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python cnn 交叉驗證
70 瀏覽
list of ans=[]
img=cv2.imread(directory name+ / +filename)
img resize = cv2.resize(img,(32,32))
list of img.append(img resize)
list of ans.append(int(filename.split( . )[0])-1)
array of img = np.array(list of img,dtype= float32 )
return(array of img,list of ans)
指定亂數種子
(input train,target train)=read directory(r C:\Users\COSH\Desktop\Ch09\train )
(input test, target test)=read directory(r C:\Users\COSH\Desktop\Ch09\test )
input shape = (img width, img height, img num channels)
epochs=10
因為是固定範圍, 所以執行正規化, 從 0-255 至 0-1
input train = input train.astype( float32 ) / 255
input test = input test.astype( float32 ) / 255
acc per fold = [] ....

K Fold Cross , Cross Validation , குறுக்கு சரிபார்த்தல் ,