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I have 2 numpy arrays, which I convert into tensors to use the TensorDataset object. import torch.utils.data as data_utils X = np.zeros((100,30)) Y = np.zeros((100,30)) train = data_utils.TensorDataset(torch.from_numpy(X).double(), torch.from_numpy(Y)) train_loader = data_utils.DataLoader(train, batch_size=50, shuffle=True) when I do: for batch_idx, (data, target) in enumerate(train_loader): ...

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Such as this, I want to using some auxiliary loss to promoting my model performance. Which type code can implement it in pytorch? #one loss1.backward() loss2.backward() loss3.backward() optimizer.step() #two loss1.backward() optimizer.step() loss2.backward() optimizer.step() loss3.backward() optimizer.step() #three loss = loss1+loss2+loss3 loss.backward() optimizer.step() Thanks for ...

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