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| author | Nathan Cassereau <84033440+ncassereau-idris@users.noreply.github.com> | 2021-12-09 17:55:12 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2021-12-09 17:55:12 +0100 |
| commit | f8d871e8c6f15009f559ece6a12eb8d8891c60fb (patch) | |
| tree | 9aa46b2fcc8046c6cddd8e9159a6f607dcf0e1e9 /ot/dr.py | |
| parent | b3dc68feac355fa94c4237f4ecad65edc9f7a7e8 (diff) | |
[MRG] Tensorflow backend & Benchmarker & Myst_parser (#316)
* First batch of tf methods (to be continued)
* Second batch of method (yet to debug)
* tensorflow for cpu
* add tf requirement
* pep8 + bug
* small changes
* attempt to solve pymanopt bug with tf2
* attempt #2
* attempt #3
* attempt 4
* docstring
* correct pep8 violation introduced in merge conflicts resolution
* attempt 5
* attempt 6
* just a random try
* Revert "just a random try"
This reverts commit 8223e768bfe33635549fb66cca2267514a60ebbf.
* GPU tests for tensorflow
* pep8
* attempt to solve issue with m2r2
* Remove transpose backend method
* first draft of benchmarker (need to correct time measurement)
* prettier bench table
* Bitsize and prettier device methods
* prettified table bench
* Bug corrected (results were mixed up in the final table)
* Better perf counter (for GPU support)
* pep8
* EMD bench
* solve bug if no GPU available
* pep8
* warning about tensorflow numpy api being required in the backend.py docstring
* Bug solve in backend docstring
* not covering code which requires a GPU
* Tensorflow gradients manipulation tested
* Number of warmup runs is now customizable
* typo
* Remove some warnings while building docs
* Change prettier_device to device_type in backend
* Correct JAX mistakes preventing to see the CPU if a GPU is present
* Attempt to solve JAX bug in case no GPU is found
* Reworked benchmarks order and results storage & clear GPU after usage by benchmark
* Add bench to backend docstring
* better benchs
* remove useless stuff
* Better device_type
* Now using MYST_PARSER and solving links issue in the README.md / online docs
Diffstat (limited to 'ot/dr.py')
| -rw-r--r-- | ot/dr.py | 2 |
1 files changed, 2 insertions, 0 deletions
@@ -16,6 +16,7 @@ Dimension reduction with OT from scipy import linalg import autograd.numpy as np +from pymanopt.function import Autograd from pymanopt.manifolds import Stiefel from pymanopt import Problem from pymanopt.solvers import SteepestDescent, TrustRegions @@ -181,6 +182,7 @@ def wda(X, y, p=2, reg=1, k=10, solver=None, maxiter=100, verbose=0, P0=None, no else: regmean = np.ones((len(xc), len(xc))) + @Autograd def cost(P): # wda loss loss_b = 0 |