X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=dagnn.git;a=blobdiff_plain;f=test-dagnn.lua;h=f7de819fb6d170afa0e0b5ce85d865a85514ecda;hp=53302fd810b1dcad950a74400ab2f678c04745e1;hb=56a476ee19396d0e7f186b238dc7d013000acb59;hpb=0a630b54355382dfa68c0f3d51729bad0b4c58e6

diff --git a/test-dagnn.lua b/test-dagnn.lua
index 53302fd..f7de819 100755
--- a/test-dagnn.lua
+++ b/test-dagnn.lua
@@ -23,9 +23,8 @@ require 'torch'
 require 'nn'
 require 'dagnn'
 
--- torch.setnumthreads(params.nbThreads)
 torch.setdefaulttensortype('torch.DoubleTensor')
-torch.manualSeed(2)
+torch.manualSeed(1)
 
 function checkGrad(model, criterion, input, target)
    local params, gradParams = model:getParameters()
@@ -76,37 +75,41 @@ function printTensorTable(t)
    end
 end
 
---               +- Linear(10, 10) -> ReLU ---> d --+
---              /                              /     \
---             /                              /       \
---  --> a --> b -----------> c --------------+         e -->
---                            \                       /
---                             \                     /
---                              +-- Mul(-1) --------+
+--               +-- Linear(10, 10) --> ReLU --> d -->
+--              /                               /
+--             /                               /
+--  --> a --> b -----------> c ---------------+
+--                            \
+--                             \
+--                              +--------------- e -->
 
-model = nn.DAG()
+dag = nn.DAG()
 
 a = nn.Linear(50, 10)
 b = nn.ReLU()
 c = nn.Linear(10, 15)
 d = nn.CMulTable()
-e = nn.CAddTable()
+e = nn.Mul(-1)
 
-model:addEdge(a, b)
-model:addEdge(b, nn.Linear(10, 15), nn.ReLU(), d)
-model:addEdge(d, e)
-model:addEdge(b, c)
-model:addEdge(c, d)
-model:addEdge(c, nn.Mul(-1), e)
+dag:connect(a, b, c)
+dag:connect(b, nn.Linear(10, 15), nn.ReLU(), d)
+dag:connect(c, d)
+dag:connect(c, e)
 
-model:setInput(a)
-model:setOutput(e)
+dag:setInput(a)
+dag:setOutput({ d, e })
+
+-- We check it works when we put it into a nn.Sequential
+model = nn.Sequential()
+   :add(nn.Linear(50, 50))
+   :add(dag)
+   :add(nn.CAddTable())
 
 local input = torch.Tensor(30, 50):uniform()
 local output = model:updateOutput(input):clone()
-
 output:uniform()
 
-print('Error = ' .. checkGrad(model, nn.MSECriterion(), input, output))
+print('Gradient estimate error ' .. checkGrad(model, nn.MSECriterion(), input, output))
 
-model:dot('/tmp/graph.dot')
+print('Writing /tmp/graph.dot')
+dag:saveDot('/tmp/graph.dot')