将上面代码写在一起,保证可以运行
时间: 2024-03-24 14:38:52 浏览: 32
好的,下面是将两个卷积层的代码与原来的代码整合在一起,可以直接运行:
```python
import torch
import torch.nn as nn
import torchvision
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
super(ConvBlock, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = ConvBlock(3, 32)
self.conv2 = ConvBlock(32, 64)
self.fc = nn.Linear(64 * 28 * 28, 10)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
transform = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.5,), (0.5,))
])
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)
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.01, momentum=0.9)
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 % 100 == 99:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
print('Finished Training')
correct = 0
total = 0
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 on test images: %d %%' % (100 * correct / total))
```