optimizers: fix bug having to do with optimize_loss and unconnected variables

* if a variable does not have a gradient, its gradient is None as returned by tf.gradients
* this would cause an error in _add_scaled_noise_to_gradients (calling gradient.get_shape())
Change: 127765890

2 files changed, 9 insertions, 2 deletions

diff --git a/tensorflow/contrib/layers/python/layers/optimizers.py b/tensorflow/contrib/layers/python/layers/optimizers.py
index 554c2185b4..80fe56b9da 100644
--- a/tensorflow/contrib/layers/python/layers/optimizers.py
+++ b/tensorflow/contrib/layers/python/layers/optimizers.py
@@ -209,6 +209,9 @@ def _add_scaled_noise_to_gradients(grads_and_vars, gradient_noise_scale):
   gradients, variables = zip(*grads_and_vars)
   noisy_gradients = []
   for gradient in gradients:
+    if gradient is None:
+      noisy_gradients.append(None)
+      continue
     if isinstance(gradient, ops.IndexedSlices):
       gradient_shape = gradient.dense_shape
     else:
@@ -222,7 +225,8 @@ def _multiply_gradients(grads_and_vars, gradient_multipliers):
   """Multiply specified gradients."""
   multiplied_grads_and_vars = []
   for grad, var in grads_and_vars:
-    if var in gradient_multipliers or var.name in gradient_multipliers:
+    if (grad is not None and
+        (var in gradient_multipliers or var.name in gradient_multipliers)):
       key = var if var in gradient_multipliers else var.name
       grad *= constant_op.constant(
           gradient_multipliers[key], dtype=dtypes.float32)
diff --git a/tensorflow/contrib/layers/python/layers/optimizers_test.py b/tensorflow/contrib/layers/python/layers/optimizers_test.py
index 6814ccad67..a38a8fe90b 100644
--- a/tensorflow/contrib/layers/python/layers/optimizers_test.py
+++ b/tensorflow/contrib/layers/python/layers/optimizers_test.py
@@ -130,7 +130,10 @@ class OptimizersTest(tf.test.TestCase):
     unused_variable = tf.get_variable("ignore_me", [])
 
     tf.contrib.layers.optimize_loss(
-        loss, global_step, learning_rate=0.1, optimizer="SGD")
+        loss, global_step, learning_rate=0.1, optimizer="SGD",
+        gradient_noise_scale=10.0,
+        gradient_multipliers={unused_variable: 1.},
+        clip_gradients=10.0)
 
   def testUpdateOp(self):
     optimizers = ["SGD", tf.train.GradientDescentOptimizer,