数据挖掘 - 如何在分类器模型中添加 Earlystopper - 吾爱随笔录

我为一项任务设计了以下二元分类器神经网络模型。我想为模型添加一个早期停止器，以便模型在它已显着停止学习的时期停止。我怎样才能做到这一点？

模型

X = badge1_data[['1','2','3','Score']] 
y = badge1_data['APR']

#Standardizing the Input Features
scaler = StandardScaler()
X = scaler.fit_transform(X)

#Train Test split
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3)

#Create Model
model = Sequential()
model.add(Dense(4, 
                  input_dim=4, 
                  kernel_initializer='normal', 
                  activation='relu'))

model.add(Dense(2, 
                  kernel_initializer='normal', 
                  activation='relu'))
   
model.add(Dense(1, 
                  activation='sigmoid',
                  kernel_initializer='normal'))
    
#Compile Model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

#Fit Model
model.fit(X_train, y_train, epochs = 5000, validation_split = 0.3, verbose=0, batch_size=256)

#Make predictions and convert to binary value
pred_train = model.predict(X_train)
pred_test = model.predict(X_test)

#ROC AUC Score
print('Train AUC = {:}'.format(roc_auc_score(y_train,pred_train)))
print('Test AUC = {:}'.format(roc_auc_score(y_test,pred_test)))

#Accuracy
print('Train Accuracy = ',accuracy_score(y_train,pred_train.round()))
print('Test Accuracy = ',accuracy_score(y_test,pred_test.round()))