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f4bffb7461
The sub-gradient of minimum norm is the least steep descent direction. ```python import torch x = torch.tensor([-2, -1, 0, 1, 2.], requires_grad=True) torch.relu(x).sum().backward() print(x.grad) # tensor([0., 0., 0., 1., 1.]) y = torch.tensor([-2, -1, 0, 1, 2.], requires_grad=True) torch.abs(y).sum().backward() print(y.grad) # tensor([-1., -1., 0., 1., 1.]) ``` (How can I request a reviewer? I don't have the button on the right) Pull Request resolved: https://github.com/pytorch/pytorch/pull/148658 Approved by: https://github.com/lezcano |
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| .. | ||
| amp_examples.rst | ||
| autograd.rst | ||
| broadcasting.rst | ||
| cpu_threading_runtimes.svg | ||
| cpu_threading_torchscript_inference.rst | ||
| cpu_threading_torchscript_inference.svg | ||
| cuda.rst | ||
| custom_operators.rst | ||
| ddp.rst | ||
| extending.func.rst | ||
| extending.rst | ||
| faq.rst | ||
| fsdp.rst | ||
| get_start_xpu.rst | ||
| gradcheck.rst | ||
| hip.rst | ||
| large_scale_deployments.rst | ||
| modules.rst | ||
| mps.rst | ||
| multiprocessing.rst | ||
| numerical_accuracy.rst | ||
| randomness.rst | ||
| serialization.rst | ||
| windows.rst | ||
