Updated the cifar10 model to match the internal version and to be compatible...

Updated the cifar10 model to match the internal version and to be compatible with the latest version of TensorFlow

tutorials/image/cifar10/cifar10.py

@@ -90,8 +90,8 @@ 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.histogram_summary(tensor_name + '/activations', x)
-  tf.scalar_summary(tensor_name + '/sparsity',
+  tf.contrib.deprecated.histogram_summary(tensor_name + '/activations', x)
+  tf.contrib.deprecated.scalar_summary(tensor_name + '/sparsity',
                                        tf.nn.zero_fraction(x))
 
 
@@ -134,7 +134,7 @@ def _variable_with_weight_decay(name, shape, stddev, wd):
       shape,
       tf.truncated_normal_initializer(stddev=stddev, dtype=dtype))
   if wd is not None:
-    weight_decay = tf.mul(tf.nn.l2_loss(var), wd, name='weight_loss')
+    weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
     tf.add_to_collection('losses', weight_decay)
   return var
 
@@ -316,8 +316,8 @@ 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.scalar_summary(l.op.name + ' (raw)', l)
-    tf.scalar_summary(l.op.name, loss_averages.average(l))
+    tf.contrib.deprecated.scalar_summary(l.op.name + ' (raw)', l)
+    tf.contrib.deprecated.scalar_summary(l.op.name, loss_averages.average(l))
 
   return loss_averages_op
 
@@ -345,7 +345,7 @@ def train(total_loss, global_step):
                                   decay_steps,
                                   LEARNING_RATE_DECAY_FACTOR,
                                   staircase=True)
-  tf.scalar_summary('learning_rate', lr)
+  tf.contrib.deprecated.scalar_summary('learning_rate', lr)
 
   # Generate moving averages of all losses and associated summaries.
   loss_averages_op = _add_loss_summaries(total_loss)
@@ -360,12 +360,12 @@ def train(total_loss, global_step):
 
   # Add histograms for trainable variables.
   for var in tf.trainable_variables():
-    tf.histogram_summary(var.op.name, var)
+    tf.contrib.deprecated.histogram_summary(var.op.name, var)
 
   # Add histograms for gradients.
   for grad, var in grads:
     if grad is not None:
-      tf.histogram_summary(var.op.name + '/gradients', grad)
+      tf.contrib.deprecated.histogram_summary(var.op.name + '/gradients', grad)
 
   # Track the moving averages of all trainable variables.
   variable_averages = tf.train.ExponentialMovingAverage(

tutorials/image/cifar10/cifar10_input.py

@@ -132,7 +132,7 @@ def _generate_image_and_label_batch(image, label, min_queue_examples,
         capacity=min_queue_examples + 3 * batch_size)
 
   # Display the training images in the visualizer.
-  tf.image_summary('images', images)
+  tf.contrib.deprecated.image_summary('images', images)
 
   return images, tf.reshape(label_batch, [batch_size])
 

tutorials/image/cifar10/cifar10_multi_gpu_train.py

@@ -93,7 +93,7 @@ def tower_loss(scope):
     # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
     # session. This helps the clarity of presentation on tensorboard.
     loss_name = re.sub('%s_[0-9]*/' % cifar10.TOWER_NAME, '', l.op.name)
-    tf.scalar_summary(loss_name, l)
+    tf.contrib.deprecated.scalar_summary(loss_name, l)
 
   return total_loss
 
@@ -187,13 +187,13 @@ def train():
     grads = average_gradients(tower_grads)
 
     # Add a summary to track the learning rate.
-    summaries.append(tf.scalar_summary('learning_rate', lr))
+    summaries.append(tf.contrib.deprecated.scalar_summary('learning_rate', lr))
 
     # Add histograms for gradients.
     for grad, var in grads:
       if grad is not None:
         summaries.append(
-            tf.histogram_summary(var.op.name + '/gradients',
+            tf.contrib.deprecated.histogram_summary(var.op.name + '/gradients',
                                                     grad))
 
     # Apply the gradients to adjust the shared variables.
@@ -202,7 +202,7 @@ def train():
     # Add histograms for trainable variables.
     for var in tf.trainable_variables():
       summaries.append(
-          tf.histogram_summary(var.op.name, var))
+          tf.contrib.deprecated.histogram_summary(var.op.name, var))
 
     # Track the moving averages of all trainable variables.
     variable_averages = tf.train.ExponentialMovingAverage(
@@ -216,7 +216,7 @@ def train():
     saver = tf.train.Saver(tf.global_variables())
 
     # Build the summary operation from the last tower summaries.
-    summary_op = tf.merge_summary(summaries)
+    summary_op = tf.contrib.deprecated.merge_summary(summaries)
 
     # Build an initialization operation to run below.
     init = tf.global_variables_initializer()