X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=dagnn.git;a=blobdiff_plain;f=test-dagnn.lua;h=5d8a309ce9769b547de0fa104b4b5f0b99157fe1;hp=a41d8802931ec51f500a42ba4e2608540addf340;hb=59490d9da93f28283c79d44a5e7c791cbd623b24;hpb=da6186a657b7563841416c42336e52937b76d67f

diff --git a/test-dagnn.lua b/test-dagnn.lua
index a41d880..5d8a309 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()
@@ -39,6 +38,8 @@ function checkGrad(model, criterion, input, target)
    model:backward(input, gradOutput)
    local analyticalGradParam = gradParams:clone()
 
+   local err = 0
+
    for i = 1, params:size(1) do
       local x = params[i]
 
@@ -54,23 +55,13 @@ function checkGrad(model, criterion, input, target)
 
       local ana = analyticalGradParam[i]
       local num = (loss1 - loss0) / (2 * epsilon)
-      local err
 
-      if num == ana then
-         err = 0
-      else
-         err = torch.abs(num - ana) / torch.abs(num)
+      if num ~= ana then
+         err = math.max(err, math.abs(num - ana) / math.abs(num))
       end
-
-      print(
-         'CHECK '
-            .. err
-            .. ' checkGrad ' .. i
-            .. ' analytical ' .. ana
-            .. ' numerical ' .. num
-      )
    end
 
+   return err
 end
 
 function printTensorTable(t)
@@ -84,35 +75,49 @@ 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)
+
+dag:connect(a, b, c)
+dag:connect(b, nn.Linear(10, 15), nn.ReLU(), d)
+dag:connect(c, d)
+dag:connect(c, e)
 
-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:setInput(a)
+dag:setOutput({ d, e })
 
-model:setInput(a)
-model:setOutput(e)
+-- Check the output of the dot file
+print('Writing /tmp/graph.dot')
+dag:saveDot('/tmp/graph.dot')
+
+-- Let's make a model where the dag is inside another nn.Container.
+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()
 
-checkGrad(model, nn.MSECriterion(), input, output)
+-- Check that DAG:accGradParameters and friends work okay
+print('Gradient estimate error ' .. checkGrad(model, nn.MSECriterion(), input, output))
+
+-- Check that we can save and reload the model
+model:clearState()
+torch.save('/tmp/test.t7', model)
+local otherModel = torch.load('/tmp/test.t7')
+print('Gradient estimate error ' .. checkGrad(otherModel, nn.MSECriterion(), input, output))