我就废话不多说了，大家还是直接看代码吧！

# 利用sklearn自建评价函数from sklearn.model_selection import train_test_splitfrom sklearn.metrics import roc_auc_scorefrom keras.callbacks import Callbackclass RocAucEvaluation(Callback): def __init__(self, validation_data=(), interval=1): super(Callback, self).__init__() self.interval = interval self.x_val,self.y_val = validation_data def on_epoch_end(self, epoch, log={}): if epoch % self.interval == 0: y_pred = self.model.predict(self.x_val, verbose=0) score = roc_auc_score(self.y_val, y_pred) print('\n ROC_AUC - epoch:%d - score:%.6f \n' % (epoch+1, score))x_train,y_train,x_label,y_label = train_test_split(train_feature, train_label, train_size=0.95, random_state=233)RocAuc = RocAucEvaluation(validation_data=(y_train,y_label), interval=1)hist = model.fit(x_train, x_label, batch_size=batch_size, epochs=epochs, validation_data=(y_train, y_label), callbacks=[RocAuc], verbose=2)

补充知识：keras用auc做metrics以及早停

我就废话不多说了，大家还是直接看代码吧！

import tensorflow as tffrom sklearn.metrics import roc_auc_scoredef auroc(y_true, y_pred): return tf.py_func(roc_auc_score, (y_true, y_pred), tf.double)# Build Model...model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy', auroc])

完整例子：

def auc(y_true, y_pred): auc = tf.metrics.auc(y_true, y_pred)[1] K.get_session().run(tf.local_variables_initializer()) return aucdef create_model_nn(in_dim,layer_size=200): model = Sequential() model.add(Dense(layer_size,input_dim=in_dim, kernel_initializer='normal')) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(Dropout(0.3)) for i in range(2): model.add(Dense(layer_size)) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(Dropout(0.3)) model.add(Dense(1, activation='sigmoid')) adam = optimizers.Adam(lr=0.01) model.compile(optimizer=adam,loss='binary_crossentropy',metrics = [auc]) return model####cv trainfolds = StratifiedKFold(n_splits=5, shuffle=False, random_state=15)oof = np.zeros(len(df_train))predictions = np.zeros(len(df_test))for fold_, (trn_idx, val_idx) in enumerate(folds.split(df_train.values, target2.values)): print("fold n°{}".format(fold_)) X_train = df_train.iloc[trn_idx][features] y_train = target2.iloc[trn_idx] X_valid = df_train.iloc[val_idx][features] y_valid = target2.iloc[val_idx] model_nn = create_model_nn(X_train.shape[1]) callback = EarlyStopping(monitor="val_auc", patience=50, verbose=0, mode='max') history = model_nn.fit(X_train, y_train, validation_data = (X_valid ,y_valid),epochs=1000,batch_size=64,verbose=0,callbacks=[callback]) print('\n Validation Max score : {}'.format(np.max(history.history['val_auc']))) predictions += model_nn.predict(df_test[features]).ravel()/folds.n_splits

以上这篇Keras 利用sklearn的ROC-AUC建立评价函数详解就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。