name scope clean-ups in regularization and resnet (#340)

examples/ResNet/imagenet_resnet_utils.py

@@ -157,6 +157,7 @@ def eval_on_ILSVRC12(model, model_file, dataflow):
 
 
 def image_preprocess(image, bgr=True):
+    with tf.name_scope('image_preprocess'):
         if image.dtype.base_dtype != tf.float32:
             image = tf.cast(image, tf.float32)
         image = image * (1.0 / 255)
@@ -177,8 +178,9 @@ def compute_loss_and_error(logits, label):
     loss = tf.reduce_mean(loss, name='xentropy-loss')
 
     def prediction_incorrect(logits, label, topk=1, name='incorrect_vector'):
-        return tf.cast(tf.logical_not(tf.nn.in_top_k(logits, label, topk)),
-                       tf.float32, name=name)
+        with tf.name_scope('prediction_incorrect'):
+            x = tf.logical_not(tf.nn.in_top_k(logits, label, topk))
+        return tf.cast(x, tf.float32, name=name)
 
     wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
     add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))

tensorpack/models/regularize.py

@@ -46,6 +46,7 @@ def regularize_cost(regex, func, name='regularize_cost'):
     # If vars are replicated, only regularize those in the current tower
     params = ctx.filter_vars_by_vs_name(params)
 
+    with tf.name_scope('regularize_cost'):
         costs = []
         for p in params:
             para_name = p.name