# Understanding gradient computation using backward() in PyTorch

I’m trying to understand the basic pytorch autograd system: x = torch.tensor(10., requires_grad=True) print(‘tensor:’,x) x.backward() print(‘gradient:’,x.grad) output: tensor: tensor(10., requires_grad=True) gradient: tensor(1.) since x is a scalar constant and no function is applied to it, I expected 0. as the gradient output. Why is the gradient 1. instead? >Solution : Whenever you are using value.backward(),… Read More Understanding gradient computation using backward() in PyTorch