您不必使用tensorflow或keras来划分数据集。如果您已安装sklearn软件包，则只​​需使用它即可：

from sklearn.model_selection import train_test_split
X = ...
Y = ...
x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.2)

您也可以将numpy用于相同目的：

import numpy
X = ...
Y = ...
test_size = 0.2
train_nsamples = (1-test_size) * len(Y)
x_train,y_test = X[:train_nsamples,:],X[train_nsamples:,Y[:train_nsamples,],Y[train_nsamples:,]

在Keras中：

from keras.datasets import mnist
import numpy as np
from sklearn.model_selection import train_test_split

(x_train,y_train),(x_test,y_test) = mnist.load_data()
x = np.concatenate((x_train,x_test))
y = np.concatenate((y_train,y_test))

train_size = 0.7
x_train,y_test = train_test_split(x,y,train_size=train_size)

经过反复试验和奋斗一天，我找到了解决方法。

第一路

import glob
horse = glob.glob('full_dataset/horse/*.*')
donkey = glob.glob('full_dataset/donkey/*.*')
cow = glob.glob('full_dataset/cow/*.*')
zebra = glob.glob('full_dataset/zebra/*.*')

data = []
labels = []

for i in horse:   
    image=tf.keras.preprocessing.image.load_img(i,color_mode='RGB',target_size= (280,280))
    image=np.array(image)
    data.append(image)
    labels.append(0)
for i in donkey:   
    image=tf.keras.preprocessing.image.load_img(i,280))
    image=np.array(image)
    data.append(image)
    labels.append(1)
for i in cow:   
    image=tf.keras.preprocessing.image.load_img(i,280))
    image=np.array(image)
    data.append(image)
    labels.append(2)
for i in zebra:   
    image=tf.keras.preprocessing.image.load_img(i,280))
    image=np.array(image)
    data.append(image)
    labels.append(3)

data = np.array(data)
labels = np.array(labels)

from sklearn.model_selection import train_test_split
X_train,X_test,ytrain,ytest = train_test_split(data,labels,test_size=0.2,random_state=42)

第二种方式

image_generator = ImageDataGenerator(rescale=1/255,validation_split=0.2)    

train_dataset = image_generator.flow_from_directory(batch_size=32,directory='full_dataset',shuffle=True,target_size=(280,280),subset="training",class_mode='categorical')

validation_dataset = image_generator.flow_from_directory(batch_size=32,subset="validation",class_mode='categorical')

第二种方法的主要缺点，不能用于显示图片。如果您写validation_dataset[1]，它将出错。但是，如果我使用第一种方法，它会起作用：X_test[1]