diff --git a/tensorflow/contrib/model_pruning/README.md b/tensorflow/contrib/model_pruning/README.md
index 86f4fd6adf6..9143d082bf0 100644
--- a/tensorflow/contrib/model_pruning/README.md
+++ b/tensorflow/contrib/model_pruning/README.md
@@ -66,10 +66,10 @@ is the sparsity_function_begin_step. In this equation, the
 sparsity_function_exponent is set to 3.
 ### Adding pruning ops to the training graph
 
-The final step involves adding ops to the training graph that monitors the
-distribution of the layer's weight magnitudes and determines the layer threshold
-such masking all the weights below this threshold achieves the sparsity level
-desired for the current training step. This can be achieved as follows:
+The final step involves adding ops to the training graph that monitor the
+distribution of the layer's weight magnitudes and determine the layer threshold,
+such that masking all the weights below this threshold achieves the sparsity
+level desired for the current training step. This can be achieved as follows:
 
 ```python
 tf.app.flags.DEFINE_string(
@@ -79,7 +79,7 @@ tf.app.flags.DEFINE_string(
 with tf.graph.as_default():
 
   # Create global step variable
-  global_step = tf.train.get_global_step()
+  global_step = tf.train.get_or_create_global_step()
 
   # Parse pruning hyperparameters
   pruning_hparams = pruning.get_pruning_hparams().parse(FLAGS.pruning_hparams)
@@ -103,6 +103,7 @@ with tf.graph.as_default():
     mon_sess.run(mask_update_op)
 
 ```
+Ensure that `global_step` is being [incremented](https://www.tensorflow.org/api_docs/python/tf/train/Optimizer#minimize), otherwise pruning will not work!
 
 ## Example: Pruning and training deep CNNs on the cifar10 dataset
 
diff --git a/tensorflow/contrib/model_pruning/python/pruning.py b/tensorflow/contrib/model_pruning/python/pruning.py
index 4b7af18b331..da9d398cbc0 100644
--- a/tensorflow/contrib/model_pruning/python/pruning.py
+++ b/tensorflow/contrib/model_pruning/python/pruning.py
@@ -518,11 +518,11 @@ class Pruning(object):
       summary.scalar('last_mask_update_step', self._last_update_step)
       masks = get_masks()
       thresholds = get_thresholds()
-      for index, mask in enumerate(masks):
+      for mask, threshold in zip(masks, thresholds):
         if not self._exists_in_do_not_prune_list(mask.name):
-          summary.scalar(mask.name + '/sparsity', nn_impl.zero_fraction(mask))
-          summary.scalar(thresholds[index].op.name + '/threshold',
-                         thresholds[index])
+          summary.scalar(mask.op.name + '/sparsity', 
+                         nn_impl.zero_fraction(mask))
+          summary.scalar(threshold.op.name + '/threshold', threshold)
 
   def print_hparams(self):
     logging.info(self._spec.to_json())