X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=test-dagnn.lua;h=d7179cc1b5110edb2b6666ce8a1604f6fe6d2102;hb=31dc42fc93ed12491ceb10ef3bfc4296878380ee;hp=a0a81ab0e146988b3bc7859d4a90366b03e57b1f;hpb=be03a73e411d18082a2dd99bff5df45c085017ca;p=dagnn.git

diff --git a/test-dagnn.lua b/test-dagnn.lua
index a0a81ab..d7179cc 100755
--- a/test-dagnn.lua
+++ b/test-dagnn.lua
@@ -5,6 +5,21 @@ 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)
+
 a = nn.Linear(10, 10)
 b = nn.ReLU()
 c = nn.Linear(10, 3)
@@ -12,19 +27,14 @@ 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 = DAG:new()
+g = nn.DAG()
 
-g:setInput(a)
-g:setOutput({ e, f })
 g:addEdge(c, e)
 g:addEdge(a, b)
 g:addEdge(d, e)
@@ -32,13 +42,22 @@ g:addEdge(b, c)
 g:addEdge(b, d)
 g:addEdge(d, f)
 
-g:order()
+g:setInput({{a}})
+g:setOutput({ e, f })
 
-g:print(graph)
+g:print()
 
 input = torch.Tensor(3, 10):uniform()
 
-output = g:updateOutput(input)
+output = g:updateOutput({{ input }})
+
+printTensorTable(output)
+
+----------------------------------------------------------------------
+
+print('******************************************************************')
+print('** updateGradInput ***********************************************')
+print('******************************************************************')
+gradInput = g:updateGradInput({{input}}, output)
 
-print(output[1])
-print(output[2])
+printTensorTable(gradInput)