summary for all parameters

example_alexnet.py

@@ -72,7 +72,7 @@ def get_model(inputs, is_training):
                      name='regularize_loss')
     tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, wd_cost)
 
-    add_histogram_summary('.*/W')   # monitor histogram of all W
+    add_param_summary('.*/W')   # monitor histogram of all W
     return [prob, nr_wrong], tf.add_n([wd_cost, cost], name='cost')
 
 def get_config():

example_cifar10.py

@@ -73,7 +73,7 @@ def get_model(inputs, is_training):
                      name='regularize_loss')
     tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, wd_cost)
 
-    add_histogram_summary('.*/W')   # monitor histogram of all W
+    add_param_summary('.*')   # monitor all variables
     return [prob, nr_wrong], tf.add_n([wd_cost, cost], name='cost')
 
 def get_config():
@@ -155,5 +155,5 @@ if __name__ == '__main__':
         if args.load:
             config.session_init = SaverRestore(args.load)
         if args.gpu:
-            config['nr_tower'] = len(args.gpu.split(','))
+            config.nr_tower = len(args.gpu.split(','))
         start_train(config)

example_mnist.py

@@ -82,7 +82,7 @@ def get_model(inputs, is_training):
                      name='regularize_loss')
     tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, wd_cost)
 
-    add_histogram_summary('.*/W')   # monitor histogram of all W
+    add_param_summary('.*/W')   # monitor histogram of all W
     return [prob, nr_wrong], tf.add_n([wd_cost, cost], name='cost')
 
 def get_config():

tensorpack/train.py

@@ -140,6 +140,7 @@ def start_train(config):
         grads = []
         for i in range(config.nr_tower):
             with tf.device('/gpu:{}'.format(i)):
+                with tf.name_scope('tower{}'.format(i)) as scope:
                     model_inputs = get_model_inputs()
                     output_vars, cost_var = config.get_model_func(model_inputs, is_training=True)
                     grads.append(

tensorpack/utils/summary.py

@@ -32,15 +32,18 @@ def add_activation_summary(x, name=None):
     tf.histogram_summary(name + '/activations', x)
     tf.scalar_summary(name + '/sparsity', tf.nn.zero_fraction(x))
 
-def add_histogram_summary(regex):
+def add_param_summary(regex):
     """
-    Add histogram summary for all trainable variables matching the regex
+    Add summary for all trainable variables matching the regex
     """
     import re
     params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
     for p in params:
         name = p.name
         if re.search(regex, name):
+            if p.get_shape().ndims == 0:
+                tf.scalar_summary(name, p)
+            else:
                 tf.histogram_summary(name, p)
 
 def summary_moving_average(cost_var):