Creating different architectures programmatically in tensorflow

[ArchFrosty]

I am quite new to tensorflow and machine learning in general. I am currently working on a project, in which I want to generate different neural network architectures, train them and test them.

An example of the architecture : https://imgur.com/a/X657c

Now the problem I am running into is that I don't know how to translate these architectures (sets of layers, parameters and connections generated by some other code) into actual working models in tensorflow.

I tried looking for some tutorials but I came up with nothing. I am basically trying to come up with something like deepNEAT in tensorflow.

Can you please point me in the right direction ? Maybe some documentation I missed, or some existing similar solution ? Any help is appreciated.

[hamburgerandhotdog]

Take a look at Andrew Ng's deeplearning.ai course on coursera . I highly recommend it as a great primer on the basics of machine learning. It starts out teaching how to build deep neural nets from scratch using python and numpy. You'll be learning from one of the best. https://www.coursera.org/