tensorflow/tensorflow/python/estimator/util_test.py at master · codeplaysoftware/tensorflow · GitHub

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# 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 util.py."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import numpy as np

from tensorflow.python.data.ops import dataset_ops

from tensorflow.python.estimator import util

from tensorflow.python.framework import constant_op

from tensorflow.python.platform import test

from tensorflow.python.training import training

class UtilTest(test.TestCase):

"""Tests for miscellaneous Estimator utils."""

def test_parse_input_fn_result_tuple(self):

def _input_fn():

features = constant_op.constant(np.arange(100))

labels = constant_op.constant(np.arange(100, 200))

return features, labels

features, labels, hooks = util.parse_input_fn_result(_input_fn())

with self.test_session() as sess:

vals = sess.run([features, labels])

self.assertAllEqual(vals[0], np.arange(100))

self.assertAllEqual(vals[1], np.arange(100, 200))

self.assertEqual(hooks, [])

def test_parse_input_fn_result_dataset(self):

def _input_fn():

features = np.expand_dims(np.arange(100), 0)

labels = np.expand_dims(np.arange(100, 200), 0)

return dataset_ops.Dataset.from_tensor_slices((features, labels))

features, labels, hooks = util.parse_input_fn_result(_input_fn())

with training.MonitoredSession(hooks=hooks) as sess:

vals = sess.run([features, labels])

self.assertAllEqual(vals[0], np.arange(100))

self.assertAllEqual(vals[1], np.arange(100, 200))

self.assertIsInstance(hooks[0], util._DatasetInitializerHook)

def test_parse_input_fn_result_features_only(self):

def _input_fn():

return constant_op.constant(np.arange(100))

features, labels, hooks = util.parse_input_fn_result(_input_fn())

with self.test_session() as sess:

vals = sess.run([features])

self.assertAllEqual(vals[0], np.arange(100))

self.assertEqual(labels, None)

self.assertEqual(hooks, [])

def test_parse_input_fn_result_features_only_dataset(self):

def _input_fn():

features = np.expand_dims(np.arange(100), 0)

return dataset_ops.Dataset.from_tensor_slices(features)

features, labels, hooks = util.parse_input_fn_result(_input_fn())

with training.MonitoredSession(hooks=hooks) as sess:

vals = sess.run([features])

self.assertAllEqual(vals[0], np.arange(100))

self.assertEqual(labels, None)

self.assertIsInstance(hooks[0], util._DatasetInitializerHook)

def test_parse_input_fn_result_invalid(self):

def _input_fn():

features = np.expand_dims(np.arange(100), 0)

labels = np.expand_dims(np.arange(100, 200), 0)

return dataset_ops.Dataset.from_tensor_slices((features, labels, labels))

with self.assertRaisesRegexp(ValueError, 'input_fn should return'):

util.parse_input_fn_result(_input_fn())

if __name__ == '__main__':

test.main()

tensorflow/tensorflow/python/estimator/util_test.py at master · codeplaysoftware/tensorflow · GitHub

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