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KFold.py
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KFold.py
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import keras
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold
#设置随机数种子
np.random.seed(7)
#数据路径
data_file="pima-indians-diabetes.csv"
#导入数据
dataset=np.loadtxt(data_file,encoding='utf-8',delimiter=',')
#分割X和y
X=dataset[:,0:8]
y=dataset[:,8]
#10折,训练集和验证集总数共为10
kfold=StratifiedKFold(n_splits=10,shuffle=True,random_state=7)
cvscores=[]
for train,validation in kfold.split(X,y):
# 创建模型
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(6, activation='relu'))
# 最后一层dense,sigmoid输出患病概率
model.add(Dense(1, activation='sigmoid'))
# 编译模型
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# 训练模型
model.fit(X, y, epochs=150, batch_size=10, verbose=0)
# 评估模型
scores = model.evaluate(X[validation], y[validation],verbose=0)
#输出评估结果
print('\n%s : %.2f%%' % (model.metrics_names[1], scores[1] * 100))
cvscores.append(scores[1]*100)
#输出均值和标准差
print("%.2f%% (+/- %.2f%%)" % (np.mean(cvscores),np.std(cvscores)))