在結束 Keras 篇章時，特別介紹『派生火炬』︰

PyTorch is a python package that provides two high-level features:

Tensor computation (like numpy) with strong GPU acceleration
Deep Neural Networks built on a tape-based autograd system

You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed.

At a granular level, PyTorch is a library that consists of the following components:

Package	Description
torch	a Tensor library like NumPy, with strong GPU support
torch.autograd	a tape based automatic differentiation library that supports all differentiable Tensor operations in torch
torch.nn	a neural networks library deeply integrated with autograd designed for maximum flexibility
torch.optim	an optimization package to be used with torch.nn with standard optimization methods such as SGD, RMSProp, LBFGS, Adam etc.
torch.multiprocessing	python multiprocessing, but with magical memory sharing of torch Tensors across processes. Useful for data loading and hogwild training.
torch.utils	DataLoader, Trainer and other utility functions for convenience
torch.legacy(.nn/.optim)	legacy code that has been ported over from torch for backward compatibility reasons

Usually one uses PyTorch either as:

A replacement for numpy to use the power of GPUs.
a deep learning research platform that provides maximum flexibility and speed

Elaborating further:

A GPU-ready Tensor library

If you use numpy, then you have used Tensors (a.k.a ndarray).

tensor_illustration

PyTorch provides Tensors that can live either on the CPU or the GPU, and accelerate compute by a huge amount.

We provide a wide variety of tensor routines to accelerate and fit your scientific computation needs such as slicing, indexing, math operations, linear algebra, reductions. And they are fast!

Dynamic Neural Networks: Tape based Autograd

PyTorch has a unique way of building neural networks: using and replaying a tape recorder.

Most frameworks such as TensorFlow, Theano, Caffe and CNTK have a static view of the world. One has to build a neural network, and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch.

With PyTorch, we use a technique called Reverse-mode auto-differentiation, which allows you to change the way your network behaves arbitrarily with zero lag or overhead. Our inspiration comes from several research papers on this topic, as well as current and past work such as autograd, autograd, Chainer, etc.

While this technique is not unique to PyTorch, it’s one of the fastest implementations of it to date. You get the best of speed and flexibility for your crazy research.

dynamic_graph

Python first

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc. You can write your new neural network layers in Python itself, using your favorite libraries and use packages such as Cython and Numba. Our goal is to not reinvent the wheel where appropriate.

‧ 安裝