ROC', num_thresholds=200):
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>`_.
Note: If input label contains values other than 0 and 1, it will be cast
to `bool`. 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
1. ROC: Receiver operating characteristic;
2. PR: Precision Recall
Args:
input(Variable): A floating-point 2D Variable, values are in the range
...
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@@ -85,9 +85,9 @@ def auc(input, label, curve='ROC', num_thresholds=200):
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)
# 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
(
...
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