Thank for your answer, ptrblck! I am using the CNN for my work in Chemistry, and the use case goes as follows:
For the I/O of the CNN, I use 3D tensors. These 3D tensors are the grid representations of the chemical molecules I want to train my network with. I initiate the 3D tensor with all zero values, and update a grid’s value to 1 if an atom (of the molecule) is present in the grid. The grid resolution is small enough such that there can only be maximum one atom in a particular grid. That is how I end up with a 3D tensor, that has values only 0 (grid in which no atom is present) and 1 (grid where 1 atom is present).
An analogy of this problem can be that, imagine if you have to train a CNN using a collection of black-and-white images. These images have pixel values either 0 or 1.