228 lines
8.9 KiB
Python
228 lines
8.9 KiB
Python
# Copyright 2015 Google Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Functional test for moving_averages.py."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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from tensorflow.python.ops import state_ops
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from tensorflow.python.training import moving_averages
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class MovingAveragesTest(tf.test.TestCase):
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def testAssignMovingAverage(self):
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with self.test_session():
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var = tf.Variable([10.0, 11.0])
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val = tf.constant([1.0, 2.0], tf.float32)
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decay = 0.25
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assign = moving_averages.assign_moving_average(var, val, decay)
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tf.initialize_all_variables().run()
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self.assertAllClose([10.0, 11.0], var.eval())
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assign.op.run()
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self.assertAllClose([10.0 * 0.25 + 1.0 * (1.0 - 0.25),
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11.0 * 0.25 + 2.0 * (1.0 - 0.25)],
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var.eval())
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def testWeightedMovingAverage(self):
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with self.test_session() as sess:
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decay = 0.5
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weight = tf.placeholder(tf.float32, [])
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val = tf.placeholder(tf.float32, [])
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wma = moving_averages.weighted_moving_average(val, decay, weight)
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tf.initialize_all_variables().run()
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# Get the first weighted moving average.
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val_1 = 3.0
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weight_1 = 4.0
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wma_array = sess.run(
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wma, feed_dict={val: val_1, weight: weight_1})
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numerator_1 = val_1 * weight_1 * (1.0 - decay)
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denominator_1 = weight_1 * (1.0 - decay)
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self.assertAllClose(numerator_1 / denominator_1, wma_array)
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# Get the second weighted moving average.
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val_2 = 11.0
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weight_2 = 22.0
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wma_array = sess.run(
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wma, feed_dict={val: val_2, weight: weight_2})
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numerator_2 = numerator_1 * decay + val_2 * weight_2 * (1.0 - decay)
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denominator_2 = denominator_1 * decay + weight_2 * (1.0 - decay)
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self.assertAllClose(numerator_2 / denominator_2, wma_array)
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def _Repeat(value, dim):
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if dim == 1:
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return value
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return [value] * dim
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class ExponentialMovingAverageTest(tf.test.TestCase):
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def _CheckDecay(self, ema, actual_decay, dim):
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tens = _Repeat(10.0, dim)
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thirties = _Repeat(30.0, dim)
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var0 = tf.Variable(tens, name="v0")
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var1 = tf.Variable(thirties, name="v1")
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tf.initialize_all_variables().run()
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# Note that tensor2 is not a Variable but just a plain Tensor resulting
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# from the sum operation.
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tensor2 = var0 + var1
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update = ema.apply([var0, var1, tensor2])
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avg0 = ema.average(var0)
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avg1 = ema.average(var1)
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avg2 = ema.average(tensor2)
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self.assertItemsEqual([var0, var1], tf.moving_average_variables())
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self.assertFalse(avg0 in tf.trainable_variables())
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self.assertFalse(avg1 in tf.trainable_variables())
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self.assertFalse(avg2 in tf.trainable_variables())
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tf.initialize_all_variables().run()
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self.assertEqual("v0/ExponentialMovingAverage:0", avg0.name)
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self.assertEqual("v1/ExponentialMovingAverage:0", avg1.name)
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self.assertEqual("add/ExponentialMovingAverage:0", avg2.name)
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# Check initial values.
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self.assertAllClose(tens, var0.eval())
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self.assertAllClose(thirties, var1.eval())
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self.assertAllClose(_Repeat(10.0 + 30.0, dim), tensor2.eval())
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# Check that averages are initialized correctly.
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self.assertAllClose(tens, avg0.eval())
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self.assertAllClose(thirties, avg1.eval())
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# Note that averages of Tensor's initialize to zeros_like since no value
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# of the Tensor is known because the Op has not been run (yet).
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self.assertAllClose(_Repeat(0.0, dim), avg2.eval())
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# Update the averages and check.
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update.run()
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dk = actual_decay
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expected = _Repeat(10.0 * dk + 10.0 * (1 - dk), dim)
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self.assertAllClose(expected, avg0.eval())
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expected = _Repeat(30.0 * dk + 30.0 * (1 - dk), dim)
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self.assertAllClose(expected, avg1.eval())
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expected = _Repeat(0.0 * dk + (10.0 + 30.0) * (1 - dk), dim)
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self.assertAllClose(expected, avg2.eval())
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# Again, update the averages and check.
