# How can I do a sequence to sequence model (RNN / LSTM) with Keras with fixed length data?

What I'm trying to do seems so simple, but I can't find any examples online. First, I'm not working in language, so all of the embedding stuff adds needless complexity to my task.

I have input, in the form of a (1, 1000) vector. They are time-series data, so I'll have 10 of them in sequence. Which, if I understand tensors correctly, gives me something of shape (10, 1, 1000), right?

I want to pass this through an RNN/LSTM and the output should be also of the same shape (10, 1, 1000). Namely, 10 vectors of 1000 dimensions each.

Can anyone guide me?

Thank you kindly.