117笔记问答要使用PyTorch构建一个简单的MNIST分类模型，你可以按照以下步骤进行：
- 导入所需库：
import torch import torch.nn as nn import torch.optim as optim import torchvision import torchvision.transforms as transforms
- 定义模型结构：
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(28 * 28, 128)
self.fc2 = nn.Linear(128, 64)
self.fc3 = nn.Linear(64, 10)
self.dropout = nn.Dropout(0.5)
def forward(self, x):
x = x.view(-1, 28 * 28)
x = torch.relu(self.fc1(x))
x = self.dropout(x)
x = torch.relu(self.fc2(x))
x = self.dropout(x)
x = self.fc3(x)
return x
- 下载并加载MNIST数据集：
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True) testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform) testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)
- 初始化模型、损失函数和优化器：
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Net().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
- 训练模型：
num_epochs = 10
for epoch in range(num_epochs):
for i, data in enumerate(trainloader, 0):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
print(f"Epoch {epoch + 1}, Loss: {loss.item():.4f}")
- 测试模型：
model.eval()
with torch.no_grad():
correct = 0
total = 0
for data in testloader:
images, labels = data
images, labels = images.to(device), labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f"Accuracy of the network on the test images: {100 * correct / total:.2f}%")
这个简单的例子展示了如何使用PyTorch构建一个基本的MNIST分类模型。你可以根据需要调整模型结构、损失函数和优化器参数以获得更好的性能。