Change default for assign_moving_average to perform 0-debiasing. This is a semantic breaking change.

Change: 139237086

Author: tensorflower-gardener

tensorflow/python/training/moving_averages.py

@@ -29,7 +29,7 @@
 
 
 # TODO(touts): switch to variables.Variable.
-def assign_moving_average(variable, value, decay, zero_debias=False, name=None):
+def assign_moving_average(variable, value, decay, zero_debias=True, name=None):
   """Compute the moving average of a variable.
 
   The moving average of 'variable' updated with 'value' is:
@@ -124,8 +124,10 @@ def weighted_moving_average(value,
                                                dtype=weight.dtype),
         trainable=False,
         collections=collections)
-    numerator = assign_moving_average(value_x_weight_var, value * weight, decay)
-    denominator = assign_moving_average(weight_var, weight, decay)
+    numerator = assign_moving_average(
+        value_x_weight_var, value * weight, decay, zero_debias=False)
+    denominator = assign_moving_average(
+        weight_var, weight, decay, zero_debias=False)
 
     if truediv:
       return math_ops.truediv(numerator, denominator, name=scope.name)

tensorflow/python/training/moving_averages_test.py

@@ -25,26 +25,26 @@
 
 class MovingAveragesTest(tf.test.TestCase):
 
-  def testAssignMovingAverage(self):
+  def testAssignMovingAverageWithoutZeroDebias(self):
     with self.test_session():
       var = tf.Variable([10.0, 11.0])
       val = tf.constant([1.0, 2.0], tf.float32)
       decay = 0.25
-      assign = moving_averages.assign_moving_average(var, val, decay)
+      assign = moving_averages.assign_moving_average(
+          var, val, decay, zero_debias=False)
       tf.global_variables_initializer().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 testAssignMovingAverageWithZeroDebias(self):
+  def testAssignMovingAverage(self):
     with self.test_session():
       var = tf.Variable([0.0, 0.0])
       val = tf.constant([1.0, 2.0], tf.float32)
       decay = 0.25
-      assign = moving_averages.assign_moving_average(
-          var, val, decay, zero_debias=True)
+      assign = moving_averages.assign_moving_average(var, val, decay)
       tf.global_variables_initializer().run()
       self.assertAllClose([0.0, 0.0], var.eval())
       assign.op.run()
@@ -293,8 +293,8 @@ def testAverageVariablesDeviceAssignment(self):
     with tf.device("/job:dev_v0"):
       v0 = tf.Variable(10.0, name="v0")
     with tf.device("/job:dev_v1"):
-      v1 = gen_state_ops._variable(shape=[1], dtype=tf.float32, 
-          name="v1", container="", shared_name="")
+      v1 = gen_state_ops._variable(shape=[1], dtype=tf.float32,
+                                   name="v1", container="", shared_name="")
       v1.set_shape([1])
     tensor2 = v0 + v1
     ema = tf.train.ExponentialMovingAverage(0.25, name="foo_avg")