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I'm implementing my own neural network (with the term implementing i mean writing the code that run the neural network itself, not training).

I implemented it for didactical purpose, but i does not know how to proceede for a validation of what i have done.

So i would ask if there is some best practice for neural network test (eg : is there a dataset of neural nets with weight, input and expected output)

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    $\begingroup$ You can try your neural net on MNIST for sure... Also you can verify your gradient calculation $\endgroup$ – Aditya Oct 7 '18 at 17:54
  • $\begingroup$ @Aditya : sorry i have not understand your comment. Isn't MINST a dataset ? At the moment i have only the raw implementation of a network nothing more. I would like to test it before proceede with the code writing, and discover at the end of develop process that my implementation have flaw in the activation function (for example) $\endgroup$ – Skary Oct 7 '18 at 18:10
  • $\begingroup$ Please note that I've edited my answer, to add a link to this discussion which is highly pertinent, and frankly better than my existing answer. $\endgroup$ – shadowtalker Oct 11 '18 at 10:24
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  1. Make your code testable. You should be able to write plenty of tests for individual components without having to mock up an entire training pipeline for each one. A "property-based testing" library like Hypothesis can be helpful for this.

  2. Train a standard model on a standard dataset and make sure the accuracy numbers are similar.

  3. See here for an in-depth discussion, with more and better advice than I've been able to provide.

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