liaosw97 PyTorch 深度学习指南
模型定义
使用Module定义模型结构
import torch.nn as nn
class CNNClassifier(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
self.fc = nn.Linear(32 * 8 * 8, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2)
x = x.view(-1, 32 * 8 * 8)
x = self.fc(x)
return x
训练循环
标准训练步骤
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = CNNClassifier().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
for epoch in range(10):
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()