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""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
Author(s): Mathieu Carriere
Copyright (C) 2020 Inria
Modification(s):
- YYYY/MM Author: Description of the modification
"""
from gudhi.wasserstein import wasserstein_distance as pot
import numpy as np
def test_wasserstein_distance_grad_tensorflow():
import tensorflow as tf
with tf.GradientTape() as tape:
diag4 = tf.convert_to_tensor(tf.Variable(initial_value=np.array([[0., 10.]]), trainable=True))
diag5 = tf.convert_to_tensor(tf.Variable(initial_value=np.array([[1., 11.], [3., 4.]]), trainable=True))
dist45 = pot(diag4, diag5, internal_p=1, order=1, enable_autodiff=True)
assert dist45 == 3.
grads = tape.gradient(dist45, [diag4, diag5])
assert np.array_equal(grads[0].values, [[-1., -1.]])
assert np.array_equal(grads[1].values, [[1., 1.], [-1., 1.]])
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