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Clarifying exceptions and visualizing tensor operations in deep learning code

explained.ai

11–13 of 13 posts

Re: Clarifying exceptions and visualizing tensor operations in deep learning code

#11
post #10

This looks really neat, but I've only felt the need for such a tool with tensorflow. With pytorch and numpy you can just use ipdb or any other debugger to be parachuted right at the exception and inspect shape and value of all the variables you need.

True, but you have to do it one at a time to look at the variables. Also, the exception message doesn't tell you which operator among several is the issue. This just makes it easier. :)

Of course, but debugging is a general skill that will work in every case.

Not trying to detract anything from this tool, I'll definitely give it a try it next time I have something that doesn't work :)

Re: Clarifying exceptions and visualizing tensor operations in deep learning code

#13
post #12

I believe problems solved by this tool generally disappear when using named tensors

Are people using named tensors a lot these days? I haven't seen a lot of code using them but I also haven't been looking at a lot of tensor code lately.
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