From e7b7947adf13ec1dcb8c126a4373fa29baaecb63 Mon Sep 17 00:00:00 2001 From: ROUVREAU Vincent Date: Tue, 29 Sep 2020 13:23:56 +0200 Subject: Added tests for wasserstein distance with tensorflow --- src/python/test/test_wasserstein_with_tensors.py | 25 ++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100755 src/python/test/test_wasserstein_with_tensors.py (limited to 'src/python/test') diff --git a/src/python/test/test_wasserstein_with_tensors.py b/src/python/test/test_wasserstein_with_tensors.py new file mode 100755 index 00000000..8957705d --- /dev/null +++ b/src/python/test/test_wasserstein_with_tensors.py @@ -0,0 +1,25 @@ +""" 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.]]) \ No newline at end of file -- cgit v1.2.3