如何通过张量流在X_train，y_train，X_test，y_test中分割图像数据集? [英] How to split an image dataset in X_train, y_train, X_test, y_test by tensorflow?

问题描述

如何将图像数据分为X_train，Y_train，X_test和Y_test?

How can I split the image data into X_train, Y_train, X_test and Y_test?

我在使用带有tensorflow后端的keras

I am using keras with tensorflow backend

谢谢.

推荐答案

例如，您具有这样的文件夹

For example, you have folder like this

full_dataset
|--horse (40 images)
|--donkey (30 images)
|--cow ((50 images)
|--zebra (70 images)

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, color_mode='RGB', 
    target_size= (280,280))
    image=np.array(image)
    data.append(image)
    labels.append(1)
for i in cow:   
    image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB', 
    target_size= (280,280))
    image=np.array(image)
    data.append(image)
    labels.append(2)
for i in zebra:   
    image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB', 
    target_size= (280,280))
    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,
                                                 directory='full_dataset',
                                                 shuffle=True,
                                                 target_size=(280, 280), 
                                                 subset="validation",
                                                 class_mode='categorical')

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

Main drawback from Second way, you can't use for display a picture. It will error if you write validation_dataset[1]. But it worked if I use first way : X_test[1]

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