本文共 3616 字，大约阅读时间需要 12 分钟。
PyTorch与torchvision的结合应用：CIFAR-10数据集的训练与测试
本文将介绍如何利用PyTorch和torchvision实现CIFAR-10数据集的高效训练与测试，并对模型的性能进行详细分析。
数据集准备与预处理
首先，我们需要准备CIFAR-10数据集。通过torchvision，我们可以直接下载并加载数据集。为了确保模型的泛化能力，需要对图像数据进行标准化处理。具体来说，我们采用如下预处理流程：
transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
训练集和测试集的加载
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)
模型设计与训练
本文设计了一个简单的卷积神经网络（CNN）作为模型架构：
class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(3, 6, 5) self.conv2 = nn.Conv2d(6, 16, 5) self.pool = nn.MaxPool2d(2, 2) self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 10) def forward(self, x): x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv2(x))) x = x.view(-1, 16 * 5 * 5) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x
损失函数与优化器的选择
采用交叉熵损失函数作为模型训练的目标函数，优化器选择随机梯度下降（SGD）算法：
criterion = nn.CrossEntropyLoss()optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
模型的训练过程
for epoch in range(10): running_loss = 0.0 for i, data in enumerate(trainloader, 0): inputs, labels = data optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() if (i + 1) % 2000 == 0: print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000)) running_loss = 0.0print("Finished Training") 模型的测试与评估
在测试阶段，我们首先加载测试数据集，并对模型的预测结果进行分析：
with torch.no_grad(): for data in testloader: images, labels = data outputs = net(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item()print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total)) 分类级别的准确率分析
为了更细致地了解模型在不同类别上的表现，我们对每个类别的准确率进行了统计：
class_correct = list(0. for _ in range(10))class_total = list(0. for _ in range(10))with torch.no_grad(): for data in testloader: images, labels = data outputs = net(images) _, predicted = torch.max(outputs.data, 1) c = (predicted == labels) for i in range(4): class_correct[labels[i]] += c[i].item() class_total[labels[i]] += 1for i in range(10): print('Accuracy of %s : %2d%%' % (classes[i], 100 * class_correct[i] / class_total[i])) GPU加速训练
为了充分发挥PyTorch的优势，我们可以将训练过程迁移到GPU上进行加速：
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")net.to(device)# 在训练过程中：for epoch in range(10): running_loss = 0.0 for i, data in enumerate(trainloader, 0): inputs, labels = data inputs, labels = inputs.to(device), labels.to(device) optimizer.zero_grad() outputs = net(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() running_loss += loss.item() if (i + 1) % 2000 == 0: print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000)) running_loss = 0.0print("Finished Training") 以上是本文的完整实现过程及结果分析，涵盖了从数据集准备到模型训练与测试的全过程，并对模型的性能进行了详细评估。
转载地址：http://wkgfk.baihongyu.com/