update docs

.github/ISSUE_TEMPLATE.md

@@ -16,11 +16,13 @@ Otherwise, you can post here for:
 
 Some typical questions that we DO NOT answer:
 
-+ "Could you improve/implement an example/paper ?" --
-
-  We have no plans to do so. We don't consider feature
-  requests for examples or implement a paper for you.
++ "Could you implement a paper / other variants of a paper / additional features of a paper ?"
+  -- The answer is: we have no plans to do so.
+	We take feature requests on the library, but not on examples.
+  We don't implement papers or variants/features of a paper for you,
+  unless it can demonstrate some Tensorpack features not yet demonstrated in the existing examples.
   If you don't know how to do something yourself, you may ask a usage question.
+
 + "The examples do not perform as expected after I change the models/dataset/parameters/etc."
 
   Tensorpack maintainers make sure the examples perform well without modifications.

.github/ISSUE_TEMPLATE/feature-requests.md

@@ -8,8 +8,9 @@ about: Suggest an idea for Tensorpack
   (See http://tensorpack.readthedocs.io/tutorial/index.html#extend-tensorpack).
   It does not have to be added to Tensorpack unless you have a good reason.
 
-+ "Could you implement a paper / other variants of a paper ?"
++ "Could you implement a paper / other variants of a paper / additional features of a paper ?"
   -- The answer is: we have no plans to do so.
-  We don't implement papers or variants of a paper for you,
-  unless it demonstrates some Tensorpack features not yet demonstrated in the existing examples.
+	We take feature requests on the library, but not on examples.
+  We don't implement papers or variants/features of a paper for you,
+  unless it can demonstrate some Tensorpack features not yet demonstrated in the existing examples.
   If you don't know how to do something yourself, you may ask a usage question.

docs/tutorial/summary.md

@@ -11,6 +11,7 @@ This is how TensorFlow summaries eventually get logged/saved/printed:
 1. __What to Log__: Define what you want to log in the graph. 
    When you call `tf.summary.xxx` in your graph code, TensorFlow adds an op to
 	`tf.GraphKeys.SUMMARIES` collection (by default).
+   Tensorpack further removes summaries not from the first training tower.
 2. __When to Log__: [MergeAllSummaries](../modules/callbacks.html#tensorpack.callbacks.MergeAllSummaries)
 	callback is one of the [default callbacks](../modules/train.html#tensorpack.train.DEFAULT_CALLBACKS).
 	It runs ops in the `tf.GraphKeys.SUMMARIES` collection (by default) every epoch (by default),
@@ -25,6 +26,8 @@ This is how TensorFlow summaries eventually get logged/saved/printed:
 		saves scalars to a JSON file.
 
 All the "what, when, where" can be customized in either the graph or with the callbacks/monitors setting.
+You can call `tf.summary.xxx(collections=[...])` to add your custom summaries a different collection,
+and use the `MergeAllSummaries(key=...)` callback to write them to monitors.
 
 The design goal to disentangle "what, when, where" is to make components reusable.
 Suppose you have `M` items to log