Comments (1)
The purpose was to make it explicit that the encoder and decoder are two separate modules working together, and secondarily to experiment with different learning rates. There have been a lot of questions about this so I'll probably change it to one module with one optimizer.
Generally you can just combine them like so:
class Seq2Seq(nn.Module):
def __init__(self, ...):
super(Seq2Seq, self).__init__()
self.encoder = Encoder(...)
self.decoder = Decoder(...)
def forward(self, inputs):
encoder_outputs = encoder(...)
decoder_outputs = decoder(...)
Mostly pseudocode but the important part is assigning the sub-modules to self
of the parent module so that their parameters are registered.
from practical-pytorch.
Related Issues (20)
- Issue on Windows
- Seq-seq not working for creating chatbot
- How to save and load train model and use it for evaluation HOT 2
- Link for Series 2 - RNNs for time-series data
- Question about Luong Attention Implementation HOT 7
- can't import torch HOT 2
- The link for Teacher Forcing in "Translation with a Sequence to Sequence Network and Attention" is broken
- Error in BahdanauAttnDecoderRNN HOT 1
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- Error in practical-pytorch/seq2seq-translation/seq2seq-translation-batched.ipynb
- Question from character level RNN classifier, why not use the hidden state across epochs? HOT 1
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from practical-pytorch.