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The current function's documentation is here. It is basically a TF layer that takes in inputs from the encoder and decoder to produce the final state of the decoder which is eventually used to provide the final word probabiliies/predictions.
The refactor should make sure that we're still using a sequence to sequence model but open to slight changes in the network architecture depending on what the new tensorflow functions need. Also, ideally we'd like to keep the data input format (described in the README) the same.
I'd also like to add, all the modifications and tests should either be performed on the python3-upgrade branch or some another branch that is directly derived from python3-upgradenot master.
Lastly, one might notice that the tensorflow library is imported as tensorflow.compat.v1, this is to follow the official migration guidelinehere. For now, we want to keep this import exactly like this so we can just emulate the tensorflow 1.0 functions. Instead of changing the functions to new tensorflow 2.0 ones, importing the library with this name ensures everything stays the same and works the same.
Except for contrib ofcourse, that's what this issue is about :)
@adeshpande3 Hello again, I rolled out a bunch of changes and ported the tensorflow 1.0 functions to tensorflow 2.0 over in the python3-upgrade branch. Right now, the functions do work, although I'm not sure if they are working as intended. I personally don't have a rig to be able to train the AI, could you please check if the behaviour of seq2seq.py is correct?
In the Seq2Seq.py file we currently use tf.contrib.legacy_seq2seq.embedding_rnn_seq2seq. We'd like to move off of this layer because it's a legacy layer and because it doesn't work with Python3 (which we'd like to move the repo towards).
The current function's documentation is here. It is basically a TF layer that takes in inputs from the encoder and decoder to produce the final state of the decoder which is eventually used to provide the final word probabiliies/predictions.
The refactor should make sure that we're still using a sequence to sequence model but open to slight changes in the network architecture depending on what the new tensorflow functions need. Also, ideally we'd like to keep the data input format (described in the README) the same.
Some places to start
If you'd like to take it on, I would also make sure that the code works on the
python3-upgrade
branch and that the code is inpython3
Feel free to post if you have any questions or need help with anything.
cc @TotallyNotChase
Additional context on this issue #53
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