太原建站模板系统seo高效优化

MNIST数据集

MNIST数据集包含了6万张手写数字（[1,28,28]尺寸），以特殊格式存储。本文首先将MNIST数据集另存为png格式，然后再读取png格式图片，开展后续训练

另存为png格式

import torch
from torch.utils.data import Dataset
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader
from tqdm import tqdm
from torchvision import models, transforms
from torchvision.utils import save_image
from PIL import Image#将MNIST数据集转换为图片
tf = transforms.Compose([transforms.ToTensor()]) # mnist is already normalised 0 to 1
datasetMNIST = MNIST("./data", train=True, download=True, transform=tf)
pbar = tqdm(datasetMNIST)
for index, (img,cl) in enumerate(pbar):save_image(img, f"./data/MNIST_PNG/x/{index}.png")# 以写入模式打开文件with open(f"./data/MNIST_PNG/c/{index}.txt", "w", encoding="utf-8") as file:# 将字符串写入文件file.write(f"{cl}")

注意：MNIST源数据存放在./data文件下，如果没有数据也没关系，代码会自动从网上下载。另存为png的数据放在了./data/MNIST_PNG/文件下。子文件夹x存放6万张图片，子文件夹c存放6万个文本文件，每个文本文件内有一行字符串，说明该对应的手写数字是几（标签）。

读取png格式数据集

class MyMNISTDataset(Dataset):def __init__(self, data):self.data = datadef __len__(self):return len(self.data)def __getitem__(self, idx):x = self.data[idx][0] #图像y = self.data[idx][1] #标签return x, ydef load_data(dataNum=60000):data = []pbar = tqdm(range(dataNum))for i in pbar:# 指定图片路径image_path = f'./data/MNIST_PNG/x/{i}.png'cond_path=f'./data/MNIST_PNG/c/{i}.txt'# 定义图像预处理preprocess = transforms.Compose([transforms.Grayscale(num_output_channels=1),  # 将图像转换为灰度图像（单通道）transforms.ToTensor()])# 使用预处理加载图像image_tensor = preprocess(Image.open(image_path))# 加载条件文档（tag）with open(cond_path, 'r') as file:line = file.readline()number = int(line)  # 将字符串转换为整数，图像的类别data.append((image_tensor, number))return datadata=load_data(60000)
# 创建数据集实例
dataset = MyMNISTDataset(data)# 创建数据加载器
dataloader = DataLoader(dataset, batch_size=4, shuffle=True)
pbar = tqdm(dataloader)for index, (img,cond) in enumerate(pbar):#这里对每一批进行训练...print(f"Batch {index}: img = {img.shape}, cond = {cond}")

load_data函数用于读取数据文件，返回一个data张量。data张量又被用于构造MyMNISTDataset类的对象dataset，dataset对象又被DataLoader函数转换为dataloader。

dataloader事实上按照batch将数据集进行了分割，4张图片一组进行训练。上述代码的输出如下：

......
Batch 7847: img = torch.Size([4, 1, 28, 28]), cond = tensor([0, 1, 5, 2])
Batch 7848: img = torch.Size([4, 1, 28, 28]), cond = tensor([2, 2, 6, 0])
Batch 7849: img = torch.Size([4, 1, 28, 28]), cond = tensor([4, 3, 0, 9])
Batch 7850: img = torch.Size([4, 1, 28, 28]), cond = tensor([6, 2, 9, 5])
Batch 7851: img = torch.Size([4, 1, 28, 28]), cond = tensor([7, 2, 4, 4])
Batch 7852: img = torch.Size([4, 1, 28, 28]), cond = tensor([1, 4, 2, 6])
Batch 7853: img = torch.Size([4, 1, 28, 28]), cond = tensor([2, 5, 3, 5])
Batch 7854: img = torch.Size([4, 1, 28, 28]), cond = tensor([7, 1, 0, 1])
Batch 7855: img = torch.Size([4, 1, 28, 28]), cond = tensor([9, 8, 9, 7])
Batch 7856: img = torch.Size([4, 1, 28, 28]), cond = tensor([4, 6, 6, 7])
Batch 7857: img = torch.Size([4, 1, 28, 28]), cond = tensor([7, 4, 1, 6])
Batch 7858: img = torch.Size([4, 1, 28, 28]), cond = tensor([5, 4, 6, 5])
Batch 7859: img = torch.Size([4, 1, 28, 28]), cond = tensor([6, 3, 1, 9])
Batch 7860: img = torch.Size([4, 1, 28, 28]), cond = tensor([5, 5, 8, 6])
Batch 7861: img = torch.Size([4, 1, 28, 28]), cond = tensor([0, 4, 8, 9])
Batch 7862: img = torch.Size([4, 1, 28, 28]), cond = tensor([2, 3, 5, 8])
Batch 7863: img = torch.Size([4, 1, 28, 28]), cond = tensor([8, 0, 0, 6])
......