Best practice to test (unit and integration) a neural network implementation 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)
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.
Train a standard model on a standard dataset and make sure the accuracy numbers are similar.
See here for an in-depth discussion, with more and better advice than I've been able to provide.