Implementation Problem of Machine Learning Theories!

Hey Guys, I am having a problem here, which bugs me now and then and it has been for about two months or so. You see, the problem is that I happen to learn a new concept in machine learning. Let's say, I learned about Generative Adversarial Network(Ian Goodfellow). Really good concept and I get its core point. I am happy that I understood the theory part, but the problem is in its implementation. I have been using Tensorflow for about 2 months now. Beginner? Yes. But, I am decent in Python. Not that expert, but I can label myself decent. But this tensorflow implementation, is what the impediment is. How can I implement them in tensorflow once I understand the core concepts? There is no decent any decent way to start them? Sometimes, I feel like using Keras and sometimes I think it is not flexible, then I go to build the architecture from scratch and again I feel like I cannot do it and then it keeps repeating. I wouldn't be making a post if this was not a serious issue but now I am tired. I cannot find a right way on how I can implement them. Okay, I somehow get the code in github and then try doing it myself by learning the code. Its okay, it works. But, what's the point if I have completely understood the theory but cannot implement them myself? . I kind of like remember the code they use for one code and then it doesn't work on other. Like even if I memorize or somehow manage to do the Generative Adversarial Network(with the help from github) ,then once I learn the autoencoder or something new, then I have to depend on other code again. I am sorry if I am redundant ,but please kindly help me out of this mess!