pytorch全连接神经网络搭建

以下是使用PyTorch搭建全连接神经网络的基本步骤：

1. 导入必要的库

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

2. 定义神经网络模型

这里我们定义一个简单的全连接神经网络，包含3层，每层分别为输入层、隐藏层和输出层：

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 256)
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 10)

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

3. 定义损失函数和优化器

model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

4. 加载数据

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False)

5. 训练模型

for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 1000 == 999:    # 每1000个batch打印一次loss
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 1000))
            running_loss = 0.0

6. 测试模型

correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        outputs = model(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))

这样就可以使用PyTorch搭建一个简单的全连接神经网络了。需要注意的是，如果要使用GPU加速训练，可以将模型和数据移动到GPU上，如：

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