Added a primary implementation which works for small models.
Main pain points (to be solved):
The second Conv2D layer
the Flatten layer is too large (depends on the number of filters used in convolutions : 32 and 64). This layer works for small number of filters (in the jupyter notebook , we use 8 and 0)
When the size of Flatter layer goes above 1000, we have some issues with the databases support for wide tables. => create separate issue (at least for some experiments. May not be solvable).
The Dense layer leads to a very large model and SQL code. Need to perform some feature selection (make sparse models) and some simplification of the SQL code (non-used columns can be deleted).
Sample use case : simple convnet on the MNIST dataset
keras example : https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py
used layers and activation functions :