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Post doesn't talk about the actual libraries, just the ecosystems surrounding them. TF has more layer types, parallelizes better, is easier to assemble w keras
by Kalanos 5y ago
Post doesn't talk about the actual libraries, just the ecosystems surrounding them.
TF has more layer types, parallelizes better, is easier to assemble w keras, and you don't have to recreate the optimizer when loading from disk. pytorch doesn't have metrics out of the box. TF all the way.
github.com/aiqc/aiqc
- SleekEagle 5y agoDo you think it's a bit easier to build custom layers in PyTorch though? Also, I think Lightning handles the issue of loading optimizers, but I'm not sure about that. It's nice to see TF get some love, but I still think PyTorch has easier debugging and is more pythonic which lowers the barrier to entry for a lot of people.
- dbish 5y agoCurious since I’ve been looking at this recently. What out of the box metrics would matter most to you? There are lots of libraries in the ecosystem for metrics but I’ve seen the request for built in metrics a few times now so it must be a clear need.
- Kalanos 5y agomostly just accuracy and r squared. i use lightning's torchmetrics.
- Kalanos 5y agochallenging to get multi-dimensional layer sizes right. torch is not compatible numpy as input, which is painful for pre and post processing. no metrics means no History object. no activation attribute for Linear layers and Softmax layer behaves weird.