Revision 30120a183c81cc5d18af0a00ae35e3ec9e06063a authored by Artem Artemev on 25 November 2017, 12:33:07 UTC, committed by Mark van der Wilk on 25 November 2017, 12:33:07 UTC
* Add a bunch of test for Parameter and DataHolder. Minibatch seed can be changed after cleaning or in defer_build. * Add dataholder tests. * Add failure creation tests to data holders. * Shape and dtype for dataholders work for external tensors. * According fix in dataholder test. * Change travis file. * Experiments with travis. * Improvements in Parameterized tests. * Exclude external optimizer from coverage report. * Improvements in optimizer tests. * Indent functions in optimizer's tests. * Increase coverage in Parameterized. * Increase codecov for Parameterized. * Developing test coverage further. * Tests Parameterized feeds. * Call parameter size in tests. * 100% for ParamList. * Minibatch tests. * Increase coverage for Minibatch. * Setup.py clean up. Stick to 1.4 tensorflow version. Remove backwards for py2.7. * Increase coverage for Parameters. * Increase coverage for Parameters build check. * Core tests for TensorConverter. * Code coverage targets. * Change initialization policy at decors and trainers. * Check initialization status of variable using tf.is_variable_initialized tensor. * [#568] Improve initialization status checking performance. * [#568, #561] Test for Dataset iterators is not possible. * Increase coverage for Parameter. method was added, therefore required some tests. method inconsistently handled session inputs. * Added more tests for Parameter object covering cases when tensor is trainable and parameter is not. Also added test for Parameter string conversion.
1 parent 01c2aa3
test_triang.py
# Copyright 2017 the GPflow authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.from __future__ import print_function
import tensorflow as tf
import numpy as np
from numpy.testing import assert_array_almost_equal
from gpflow import misc
from gpflow.test_util import GPflowTestCase
class TestVecToTri(GPflowTestCase):
def reference_inverse(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 get_example_matrices(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 test_basic_functionality(self):
with self.test_context() as sess:
N = 3
D = 3
reference_matrices = self.get_example_matrices(D, N)
input_vector_tensor = tf.constant(self.reference_inverse(reference_matrices))
test_matrices_tensor = misc.vec_to_tri(input_vector_tensor, N)
test_matrices = sess.run(test_matrices_tensor)
assert_array_almost_equal(reference_matrices, test_matrices)
def test_differentiable(self):
with self.test_context() as sess:
N = 3
D = 3
reference_matrices = self.get_example_matrices(D, N)
input_vector_array = self.reference_inverse(reference_matrices)
input_vector_tensor = tf.constant(input_vector_array)
test_matrices_tensor = misc.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)
assert_array_almost_equal(reference_gradient, test_gradient)
if __name__ == "__main__":
tf.test.main()
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