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| d = 3 | |
| q = torch.rand(d) | |
| q /= q.sum() # empirical distribution (soft labels) | |
| theta = torch.randn(d, requires_grad=True) | |
| p = F.softmax(theta, dim=0) | |
| loss = -torch.sum(q*torch.log(p)) | |
| g = p - q | |
| H = torch.diag(p) - outer(p) | |
| torch.allclose(H, hessian(loss, theta)) |
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