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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.
# ==============================================================================
"""Tests for predictor.core_estimator_predictor."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tempfile
import numpy as np
from tensorflow.contrib.predictor import core_estimator_predictor
from tensorflow.contrib.predictor import testing_common
from tensorflow.python.platform import test
KEYS_AND_OPS = (('sum', lambda x, y: x + y),
('product', lambda x, y: x * y,),
('difference', lambda x, y: x - y))
class CoreEstimatorPredictorTest(test.TestCase):
"""Test fixture for `CoreEstimatorPredictor`."""
def setUp(self):
model_dir = tempfile.mkdtemp()
self._estimator = testing_common.get_arithmetic_estimator(
core=True, model_dir=model_dir)
self._serving_input_receiver_fn = testing_common.get_arithmetic_input_fn(
core=True, train=False)
def testDefault(self):
"""Test prediction with default signature."""
np.random.seed(1111)
x = np.random.rand()
y = np.random.rand()
predictor = core_estimator_predictor.CoreEstimatorPredictor(
estimator=self._estimator,
serving_input_receiver_fn=self._serving_input_receiver_fn)
output = predictor({'x': x, 'y': y})['sum']
self.assertAlmostEqual(output, x + y, places=3)
def testSpecifiedSignatureKey(self):
"""Test prediction with spedicified signatures."""
np.random.seed(1234)
for output_key, op in KEYS_AND_OPS:
x = np.random.rand()
y = np.random.rand()
expected_output = op(x, y)
predictor = core_estimator_predictor.CoreEstimatorPredictor(
estimator=self._estimator,
serving_input_receiver_fn=self._serving_input_receiver_fn,
output_key=output_key)
output_tensor_name = predictor.fetch_tensors[output_key].name
self.assertRegexpMatches(
output_tensor_name,
output_key,
msg='Unexpected fetch tensor.')
output = predictor({'x': x, 'y': y})[output_key]
self.assertAlmostEqual(
expected_output, output, places=3,
msg='Failed for output key "{}." '
'Got output {} for x = {} and y = {}'.format(
output_key, output, x, y))
if __name__ == '__main__':
test.main()
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