最近訓練嘗試 SGD

這邊做一個註記
發現過擬合一直都是難解的問題啊~~
尤其在小資料上

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

# 定義一個簡單的神經網絡模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(10, 50)
        self.fc2 = nn.Linear(50, 2)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 創建模型實例
model = Net()

# 定義訓練數據和標簽
input_data = torch.randn(32, 10)
labels = torch.randn(32, 2)

# 定義損失函數
criterion = nn.MSELoss()

# 定義優化器，同時添加權重衰減
optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=0.001)  # 設置 weight_decay 參數來添加權重衰減項

# 進行訓練
for epoch in range(num_epochs):
    # 前向傳播
    outputs = model(input_data)
    loss = criterion(outputs, labels)
    
    # 反向傳播
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # 打印損失值
    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

感謝 chat GPT