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# Copyright 2016 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops.linalg import linalg as linalg_lib
from tensorflow.python.ops.linalg import linear_operator_test_util
from tensorflow.python.platform import test
linalg = linalg_lib
rng = np.random.RandomState(0)
class SquareLinearOperatorCompositionTest(
linear_operator_test_util.SquareLinearOperatorDerivedClassTest):
"""Most tests done in the base class LinearOperatorDerivedClassTest."""
def setUp(self):
# Increase from 1e-6 to 1e-4
self._atol[dtypes.float32] = 1e-4
self._atol[dtypes.complex64] = 1e-4
self._rtol[dtypes.float32] = 1e-4
self._rtol[dtypes.complex64] = 1e-4
def _operator_and_matrix(self, build_info, dtype, use_placeholder):
sess = ops.get_default_session()
shape = list(build_info.shape)
# Either 1 or 2 matrices, depending.
num_operators = rng.randint(low=1, high=3)
matrices = [
linear_operator_test_util.random_positive_definite_matrix(
shape, dtype, force_well_conditioned=True)
for _ in range(num_operators)
]
lin_op_matrices = matrices
if use_placeholder:
lin_op_matrices = [
array_ops.placeholder_with_default(
matrix, shape=None) for matrix in matrices]
operator = linalg.LinearOperatorComposition(
[linalg.LinearOperatorFullMatrix(l) for l in lin_op_matrices],
is_square=True)
matmul_order_list = list(reversed(matrices))
mat = matmul_order_list[0]
for other_mat in matmul_order_list[1:]:
mat = math_ops.matmul(other_mat, mat)
return operator, mat
def test_is_x_flags(self):
# Matrix with two positive eigenvalues, 1, and 1.
# The matrix values do not effect auto-setting of the flags.
matrix = [[1., 0.], [1., 1.]]
operator = linalg.LinearOperatorComposition(
[linalg.LinearOperatorFullMatrix(matrix)],
is_positive_definite=True,
is_non_singular=True,
is_self_adjoint=False)
self.assertTrue(operator.is_positive_definite)
self.assertTrue(operator.is_non_singular)
self.assertFalse(operator.is_self_adjoint)
def test_is_non_singular_auto_set(self):
# Matrix with two positive eigenvalues, 11 and 8.
# The matrix values do not effect auto-setting of the flags.
matrix = [[11., 0.], [1., 8.]]
operator_1 = linalg.LinearOperatorFullMatrix(matrix, is_non_singular=True)
operator_2 = linalg.LinearOperatorFullMatrix(matrix, is_non_singular=True)
operator = linalg.LinearOperatorComposition(
[operator_1, operator_2],
is_positive_definite=False, # No reason it HAS to be False...
is_non_singular=None)
self.assertFalse(operator.is_positive_definite)
self.assertTrue(operator.is_non_singular)
with self.assertRaisesRegexp(ValueError, "always non-singular"):
linalg.LinearOperatorComposition(
[operator_1, operator_2], is_non_singular=False)
def test_name(self):
matrix = [[11., 0.], [1., 8.]]
operator_1 = linalg.LinearOperatorFullMatrix(matrix, name="left")
operator_2 = linalg.LinearOperatorFullMatrix(matrix, name="right")
operator = linalg.LinearOperatorComposition([operator_1, operator_2])
self.assertEqual("left_o_right", operator.name)
def test_different_dtypes_raises(self):
operators = [
linalg.LinearOperatorFullMatrix(rng.rand(2, 3, 3)),
linalg.LinearOperatorFullMatrix(rng.rand(2, 3, 3).astype(np.float32))
]
with self.assertRaisesRegexp(TypeError, "same dtype"):
linalg.LinearOperatorComposition(operators)
def test_empty_operators_raises(self):
with self.assertRaisesRegexp(ValueError, "non-empty"):
linalg.LinearOperatorComposition([])
class NonSquareLinearOperatorCompositionTest(
linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest):
"""Most tests done in the base class LinearOperatorDerivedClassTest."""
def setUp(self):
# Increase from 1e-6 to 1e-4
self._atol[dtypes.float32] = 1e-4
self._atol[dtypes.complex64] = 1e-4
self._rtol[dtypes.float32] = 1e-4
self._rtol[dtypes.complex64] = 1e-4
def _operator_and_matrix(self, build_info, dtype, use_placeholder):
sess = ops.get_default_session()
shape = list(build_info.shape)
# Test only the case of 2 matrices.
# The Square test uses either 1 or 2, so we have tested the case of 1 matrix
# sufficiently.
num_operators = 2
# Create 2 matrices/operators, A1, A2, which becomes A = A1 A2.
# Use inner dimension of 2.
k = 2
batch_shape = shape[:-2]
shape_1 = batch_shape + [shape[-2], k]
shape_2 = batch_shape + [k, shape[-1]]
matrices = [
linear_operator_test_util.random_normal(
shape_1, dtype=dtype), linear_operator_test_util.random_normal(
shape_2, dtype=dtype)
]
lin_op_matrices = matrices
if use_placeholder:
lin_op_matrices = [
array_ops.placeholder_with_default(
matrix, shape=None) for matrix in matrices]
operator = linalg.LinearOperatorComposition(
[linalg.LinearOperatorFullMatrix(l) for l in lin_op_matrices])
matmul_order_list = list(reversed(matrices))
mat = matmul_order_list[0]
for other_mat in matmul_order_list[1:]:
mat = math_ops.matmul(other_mat, mat)
return operator, mat
def test_static_shapes(self):
operators = [
linalg.LinearOperatorFullMatrix(rng.rand(2, 3, 4)),
linalg.LinearOperatorFullMatrix(rng.rand(2, 4, 5))
]
operator = linalg.LinearOperatorComposition(operators)
self.assertAllEqual((2, 3, 5), operator.shape)
def test_shape_tensors_when_statically_available(self):
operators = [
linalg.LinearOperatorFullMatrix(rng.rand(2, 3, 4)),
linalg.LinearOperatorFullMatrix(rng.rand(2, 4, 5))
]
operator = linalg.LinearOperatorComposition(operators)
with self.cached_session():
self.assertAllEqual((2, 3, 5), operator.shape_tensor().eval())
def test_shape_tensors_when_only_dynamically_available(self):
mat_1 = rng.rand(1, 2, 3, 4)
mat_2 = rng.rand(1, 2, 4, 5)
mat_ph_1 = array_ops.placeholder(dtypes.float64)
mat_ph_2 = array_ops.placeholder(dtypes.float64)
feed_dict = {mat_ph_1: mat_1, mat_ph_2: mat_2}
operators = [
linalg.LinearOperatorFullMatrix(mat_ph_1),
linalg.LinearOperatorFullMatrix(mat_ph_2)
]
operator = linalg.LinearOperatorComposition(operators)
with self.cached_session():
self.assertAllEqual(
(1, 2, 3, 5), operator.shape_tensor().eval(feed_dict=feed_dict))
if __name__ == "__main__":
test.main()
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