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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.argmax_op."""
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.ops import math_ops
from tensorflow.python.platform import test
class ArgMaxTest(test.TestCase):
def _testArg(self,
method,
x,
axis,
expected_values,
use_gpu=False,
expected_err_re=None):
with self.test_session(use_gpu=use_gpu):
ans = method(x, axis=axis)
if expected_err_re is None:
tf_ans = ans.eval()
# Defaults to int64 output.
self.assertEqual(np.int64, tf_ans.dtype)
self.assertAllEqual(tf_ans, expected_values)
self.assertShapeEqual(expected_values, ans)
else:
with self.assertRaisesOpError(expected_err_re):
ans.eval()
def _testBothArg(self,
method,
x,
axis,
expected_values,
expected_err_re=None):
self._testArg(method, x, axis, expected_values, True, expected_err_re)
self._testArg(method, x, axis, expected_values, False, expected_err_re)
def _testBasic(self, dtype):
x = np.asarray(100 * np.random.randn(200), dtype=dtype)
# Check that argmin and argmax match numpy along the primary axis
self._testBothArg(math_ops.argmax, x, 0, x.argmax())
self._testBothArg(math_ops.argmin, x, 0, x.argmin())
def _testDim(self, dtype):
x = np.asarray(100 * np.random.randn(3, 2, 4, 5, 6), dtype=dtype)
# Check that argmin and argmax match numpy along all axes
for axis in range(-5, 5):
self._testBothArg(math_ops.argmax, x, axis, x.argmax(axis))
self._testBothArg(math_ops.argmin, x, axis, x.argmin(axis))
def testFloat(self):
self._testBasic(np.float32)
self._testDim(np.float32)
def testFloatInt32Output(self):
x = np.asarray(100 * np.random.randn(200), dtype=np.float32)
expected_values = x.argmax()
with self.test_session(use_gpu=True):
ans = math_ops.argmax(x, axis=0, output_type=dtypes.int32)
tf_ans = ans.eval()
self.assertEqual(np.int32, tf_ans.dtype)
# The values are equal when comparing int32 to int64 because
# the values don't have a range that exceeds 32-bit integers.
self.assertAllEqual(tf_ans, expected_values)
expected_values = x.argmin()
with self.test_session(use_gpu=True):
ans = math_ops.argmin(x, axis=0, output_type=dtypes.int32)
tf_ans = ans.eval()
self.assertEqual(np.int32, tf_ans.dtype)
self.assertAllEqual(tf_ans, expected_values)
def testDouble(self):
self._testBasic(np.float64)
self._testDim(np.float64)
def testInt32(self):
self._testBasic(np.int32)
self._testDim(np.int32)
def testInt64(self):
self._testBasic(np.int64)
self._testDim(np.int64)
def testEmpty(self):
with self.test_session():
for op in math_ops.argmin, math_ops.argmax:
with self.assertRaisesOpError(
r"Reduction axis 0 is empty in shape \[0\]"):
op([], 0).eval()
def testDefaultAxis(self):
with self.test_session():
for op in math_ops.argmin, math_ops.argmax:
ans = op([1]).eval()
self.assertAllEqual(ans, 0)
if __name__ == "__main__":
test.main()
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