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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 PrecisionOp."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import errors_impl
from tensorflow.python.ops import nn_ops
from tensorflow.python.platform import test
class InTopKTest(test.TestCase):
def _validateInTopK(self, predictions, target, k, expected):
np_ans = np.array(expected)
with self.test_session():
precision = nn_ops.in_top_k(predictions, target, k)
out = precision.eval()
self.assertAllClose(np_ans, out)
self.assertShapeEqual(np_ans, precision)
def testInTop1(self):
predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]]
target = [3, 1]
self._validateInTopK(predictions, target, 1, [True, False])
def testInTop2(self):
predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]]
target = [0, 2]
self._validateInTopK(predictions, target, 2, [False, True])
def testInTop2Tie(self):
# Class 2 and 3 tie for 2nd, so both are considered in top 2.
predictions = [[0.1, 0.3, 0.2, 0.2], [0.1, 0.3, 0.2, 0.2]]
target = [2, 3]
self._validateInTopK(predictions, target, 2, [True, True])
def testInTop2_int64Target(self):
predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]]
target = np.asarray([0, 2]).astype(np.int64)
self._validateInTopK(predictions, target, 2, [False, True])
def testInTopNan(self):
predictions = [[0.1, float("nan"), 0.2, 0.4], [0.1, 0.2, 0.3, float("inf")]]
target = [0, 2]
self._validateInTopK(predictions, target, 2, [False, False])
def testBadTarget(self):
predictions = [[0.1, 0.3, 0.2, 0.4], [0.1, 0.2, 0.3, 0.4]]
target = [0, 80000]
with self.test_session():
with self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
"target.*out of range"):
nn_ops.in_top_k(predictions, target, 2).eval()
if __name__ == "__main__":
test.main()
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