Shift tensorflow/examples over to the new summary ops.

This is a fairly minimal change, some functions have been cleaned up slightly
because the new summary ops dont need manual namespacing.

Behavior is approximately identical.
Change: 136745220

1 files changed, 13 insertions, 13 deletions

diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index d52a23fd15..4f06cb8add 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -647,17 +647,17 @@ def add_input_distortions(flip_left_right, random_crop, random_scale,
   return jpeg_data, distort_result
 
 
-def variable_summaries(var, name):
+def variable_summaries(var):
   """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
   with tf.name_scope('summaries'):
     mean = tf.reduce_mean(var)
-    tf.scalar_summary('mean/' + name, mean)
+    tf.summary.scalar('mean', mean)
     with tf.name_scope('stddev'):
       stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
-    tf.scalar_summary('stddev/' + name, stddev)
-    tf.scalar_summary('max/' + name, tf.reduce_max(var))
-    tf.scalar_summary('min/' + name, tf.reduce_min(var))
-    tf.histogram_summary(name, var)
+    tf.summary.scalar('stddev', stddev)
+    tf.summary.scalar('max', tf.reduce_max(var))
+    tf.summary.scalar('min', tf.reduce_min(var))
+    tf.summary.histogram('histogram', var)
 
 
 def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor):
@@ -695,23 +695,23 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor):
   with tf.name_scope(layer_name):
     with tf.name_scope('weights'):
       layer_weights = tf.Variable(tf.truncated_normal([BOTTLENECK_TENSOR_SIZE, class_count], stddev=0.001), name='final_weights')
-      variable_summaries(layer_weights, layer_name + '/weights')
+      variable_summaries(layer_weights)
     with tf.name_scope('biases'):
       layer_biases = tf.Variable(tf.zeros([class_count]), name='final_biases')
-      variable_summaries(layer_biases, layer_name + '/biases')
+      variable_summaries(layer_biases)
     with tf.name_scope('Wx_plus_b'):
       logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases
-      tf.histogram_summary(layer_name + '/pre_activations', logits)
+      tf.summary.histogram('pre_activations', logits)
 
   final_tensor = tf.nn.softmax(logits, name=final_tensor_name)
-  tf.histogram_summary(final_tensor_name + '/activations', final_tensor)
+  tf.summary.histogram('activations', final_tensor)
 
   with tf.name_scope('cross_entropy'):
     cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
       logits, ground_truth_input)
     with tf.name_scope('total'):
       cross_entropy_mean = tf.reduce_mean(cross_entropy)
-    tf.scalar_summary('cross entropy', cross_entropy_mean)
+  tf.summary.scalar('cross_entropy', cross_entropy_mean)
 
   with tf.name_scope('train'):
     train_step = tf.train.GradientDescentOptimizer(FLAGS.learning_rate).minimize(
@@ -738,7 +738,7 @@ def add_evaluation_step(result_tensor, ground_truth_tensor):
         tf.argmax(ground_truth_tensor, 1))
     with tf.name_scope('accuracy'):
       evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
-    tf.scalar_summary('accuracy', evaluation_step)
+  tf.summary.scalar('accuracy', evaluation_step)
   return evaluation_step
 
 
@@ -792,7 +792,7 @@ def main(_):
   evaluation_step = add_evaluation_step(final_tensor, ground_truth_input)
 
   # Merge all the summaries and write them out to /tmp/retrain_logs (by default)
-  merged = tf.merge_all_summaries()
+  merged = tf.summary.merge_all()
   train_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/train',
                                         sess.graph)
   validation_writer = tf.train.SummaryWriter(FLAGS.summaries_dir + '/validation')