X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=README.md;h=869a1d97c09df81fd886168e78159bc56b7f8bdf;hb=c1de441737656e7268f65116218cec6cdb304757;hp=e18ea1f91e70bfdb0ebc372073ea5d74e32128ba;hpb=be353fdfc2a57172064a024f8cec6015c9d908e5;p=dagnn.git diff --git a/README.md b/README.md index e18ea1f..869a1d9 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,15 @@ -This package implements a new module nn.DAG which inherits from nn.Container and allows to combine modules in an arbitrary graph without cycle. +# Introduction # -#Example# +This package implements a new module nn.DAG for the torch framework, +which inherits from nn.Container and allows to combine modules in an +arbitrary graph without cycle. + +## Example ## A typical use would be: -```Lua +```lua model = nn.DAG() a = nn.Linear(100, 10) @@ -14,11 +18,11 @@ c = nn.Linear(10, 15) d = nn.CMulTable() e = nn.Linear(15, 15) -model:addEdge(a, b) -model:addEdge(b, nn.Linear(10, 15), nn.ReLU(), d) -model:addEdge(b, c) -model:addEdge(c, d) -model:addEdge(c, nn.Mul(-1), e) +model:connect(a, b) +model:connect(b, nn.Linear(10, 15), nn.ReLU(), d) +model:connect(b, c) +model:connect(c, d) +model:connect(c, nn.Mul(-1), e) model:setInput(a) model:setOutput({ d, e }) @@ -40,14 +44,71 @@ which would encode the following graph and run a forward pass with a random batch of 30 samples. -Note that DAG:addEdge +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. + +# 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, 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 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 +table composed of two tensors, corresponding to the outputs of d and e +respectively. + +## Functions ## + +### nn.DAG() ### + +Create a new empty DAG, which inherits from nn.Container. -#Input and output# +### nn.DAG:connect([module1 [, module2 [, ...]]]) ### -If a node has a single successor, its output is sent unchanged as input to that successor. If it has multiple successors, the outputs are collected into a table, and the table is used as input to the successor node. The indexes of the outputs in that table reflects the order in which they appear in the addEdge commands. +Add new nodes corresponding to the modules passed as arguments if they +have not been already added in a previous call. Add edges between +every two nodes associated to two successive modules in the arguments. -The expected input (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) +Calling this function with n > 2 arguments is strictly equivalent to +calling it n-1 times on the pairs of successive arguments. -So for instance, in the example above, the DAG expects a tensor as 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 e and f respectively. +### nn.DAG:setInput(i) ### + +Defines 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() ### + +Prints the list of nodes. + +### 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 + +``` +dot graph.dot -T pdf -o graph.pdf +``` -*Francois Fleuret, Jan 12th, 2017* +-- +*Francois Fleuret, Jan 13th, 2017*