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update.run()
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expected = _Repeat((10.0 * dk + 10.0 * (1 - dk)) * dk + 10.0 * (1 - dk),
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dim)
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self.assertAllClose(expected, avg0.eval())
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expected = _Repeat((30.0 * dk + 30.0 * (1 - dk)) * dk + 30.0 * (1 - dk),
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dim)
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self.assertAllClose(expected, avg1.eval())
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expected = _Repeat(((0.0 * dk + (10.0 + 30.0) * (1 - dk)) * dk +
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(10.0 + 30.0) * (1 - dk)),
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dim)
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self.assertAllClose(expected, avg2.eval())
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def testAverageVariablesNoNumUpdates_Scalar(self):
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with self.test_session():
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ema = tf.train.ExponentialMovingAverage(0.25)
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self._CheckDecay(ema, actual_decay=0.25, dim=1)
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def testAverageVariablesNoNumUpdates_Vector(self):
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with self.test_session():
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ema = tf.train.ExponentialMovingAverage(0.25)
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self._CheckDecay(ema, actual_decay=0.25, dim=5)
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def testAverageVariablesNumUpdates_Scalar(self):
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with self.test_session():
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# With num_updates 1, the decay applied is 0.1818
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ema = tf.train.ExponentialMovingAverage(0.25, num_updates=1)
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self._CheckDecay(ema, actual_decay=0.181818, dim=1)
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def testAverageVariablesNumUpdates_Vector(self):
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with self.test_session():
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# With num_updates 1, the decay applied is 0.1818
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ema = tf.train.ExponentialMovingAverage(0.25, num_updates=1)
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self._CheckDecay(ema, actual_decay=0.181818, dim=5)
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def testAverageVariablesWithControlDeps(self):
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with self.test_session() as sess:
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v0 = tf.Variable(0, name="v0")
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add_to_v0 = v0.assign_add(1)
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v1 = tf.Variable([10.0], name="v1")
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assign_to_v1 = v1.assign([20.0])
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ema = tf.train.ExponentialMovingAverage(0.25)
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with tf.control_dependencies([add_to_v0]):
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ema_op = ema.apply([v1])
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# the moving average of v1 should not have any control inputs
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v1_avg = ema.average(v1)
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self.assertEqual([], v1_avg.initializer.control_inputs)
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self.assertEqual([], v1_avg.value().op.control_inputs)
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self.assertEqual([], v1_avg.ref().op.control_inputs)
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# We should be able to initialize v1_avg before v0.
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sess.run(v1_avg.initializer)
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sess.run(v0.initializer)
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self.assertEqual([10.0], sess.run(v1_avg))
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# running ema_op should add to v0 (in addition to updating v1_avg)
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sess.run(assign_to_v1)
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sess.run(ema_op)
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self.assertEqual(1, sess.run(v0))
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self.assertEqual([17.5], sess.run(v1_avg))
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def testAverageVariablesNames(self):
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v0 = tf.Variable(10.0, name="v0")
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v1 = tf.Variable(30.0, name="v1")
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# Add a non-trainable variable.
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v2 = tf.Variable(20.0, name="v2", trainable=False)
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tensor2 = v0 + v1
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ema = tf.train.ExponentialMovingAverage(0.25, name="foo_avg")
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self.assertEqual("v0/foo_avg", ema.average_name(v0))
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self.assertEqual("v1/foo_avg", ema.average_name(v1))
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self.assertEqual("add/foo_avg", ema.average_name(tensor2))
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ema.apply([v0, v1, tensor2])
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vars_to_restore = ema.variables_to_restore()
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# vars_to_restore should contain the following:
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# {v0/foo_avg : v0,
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# v1/foo_avg : v1,
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# add/foo_avg : add/foo_avg
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# v2 : v2}
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self.assertEqual(sorted(vars_to_restore.keys()),
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sorted([ema.average_name(v0),
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ema.average_name(v1),
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ema.average_name(tensor2),
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v2.op.name]))
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self.assertEqual(ema.average_name(v0), ema.average(v0).op.name)
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self.assertEqual(ema.average_name(v1), ema.average(v1).op.name)
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self.assertEqual(ema.average_name(tensor2), ema.average(tensor2).op.name)
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def testAverageVariablesDeviceAssignment(self):
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with tf.device("/job:dev_v0"):
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v0 = tf.Variable(10.0, name="v0")
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with tf.device("/job:dev_v1"):
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v1 = state_ops.variable_op(shape=[1], dtype=tf.float32, name="v1")
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tensor2 = v0 + v1
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ema = tf.train.ExponentialMovingAverage(0.25, name="foo_avg")
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with tf.device("/job:default"):
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ema.apply([v0, v1, tensor2])
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self.assertDeviceEqual("/job:dev_v0", ema.average(v0).device)
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self.assertDeviceEqual("/job:dev_v1", ema.average(v1).device)
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# However, the colocation property is maintained.
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self.assertEqual([b"loc:@v1"],
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ema.average(v1).op.colocation_groups())
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self.assertDeviceEqual("/job:default", ema.average(tensor2).device)
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if __name__ == "__main__":
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tf.test.main()
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