python-手写识别模型

import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import MNIST
from torch import optim

import torch.nn as nn
import torch.nn.functional as F
from PIL import Image

# 构建模型（简单的卷积神经网络）
class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, padding=2)  # 卷积
        self.conv2 = nn.Conv2d(6, 16, 5)
        # Linear(in_feactures(输入的二维张量大小), out_feactures)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)  # 全连接
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)  # 最后输出10个类

    def forward(self, x):
        # 激活函数
        out = F.relu(self.conv1(x))
        # max_pool2d(input, kernel_size（卷积核）, stride(卷积核步长)=None, padding=0, dilation=1, ceil_mode（空间输入形状）=False, return_ind