Update.

diff --git a/dagnn.lua b/dagnn.lua
index 1ec9b4e..52913ad 100755 (executable)
--- a/dagnn.lua
+++ b/dagnn.lua
@@ -1,21 +1,16 @@
-#!/usr/bin/env luajit

 
 require 'torch'
 require 'nn'
 
 require 'torch'
 require 'nn'

-require 'image'
-require 'optim'

 
 

-----------------------------------------------------------------------
+local DAG, parent = torch.class('nn.DAG', 'nn.Container')

 
 

-local Graph, parent = torch.class('nn.Graph', 'nn.Container')
-
-function Graph:__init()
+function DAG:__init()

    parent.__init(self)
    self.pred = {}
    self.succ = {}
 end
 
    parent.__init(self)
    self.pred = {}
    self.succ = {}
 end
 

-function Graph:addEdge(a, b)
+function DAG:addEdge(a, b)

    local pred, succ = self.pred, self.succ
    if not pred[a] and not succ[a] then
       self:add(a)
    local pred, succ = self.pred, self.succ
    if not pred[a] and not succ[a] then
       self:add(a)


@@ -29,7 +24,7 @@ function Graph:addEdge(a, b)
    succ[a][#succ[a] + 1] = b
 end
 
    succ[a][#succ[a] + 1] = b
 end
 

-function Graph:setInput(i)
+function DAG:setInput(i)

    if torch.type(i) == 'table' then
       self.inputModules = i
       for _, m in ipairs(i) do
    if torch.type(i) == 'table' then
       self.inputModules = i
       for _, m in ipairs(i) do


@@ -42,7 +37,7 @@ function Graph:setInput(i)
    end
 end
 
    end
 end
 

-function Graph:setOutput(o)
+function DAG:setOutput(o)

    if torch.type(o) == 'table' then
       self.outputModules = o
       for _, m in ipairs(o) do
    if torch.type(o) == 'table' then
       self.outputModules = o
       for _, m in ipairs(o) do


@@ -55,7 +50,7 @@ function Graph:setOutput(o)
    end
 end
 
    end
 end
 

-function Graph:order()
+function DAG:order()

    local distance = {}
 
    for _, a in pairs(self.inputModules) do
    local distance = {}
 
    for _, a in pairs(self.inputModules) do


@@ -85,13 +80,13 @@ function Graph:order()
    for i, a in ipairs(self.sorted) do self.sorted[i] = a[2] end
 end
 
    for i, a in ipairs(self.sorted) do self.sorted[i] = a[2] end
 end
 

-function Graph:print()
+function DAG:print()

    for i, d in ipairs(self.sorted) do
       print('#' .. i .. ' -> ' .. torch.type(d))
    end
 end
 
    for i, d in ipairs(self.sorted) do
       print('#' .. i .. ' -> ' .. torch.type(d))
    end
 end
 

-function Graph:updateOutput(input)
+function DAG:updateOutput(input)

    if #self.inputModules == 1 then
       self.inputModules[1]:updateOutput(input)
    else
    if #self.inputModules == 1 then
       self.inputModules[1]:updateOutput(input)
    else


@@ -125,43 +120,3 @@ function Graph:updateOutput(input)
 
    return self.output
 end
 
    return self.output
 end

-
-----------------------------------------------------------------------
-
-a = nn.Linear(10, 10)
-b = nn.ReLU()
-c = nn.Linear(10, 3)
-d = nn.Linear(10, 3)
-e = nn.CMulTable()
-f = nn.Linear(3, 2)
-
---[[
-
-   a -----> b ---> c ----> e ---
-             \           /
-              \--> d ---/
-                    \
-                     \---> f ---
-]]--
-
-g = Graph:new()
-
-g:setInput(a)
-g:setOutput({ e, f })
-g:addEdge(c, e)
-g:addEdge(a, b)
-g:addEdge(d, e)
-g:addEdge(b, c)
-g:addEdge(b, d)
-g:addEdge(d, f)
-
-g:order()
-
-g:print(graph)
-
-input = torch.Tensor(3, 10):uniform()
-
-output = g:updateOutput(input)
-
-print(output[1])
-print(output[2])

diff --git a/test-dagnn.lua b/test-dagnn.lua
new file mode 100755 (executable)
index 0000000..a0a81ab
--- /dev/null
+++ b/test-dagnn.lua
@@ -0,0 +1,44 @@
+#!/usr/bin/env luajit
+
+require 'torch'
+require 'nn'
+
+require 'dagnn'
+
+a = nn.Linear(10, 10)
+b = nn.ReLU()
+c = nn.Linear(10, 3)
+d = nn.Linear(10, 3)
+e = nn.CMulTable()
+f = nn.Linear(3, 2)
+
+--[[
+
+   a -----> b ---> c ----> e ---
+             \           /
+              \--> d ---/
+                    \
+                     \---> f ---
+]]--
+
+g = DAG:new()
+
+g:setInput(a)
+g:setOutput({ e, f })
+g:addEdge(c, e)
+g:addEdge(a, b)
+g:addEdge(d, e)
+g:addEdge(b, c)
+g:addEdge(b, d)
+g:addEdge(d, f)
+
+g:order()
+
+g:print(graph)
+
+input = torch.Tensor(3, 10):uniform()
+
+output = g:updateOutput(input)
+
+print(output[1])
+print(output[2])