Suppose I want to build a timeseries where each timestep is represented by a categorical array: the encoded sequences look like
[[2, 0, 5],[3, 1, 4],..] and each entry has a different number of possible values (categories).
For example the first entry has 0-3 values, the second 0-1 and so on...
I want to train an LSTM model in order to predict the next timestep. So I defined a one hot encoding of each entry by means of the maximum number of classes:
[2, 0, 5] becomes
[[0. 0. 1. 0. 0. 0.], [1. 0. 0. 0. 0. 0.], [0. 0. 0. 0. 0. 1.]]
Unfortunately this kind of representation raises the error
ValueError: Invalid shape for y: (1, 3, 5)
I have three questions:
- Is it possible to pass a 3d y target to Keras?
- Should I define a single one-hot encoding which combines all the possible triplets of categorical values instead? The problem is that in this case I would lose the correlation between the occurrences of the labels in the same category, because each possible combination of labels would become independent from the other ones.
- Should I only one-hot encode the target y or also the input X?