-#Introduction#
+# Introduction #
-This package implements a new module nn.DAG which inherits from
-nn.Container and allows to combine modules in an arbitrary graph
-without cycle.
+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)
-##Example##
+## Example ##
A typical use would be:
-```Lua
+```lua
model = nn.DAG()
a = nn.Linear(100, 10)
particularly useful to add anonymous modules which have a single
predecessor and successor.
-##Input and output##
+# Usage #
-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
+## 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, 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 [, ...]]]) ###
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)###
+### 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.
+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)###
+### 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
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.
-
--
*Francois Fleuret, Jan 13th, 2017*