a problem about Mem2Seq.py

[bing-95]

Hi, in the Mem2Seq.py, I have a question about the GRU's hidden layer.
In the DecoderMemNN init function, the code self.gru = nn.GRU(embedding_dim, embedding_dim, dropout=dropout) doesn't define the GRU's layer_num, so does it have a default one hidden layer?
And in the train_batch and evaluate_batch function, the code decoder_hidden = self.encoder(input_batches).unsqueeze(0) also implies the GRU has one hidden layer.
So, I think, during training and evaluating, the GRU always has only one hidden layer regardless of the args['layer']. Should the code be modified?

Look forward to your reply, thanks.

[jasonwu0731]

Yes for all the experiments we did are using one layer for the GRU. The args["layer"] we set is for the hyperparameter in the memory network setting, that is, it is for the number of "hops".

Please feel free to try a different number of RNN layers to see if the results can be improved. :)

[bing-95]

Yes for all the experiments we did are using one layer for the GRU. The args["layer"] we set is for the hyperparameter in the memory network setting, that is, it is for the number of "hops".

Please feel free to try a different number of RNN layers to see if the results can be improved. :)

OK, I understantd. Thank you~ :)