我就廢話不多說了，大家還是直接看程式碼吧~

import tensorflow as tf
from sklearn.metrics import roc_auc_score

def 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 auc

def 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 train
folds = 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,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

補充知識：Keras可使用的評價函式

1：binary_accuracy（對二分類問題，計算在所有預測值上的平均正確率）

binary_accuracy(y_true,y_pred)

2：categorical_accuracy（對多分類問題，計算在所有預測值上的平均正確率）

categorical_accuracy(y_true,y_pred)

3：sparse_categorical_accuracy（與categorical_accuracy相同，在對稀疏的目標值預測時有用 ）

sparse_categorical_accuracy(y_true,y_pred)

4：top_k_categorical_accuracy（計算top-k正確率，當預測值的前k個值中存在目標類別即認為預測正確 ）

top_k_categorical_accuracy(y_true,y_pred,k=5)

5：sparse_top_k_categorical_accuracy（與top_k_categorical_accracy作用相同，但適用於稀疏情況）

sparse_top_k_categorical_accuracy(y_true,k=5)

以上這篇keras用auc做metrics以及早停例項就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。