小编Joi*_*ish的帖子

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

dataset = pd.read_csv("Churn_Modelling.csv")
X = dataset.iloc[:,3:13].values
Y = dataset.iloc[:,13:].values

from sklearn.preprocessing import OneHotEncoder,LabelEncoder,StandardScaler

enc1=LabelEncoder()
enc2=LabelEncoder()
X[:,1] = enc1.fit_transform(X[:,1])
X[:,2] = enc2.fit_transform(X[:,2])

one = OneHotEncoder(categorical_features=[1])
X=one.fit_transform(X).toarray()

X = X[:,1:]

from sklearn.model_selection import train_test_split
Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,Y,random_state=0,test_size=0.2)

scale = StandardScaler()
scale.fit_transform(Xtrain)
scale.transform(Xtest)

from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score 
from keras.models import Sequential
from keras.layers import Dense

def func1():
    net = Sequential()
    net.add(Dense(input_dim=11,units=6,activation="relu",kernel_initializer='uniform'))
    net.add(Dense(units=6,activation="relu",kernel_initializer='uniform'))
    net.add(Dense(units=1,activation="sigmoid",kernel_initializer='uniform'))
    net.compile(optimizer='adam',metrics=['accuracy'],loss='binary_crossentropy')

    return net

classfier …