keras 上添加 roc auc指标

https://*.com/questions/41032551/how-to-compute-receiving-operating-characteristic-roc-and-auc-in-keras

https://github.com/keras-team/keras/issues/3230#issuecomment-319208366

http://www.luozhipeng.com/?p=1225

http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html

http://blog.csdn.net/zdy0_2004/article/details/44948511

keras 输出roc指标，不能每个batch输出一次，需要全部计算完再一次计算一次。使用sklearn中的metrics roc来计算。几个帖子类似

class roc_callback(keras.callbacks.Callback):
def __init__(self,training_data,validation_data):

        self.x = training_data[0]
        self.y = training_data[1]
        self.x_val = validation_data[0]
        self.y_val = validation_data[1]


def on_train_begin(self, logs={}):
return

def on_train_end(self, logs={}):
return

def on_epoch_begin(self, epoch, logs={}):
return

def on_epoch_end(self, epoch, logs={}):        
        y_pred = self.model.predict(self.x)
        roc = roc_auc_score(self.y, y_pred)      

        y_pred_val = self.model.predict(self.x_val)
        roc_val = roc_auc_score(self.y_val, y_pred_val)      

print('\rroc-auc: %s - roc-auc_val: %s' % (str(round(roc,4)),str(round(roc_val,4))),end=100*' '+'\n')
return

def on_batch_begin(self, batch, logs={}):
return

def on_batch_end(self, batch, logs={}):
return   


callbacks=[roc_callback(training_data=training_data,validation_data=validation_data)]

首先建立callbacks脚本，

my_callbacks.py如下：

import keras
from sklearn.metrics import roc_auc_score
import numpy as np



class Histories(keras.callbacks.Callback):
6
def on_train_begin(self, logs={}):
7
        self.aucs = []
8
        self.losses = []
9

10
def on_train_end(self, logs={}):
11
return
12

13
def on_epoch_begin(self, epoch, logs={}):
14
return
15

16
def on_epoch_end(self, epoch, logs={}):
17
        self.losses.append(logs.get('loss'))
18
        y_pred = self.model.predict(self.validation_data[0:2])
19

20
        yp = []
21
for i in xrange(0, len(y_pred)):
22
            yp.append(y_pred[i][0])
23
        yt = []
24
for x in self.validation_data[2]:
25
            yt.append(x[0])
26

27
        auc = roc_auc_score(yt, yp)
28
        self.aucs.append(auc)
29
print 'val-loss',logs.get('loss'), ' val-auc: ',auc,
30
print '\n'
31

32
return
33

34
def on_batch_begin(self, batch, logs={}):
35
return
36

37
def on_batch_end(self, batch, logs={}):
38
return

模型的输入为：

 
1 
      model = Model(inputs=[keyword1, keyword2], outputs=y)

在每个epoch结束时计算auc并输出：

 
1 
      histories = my_callbacks.Histories() 
     
2 
        
3 
      model.fit(train_x, train_y, batch_size=1024, epochs=20,shuffle=True, class_weight={1:1.0, 0:0.25}, validation_split=0.2, callbacks=[histories, model_check, lr])

秒客网

keras 上添加 roc auc指标

相关文章