附上资源下载链接：pytorch实现cnn手写识别-深度学习文档类资源-CSDN文库

?1.代码

import torch.nn as nn
import torch
from torch.utils.data import DataLoader
import torchvision
import matplotlib.pyplot as plt
from tqdm import tqdm

# 神经网络模型
class Net(nn.Module):
    def __init__(self):
        # 调用父类的构造函数
        super(Net, self).__init__()
        # 将方法进行包装
        self.model = torch.nn.Sequential(
            # 像素是 28x28
            nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1),  # 卷积层
            # （28像素-3卷积核大小+2*1pad）/1步长 + 1 = 28
            nn.ReLU(),  # 激活函数
            nn.MaxPool2d(kernel_size=2, stride=2),  # 池化层
            # （28像素-2池化核大小）/2步长 + 1 = 14

            # 像素是 14x14
            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1),  # 卷积层
            # （14像素-3卷积核大小+2*1pad）/1步长 + 1 = 14
            nn.ReLU(),  # 激活函数
            nn.MaxPool2d(kernel_size=2, stride=2),  # 池化层
            # （14像素-2池化核大小）/2步长 + 1 = 7

            # 像素是 7x7
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1),  # 卷积层
            nn.ReLU(),  # 激活函数

            nn.Flatten(),  # 展平
            nn.Linear(in_features=7 * 7 * 64, out_features=128),  # 全连接层
            nn.ReLU(),  # 激活函数
            nn.Linear(in_features=128, out_features=10),  # 全连接层
            nn.Softmax(dim=1)  # 多分类器
        )

    # forward方法是必须要重写的，它是实现模型的功能，实现各个层之间的连接关系的核心
    def forward(self, input):
        output = self.model(input)
        return output


# 超参数设置
BATCH_SIZE = 250
EPOCHS = 10

# 创建实例
NET = Net()


# 利用gpu进行加速，没有的话就用cpu
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# 图像处理成张量并进行归一化处理
transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor(),
                                                torchvision.transforms.Normalize(mean=[0.5], std=[0.5])])
preTrainData = torchvision.datasets.MNIST('./data/', train=True, transform=transform, download=True)
preTestData = torchvision.datasets.MNIST('./data/', train=False, transform=transform)

trainData = DataLoader(dataset=preTrainData, batch_size=BATCH_SIZE, shuffle=True)
testData = DataLoader(dataset=preTestData, batch_size=BATCH_SIZE)

NET.to(device)  # gpu或cpu

criterion = nn.CrossEntropyLoss()  # 交叉熵损失函数，与softmax激活函数一起用，一起构成softmax损失函数；分类器
optimizer = torch.optim.Adam(NET.parameters())  # 调用优化器Adam调整参数
history = {'Test Loss': [], 'Test Accuracy': []}  # 用字典来存储测试损失历史值和精度历史值

for epoch in range(EPOCHS):  # 循环10次
    processBar = tqdm(trainData, unit='batch_idx')  # 进度条过程可视化 
    NET.train(True)
    for batch_idx, (trainImgs, labels) in enumerate(processBar):  # 迭代器
        trainImgs = trainImgs.to(device)
        labels = labels.to(device)

        outputs = NET.forward(trainImgs)  # 输入图片数据，进行训练
        loss = criterion(outputs, labels)  # 计算损失值
        predictions = torch.argmax(outputs, dim=1)
        accuracy = torch.sum(predictions == labels) / labels.shape[0]

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        processBar.set_description("[%d/%d] Loss: %.4f, Acc: %.4f" %
                                   (epoch+1, EPOCHS, loss.item(), accuracy.item()))
        if batch_idx == len(processBar) - 1:
            correct, totalLoss = 0, 0
            NET.train(False)
            with torch.no_grad():
                for testImgs, labels in testData:
                    testImgs = testImgs.to(device)
                    labels = labels.to(device)
                    outputs = NET(testImgs)
                    loss = criterion(outputs, labels)

                    predictions = torch.argmax(outputs, dim=1)

                    totalLoss += loss
                    correct += torch.sum(predictions == labels)

                    testAccuracy = correct / (BATCH_SIZE * len(testData))
                    testLoss = totalLoss / len(testData)
                    processBar.set_description("[%d/%d] Loss: %.4f, Acc: %.4f, Test Loss: %.4f, Test Acc: %.4f" %
                                           (epoch+1, EPOCHS, loss.item(), accuracy.item(), testLoss.item(), testAccuracy.item()))
                testAccuracy = correct / (BATCH_SIZE * len(testData))
                testLoss = totalLoss / len(testData)
                history['Test Loss'].append(testLoss.item())
                history['Test Accuracy'].append(testAccuracy.item())

# 测试结果可视化
plt.subplot(1, 2, 1)
plt.plot(history['Test Loss'], color='red', label='Test Loss')
plt.legend(loc='best')
plt.grid(True)
plt.xlabel('EPOCHS')
plt.ylabel('testLoss')

plt.subplot(1, 2, 2)
plt.plot(history['Test Accuracy'], color='green', label='Test Accuracy')
plt.legend(loc='best')
plt.grid(True)
plt.xlabel('EPOCHS')
plt.ylabel('testAccuracy')
plt.show()

# 保存模型
torch.save(NET, './model.pth')