importpandasaspdimportnumpyasnpimportmatplotlib.pyplotaspltdataset=pd.read_csv("Churn_Modelling.csv")X=dataset.iloc[:,3:13].valuesY=dataset.iloc[:,13:].valuesfromsklearn.preprocessingimportOneHotEncoder,LabelEncoder,StandardScalerenc1=LabelEncoder()enc2=LabelEncoder()X[:,1]=enc1.fit_transform(X[

如果耐心达到我设置的数字，我希望分类器运行得更快并早点停止。在下面的代码中，它进行了10次拟合模型的迭代。importnumpyimportpandasfromkeras.modelsimportSequentialfromkeras.layersimportDensefromkeras.layersimportDropoutfromkeras.wrappers.scikit_learnimportKerasClassifierfromkeras.callbacksimportEarlyStopping,ModelCheckpointfromkeras.constraintsimpor