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Recently I found that a lot of TF2.0 Keras' functionaly does not support eager execution. This makes Pytorch still significantly easier to prototype with than T
by abakus 7y ago
Recently I found that a lot of TF2.0 Keras' functionaly does not support eager execution. This makes Pytorch still significantly easier to prototype with than TF2.0.
If you miss Keras' way of defining NNs, you can use PyWarm: https://github.com/blue-season/pywarm https://github.com/blue-season/pywarm which offers a fully functional NN building API for pytorch.
- choppaface 7y agoDoes pytorch have a Tensorboard equivalent? For rapid prototyping, I find Tensorboard a lot more useful than, say, print statements and such that you can get through eager execution. Tensorboard is also crucial for post-hoc analysis, and the Summary format is clean enough to use as a primary data artifact (e.g. use loss recorded in summaries versus some alternative hand-crafted text file).
- smhx 7y agoIt has TensorBoard integration itself (which can be installed independently). https://pytorch.org/docs/stable/tensorboard.html https://pytorch.org/docs/stable/tensorboard.html
- abakus 7y agoThere is also tensorboardX: https://github.com/lanpa/tensorboardX https://github.com/lanpa/tensorboardX
- wdroz 7y ago"Visdom" [0] isn't well know, but it's powerful and easy to use. You can even centralize remote multiple experiments in the same dashboard. Very useful for following in real-time what is happening to your networks. [0] - https://github.com/facebookresearch/visdom https://github.com/facebookresearch/visdom