I started using Tensorflow/Keras for basics Neural Networks architectures such as Feed-Forward networks or RNN.

Although it is working well and there are plenty of information on how this works, in principle, on the internet, I could not find any direct explanation of Tensorflow/Keras source code.

When I have a look in the source directory of the package, there are thousands of files and there is virtually no way (at least for me) to find relevant information in this. It seems everything is highly nested and I can't find the code corresponding to the maths behind layers I call.

So I'd like someone to provide tips on how to find such information in Tensorflow/Keras code or any resource that comments the inner working of basic networks directly linked with the source of the API implementation.

CodePudding user response：

Well I am unsure Tensorflow/Keras has ever been made to be "used" for this purpose.

You may want to look at EpyNN.

This is an educational project which provides API, examples and documentation website. The source code is written to be read.

While this is basic compared to Tensorflow/Keras, what it provides has been validated against it. For identical configurations results are identical. So you could use that to understand, somehow, the necessary part of what's behind Tensorflow/Keras API for the basic use you mention.

Disclaimer: I am the main author of EpyNN.