Merge changes from github.

PiperOrigin-RevId: 189945839

2 files changed, 9 insertions, 2 deletions

diff --git a/tensorflow/python/layers/base.py b/tensorflow/python/layers/base.py
index e9066d3fda..e4395bea92 100644
--- a/tensorflow/python/layers/base.py
+++ b/tensorflow/python/layers/base.py
@@ -578,7 +578,7 @@ class Layer(checkpointable.CheckpointableBase):
           if isinstance(variable, tf_variables.PartitionedVariable):
             raise RuntimeError(
                 'Partitioned variable regularization is not yet '
-                'supported when executing eagerly. File a feature request'
+                'supported when executing eagerly. File a feature request '
                 'if this is important to you.')
           # Save a zero-argument lambda which runs the regularizer on the
           # variable, to be executed when `Layer.losses` is requested.
diff --git a/tensorflow/python/layers/normalization.py b/tensorflow/python/layers/normalization.py
index 11daf01670..29fb92ccb5 100644
--- a/tensorflow/python/layers/normalization.py
+++ b/tensorflow/python/layers/normalization.py
@@ -664,9 +664,16 @@ def batch_normalization(inputs,
 
   Note: when training, the moving_mean and moving_variance need to be updated.
   By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they
-  need to be added as a dependency to the `train_op`. For example:
+  need to be added as a dependency to the `train_op`. Also, be sure to add
+  any batch_normalization ops before getting the update_ops collection.
+  Otherwise, update_ops will be empty, and training/inference will not work
+  properly. For example:
 
   ```python
+    x_norm = tf.layers.batch_normalization(x, training=training)
+
+    # ...
+
     update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
     with tf.control_dependencies(update_ops):
       train_op = optimizer.minimize(loss)