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  • model.eval() will notify all your layers that you are in eval mode, that way, batchnorm or dropout layers will work in eval mode instead of training mode.
  • torch.no_grad() impacts the autograd engine and deactivate it. It will reduce memory usage and speed up computations but you won’t be able to backprop (which you don’t want in an eval script).
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https://www.analyticsvidhya.com/blog/2017/09/pseudo-labelling-semi-supervised-learning-technique/

 

Introduction to Pseudo-Labelling : A Semi-Supervised learning technique

Introduction to pseudo-labeling and semi-supervised machine learning algorithms. We discuss basics of SSL with implementation code in Python.

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https://www.quora.com/What-is-an-explicit-and-implicit-solution-in-differential-equations

 

What is an explicit and implicit solution in differential equations?

Answer: Let's say that y is the dependent variable and x is the independent variable. An explicit solution would be y=f(x), i.e. y is expressed in terms of x only. An implicit solution is when you have f(x,y)=g(x,y) which means that y and x are mixed toget

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https://medium.com/@aptrishu/understanding-principle-component-analysis-e32be0253ef0

 

Understanding Principal Component Analysis

The purpose of this post is to give the reader detailed understanding of Principal Component Analysis with the necessary mathematical…

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Spectral Clustering

Foundation and Application

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https://discuss.pytorch.org/t/about-normalization-using-pre-trained-vgg16-networks/23560/2

 

About Normalization using pre-trained vgg16 networks

Usually if your use case stays in the same data domain, the mean and std won’t be that different and you can try to use the ImageNet statistics. I would recommend to use your own data statistics if you are dealing with another domain, e.g. medical images.

discuss.pytorch.org

 

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PyTorch Autograd

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