update docs about naming rules (fix #1154)

docs/tutorial/trainer.md

@@ -43,9 +43,18 @@ The tower function needs to follow some rules:
 2. It must __respect variable collections__:
    * (Required) Only put variables __trainable by gradient descent__ into `TRAINABLE_VARIABLES`.
    * (Recommended) Put non-trainable variables that need to be used in inference into `MODEL_VARIABLES`.
-3. It must __respect variable scopes__:
-   * The name of any trainable variables created in the function must be like "variable_scope_name/custom/scopes/name".
-     Don't depend on name_scope's name. Don't depend on some tensor's name. Don't use variable_scope's name twice.
+3. It must __respect variable scope names__:
+
+   The name of any trainable variables created in the function must be like "variable_scope_name/custom/scopes/name".
+	 Therefore, the name of any trainable variables must:
+
+	 * Not depend on name_scope's name.
+	 * Not depend on some tensor's name.
+	 * Not use the same variable_scope's name twice.
+
+	 Tensorpack layers create variables based on the name given to the layer  (i.e., `Conv2D('name', x)`).
+	 So the name of the layer needs to follow the above rules as well.
+4. It must __respect variable scope reuse__:
    * The creation of any trainable variables must __respect reuse__ variable scope.
      To respect variable reuse (i.e. sharing), use `tf.get_variable` instead of `tf.Variable` in the function.
 
@@ -54,7 +63,7 @@ The tower function needs to follow some rules:
    * Do not modify the reuse option (e.g., by `scope.reuse_variables()`) of a variable
      scope that is not created by you. This affects other's code. You can always
      open new scopes if you need the reuse option.
-4. It cannot create scopes or variables containing the name 'tower', as it is
+5. It must not create scopes or variables containing the name 'tower', as it is
    reserved for special use.
 
 These conventions are easy to follow, and most layer wrappers (e.g.,