From: Francois Fleuret <francois@fleuret.org>
Date: Fri, 13 Jan 2017 22:14:08 +0000 (+0100)
Subject: Cosmetics.
X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=dagnn.git;a=commitdiff_plain;h=5a149d40e14c1931103514b41c56a4139f06973a

Cosmetics.
---

diff --git a/README.md b/README.md
index f370b71..436aa63 100644
--- a/README.md
+++ b/README.md
@@ -65,13 +65,13 @@ input, since it is the input to the module a, and its output will is a
 table composed of two tensors, corresponding to the outputs of d and e
 respectively.
 
-#Usage#
+##Usage##
 
-##nn.DAG()##
+###nn.DAG()###
 
 Create a new empty DAG, which inherits from nn.Container.
 
-##nn.DAG:connect([module1 [, module2 [, ...]]])##
+###nn.DAG:connect([module1 [, module2 [, ...]]])###
 
 Add new nodes corresponding to the modules passed as arguments if they
 are not already existing. Add edges between every two nodes
@@ -80,21 +80,21 @@ corresponding to a pair of successive modules in the arguments.
 Calling it with n > 2 arguments is strictly equivalent to calling it
 n-1 times on the pairs of successive arguments.
 
-##nn.DAG:setInput(i)##
+###nn.DAG:setInput(i)###
 
 Defines the content and structure of the input. The argument should be
 either a module, or a (nested) table of module. The input to the DAG
 should be a (nested) table of inputs with the corresponding structure.
 
-##nn.DAG:setOutput(o)##
+###nn.DAG:setOutput(o)###
 
 Similar to DAG:setInput().
 
-##nn.DAG:print()##
+###nn.DAG:print()###
 
 Prints the list of nodes.
 
-##nn.DAG:saveDot(filename)##
+###nn.DAG:saveDot(filename)###
 
 Save a dot file to be used by the Graphviz set of tools for graph
 visualization. This dot file can than be used for instance to produce
@@ -104,15 +104,15 @@ a pdf file with
 dot graph.dot -T pdf -o graph.pdf
 ```
 
-##nn.DAG:updateOutput(input)##
+###nn.DAG:updateOutput(input)###
 
 See the torch documentation.
 
-##nn.DAG:updateGradInput(input, gradOutput)##
+###nn.DAG:updateGradInput(input, gradOutput)###
 
 See the torch documentation.
 
-##nn.DAG:accGradParameters(input, gradOutput, scale)##
+###nn.DAG:accGradParameters(input, gradOutput, scale)###
 
 See the torch documentation.