Artificial Intelligence: PyTorch Deep Learning Framework

Today I will explain to you some basic knowledge of the PyTorch deep learning framework. I hope it will be helpful to everyone in understanding PyTorch!

1. PyTorch

PyTorch is a Python machine learning framework based on Torch. It was developed by Facebook's artificial intelligence research team in 2016. It solved the problem of Torch's low popularity due to the use of the Lua programming language, so it was implemented using the Python programming language, which is very widely integrated.

2. PyTorch commonly used toolkit

• torch: a general array library similar to Numpy, which can convert tensor types to (torch.cuda.TensorFloat) and supports Compute on GPU.
• torch.autograd: A package mainly used to build computational graphs and automatically obtain gradients
• torch.nn: Neural network library with common layers and cost functions
• torch. optim: Optimization package with general optimization algorithms (such as SGD, Adam, etc.)
• torch.utils: Data loader. Has trainers and other convenient functions
• torch.legacy(.nn/.optim): Legacy code ported from Torch for backward compatibility reasons
• torch.multiprocessing: python more Process concurrency enables memory sharing of torch Tensors between processes

3. PyTorch features

• Dynamic neural structure: PyTorch uses a reverse automatic derivation technology. You can arbitrarily change the behavior of the neural network with zero delay, avoiding the trouble of having to start from scratch if you need to adjust the neural network structure after the neural network is built. Using PyTorch greatly saves manpower and time costs.
• Debug is easy to debug: PyTorch’s design idea is linear, intuitive and easy to use. When you execute a line of code, you execute it step by step without the tediousness of asynchronous debugging, so when a bug appears in the code, By easily locating the code, you can avoid the trouble of query problems caused by bugs, incorrect pointing or asynchronous and opaque engines.
• The code is concise and easy to understand: PyTorch's code is more concise and easier to read than Tensorflow, and the source code of PyTorch itself is much friendlier to read, making it easier to understand PyTorch in depth.
• Highly active community: PyTorch has a very active community and forum (discuss.pytorch.org). Its documentation (pytorch.org) is very clear, and beginners can get started quickly; and it keeps pace with the PyTorch version and provides a complete set of tutorials. PyTorch is very simple to use, so the learning cost is relatively low.

4. Shortcomings of PyTorch

• Weak visual monitoring capabilities: Lack of directly available monitoring and visualization interfaces:
• Not as widely used as TensorFlow: PyTorch is not End-to-end machine learning development tool; actual application development requires converting PyTorch code to another framework, such as Caffe2, and deploying the application to servers, workstations, and mobile devices.

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