apeforest commented on issue #15288: [MXNET-978] Higher order gradient for sigmoid URL: https://github.com/apache/incubator-mxnet/pull/15288#issuecomment-507516653 I verified the result is the same as pytorch ``` import torch import numpy as np import math op = lambda x: torch.sigmoid(x) grad_op = lambda x: op(x) * (1 - op(x)) grad_grad_op = lambda x: grad_op(x) * (1 - 2 * op(x)) grad_grad_grad_op = lambda x: grad_grad_op(x) - 2 * ( grad_op(x)**2 + grad_grad_op(x) * op(x)) x = torch.tensor(np.array([1, 2, 3]), dtype=torch.float32) head_grads = torch.tensor(np.array([1, 1, 1]), dtype=torch.float32) * 0.5 head_grad_grads = torch.tensor(np.array([1, 1, 1]), dtype=torch.float32) * 0.6 head_grad_grad_grads = torch.tensor(np.array([1, 1, 1]), dtype=torch.float32) * 0.7 x.requires_grad = True head_grads.requires_grad = True y = op(x) x_grad = torch.autograd.grad(y, x, grad_outputs= head_grads, create_graph=True, retain_graph=True)[0] expected_grad_x = (grad_op(x) * head_grads).detach().numpy() print('expected_grad_x = {}'.format(expected_grad_x)) print('grad_x = {}'.format(x_grad.detach().numpy())) x_grad_grad = torch.autograd.grad(x_grad, x, grad_outputs= head_grad_grads, create_graph=True, retain_graph=True)[0] x_grad_grad.backward(head_grad_grad_grads) expected_grad_grad_x = (grad_grad_op(x) * head_grads * head_grad_grads).detach().numpy() expected_head_grad = (grad_op(x) * head_grad_grads).detach().numpy() expected_grad_grad_grad_x = (grad_grad_grad_op(x) * head_grads * head_grad_grads * head_grad_grad_grads).detach().numpy() print('expected_grad_grad_x = {}'.format(expected_grad_grad_x)) print('grad_grad_x = {}'.format(x_grad_grad.detach().numpy())) print('expected_grad_grad_grad_x = {}'.format(expected_grad_grad_grad_x)) print('grad_grad_grad_x = {}'.format(x.grad.detach().numpy())) ```
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