我就废话不多说了，大家还是直接看代码吧~
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,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
补充知识：Keras可使用的评价函数
1：binary_accuracy（对二分类问题，计算在所有预测值上的平均正确率）
binary_accuracy(y_true, y_pred)
2：categorical_accuracy（对多分类问题，计算在所有预测值上的平均正确率）
categorical_accuracy(y_true, y_pred)
3：sparse_categorical_accuracy（与categorical_accuracy相同，在对稀疏的目标值预测时有用 ）
sparse_categorical_accuracy(y_true, y_pred)
4：top_k_categorical_accuracy（计算top-k正确率，当预测值的前k个值中存在目标类别即认为预测正确 ）
top_k_categorical_accuracy(y_true, y_pred, k=5)
5：sparse_top_k_categorical_accuracy（与top_k_categorical_accracy作用相同，但适用于稀疏情况）
sparse_top_k_categorical_accuracy(y_true, y_pred, k=5)
以上这篇keras用auc做metrics以及早停实例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。