Fix bad unpack in error message generation when no gradients are provided

for any variables in apply_gradients().
Add tests.
Change: 141480285

tensorflow/python/training/optimizer.py

@@ -388,7 +388,7 @@ class Optimizer(object):
     var_list = [v for g, v, _ in converted_grads_and_vars if g is not None]
     if not var_list:
       raise ValueError("No gradients provided for any variable: %s." %
-                       ([str(v) for _, v in converted_grads_and_vars],))
+                       ([str(v) for _, _, v in converted_grads_and_vars],))
     with ops.control_dependencies(None):
       self._create_slots(var_list)
     update_ops = []

tensorflow/python/training/optimizer_test.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Functional test for optimizer."""
 from __future__ import absolute_import
 from __future__ import division
@@ -52,8 +51,7 @@ class OptimizerTest(tf.test.TestCase):
         sgd_op = tf.train.GradientDescentOptimizer(3.0)
         opt_op = sgd_op.minimize(
             cost,
-            global_step,
-            [var0, var1],
+            global_step, [var0, var1],
             aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)
 
         tf.global_variables_initializer().run()
@@ -75,9 +73,8 @@ class OptimizerTest(tf.test.TestCase):
         grad_loss = tf.constant([42, -42], dtype=dtype)
         global_step = tf.Variable(tf.zeros([], tf.int64), name='global_step')
         sgd_op = tf.train.GradientDescentOptimizer(3.0)
-        opt_op = sgd_op.minimize(cost,
-                                 global_step, [var0, var1],
-                                 grad_loss=grad_loss)
+        opt_op = sgd_op.minimize(
+            cost, global_step, [var0, var1], grad_loss=grad_loss)
 
         tf.global_variables_initializer().run()
         # Fetch params to validate initial values
@@ -86,10 +83,10 @@ class OptimizerTest(tf.test.TestCase):
         # Run 1 step of sgd through optimizer
         opt_op.run()
         # Validate updated params
-        self.assertAllClose(
-            [1.0 - 3 * 5 * 42.0, 2.0 - 3 * 5 * (-42.0)], var0.eval())
-        self.assertAllClose(
-            [3.0 - 3 * 3 * 42.0, 4.0 - 3 * 3 * (-42.0)], var1.eval())
+        self.assertAllClose([1.0 - 3 * 5 * 42.0, 2.0 - 3 * 5 * (-42.0)],
+                            var0.eval())
+        self.assertAllClose([3.0 - 3 * 3 * 42.0, 4.0 - 3 * 3 * (-42.0)],
+                            var1.eval())
 
   def testNoVariables(self):
     for dtype in [tf.half, tf.float32, tf.float64]:
@@ -113,6 +110,28 @@ class OptimizerTest(tf.test.TestCase):
           # var1 has no gradient
           sgd_op.minimize(cost, global_step, [var1])
 
+  def testNoGradientsForAnyVariables_Minimize(self):
+    for dtype in [tf.half, tf.float32, tf.float64]:
+      with self.test_session():
+        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
+        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
+        cost = tf.constant(5.0)
+        global_step = tf.Variable(tf.zeros([], tf.int64), name='global_step')
+        sgd_op = tf.train.GradientDescentOptimizer(3.0)
+        with self.assertRaisesRegexp(ValueError,
+                                     'No gradients provided for any variable'):
+          sgd_op.minimize(cost, global_step, [var0, var1])
+
+  def testNoGradientsForAnyVariables_ApplyGradients(self):
+    for dtype in [tf.half, tf.float32, tf.float64]:
+      with self.test_session():
+        var0 = tf.Variable([1.0, 2.0], dtype=dtype)
+        var1 = tf.Variable([3.0, 4.0], dtype=dtype)
+        sgd_op = tf.train.GradientDescentOptimizer(3.0)
+        with self.assertRaisesRegexp(ValueError,
+                                     'No gradients provided for any variable'):
+          sgd_op.apply_gradients([(None, var0), (None, var1)])
+
   def testGradientsAsVariables(self):
     for dtype in [tf.half, tf.float32, tf.float64]:
       with self.test_session() as sess:
@@ -123,8 +142,13 @@ class OptimizerTest(tf.test.TestCase):
         sgd_op = tf.train.GradientDescentOptimizer(3.0)
         grads_and_vars = sgd_op.compute_gradients(cost, [var0, var1])
         # Convert gradients to tf.Variables
-        converted_grads = [tf.Variable(tf.zeros([2], dtype)) for i in grads_and_vars]
-        convert_ops = [tf.assign(converted_grads[i], gv[0]) for i,gv in enumerate(grads_and_vars)]
+        converted_grads = [
+            tf.Variable(tf.zeros([2], dtype)) for i in grads_and_vars
+        ]
+        convert_ops = [
+            tf.assign(converted_grads[i], gv[0])
+            for i, gv in enumerate(grads_and_vars)
+        ]
 
         converted_grads_and_vars = list(zip(converted_grads, [var0, var1]))
         opt_op = sgd_op.apply_gradients(converted_grads_and_vars, global_step)