roc_auc_score_Metrics

from sklearn.metrics import roc_auc_score
import keras
class RocAucMetricCallback(keras.callbacks.Callback):
    def __init__(self, predict_batch_size=1024, include_on_batch=False):
        super(RocAucMetricCallback, self).__init__()
        self.predict_batch_size=predict_batch_size
        self.include_on_batch=include_on_batch
 
    def on_batch_begin(self, batch, logs={}):
        pass
 
    def on_batch_end(self, batch, logs={}):
        if(self.include_on_batch):
            logs['roc_auc_val']=float('-inf')
            if(self.validation_data):
                logs['roc_auc_val']=roc_auc_score(self.validation_data[1], 
                                                  self.model.predict(self.validation_data[0],
                                                                     batch_size=self.predict_batch_size))
 
    def on_train_begin(self, logs={}):
        if not ('roc_auc_val' in self.params['metrics']):
            self.params['metrics'].append('roc_auc_val')
 
    def on_train_end(self, logs={}):
        pass
 
    def on_epoch_begin(self, epoch, logs={}):
        pass
 
    def on_epoch_end(self, epoch, logs={}):
        logs['roc_auc_val']=float('-inf')
        if(self.validation_data):
            logs['roc_auc_val']=roc_auc_score(self.validation_data[1], 
                                              self.model.predict(self.validation_data[0],
                                                                 batch_size=self.predict_batch_size))
 
 
import numpy as np
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import GRU
import keras
from keras.callbacks import EarlyStopping
from sklearn.metrics import roc_auc_score
from keras import metrics
 
cb = [
    my_callbacks.RocAucMetricCallback(), # include it before EarlyStopping!
    EarlyStopping(monitor='roc_auc_val',patience=300, verbose=2,mode='max')
]
model = Sequential()
model.add(keras.layers.core.Masking(mask_value=0., input_shape=(max_lenth, max_features)))
# model.add(Embedding(input_dim=max_features+1, output_dim=64,mask_zero=True))
model.add(GRU(units=n_hidden_units,activation='selu',kernel_initializer='orthogonal', recurrent_initializer='orthogonal',
              bias_initializer='zeros', kernel_regularizer=regularizers.l2(0.01), recurrent_regularizer=regularizers.l2(0.01),
              bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, recurrent_constraint=None,
              bias_constraint=None, dropout=0.5, recurrent_dropout=0.0, implementation=1, return_sequences=False,
              return_state=False, go_backwards=False, stateful=False, unroll=False))   #input_shape=(max_lenth, max_features),
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
 
model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy']) #这里就可以写其他评估标准
model.fit(x_train, y_train, batch_size=train_batch_size, epochs=training_iters, verbose=2,
          callbacks=cb,validation_split=0.2,
          shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0)