LeNet-5 手写数字识别模型训练指南 - PyTorch 实现

{ "title": "LeNet-5 手写数字识别模型训练指南 - PyTorch 实现", "description": "本指南详细介绍了使用 PyTorch 训练 LeNet-5 模型识别手写数字的步骤，包括环境准备、数据加载、模型定义、训练和测试。", "keywords": "LeNet-5, 手写数字识别, PyTorch, 深度学习, 卷积神经网络, MNIST 数据集, 训练, 测试, 代码示例", "content": """"\n# 环境准备\n\n首先，确保你已经安装了以下库：\n- PyTorch：用于构建和训练神经网络模型\n- torchvision：用于加载和预处理图像数据集\n\n你可以使用以下命令安装这些库：\n\nshell\npip install torch torchvision\n\n\n# 导入库和数据集\n\n首先，让我们导入所需的库并加载 MNIST 手写数字数据集。PyTorch 的 torchvision 库提供了方便的函数来加载和预处理 MNIST 数据集。\n\npython\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision.transforms as transforms\nfrom torchvision.datasets import MNIST\nfrom torch.utils.data import DataLoader\n\n# 设置随机种子以确保可重复性\ntorch.manual_seed(42)\n\n# 定义训练和测试数据的转换器\ntransform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.1307,), (0.3081,))\n])\n\n# 加载 MNIST 数据集\ntrain_dataset = MNIST(root='data/', train=True, transform=transform, download=True)\ntest_dataset = MNIST(root='data/', train=False, transform=transform)\n\n# 创建数据加载器\ntrain_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\ntest_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n\n\n# 定义 LeNet-5 模型\n\n接下来，我们定义 LeNet-5 模型。LeNet-5 由卷积层、池化层和全连接层组成。\n\npython\nclass LeNet5(nn.Module):\n def __init__(self):\n super(LeNet5, self).__init__()\n self.conv1 = nn.Conv2d(1, 6, kernel_size=5)\n self.avgpool1 = nn.AvgPool2d(kernel_size=2, stride=2)\n self.conv2 = nn.Conv2d(6, 16, kernel_size=5)\n self.avgpool2 = nn.AvgPool2d(kernel_size=2, stride=2)\n self.fc1 = nn.Linear(16 * 4 * 4, 120)\n self.fc2 = nn.Linear(120, 84)\n self.fc3 = nn.Linear(84, 10)\n\n def forward(self, x):\n x = self.avgpool1(torch.relu(self.conv1(x)))\n x = self.avgpool2(torch.relu(self.conv2(x)))\n x = x.view(x.size(0), -1)\n x = torch.relu(self.fc1(x))\n x = torch.relu(self.fc2(x))\n x = self.fc3(x)\n return x\n\n# 创建 LeNet-5 模型实例\nmodel = LeNet5()\n\n\n# 定义损失函数和优化器\n\n我们使用交叉熵损失函数和随机梯度下降（SGD）优化器来训练 LeNet-5 模型。\n\npython\n# 定义损失函数和优化器\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n\n\n# 训练模型\n\n接下来，我们定义训练函数来训练 LeNet-5 模型。\n\npython\ndef train(model, train_loader, criterion, optimizer, num_epochs):\n model.train()\n\n for epoch in range(num_epochs):\n running_loss = 0.0\n for i, (inputs, labels) in enumerate(train_loader):\n optimizer.zero_grad()\n\n # 前向传播和计算损失\n outputs = model(inputs)\n loss = criterion(outputs, labels)\n\n # 反向传播和优化\n loss.backward()\n optimizer.step()\n\n running_loss += loss.item()\n\n if (i + 1) % 100 == 0:\n print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, i + 1, len(train_loader), running_loss / 100))\n running_loss = 0.0\n\n# 训练模型\ntrain(model, train_loader, criterion, optimizer, num_epochs=10)\n\n\n# 测试模型\n\n最后，我们定义测试函数来评估 LeNet-5 模型的性能。\n\npython\ndef test(model, test_loader):\n model.eval()\n correct = 0\n total = 0\n\n with torch.no_grad():\n for inputs, labels in test_loader:\n outputs = model(inputs)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted == labels).sum().item()\n\n accuracy = 100 * correct / total\n print('Test Accuracy: {:.2f}%'.format(accuracy))\n\n# 测试模型\ntest(model, test_loader)\n\n\n这就是使用 PyTorch 实现 LeNet-5 模型的示例代码和训练文档。你可以根据自己的需求进行修改和扩展。\n"""\n