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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
import tensorflow as tf
from tensorflow.contrib.distributions.python.ops import operator_pd_identity
from tensorflow.contrib.distributions.python.ops import operator_test_util
distributions = tf.contrib.distributions
class OperatorPDIdentityTest(operator_test_util.OperatorPDDerivedClassTest):
"""Most tests done in the base class."""
def _build_operator_and_mat(self, batch_shape, k, dtype=np.float64):
# Build an identity matrix with right shape and dtype.
# Build an operator that should act the same way.
batch_shape = list(batch_shape)
diag_shape = batch_shape + [k]
matrix_shape = batch_shape + [k, k]
diag = tf.ones(diag_shape, dtype=dtype)
scale = tf.constant(2.0, dtype=dtype)
scaled_identity_matrix = scale * tf.matrix_diag(diag)
operator = operator_pd_identity.OperatorPDIdentity(
matrix_shape, dtype, scale=scale)
return operator, scaled_identity_matrix.eval()
def testBadDtypeArgsRaise(self):
dtype = np.float32
batch_shape = [2, 3]
k = 4
with self.test_session():
operator, _ = self._build_operator_and_mat(batch_shape, k, dtype=dtype)
x_good_shape = batch_shape + [k, 5]
x_good = self._rng.randn(*x_good_shape).astype(dtype)
x_bad = x_good.astype(np.float64)
operator.matmul(x_good).eval() # Should not raise.
with self.assertRaisesRegexp(TypeError, "dtype"):
operator.matmul(x_bad)
with self.assertRaisesRegexp(TypeError, "dtype"):
operator.solve(x_bad)
with self.assertRaisesRegexp(TypeError, "dtype"):
operator.sqrt_solve(x_bad)
def testBadRankArgsRaise(self):
# Prepend a singleton dimension, changing the rank of "x", but not the size.
dtype = np.float32
batch_shape = [2, 3]
k = 4
with self.test_session():
operator, _ = self._build_operator_and_mat(batch_shape, k, dtype=dtype)
x_good_shape = batch_shape + [k, 5]
x_good = self._rng.randn(*x_good_shape).astype(dtype)
x_bad = x_good.reshape(1, 2, 3, 4, 5)
operator.matmul(x_good).eval() # Should not raise.
with self.assertRaisesRegexp(ValueError, "tensor rank"):
operator.matmul(x_bad)
with self.assertRaisesRegexp(ValueError, "tensor rank"):
operator.solve(x_bad)
with self.assertRaisesRegexp(ValueError, "tensor rank"):
operator.sqrt_solve(x_bad)
def testIncompatibleShapeArgsRaise(self):
# Test shapes that are the same rank but incompatible for matrix
# multiplication.
dtype = np.float32
batch_shape = [2, 3]
k = 4
with self.test_session():
operator, _ = self._build_operator_and_mat(batch_shape, k, dtype=dtype)
x_good_shape = batch_shape + [k, 5]
x_good = self._rng.randn(*x_good_shape).astype(dtype)
x_bad_shape = batch_shape + [5, k]
x_bad = x_good.reshape(*x_bad_shape)
operator.matmul(x_good).eval() # Should not raise.
with self.assertRaisesRegexp(ValueError, "Incompatible"):
operator.matmul(x_bad)
with self.assertRaisesRegexp(ValueError, "Incompatible"):
operator.solve(x_bad)
with self.assertRaisesRegexp(ValueError, "Incompatible"):
operator.sqrt_solve(x_bad)
if __name__ == "__main__":
tf.test.main()
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