* Consistent GPU classes
Convert the existing GPU classes into one standard format.
Remove duplicated functions in `test_mnist` and create a TestMNISTGPU
class. This reduces line count and ensures consistency.
Use `@unittest.skipUnless(GPU, "Requires GPU")` instead of `if GPU:` to
skip GPU testing. This will ensure that skipped tests are displayed
accordingly in the pytest output.
* Optim Testing now supports GPU
* Tensor testing now supports GPU
jacobian and gradcheck auto skipped until GPU float64 support added.
* GPU support for custom constructor methods
* Remove GPU flag from Model constructors
It was requested that the `gpu` kwarg be removed from the model
constructor. GPU conversion is now handled in the train function.
This also required the conversion of Optimizer parameters as they are
constructed prior to execution of the `train` function and are dependant
on the model GPU state.
* Fix typo: float32->float64
* Clean `get_parameters` utility
Just a quick refactor w/ the new support for optimizers.
* Remove GPU kwarg from TinyNet
Remove `gpu` kwarg from tiny net to match test_mnist `train` function.
* copy tensors to and from gpu
* add on GPU
* adding works
* we stick shapes in
* works on cpu and gpu
* test changes, not passing yet
* something else
* op tests pass
* add, mean, and sum have working forward/backward
* mul ops test
* no gpu support, no problem
* test pass, clean up later
* gpu cleanup
* cleanup test ops, don't let div fail
* revert more
* aimpler dispatcher
* clean up grad
* GPU and
* grad is a Tensor now
* gate test on GPU
* cleanups
* late loading gpu
* GPU as input option
* last cleanups
