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# Copyright 2015 The TensorFlow Authors. 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.math_ops.matrix_solve."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.ops import linalg_ops
from tensorflow.python.platform import test
class MatrixSolveOpTest(test.TestCase):
def _verifySolve(self, x, y, batch_dims=None):
for adjoint in False, True:
for np_type in [np.float32, np.float64, np.complex64, np.complex128]:
if np_type is [np.float32, np.float64]:
a = x.real().astype(np_type)
b = y.real().astype(np_type)
else:
a = x.astype(np_type)
b = y.astype(np_type)
if adjoint:
a_np = np.conj(np.transpose(a))
else:
a_np = a
if batch_dims is not None:
a = np.tile(a, batch_dims + [1, 1])
a_np = np.tile(a_np, batch_dims + [1, 1])
b = np.tile(b, batch_dims + [1, 1])
np_ans = np.linalg.solve(a_np, b)
with self.test_session():
tf_ans = linalg_ops.matrix_solve(a, b, adjoint=adjoint)
out = tf_ans.eval()
self.assertEqual(tf_ans.get_shape(), out.shape)
self.assertEqual(np_ans.shape, out.shape)
self.assertAllClose(np_ans, out)
def testSolve(self):
matrix = np.array([[1. + 5.j, 2. + 6.j], [3. + 7j, 4. + 8.j]])
# 2x1 right-hand side.
rhs1 = np.array([[1. + 0.j], [1. + 0.j]])
self._verifySolve(matrix, rhs1)
# 2x3 right-hand sides.
rhs3 = np.array(
[[1. + 0.j, 0. + 0.j, 1. + 0.j], [0. + 0.j, 1. + 0.j, 1. + 0.j]])
self._verifySolve(matrix, rhs3)
def testSolveBatch(self):
matrix = np.array([[1. + 5.j, 2. + 6.j], [3. + 7j, 4. + 8.j]])
rhs = np.array([[1. + 0.j], [1. + 0.j]])
# Batch of 2x3x2x2 matrices, 2x3x2x3 right-hand sides.
self._verifySolve(matrix, rhs, batch_dims=[2, 3])
# Batch of 3x2x2x2 matrices, 3x2x2x3 right-hand sides.
self._verifySolve(matrix, rhs, batch_dims=[3, 2])
def testNonSquareMatrix(self):
# When the solve of a non-square matrix is attempted we should return
# an error
with self.test_session():
with self.assertRaises(ValueError):
matrix = constant_op.constant([[1., 2., 3.], [3., 4., 5.]])
linalg_ops.matrix_solve(matrix, matrix)
def testWrongDimensions(self):
# The matrix and right-hand sides should have the same number of rows.
with self.test_session():
matrix = constant_op.constant([[1., 0.], [0., 1.]])
rhs = constant_op.constant([[1., 0.]])
with self.assertRaises(ValueError):
linalg_ops.matrix_solve(matrix, rhs)
def testNotInvertible(self):
# The input should be invertible.
with self.test_session():
with self.assertRaisesOpError("Input matrix is not invertible."):
# All rows of the matrix below add to zero
matrix = constant_op.constant(
[[1., 0., -1.], [-1., 1., 0.], [0., -1., 1.]])
linalg_ops.matrix_solve(matrix, matrix).eval()
if __name__ == "__main__":
test.main()
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