https://github.com/GPflow/GPflow
Tip revision: 445112bcb708b6ddce327577cdd9c4a76a185fdf authored by John Bradshaw on 24 October 2017, 10:52:48 UTC
Merge remote-tracking branch 'origin/GPflow-1.0-RC' into john-bradshaw/binary-class-GP
Merge remote-tracking branch 'origin/GPflow-1.0-RC' into john-bradshaw/binary-class-GP
Tip revision: 445112b
test_triang.py
import unittest
from gpflow.tf_wraps import vec_to_tri
import tensorflow as tf
import numpy as np
from gpflow.test_util import GPflowTestCase
from gpflow.tf_wraps import vec_to_tri
class TestVecToTri(GPflowTestCase):
def referenceInverse(self, matrices):
#this is the inverse operation of the vec_to_tri
#op being tested.
D, N, _ = matrices.shape
M = (N * (N + 1)) // 2
tril_indices = np.tril_indices(N)
output = np.zeros((D, M))
for vector_index in range(D):
matrix = matrices[vector_index, :]
output[vector_index, :] = matrix[tril_indices]
return output
def getExampleMatrices(self, D, N ):
rng = np.random.RandomState(1)
random_matrices = rng.randn(D, N, N)
for matrix_index in range(D):
for row_index in range(N):
for col_index in range(N):
if col_index > row_index:
random_matrices[matrix_index, row_index, col_index] = 0.
return random_matrices
def testBasicFunctionality(self):
with self.test_context() as sess:
N = 3
D = 3
reference_matrices = self.getExampleMatrices(D, N)
input_vector_tensor = tf.constant(self.referenceInverse(reference_matrices))
test_matrices_tensor = vec_to_tri(input_vector_tensor, N)
test_matrices = sess.run(test_matrices_tensor)
np.testing.assert_array_almost_equal(reference_matrices, test_matrices)
def testDifferentiable(self):
with self.test_context() as sess:
N = 3
D = 3
reference_matrices = self.getExampleMatrices(D, N)
input_vector_array = self.referenceInverse(reference_matrices)
input_vector_tensor = tf.constant(input_vector_array)
test_matrices_tensor = vec_to_tri(input_vector_tensor, N)
reduced_sum = tf.reduce_sum(test_matrices_tensor)
gradient = tf.gradients(reduced_sum, input_vector_tensor)[0]
reference_gradient = np.ones_like(input_vector_array)
test_gradient = sess.run(gradient)
np.testing.assert_array_almost_equal(reference_gradient, test_gradient)
if __name__ == "__main__":
unittest.main()