update resnet with better init

examples/cifar10_resnet.py

@@ -46,7 +46,7 @@ class Model(ModelDesc):
         def conv(name, l, channel, stride):
             return Conv2D(name, l, channel, 3, stride=stride,
                           nl=tf.identity, use_bias=False,
-                          W_init=tf.contrib.layers.xavier_initializer_conv2d(False))
+                          W_init=tf.random_normal_initializer(stddev=2.0/9/channel))
 
         def residual(name, l, increase_dim=False):
             shape = l.get_shape().as_list()
@@ -124,7 +124,6 @@ def get_data(train_or_test):
             imgaug.RandomCrop((32, 32)),
             imgaug.Flip(horiz=True),
             imgaug.BrightnessAdd(20),
-            imgaug.Contrast((0.6,1.4)),
             imgaug.MapImage(lambda x: x - pp_mean),
         ]
     else:
@@ -147,7 +146,8 @@ def get_config():
 
     sess_config = get_default_sess_config(0.9)
 
-    lr = tf.Variable(0.1, trainable=False, name='learning_rate')
+    # warm up with small LR for 1 epoch
+    lr = tf.Variable(0.01, trainable=False, name='learning_rate')
     tf.scalar_summary('learning_rate', lr)
 
     return TrainConfig(
@@ -158,7 +158,7 @@ def get_config():
             PeriodicSaver(),
             ValidationError(dataset_test, prefix='test'),
             ScheduledHyperParamSetter('learning_rate',
-                                      [(82, 0.01), (123, 0.001), (300, 0.0001)])
+                                      [(1, 0.1), (82, 0.01), (123, 0.001), (300, 0.0001)])
         ]),
         session_config=sess_config,
         model=Model(n=18),

tensorpack/callbacks/base.py

@@ -56,6 +56,9 @@ class Callback(object):
         return self.trainer.global_step
 
     def trigger_epoch(self):
+        """
+        epoch_num is the number of epoch finished.
+        """
         self.epoch_num += 1
         self._trigger_epoch()
 

tensorpack/callbacks/param.py

@@ -25,7 +25,6 @@ class HyperParamSetter(Callback):
     def _before_train(self):
         all_vars = tf.all_variables()
         for v in all_vars:
-            print v.name
             if v.name == self.var_name:
                 self.var = v
                 break

tensorpack/train/base.py

@@ -49,7 +49,7 @@ class Trainer(object):
         if not hasattr(logger, 'LOG_DIR'):
             raise RuntimeError("Please use logger.set_logger_dir at the beginning of your script.")
         self.summary_writer = tf.train.SummaryWriter(
-            logger.LOG_DIR, graph_def=self.sess.graph_def)
+            logger.LOG_DIR, graph_def=self.sess.graph)
         self.summary_op = tf.merge_all_summaries()
         # create an empty StatHolder
         self.stat_holder = StatHolder(logger.LOG_DIR, [])