1 files changed, 130 insertions, 0 deletions
diff --git a/tensorflow/python/training/moving_averages_test.py b/tensorflow/python/training/moving_averages_test.py
new file mode 100644
index 0000000000..73ee94b400
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+++ b/tensorflow/python/training/moving_averages_test.py
@@ -0,0 +1,130 @@
+"""Functional test for moving_averages.py."""
+import tensorflow.python.platform
+
+from tensorflow.python.framework import test_util
+from tensorflow.python.framework import types
+from tensorflow.python.ops import constant_op
+from tensorflow.python.ops import variables
+from tensorflow.python.platform import googletest
+from tensorflow.python.training import moving_averages
+
+
+class MovingAveragesTest(test_util.TensorFlowTestCase):
+
+  def testAssignMovingAverage(self):
+    with self.test_session():
+      var = variables.Variable([10.0, 11.0])
+      val = constant_op.constant([1.0, 2.0], types.float32)
+      decay = 0.25
+      assign = moving_averages.assign_moving_average(var, val, decay)
+      variables.initialize_all_variables().run()
+      self.assertAllClose([10.0, 11.0], var.eval())
+      assign.op.run()
+      self.assertAllClose([10.0 * 0.25 + 1.0 * (1.0 - 0.25),
+                           11.0 * 0.25 + 2.0 * (1.0 - 0.25)],
+                          var.eval())
+
+def _Repeat(value, dim):
+  if dim == 1:
+    return value
+  return [value for _ in xrange(dim)]
+
+class ExponentialMovingAverageTest(test_util.TensorFlowTestCase):
+
+  def _CheckDecay(self, ema, actual_decay, dim):
+    tens = _Repeat(10.0, dim)
+    thirties = _Repeat(30.0, dim)
+    var0 = variables.Variable(tens, name="v0")
+    var1 = variables.Variable(thirties, name="v1")
+    variables.initialize_all_variables().run()
+    # Note that tensor2 is not a Variable but just a plain Tensor resulting
+    # from the sum operation.
+    tensor2 = var0 + var1
+    update = ema.apply([var0, var1, tensor2])
+    avg0 = ema.average(var0)
+    avg1 = ema.average(var1)
+    avg2 = ema.average(tensor2)
+
+    self.assertFalse(avg0 in variables.trainable_variables())
+    self.assertFalse(avg1 in variables.trainable_variables())
+    self.assertFalse(avg2 in variables.trainable_variables())
+    variables.initialize_all_variables().run()
+
+    self.assertEqual("v0/ExponentialMovingAverage:0", avg0.name)
+    self.assertEqual("v1/ExponentialMovingAverage:0", avg1.name)
+    self.assertEqual("add/ExponentialMovingAverage:0", avg2.name)
+
+    # Check initial values.
+    self.assertAllClose(tens, var0.eval())
+    self.assertAllClose(thirties, var1.eval())
+    self.assertAllClose(_Repeat(10.0 + 30.0, dim), tensor2.eval())
+
+    # Check that averages are initialized correctly.
+    self.assertAllClose(tens, avg0.eval())
+    self.assertAllClose(thirties, avg1.eval())
+    # Note that averages of Tensor's initialize to zeros_like since no value
+    # of the Tensor is known because the Op has not been run (yet).
+    self.assertAllClose(_Repeat(0.0, dim), avg2.eval())
+
+    # Update the averages and check.
+    update.run()
+    dk = actual_decay
+
+    expected = _Repeat(10.0 * dk + 10.0 * (1 - dk), dim)
+    self.assertAllClose(expected, avg0.eval())
+    expected = _Repeat(30.0 * dk + 30.0 * (1 - dk), dim)
+    self.assertAllClose(expected, avg1.eval())
+    expected = _Repeat(0.0 * dk + (10.0 + 30.0) * (1 - dk), dim)
+    self.assertAllClose(expected, avg2.eval())
+
+    # Again, update the averages and check.
+    update.run()
+    expected = _Repeat((10.0 * dk + 10.0 * (1 - dk)) * dk + 10.0 * (1 - dk),
+                       dim)
+    self.assertAllClose(expected, avg0.eval())
+    expected = _Repeat((30.0 * dk + 30.0 * (1 - dk)) * dk + 30.0 * (1 - dk),
+                       dim)
+    self.assertAllClose(expected, avg1.eval())
+    expected = _Repeat(((0.0 * dk + (10.0 + 30.0) * (1 - dk)) * dk +
+                        (10.0 + 30.0) * (1 - dk)),
+                       dim)
+    self.assertAllClose(expected, avg2.eval())
+
+  def testAverageVariablesNoNumUpdates_Scalar(self):
+    with self.test_session():
+      ema = moving_averages.ExponentialMovingAverage(0.25)
+      self._CheckDecay(ema, actual_decay=0.25, dim=1)
+
+  def testAverageVariablesNoNumUpdates_Vector(self):
+    with self.test_session():
+      ema = moving_averages.ExponentialMovingAverage(0.25)
+      self._CheckDecay(ema, actual_decay=0.25, dim=5)
+
+  def testAverageVariablesNumUpdates_Scalar(self):
+    with self.test_session():
+      # With num_updates 1, the decay applied is 0.1818
+      ema = moving_averages.ExponentialMovingAverage(0.25, num_updates=1)
+      self._CheckDecay(ema, actual_decay=0.181818, dim=1)
+
+  def testAverageVariablesNumUpdates_Vector(self):
+    with self.test_session():
+      # With num_updates 1, the decay applied is 0.1818
+      ema = moving_averages.ExponentialMovingAverage(0.25, num_updates=1)
+      self._CheckDecay(ema, actual_decay=0.181818, dim=5)
+
+  def testAverageVariablesNames(self):
+    v0 = variables.Variable(10.0, name="v0")
+    v1 = variables.Variable(30.0, name="v1")
+    tensor2 = v0 + v1
+    ema = moving_averages.ExponentialMovingAverage(0.25, name="foo_avg")
+    self.assertEqual("v0/foo_avg", ema.average_name(v0))
+    self.assertEqual("v1/foo_avg", ema.average_name(v1))
+    self.assertEqual("add/foo_avg", ema.average_name(tensor2))
+    ema.apply([v0, v1, tensor2])
+    self.assertEqual(ema.average_name(v0), ema.average(v0).op.name)
+    self.assertEqual(ema.average_name(v1), ema.average(v1).op.name)
+    self.assertEqual(ema.average_name(tensor2), ema.average(tensor2).op.name)
+
+
+if __name__ == "__main__":
+  googletest.main()