Last active
October 20, 2018 18:53
-
-
Save lfrati/d271fa868b6373d1c5af968af5951ad7 to your computer and use it in GitHub Desktop.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| """Toy examples for torch.autograd.grad.""" | |
| import torch | |
| from torch.autograd import Variable | |
| # Input | |
| x = torch.tensor([1., 2., 3.], requires_grad=True) | |
| # Weights | |
| w1 = torch.tensor([2., 3., 4.]), requires_grad=True) | |
| w2 = torch.tensor([3., 4., 5.]), requires_grad=True) | |
| # Create a computational graph | |
| h = w1 * x | |
| y = w2 * h | |
| # Compute dy/dh. Since dy/dh is w2, it returns [3, 4, 5]. | |
| # If y is a vector, you should provide torch.ones(y.size()). If y is a scalar, you can skip the "grad_outputs" arguments. | |
| dydh = torch.autograd.grad(outputs=y, | |
| inputs=h, | |
| grad_outputs=torch.ones(y.size()), | |
| retain_graph=True) # To prevent buffer initialization | |
| # Compute dh/dx. Since dh/dx is w1, it returns [2, 3, 4]. | |
| dhdx = torch.autograd.grad(outputs=h, | |
| inputs=x, | |
| grad_outputs=torch.ones(h.size())) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment