Seq2Seq(Attention)Input Shape Question

[dpyneo]

Seq2Seq(Attention)\Seq2Seq(Attention)-Tensor.py

The shape of the input should be [max_time, batch_size,...]. The input = tf. transpose (dec_inputs, [1, 0, 2]) has already been transformed. In tf. expand_dims (inputs [i], 1), the expansion is indeed one dimension. It seems that there should be zero dimension expansion here. Although the final shape is correct, whether it is intentional or not is here. What about a little trick?

[dpyneo]

I would also like to ask if this is attention can be used for LSTM timing prediction. The main problem that bothers me is this n_class = len (word_dict). Can this be considered as a different feature of input? In the end, your project is very good. It has benefited a lot. It's very good. Thank you very much.

[dpyneo]

'S i want a beer', 'i want a beer E'，Is this the last attention mechanism not aligned?

[searchlink]

Seq2Seq(Attention)\Seq2Seq(Attention)-Tensor.py

The shape of the input should be [max_time, batch_size,...]. The input = tf. transpose (dec_inputs, [1, 0, 2]) has already been transformed. In tf. expand_dims (inputs [i], 1), the expansion is indeed one dimension. It seems that there should be zero dimension expansion here. Although the final shape is correct, whether it is intentional or not is here. What about a little trick?

I have the same question. Do you solve it ?

Oh, I have solve it.You are right.However, batch_size=1,so it has no effect。

[cqray1990]

i think the code is only for batch)size=1,isn't is ?