Change if autograd should record operations on this tensor: sets this tensor’s requires_grad attribute in-place. Returns this tensor.
requires_grad_()’s main use case is to tell autograd to begin recording operations on a Tensor tensor. If tensor has requires_grad=False (because it was obtained through a DataLoader, or required preprocessing or initialization), tensor.requires_grad_() makes it so that autograd will begin to record operations on tensor.
>>> # Let's say we want to preprocess some saved weights and use >>> # the result as new weights. >>> saved_weights = [0.1, 0.2, 0.3, 0.25] >>> loaded_weights = torch.tensor(saved_weights) >>> weights = preprocess(loaded_weights) # some function >>> weights tensor([-0.5503, 0.4926, -2.1158, -0.8303]) >>> # Now, start to record operations done to weights >>> weights.requires_grad_() >>> out = weights.pow(2).sum() >>> out.backward() >>> weights.grad tensor([-1.1007, 0.9853, -4.2316, -1.6606])
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