Hi,
I am working on natural language entailments tasks with keras but despite some searching around I couldn't find any convincing example of an seq2seq with attention model in Keras (or a neural cache model for that matter)
On the other hand I just looked at pytorch for a couple of hours and dynamic/control flow operations seems really easy to code in pytorch .
Is everyone progressively moving to pytorch while only using tensorflow as a backend for keras ? Is the pytorch movement even spreading inside google ?
Is deepmind moving back to (py)torch?
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Re: Is deepmind moving back to (py)torch?
#2I'm not a Googler/Deepminder, so I can't comment about what goes on inside the company.
However, Google is making a hardware investment in Tensor Processing Units. These presumably offer hardware acceleration for the static computation graphs that TensorFlow produces, and PyTorch wouldn't be any good with them.
You're right that--as of this time of writing--there are no good seq2seq with attention models in Keras. I think there are a few attempts on Github, but I haven't tried them yet. I don't know anybody else that has tried seq2seq w/ attention in Keras yet either.
Additionally, TensorFlow has a seq2seq module, and it does come with an attention mechanism. See https://github.com/google/seq2seq/blob/master/seq2seq/models...
Anyway, I think the best thing for folks like you and me is to just keep using PyTorch for research work, and use TensorFlow for certain deployment scenarios where TensorFlow is superior--like mobile apps and Google Cloud.