What does doing .backward twice do?

[pinocchio (Rene Sandoval)]

need to read:

How does one obtain gradients as data efficiently?

the whole code just for reference: ''' This script is written to test: 1)what happens if we differentiate through pytorch's backward pass and to see if its consistent with the doing a second derivative ''' import torch import torch.nn as nn from grad_test_sympy import symbolic_test import sys from pdb import set_trace as st def test_does_backward_backward_match_second_derivative(experiment_type,grad_computation_type='dl_dw_zero_grad'): ''' 1)what happens if we differentiate th…

and

Clone and detach in v0.4.0

We’ll provide a migration guide when 0.4.0 is officially released. Here are the answers to your questions: tensor.detach() creates a tensor that shares storage with tensor that does not require grad. tensor.clone()creates a copy of tensor that imitates the original tensor's requires_grad field. You should use detach() when attempting to remove a tensor from a computation graph, and clone as a way to copy the tensor while still keeping the copy as a part of the computation graph it came from. …