dboyliao

import torch

x = torch.tensor(0, requires_grad=True, dtype=torch.float64)
y = torch.tensor(0, requires_grad=True, dtype=torch.float64)
l = torch.tensor(0, requires_grad=True, dtype=torch.float64)
lr = 0.1

# min x^2+y^2 s.t x+y = 1
for i in range(100):
    L = x**2 + y**2 + l*(1-x-y)

sbarratt

def get_jacobian(net, x, noutputs):
    x = x.squeeze()
    n = x.size()[0]
    x = x.repeat(noutputs, 1)
    x.requires_grad_(True)
    y = net(x)
    y.backward(torch.eye(noutputs))
    return x.grad.data