在PyTorch中搭建卷积神经网络通常涉及以下步骤：
- 导入必要的库和模块：
import torch
import torch.nn as nn
import torch.nn.functional as F
- 定义卷积神经网络模型类：
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(32*7*7, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, kernel_size=2, stride=2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, kernel_size=2, stride=2)
x = x.view(-1, 32*7*7)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
- 实例化模型类并定义损失函数和优化器：
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
- 训练模型：
for epoch in range(num_epochs):
for images, labels in train_loader:
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
- 测试模型：
correct = 0
total = 0
with torch.no_grad():
for images, labels in test_loader:
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = correct / total
print('Accuracy: {:.2f}%'.format(100 * accuracy))
以上是一个简单的卷积神经网络的搭建过程，你可以根据具体的任务和数据集自行调整网络结构和超参数。
