I need to use encoder-decoder structure to predict 2D trajectories. As almost all available tutorials are related to NLP -with sparse vectors-, I couldn't be sure about how to adapt the solutions to a continuous data.
In addition to my ignorance in seqence-to-sequence models,
embedding process for words confused me more. I have a dataset that consists of 3,000,000 samples each having
x-y coordinates (-1, 1) with
125 observations, which means the shape of each sample is
(125, 2). I thought I could think of this as 125 words with 2 dimensional already embedded words, but the encoder and the decoder in this Keras Tutorial expect 3D arrays as
(num_pairs, max_english_sentence_length, num_english_characters).
I doubt I need to train each sample
(125, 2) separately with this model, as the way Google's search bar does with only one word written.
As far as I understood, an encoder is
many-to-one type model and a decoder is
one-to-many type model. I need to get a memory state
c and a hiddenstate
h as vectors(?). Then I should use those vectors as input to decoder and extract predictions in the shape of (x,y) as many as I determine as encoder output.
I'd be so thankful if someone could give an example of an encoder-decoder LSTM architecture over the shape of my dataset, especially in terms of dimensions required for encoder-decoder inputs and outputs, particulary on Keras model if possible.