尝试使用我自己的数据而不是cifar10生成Keras模型

假设您的图像为。jpg格式,您的标签为csv格式,称为 label.csv ,并将它们分为两个文件夹, train folder 和 test folder .

然后,您可以执行以下操作以获取 x_train

import cv2 #library for reading images
import numpy as np
import glob #library for reading files in a folder
x_train= []
for file in glob.glob("train/*.jpg"):
    im = cv2.imread(file) #reading each image from the folder
    x_train.append(im)
x_train = np.array(x_train)

您可以执行以下操作以获得 y_train

import csv
y_train= []
with open('train/label.csv', 'r') as csvfile:
    reader = csv.reader(csvfile)
    for row in reader:
        y_train.append([int(row[0])]) #converting the string to int (otherwise the csv data will be read as string)
y_train = np.array(y_train)

您可以对测试文件夹执行相同的操作,只需更改参数和参数的名称。