Current progress
- load/save files not yet implemented
- many tests need reorganising
- More tests, particularly of UESMANN proper: I predict some will be hard to pass (real networks need multiple attempts to train)
This is the new C++ implementation of UESMANN, based on the original code used in my thesis. The code is very simplistic, using scalar as opposed to matrix operations and no GPU acceleration. This is to make the code as clear as possible, as befits a reference implementation, and also to match the implementation used in the thesis. There are no dependencies on any libraries beyond those found in a standard C++ install, and libboost-test for testing. You may find the code somewhat lacking in modern C++ style because I'm an 80's coder.
A rather more complete set of documentation, including a description of the network and a Doxygen docs, can be found at
https://jimfinnis.github.io/uesmanncpp/html/index.html
I originally intended to use Keras/Tensorflow, but would have been limited to using the low-level Tensorflow operations because of the somewhat peculiar nature of optimisation in UESMANN: we descend the gradient relative to the weights for one function, and the gradient relative to the weights times some constant for the other. A Keras/Tensorflow implementation is planned.
Implementations of the other network types mentioned in the thesis are also included.
