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)
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)
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)