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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.linalg_grad."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
class MatrixInverseGradientTest(tf.test.TestCase):
pass # Filled in below
def _GetMatrixInverseGradientTest(dtype_, shape_):
def Test(self):
with self.test_session():
np.random.seed(1)
m = np.random.uniform(low=1.0,
high=100.0,
size=np.prod(shape_)).reshape(shape_).astype(dtype_)
a = tf.constant(m)
epsilon = np.finfo(dtype_).eps
# Optimal stepsize for central difference is O(epsilon^{1/3}).
delta = epsilon**(1.0 / 3.0)
tol = 1e-3
if len(shape_) == 2:
ainv = tf.matrix_inverse(a)
else:
ainv = tf.batch_matrix_inverse(a)
theoretical, numerical = tf.test.compute_gradient(a,
shape_,
ainv,
shape_,
delta=delta)
self.assertAllClose(theoretical, numerical, atol=tol, rtol=tol)
return Test
class MatrixDeterminantGradientTest(tf.test.TestCase):
pass # Filled in below
def _GetMatrixDeterminantGradientTest(dtype_, shape_):
def Test(self):
with self.test_session():
np.random.seed(1)
m = np.random.uniform(low=1.0,
high=100.0,
size=np.prod(shape_)).reshape(shape_).astype(dtype_)
a = tf.constant(m)
epsilon = np.finfo(dtype_).eps
# Optimal stepsize for central difference is O(epsilon^{1/3}).
delta = epsilon**(1.0 / 3.0)
# tolerance obtained by looking at actual differences using
# np.linalg.norm(theoretical-numerical, np.inf) on -mavx build
tol = 1e-3
if len(shape_) == 2:
c = tf.matrix_determinant(a)
else:
c = tf.batch_matrix_determinant(a)
out_shape = shape_[:-2] # last two dimensions hold matrices
theoretical, numerical = tf.test.compute_gradient(a,
shape_,
c,
out_shape,
delta=delta)
self.assertAllClose(theoretical, numerical, atol=tol, rtol=tol)
return Test
if __name__ == '__main__':
# TODO(rmlarsen,irving): Reenable float32 once tolerances are fixed
# The test used to loop over (np.float, np.double), both of which are float64.
for dtype in (np.float64,):
for size in 2, 3, 5, 10:
# We skip the rank 4, size 10 case: it is slow and conceptually covered
# by the other cases.
for extra in [(), (2,), (3,)] + [(3, 2)] * (size < 10):
shape = extra + (size, size)
name = '%s_%s' % (dtype.__name__, '_'.join(map(str, shape)))
setattr(MatrixInverseGradientTest, 'testMatrixInverseGradient_' + name,
_GetMatrixInverseGradientTest(dtype, shape))
for dtype in (np.float64,):
for size in 2, 5, 10:
# increase this list to check batch version
for extra in [()]:
shape = extra+(size, size)
name = '%s_%s' % (dtype.__name__, '_'.join(map(str, shape)))
setattr(MatrixDeterminantGradientTest,
'testMatrixDeterminantGradient_' + name,
_GetMatrixDeterminantGradientTest(dtype, shape))
tf.test.main()
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