Hi guys,   could you please shed light on the following?
Requirement:

Create a sequence predictor based on geometric shapes
Shapes can be circles, rectangles and triangles
Such  shapes can vary in size and angles if they have
The sequencer should try to predict the most likely next shape and its size.

What has been tried:
Basics:

The data has been normalized
Training data,  validation data and test data are separate and different sets

Implementation:

Passed the shape’s numeric data as part of the sequence e.g. the points that represent the triangle,  it seems the LSTM is trying to do some math with it and it never picks a pattern, I guess, given the different shapes
Make the shape’s point strings and use a categorical LSTM:  the strings are so varied that no pattern is picked
Round the numbers of the dimensions and make them string: too much precision gets lost and  I get a crazy big dictionary , no pattern is picked

In all cases the Test data evaluation is no more than 40% accurate
Any ideas to tackle this type of requirement?
Thanks!