Update cifar10.py

bug fix for contrib.deprecated eliminatation in tf version 12.

tutorials/image/cifar10/cifar10.py

@@ -91,8 +91,8 @@ def _activation_summary(x):
   # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
   # session. This helps the clarity of presentation on tensorboard.
   tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)
-  tf.contrib.deprecated.histogram_summary(tensor_name + '/activations', x)
-  tf.contrib.deprecated.scalar_summary(tensor_name + '/sparsity',
+  tf.histogram_summary(tensor_name + '/activations', x)
+  tf.scalar_summary(tensor_name + '/sparsity',
                                        tf.nn.zero_fraction(x))
 
 
@@ -317,8 +317,8 @@ def _add_loss_summaries(total_loss):
   for l in losses + [total_loss]:
     # Name each loss as '(raw)' and name the moving average version of the loss
     # as the original loss name.
-    tf.contrib.deprecated.scalar_summary(l.op.name + ' (raw)', l)
-    tf.contrib.deprecated.scalar_summary(l.op.name, loss_averages.average(l))
+    tf.scalar_summary(l.op.name + ' (raw)', l)
+    tf.scalar_summary(l.op.name, loss_averages.average(l))
 
   return loss_averages_op
 
@@ -346,7 +346,7 @@ def train(total_loss, global_step):
                                   decay_steps,
                                   LEARNING_RATE_DECAY_FACTOR,
                                   staircase=True)
-  tf.contrib.deprecated.scalar_summary('learning_rate', lr)
+  tf.scalar_summary('learning_rate', lr)
 
   # Generate moving averages of all losses and associated summaries.
   loss_averages_op = _add_loss_summaries(total_loss)
@@ -361,12 +361,12 @@ def train(total_loss, global_step):
 
   # Add histograms for trainable variables.
   for var in tf.trainable_variables():
-    tf.contrib.deprecated.histogram_summary(var.op.name, var)
+    tf.histogram_summary(var.op.name, var)
 
   # Add histograms for gradients.
   for grad, var in grads:
     if grad is not None:
-      tf.contrib.deprecated.histogram_summary(var.op.name + '/gradients', grad)
+      tf.histogram_summary(var.op.name + '/gradients', grad)
 
   # Track the moving averages of all trainable variables.
   variable_averages = tf.train.ExponentialMovingAverage(