That’s something I don’t understand. Could you please explain?
My understanding that net_out is not too large (51210) compared to LSTM unit itself which has 4(512*10 + 512^2) i.e. hos 200 times more parameters. Additionally, with trainer mode of seq2seq, on every step of decoder RNN I need to apply linear layer to calculate next token for this step to feed in as input on next step.
Of course, in real-world applications, when count of output tokens will be full vocabulary (hundreeds of thousands or even millions of words), output layer will dominate. But this is completely different story which can be solved by hierarchical or sampled softmax.