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# Copyright 2015 Google Inc. 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.
# ==============================================================================
"""Functional tests for Unpack Op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.python.platform
import numpy as np
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
from tensorflow.python.kernel_tests import gradient_checker
class UnpackOpTest(tf.test.TestCase):
def testSimple(self):
np.random.seed(7)
for use_gpu in False, True:
with self.test_session(use_gpu=use_gpu):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
data = np.random.randn(*shape)
# Convert data to a single tensorflow tensor
x = tf.constant(data)
# Unpack into a list of tensors
cs = tf.unpack(x, num=shape[0])
self.assertEqual(type(cs), list)
self.assertEqual(len(cs), shape[0])
cs = [c.eval() for c in cs]
self.assertAllEqual(cs, data)
def testGradients(self):
for use_gpu in False, True:
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
data = np.random.randn(*shape)
shapes = [shape[1:]] * shape[0]
for i in xrange(shape[0]):
with self.test_session(use_gpu=use_gpu):
x = tf.constant(data)
cs = tf.unpack(x, num=shape[0])
err = gradient_checker.ComputeGradientError(x, shape, cs[i],
shapes[i])
self.assertLess(err, 1e-6)
def testInferNum(self):
with self.test_session():
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
x = tf.placeholder(np.float32, shape=shape)
cs = tf.unpack(x)
self.assertEqual(type(cs), list)
self.assertEqual(len(cs), shape[0])
def testCannotInferNum(self):
x = tf.placeholder(np.float32)
with self.assertRaisesRegexp(
ValueError, r'Cannot infer num from shape TensorShape\(None\)'):
tf.unpack(x)
if __name__ == '__main__':
tf.test.main()
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