Backends
The general idea is to provide different backends such that different tensor frameworks can be used. These include for
instance numpy (with autograd), pytorch, and tensorflow. These frameworks use different data structures and different
method signatures. By defining a common API, it would ease the use of these various frameworks as the syntax
would be the same. For instance, in numpy, the outer product between two arrays is performed using np.outer
while in pytorch it is carried out by calling torch.ger. Defining a common API would solve these issues.
Also, using backends, we could convert inside each function the given data to the appropriate data structure.
For instance, a pytorch function defined in the backend could easily accept a np.array as input and convert it
automatically to a torch.Tensor.
It would be nice to have learning models that are more or less independent of the tensor framework as done in Keras.
This is mostly an idea that I had in a later stage, and thus is not operational for the moment. It would require to refactor a bit the code, as currently our code is coupled to the pytorch and numpy frameworks. Also, it would require to provide the same functionalities in the various frameworks, and thus implement their missing functionalities.