X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=dagnn.git;a=blobdiff_plain;f=README.md;h=fa77a7e785ccdb23880cacdea55e5d609f294399;hp=436aa63d1c585c267e8ee40756f23588a01765b0;hb=HEAD;hpb=5a149d40e14c1931103514b41c56a4139f06973a

diff --git a/README.md b/README.md
index 436aa63..fa77a7e 100644
--- a/README.md
+++ b/README.md
@@ -1,15 +1,14 @@
+# Introduction #
 
-#Introduction#
+This package implements a new module nn.DAG for the [torch framework](https://torch.ch),
+which inherits from [nn.Container](https://github.com/torch/nn/blob/master/Container.lua) and allows to combine modules in an
+arbitrary [Directed Acyclic Graph (DAG).](https://en.wikipedia.org/wiki/Directed_acyclic_graph)
 
-This package implements a new module nn.DAG which inherits from
-nn.Container and allows to combine modules in an arbitrary graph
-without cycle.
-
-##Example##
+## Example ##
 
 A typical use would be:
 
-```Lua
+```lua
 model = nn.DAG()
 
 a = nn.Linear(100, 10)
@@ -48,73 +47,75 @@ Note that DAG:connect allows to add a bunch of edges at once. This is
 particularly useful to add anonymous modules which have a single
 predecessor and successor.
 
-##Input and output##
+# Usage #
+
+## Input and output ##
+
+The DAG can deal with modules which take as input and produce as
+output tensors and nested tables of tensors.
 
-If a node has a single predecessor, its output is taken as-is. If it
-has multiple predecessors, all the outputs are collected into a table,
-and the table is used as input. The indexes of the outputs in that
-table reflects the order in which the predecessors appeared in the
+If a node has a single predecessor, the output of the latter is taken
+as-is as the input to the former. If it has multiple predecessors, all
+the outputs are collected into a table, and the table is used as
+input. The indexes of the outputs in that table reflect the
+chronological order in which the edges where created in the
 DAG:connect() commands.
 
 The input to the DAG (respectively the produced output) is a nested
 table of inputs reflecting the structure of the nested table of
-modules provided to DAG:setInput (respectively DAG:setOutput)
+modules given as argument to DAG:setInput (respectively DAG:setOutput)
 
 So for instance, in the example above, the model expects a tensor as
-input, since it is the input to the module a, and its output will is a
+input, since it is the input to the module a, and its output is a
 table composed of two tensors, corresponding to the outputs of d and e
 respectively.
 
-##Usage##
+## Functions ##
 
-###nn.DAG()###
+### nn.DAG() ###
 
 Create a new empty DAG, which inherits from nn.Container.
 
-###nn.DAG:connect([module1 [, module2 [, ...]]])###
+### nn.DAG:connect(module1, module2 [, module3, [...]]) ###
 
 Add new nodes corresponding to the modules passed as arguments if they
-are not already existing. Add edges between every two nodes
-corresponding to a pair of successive modules in the arguments.
+have not been already added in a previous call. Add edges between
+every two nodes associated to two 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.
+Calling this function with n > 2 arguments is strictly equivalent to
+calling it n-1 times on the pairs of successive arguments.
 
-###nn.DAG:setInput(i)###
+Accepting more than two arguments allows in particular to add
+anonymous modules, which are not associated to variables. In principle
+the only ones that have to be non-anonymous are those that have more
+than one successor/predecessor and/or are inputs/outputs.
 
-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:setInput(i) ###
 
-###nn.DAG:setOutput(o)###
+Define the content and structure of the input. The argument should be
+either a module, or a (nested) table of modules. The input to the DAG
+should be a (nested) table of inputs, with the corresponding
+structure.
+
+### nn.DAG:setOutput(o) ###
 
 Similar to DAG:setInput().
 
-###nn.DAG:print()###
+### nn.DAG:print() ###
 
-Prints the list of nodes.
+Print 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
-a pdf file with
+a pdf file such as [this one](https://fleuret.org/git-extract/dagnn/graph.pdf) with
 
 ```
 dot graph.dot -T pdf -o graph.pdf
 ```
 
-###nn.DAG:updateOutput(input)###
-
-See the torch documentation.
-
-###nn.DAG:updateGradInput(input, gradOutput)###
-
-See the torch documentation.
-
-###nn.DAG:accGradParameters(input, gradOutput, scale)###
-
-See the torch documentation.
+### nn.DAG:setLabel(module, name) ###
 
--- 
-*Francois Fleuret, Jan 13th, 2017*
+Add a label to the given module, that will be used for DAG:print() and DAG:saveDot()