使用pytorch训练全连接神经网络的代码

以下是使用 PyTorch 训练全连接神经网络的代码：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义全连接神经网络
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(in_features=784, out_features=256)
        self.fc2 = nn.Linear(in_features=256, out_features=128)
        self.fc3 = nn.Linear(in_features=128, out_features=10)

    def forward(self, x):
        x = x.view(-1, 784)
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 加载MNIST数据集
trainset = torch.utils.data.DataLoader(
    torchvision.datasets.MNIST(root='./data', train=True, download=True,
                               transform=torchvision.transforms.Compose([
                                   torchvision.transforms.ToTensor(),
                                   torchvision.transforms.Normalize((0.1307,), (0.3081,))
                               ])), batch_size=64, shuffle=True)

# 定义损失函数和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

# 训练神经网络
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainset, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0
print('Finished Training')

在这个例子中，我们定义了一个三层全连接神经网络，使用 MNIST 数据集进行训练。我们使用交叉熵损失函数和随机梯度下降优化器进行训练。训练过程中，我们迭代了 10 个 epoch，每个 epoch 中进行多次 mini-batch 训练。