Update. The input/output can now be nested tables.

[dagnn.git] / test-dagnn.lua

diff --git a/test-dagnn.lua b/test-dagnn.lua
index 262ea6f..a45d636 100755 (executable)
--- a/test-dagnn.lua
+++ b/test-dagnn.lua
@@ -5,6 +5,10 @@ require 'nn'
 
 require 'dagnn'
 
+-- torch.setnumthreads(params.nbThreads)
+torch.setdefaulttensortype('torch.DoubleTensor')
+torch.manualSeed(2)
+
 a = nn.Linear(10, 10)
 b = nn.ReLU()
 c = nn.Linear(10, 3)
@@ -24,14 +28,16 @@ f = nn.Linear(3, 2)
 g = nn.DAG:new()
 
 g:setInput(a)
-g:setOutput({ e, f })
+g:setOutput({ e })
 
 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:print()
 
@@ -39,5 +45,12 @@ input = torch.Tensor(3, 10):uniform()
 
 output = g:updateOutput(input)
 
-print(output[1])
-print(output[2])
+if torch.type(output) == 'table' then
+   for i, t in pairs(output) do
+      print(tostring(i) .. ' -> ' .. tostring(t))
+   end
+else
+   print(tostring(output))
+end
+
+torch.save('dag.t7', g)