Update.

[dagnn.git] / test-dagnn.lua

diff --git a/test-dagnn.lua b/test-dagnn.lua
index a45d636..6c09f95 100755 (executable)
--- a/test-dagnn.lua
+++ b/test-dagnn.lua
@@ -5,6 +5,17 @@ require 'nn'
 
 require 'dagnn'
 
+function printTensorTable(t)
+   if torch.type(t) == 'table' then
+      for i, t in pairs(t) do
+         print('-- ELEMENT [' .. i .. '] --')
+         printTensorTable(t)
+      end
+   else
+      print(tostring(t))
+   end
+end
+
 -- torch.setnumthreads(params.nbThreads)
 torch.setdefaulttensortype('torch.DoubleTensor')
 torch.manualSeed(2)
@@ -16,41 +27,32 @@ d = nn.Linear(10, 3)
 e = nn.CMulTable()
 f = nn.Linear(3, 2)
 
---[[
-
-   a -----> b ---> c ----> e ---
-             \           /
-              \--> d ---/
-                    \
-                     \---> f ---
-]]--
+--   a -----> b ---> c ----> e ---
+--             \           /
+--              \--> d ---/
+--                    \
+--                     \---> f ---
 
-g = nn.DAG:new()
-
-g:setInput(a)
-g:setOutput({ e })
+g = nn.DAG()
 
 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:addEdge(d, f)
 
--- g = torch.load('dag.t7')
+g:setInput({a})
+g:setOutput({e,f})
 
 g:print()
 
 input = torch.Tensor(3, 10):uniform()
 
-output = g:updateOutput(input)
+output = g:updateOutput({input})
 
-if torch.type(output) == 'table' then
-   for i, t in pairs(output) do
-      print(tostring(i) .. ' -> ' .. tostring(t))
-   end
-else
-   print(tostring(output))
-end
+printTensorTable(output)
+
+----------------------------------------------------------------------
 
-torch.save('dag.t7', g)
+-- gradInput = g:updateGradInput({ input }, output)