[In-depth understanding of PyTorch] PyTorch automatic derivation: tensor gradient calculation, backpropagation and the use of optimizers

PyTorch automatic derivation: Tensor gradient calculation, back propagation and use of optimizer

PyTorch is an open source deep learning framework that provides a powerful automatic derivation mechanism that allows us to easily calculate the gradient of tensors, perform backpropagation, and use optimizers to update model parameters. In this article, we will explain PyTorch's automatic derivation mechanism and related concepts in detail.

Gradient Computation of Tensors

In PyTorch, tensor is the basic unit of storing and transforming data. Each tensor has an requires_gradattribute that indicates whether gradients need to be computed. By default, this property is False, i.e. no gradients are computed. We can requires_grad=Trueenable gradient calculation by setting.

import torch

x = torch.tensor(