Notes on Pytorch

Notes on Pytorch

基础

Why GPU

GPU擅长处理可以并行化的任务，比如渲染，而AI模型的一些计算操作，比如池化、卷积是可以高度并行化的，因为计算的先后并不会影响最终的结果，因此训练模型适合用GPU

常用的torch包

Package	Description
torch	The top-level PyTorch package and tensor library.
torch.nn	A subpackage that contains modules and extensible classes for building neural networks.
torch.autograd	A subpackage that supports all the differentiable Tensor operations in PyTorch.
torch.nn.functional	A functional interface that contains typical operations used for building neural networks like loss functions, activation functions, and convolution operations.
torch.optim	A subpackage that contains standard optimization operations like SGD and Adam.
torch.utils	A subpackage that contains utility classes like data sets and data loaders that make data preprocessing easier.
torchvision	A package that provides access to popular datasets, model architectures, and image transformations for computer vision.

动态计算图(Dynamic Computing Graph)

能够在运行时动态地构建计算图

Tensor

• 对于多维tensor来说，其每个维度的长度要相同

• rank：表示tensor的维数
• axis：用于指定特定的tensor维度