Comments (4)
spro
commented on May 18, 2024
Yes it is, though looking back at it I'm missing one layer between the context vector c_t and the softmax layer, to create the "attentional hidden state" ~h_t, which is what they use for input feeding.
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TinaB19
commented on May 18, 2024
It would be great if you add it to the tutorial later, thank you very much.
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TinaB19
commented on May 18, 2024
I just saw seq2seq-translation-batched.
concat_input = torch.cat((rnn_output, context), 1)
concat_output = F.tanh(self.concat(concat_input))
So I guess in this case we should concatenate concat_output with embedded in the next time step and then feed them to gru. Is this correct?
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spro
commented on May 18, 2024
Correct.
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