keras 上添加 roc auc指标

https://stackoverflow.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('
roc-auc: %s - roc-auc_val: %s' % (str(round(roc,4)),str(round(roc_val,4))),end=100*' '+'
')
        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={}):
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        self.aucs = []
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        self.losses = []
9
 
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    def on_train_end(self, logs={}):
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        return
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    def on_epoch_begin(self, epoch, logs={}):
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        return
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    def on_epoch_end(self, epoch, logs={}):
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        self.losses.append(logs.get('loss'))
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        y_pred = self.model.predict(self.validation_data[0:2])
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        yp = []
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        for i in xrange(0, len(y_pred)):
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            yp.append(y_pred[i][0])
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        yt = []
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        for x in self.validation_data[2]:
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            yt.append(x[0])
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        auc = roc_auc_score(yt, yp)
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        self.aucs.append(auc)
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        print 'val-loss',logs.get('loss'), ' val-auc: ',auc,
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        print '
'
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        return
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    def on_batch_begin(self, batch, logs={}):
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        return
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    def on_batch_end(self, batch, logs={}):
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        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])