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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.tf.norm."""
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 array_ops
from tensorflow.python.ops import linalg_ops
from tensorflow.python.platform import test as test_lib
def _AddTest(test, test_name, fn):
test_name = "_".join(["test", test_name])
if hasattr(test, test_name):
raise RuntimeError("Test %s defined more than once" % test_name)
setattr(test, test_name, fn)
class NormOpTest(test_lib.TestCase):
def testBadOrder(self):
matrix = [[0., 1.], [2., 3.]]
for ord_ in "fro", -7, -1.1, 0:
with self.assertRaisesRegexp(ValueError,
"'ord' must be a supported vector norm"):
linalg_ops.norm(matrix, ord=ord_)
for ord_ in "fro", -7, -1.1, 0:
with self.assertRaisesRegexp(ValueError,
"'ord' must be a supported vector norm"):
linalg_ops.norm(matrix, ord=ord_, axis=-1)
for ord_ in "foo", -7, -1.1, 1.1:
with self.assertRaisesRegexp(ValueError,
"'ord' must be a supported matrix norm"):
linalg_ops.norm(matrix, ord=ord_, axis=[-2, -1])
def testInvalidAxis(self):
matrix = [[0., 1.], [2., 3.]]
for axis_ in [], [1, 2, 3], [[1]], [[1], [2]], [3.1415], [1, 1]:
error_prefix = ("'axis' must be None, an integer, or a tuple of 2 unique "
"integers")
with self.assertRaisesRegexp(ValueError, error_prefix):
linalg_ops.norm(matrix, axis=axis_)
def _GetNormOpTest(dtype_, shape_, ord_, axis_, keep_dims_, use_static_shape_):
def _CompareNorm(self, matrix):
np_norm = np.linalg.norm(matrix, ord=ord_, axis=axis_, keepdims=keep_dims_)
with self.test_session(use_gpu=True) as sess:
if use_static_shape_:
tf_matrix = constant_op.constant(matrix)
tf_norm = linalg_ops.norm(
tf_matrix, ord=ord_, axis=axis_, keepdims=keep_dims_)
tf_norm_val = sess.run(tf_norm)
else:
tf_matrix = array_ops.placeholder(dtype_)
tf_norm = linalg_ops.norm(
tf_matrix, ord=ord_, axis=axis_, keepdims=keep_dims_)
tf_norm_val = sess.run(tf_norm, feed_dict={tf_matrix: matrix})
self.assertAllClose(np_norm, tf_norm_val, rtol=1e-5, atol=1e-5)
def Test(self):
is_matrix_norm = (isinstance(axis_, tuple) or
isinstance(axis_, list)) and len(axis_) == 2
is_fancy_p_norm = np.isreal(ord_) and np.floor(ord_) != ord_
if ((not is_matrix_norm and ord_ == "fro") or
(is_matrix_norm and is_fancy_p_norm)):
self.skipTest("Not supported by neither numpy.linalg.norm nor tf.norm")
if ord_ == 'euclidean' or (axis_ is None and len(shape) > 2):
self.skipTest("Not supported by numpy.linalg.norm")
matrix = np.random.randn(*shape_).astype(dtype_)
if dtype_ in (np.complex64, np.complex128):
matrix += 1j * np.random.randn(*shape_).astype(dtype_)
_CompareNorm(self, matrix)
return Test
# pylint: disable=redefined-builtin
if __name__ == "__main__":
for use_static_shape in False, True:
for dtype in np.float32, np.float64, np.complex64, np.complex128:
for rows in 2, 5:
for cols in 2, 5:
for batch in [], [2], [2, 3]:
shape = batch + [rows, cols]
for ord in "euclidean", "fro", 0.5, 1, 2, np.inf:
for axis in [
None, (-2, -1), (-1, -2), -len(shape), 0, len(shape) - 1
]:
for keep_dims in False, True:
name = "%s_%s_ord_%s_axis_%s_%s_%s" % (
dtype.__name__, "_".join(map(str, shape)), ord, axis,
keep_dims, use_static_shape)
_AddTest(NormOpTest, "Norm_" + name,
_GetNormOpTest(dtype, shape, ord, axis, keep_dims,
use_static_shape))
test_lib.main()
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