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# Copyright 2015 Google Inc. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Tests for tensorflow.ops.tf.MatrixDeterminant."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
class DeterminantOpTest(tf.test.TestCase):
def _compareDeterminant(self, matrix_x):
with self.test_session():
if matrix_x.ndim == 2:
tf_ans = tf.matrix_determinant(matrix_x)
else:
tf_ans = tf.batch_matrix_determinant(matrix_x)
out = tf_ans.eval()
shape = matrix_x.shape
if shape[-1] == 0 and shape[-2] == 0:
np_ans = np.ones(shape[:-2]).astype(matrix_x.dtype)
else:
np_ans = np.array(np.linalg.det(matrix_x)).astype(matrix_x.dtype)
self.assertAllClose(np_ans, out)
self.assertShapeEqual(np_ans, tf_ans)
def testBasic(self):
# 2x2 matrices
self._compareDeterminant(np.array([[2., 3.], [3., 4.]]).astype(np.float32))
self._compareDeterminant(np.array([[0., 0.], [0., 0.]]).astype(np.float32))
# 5x5 matrices (Eigen forces LU decomposition)
self._compareDeterminant(np.array(
[[2., 3., 4., 5., 6.], [3., 4., 9., 2., 0.], [2., 5., 8., 3., 8.],
[1., 6., 7., 4., 7.], [2., 3., 4., 5., 6.]]).astype(np.float32))
# A multidimensional batch of 2x2 matrices
self._compareDeterminant(np.random.rand(3, 4, 5, 2, 2).astype(np.float32))
def testBasicDouble(self):
# 2x2 matrices
self._compareDeterminant(np.array([[2., 3.], [3., 4.]]).astype(np.float64))
self._compareDeterminant(np.array([[0., 0.], [0., 0.]]).astype(np.float64))
# 5x5 matrices (Eigen forces LU decomposition)
self._compareDeterminant(np.array(
[[2., 3., 4., 5., 6.], [3., 4., 9., 2., 0.], [2., 5., 8., 3., 8.],
[1., 6., 7., 4., 7.], [2., 3., 4., 5., 6.]]).astype(np.float64))
# A multidimensional batch of 2x2 matrices
self._compareDeterminant(np.random.rand(3, 4, 5, 2, 2).astype(np.float64))
def testOverflow(self):
max_double = np.finfo("d").max
huge_matrix = np.array([[max_double, 0.0], [0.0, max_double]])
with self.assertRaisesOpError("not finite"):
self._compareDeterminant(huge_matrix)
def testNonSquareMatrix(self):
# When the determinant of a non-square matrix is attempted we should return
# an error
with self.assertRaises(ValueError):
tf.matrix_determinant(
np.array([[1., 2., 3.], [3., 5., 4.]]).astype(np.float32))
def testWrongDimensions(self):
# The input to the determinant should be a 2-dimensional tensor.
tensor1 = tf.constant([1., 2.])
with self.assertRaises(ValueError):
tf.matrix_determinant(tensor1)
def testEmpty(self):
self._compareDeterminant(np.empty([0, 2, 2]))
self._compareDeterminant(np.empty([2, 0, 0]))
if __name__ == "__main__":
tf.test.main()
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