Pytorch深度学习——用卷积神经网络实现MNIST数据集分类

学习CV的研一小白

已于 2025-04-03 17:52:01 修改

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分类专栏： PyTorch学习笔记文章标签： pytorch 深度学习 cnn

于 2022-01-24 17:42:19 首次发布

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选择下图结构的卷积神经网络来进行训练：

步骤：

选择 5 x 5 的卷积核，输入通道为 1，输出通道为 10：此时图像矩阵经过 5 x 5 的卷积核后会小两圈，也就是4个数位，变成 24 x 24，输出通道为10；
选择 2 x 2 的最大池化层：此时图像大小缩短一半，变成 12 x 12，通道数不变；
再次经过 5 x 5 的卷积核，输入通道为 10，输出通道为 20：此时图像再小两圈，变成 8 *8，输出通道为20；
再次经过 2 x 2 的最大池化层：此时图像大小缩短一半，变成 4 x 4，通道数不变；
最后将图像整型变换成向量，输入到全连接层中：输入一共有 4 x 4 x 20 = 320 个元素，输出为 10.

具体代码如下：

1 准备数据集

# 准备数据集
batch_size = 64
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])
train_dataset = datasets.MNIST(root='../dataset/mnist/',
                               train=True,
                               download=True,
                               transform=transform)
train_loader = DataLoader(train_dataset,
                          shuffle=True,
                          batch_size=batch_size)
test_dataset = datasets.MNIST(root='../dataset/mnist',
                              train=False,
                              download=True,
                              transform=transform)
test_loader = DataLoader(test_dataset,
                         shuffle=False,
                         batch_size=batch_size)

2 建立模型

class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)
        self.pooling = torch.nn.MaxPool2d(2)
        self.fc = torch.nn.Linear(320, 10)

    def forward(self, x):
        batch_size = x.size(0)
        x = F.relu(self.pooling(self.conv1(x)))
        x = F.relu(self.pooling(self.conv2(x)))
        x = x.view(batch_size, -1)
        x = self.fc(x)
        return x


model = Net()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

3 构造损失函数+优化器

criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

4 训练+测试

def train(epoch):
    running_loss = 0.0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        inputs,target=inputs.to(device),target.to(device)
        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d,%.5d] loss:%.3f' % (epoch + 1, batch_idx + 1, running_loss / 2000))
            running_loss = 0.0

def test():
    correct=0
    total=0
    with torch.no_grad():
        for data in test_loader:
            inputs,target=data
            inputs,target=inputs.to(device),target.to(device)
            outputs=model(inputs)
            _,predicted=torch.max(outputs.data,dim=1)
            total+=target.size(0)
            correct+=(predicted==target).sum().item()
    print('Accuracy on test set:%d %% [%d%d]' %(100*correct/total,correct,total))

if __name__ =='__main__':
    for epoch in range(10):
        train(epoch)
        test()

5 完整代码+运行结果

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim

# 准备数据集
batch_size = 64
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])
train_dataset = datasets.MNIST(root='../dataset/mnist/',
                               train=True,
                               download=True,
                               transform=transform)
train_loader = DataLoader(train_dataset,
                          shuffle=True,
                          batch_size=batch_size)
test_dataset = datasets.MNIST(root='../dataset/mnist',
                              train=False,
                              download=True,
                              transform=transform)
test_loader = DataLoader(test_dataset,
                         shuffle=False,
                         batch_size=batch_size)


class Net(torch.nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)
        self.pooling = torch.nn.MaxPool2d(2)
        self.fc = torch.nn.Linear(320, 10)

    def forward(self, x):
        batch_size = x.size(0)
        x = F.relu(self.pooling(self.conv1(x)))
        x = F.relu(self.pooling(self.conv2(x)))
        x = x.view(batch_size, -1)
        x = self.fc(x)
        return x


model = Net()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)


def train(epoch):
    running_loss = 0.0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        inputs,target=inputs.to(device),target.to(device)
        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d,%.5d] loss:%.3f' % (epoch + 1, batch_idx + 1, running_loss / 2000))
            running_loss = 0.0

def test():
    correct=0
    total=0
    with torch.no_grad():
        for data in test_loader:
            inputs,target=data
            inputs,target=inputs.to(device),target.to(device)
            outputs=model(inputs)
            _,predicted=torch.max(outputs.data,dim=1)
            total+=target.size(0)
            correct+=(predicted==target).sum().item()
    print('Accuracy on test set:%d %% [%d%d]' %(100*correct/total,correct,total))

if __name__ =='__main__':
    for epoch in range(10):
        train(epoch)
        test()

运行结果如下：