I am new to Autoencoders and I am a bit confused on which model to try for my situation and what is the difference between all the different models I have seen in tutorials.
So, I have a set of time-series of a certain length
w, so my input dataset
X has a shape
(5000,50,1), where 5000 is the amount of small time-series and
I have seen the following options:
- LSTM -> Dense -> Linear Activation. For me it seems that the decoding layer is missing here. Or this would still work?
- LSTM(64) -> RepeatVector(
w) -> LSTM(
w). This one gives me the error, because it's output seems to be of shape (
None, w, w), while I would actually expect (
None, w, 1), corresponding to the original input. How to fix this problem? Or it is actually supposed to be like that and I just don't understand something about Autoencoders? Here is the code that I used:
model = Sequential()
- Same architecture but divided into separate Encoder and Decoder model. I haven't tried this because I would prefer just one model. Or is this option also worth trying?