Wrap the cifar10 multigpu model construction part with a variable_scope

Without the new variable_scope, creating apply_gradient_op raises
an error that additional moving average or slot variables could not
be created. This is because of the 'leaky reuse' of variable scope,
so we correct the problem by explicitly introducing a new variable scope.

Related issues: tensorflow/models#901, tensorflow/tensorflow#6220

tutorials/image/cifar10/cifar10_multi_gpu_train.py

@@ -162,6 +162,7 @@ def train():
 
     # Calculate the gradients for each model tower.
     tower_grads = []
+    with tf.variable_scope(tf.get_variable_scope()):
       for i in xrange(FLAGS.num_gpus):
         with tf.device('/gpu:%d' % i):
           with tf.name_scope('%s_%d' % (cifar10.TOWER_NAME, i)) as scope: