CODEWITHSUNDEEP
pythonvisual-studiodebuggingtensorflow
AOK
AOK

Reputation: 503

Inspecting the values of constant or variable tensors during debug

I implemented a model in TensorFlow (Python) that I previously programmed in C++ using Eigen, where it worked as expected. But the model is not working as expected in Python, and it's probably because I am defining tensors incorrectly or I am mixing up dimensions.

I am trying to get a feel for the problems by using Visual Studio's (2017) debugger (if a different IDE is better for this then I'm all ears, but I would prefer to stick with VS), but tensors do not evaluate to anything - and I can understand this because the tensor defines an operation and not a data object (well it only produces a data object after calling a session.run).

However, constant and variable tensors - and any other tensors built solely on top of such tensors - come with predefined data. So hey, why not be able to inspect the value through the debugging UI?

So my question: is there a way to inpect the data with some extension?

For example, if I was working in C++ and with Eigen, I can use Eigen.natvis as described here. Anything similar for TensorFlow? It's not just a matter of seeing the evaluated value, either. It would be nice to see things like the shape, etc... while debugging.

I would also be open to other debugging techniques of TensorFlow code, if anyone has a good suggestion.

Upvotes: 3

Views: 3185

Answers (1)

javidcf
javidcf

Reputation: 59691

TensorFlow includes tfdbg, a debugger for TensorFlow models, where you can step through each execution step, check values, stop on NaN, etc. See the programmer's guide TensorFlow Debugger and The Debugger Dashboard for more information.

tfdbg can be a bit cumbersome to setup and use though. A quick alternative to check intermediate values is to use tf.Print operations. TensorFlow includes a few other debugging operations that you may find useful to check for some basic things.

EDIT: Another tool that can be useful is eager execution. This allows you to use TensorFlow operations as if they were regular Python operations (they return the result of the operation instead of the graph object), so it is a good way to check if some particular code does what you expect.

Upvotes: 4

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