如何使我的数据适应这种线性矩阵pytorch优化方法？

Question

我尝试我的代码不起作用，我正在使用一个简单的数据集：

epochs = 100 
losses = [] 
for i in range(epochs):   
  y_pred = model.forward(X)   
  loss = criterion(y_pred, y)   
  print("epoch:", i, "loss:", loss.item())      
  losses.append(loss)     
  optimizer.zero_grad()   
  loss.backward()   
  optimizer.step()

谢谢，

菲利普。

Answer 1

您可以使用自定义数据生成器加载数据集

train_dataset = DatasetGenerator()

或加载的数据

inps = torch.arange(10 * 5, dtype=torch.float32).view(10, 5)
tgts = torch.arange(10 * 5, dtype=torch.float32).view(10, 5)
train_dataset = TensorDataset(inps, tgts)


train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)

epochs = 100 
losses = [] 
for i in range(epochs):   
    for ii, (data, target) in enumerate(train_loader):
        y_pred = model(data)  
        optimizer.zero_grad()
        loss = criterion(y_pred, target)
        losses.append(loss) 
        loss.backward()
        optimizer.step()