用卷积神经网络代替全连接神经网络的代码
时间: 2023-10-22 16:20:27 浏览: 36
以下是一个使用卷积神经网络代替全连接神经网络的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
# 定义卷积神经网络模型
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
self.pool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = nn.Conv2d(32, 64, kernel_size=5)
self.pool2 = nn.MaxPool2d(kernel_size=2)
self.fc1 = nn.Linear(1024, 512)
self.fc2 = nn.Linear(512, 10)
def forward(self, x):
x = self.pool1(torch.relu(self.conv1(x)))
x = self.pool2(torch.relu(self.conv2(x)))
x = x.view(-1, 1024)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# 加载MNIST数据集
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transforms.ToTensor())
# 定义数据加载器
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=True)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
num_epochs = 5
model = ConvNet()
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))
# 测试模型
model.eval()
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
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: {} %'.format(100 * correct / total))
```