Wow, that was quick!
Yes I get about the same,
22-Mar 21:36:03:INFO:root:## Performance on Validation Set after 10000 iterations
22-Mar 21:36:03:INFO:root:---------------------------------------------------------
22-Mar 21:36:03:INFO:root:  Accuracy (avg over 1000) = 95.8000%
22-Mar 21:36:03:INFO:root:---------------------------------------------------------

I’m still going over Marco Cuturi’s Matlab code - implementing a Wasserstein layers quite a long term project - unless you’ve got an immediate application in mind?
For the time being I’m content with just understand it mathematically.
It does seem to have a lot of potential if you want to train a network to give fast approximations to - existing slow simulation algorithms, or algorithms that currently calculate a Wasserstein metric using a linear program - there’s a lot of things like this in scientific computing - that’s actually the application I’ve got in mind.
Chiyuan Zhang (pluskid) has already applied this to computationally intensive geological simulations,
http://pluskid.org/papers/TLE2017-seismic.pdf 28
So basically - once your nets trained - you can run it forward in batch mode, and get output in second, which previously would have taken hours. Here’s a quote from the peper,
Recent work (Zhang et al., 2014; Frogner et al., 2015;
Dahlke et al., 2016) demonstrates a new approach that builds
and uses a deep neural network (DNN) statistical model to
transform raw-input seismic data directly to the final mapping
of faults in 2D. In this case, fault locations were chosen as the
output because of their relevance to optimizing production in
existing fields. DNNs are built on the premise that they can
be used to replicate any function (in theory, even a nonlinear
one like acoustic wave propagation). We show that DNNs can
be used to identify fault structure in 3D volumes with reasonable
accuracy. The greater promise is that as computational tools
improve, we can use even more complex neural networks to
improve accuracy. The costs are all computational, mostly in
the form of training incurred only once up front. Once the
neural network is trained, predictions can be produced in a
fraction of the training time and of the time needed for traditional
physics modeling with partial differential equations.
This means that nearly instantaneous earth models could be
created as acquisition progresses.