update docs

tensorpack/callbacks/saver.py

@@ -46,7 +46,7 @@ class ModelSaver(Callback):
 
     def _setup_graph(self):
         assert self.checkpoint_dir is not None, \
-            "ModelSaver() doesn't have a valid checkpoint directory."
+            "Please provide 'checkpoint_dir' for ModelSaver, or use logger.set_logger_dir()"
         vars = []
         for key in self.var_collections:
             vars.extend(tf.get_collection(key))

tensorpack/models/batch_norm.py

@@ -80,8 +80,8 @@ def BatchNorm(inputs, axis=None, training=None, momentum=0.9, epsilon=1e-5,
     1. Accepts an alternative `data_format` option when `axis` is None. For 2D input, this argument will be ignored.
     2. Default value for `momentum` and `epsilon` is different.
     3. Default value for `training` is automatically obtained from tensorpack's `TowerContext`, but can be overwritten.
-    4. Support the `internal_update` option, which cover more use cases than the standard collection-based update.
-    5. Support the `sync_statistics` option, which is very useful in small-batch models.
+    4. Support the ``internal_update`` option, which cover more use cases than the standard collection-based update.
+    5. Support the ``sync_statistics`` option, which is very useful in small-batch models.
 
     Args:
         internal_update (bool): if False, add EMA update ops to