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TensorFlow Filesystem – Access Tensors Differently

TensorFlow Filesystem – Access Tensors Differently

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Tensorflow is great. Really, I mean it. The problem is it’s great up to a point. Sometimes you want to do very simple things, but tensorflow is giving you a hard time. The motivation I had behind writing TFFS (TensorFlow File System) can be shared by anyone who has used tensorflow, including you.

All I wanted was to know what the name of a specific tensor is; or what its input tensors are (ignoring operations).

All of these questions can be easily answered using tensorboard. Sure, you just open the graph tab, and visually examine the graph. Really convenient, right? Well, only if you want to have a bird overview of the graph. But if you’re focused and have a specific question you want to answer, using the keyboard is the way to go.

So why not load the graph inside a python shell and examine it? That’s doable, but writing these lines of code every time I want to do that task? Having to remember how to load a graph, how to look for a tensor, how to get its inputs… Sure, it’s only a couple of lines of code, but once you repeat the same task over and over again, it’s time to write a script!

So why not write an interactive script? You mean a script that given the path to your model loads it for you, and provides utility functions to ease your pain of writing tensorflow code? Well, we could do that, but that’s not gonna be as awesome as what I’m gonna show you!

Disclaimer: if you want a solution that makes sense, stop reading here and just use the interactive script approach. Continue reading only if you want to learn something a bit different 😉

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