X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=test-dagnn.lua;h=366e98f620b89b1f793284e3317489909a371ce4;hb=9dad4fa1118632bfa02c01e4d6a8a5a129061a54;hp=5b266da3ea9e03a6b837f63022ee65b9eecc4198;hpb=e6516772e13dd5424f0a1b7e2063a7417614844c;p=dagnn.git

diff --git a/test-dagnn.lua b/test-dagnn.lua
index 5b266da..366e98f 100755
--- a/test-dagnn.lua
+++ b/test-dagnn.lua
@@ -21,9 +21,12 @@
 
 require 'torch'
 require 'nn'
-
 require 'dagnn'
 
+-- torch.setnumthreads(params.nbThreads)
+torch.setdefaulttensortype('torch.DoubleTensor')
+torch.manualSeed(2)
+
 function checkGrad(model, criterion, input, target)
    local params, gradParams = model:getParameters()
 
@@ -36,6 +39,8 @@ 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]
 
@@ -51,23 +56,13 @@ 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, torch.abs(num - ana) / torch.abs(num))
       end
-
-      print(
-         'CHECK '
-            .. err
-            .. ' checkGrad ' .. i
-            .. ' analytical ' .. ana
-            .. ' numerical ' .. num
-      )
    end
 
+   return err
 end
 
 function printTensorTable(t)
@@ -81,62 +76,38 @@ function printTensorTable(t)
    end
 end
 
--- torch.setnumthreads(params.nbThreads)
-torch.setdefaulttensortype('torch.DoubleTensor')
-torch.manualSeed(2)
+--               +-- Linear(10, 10) --> ReLU --> d --+
+--              /                              /      \
+--             /                              /        \
+--  --> a --> b -----------> c --------------+          e -->
+--                            \                        /
+--                             \                      /
+--                              +----- Mul(-1) ------+
 
---                     +--> c ----> e --+
---                    /            /     \
---                   /            /       \
---  input --> a --> b ---> d ----+         g --> output
---                          \             /
---                           \           /
---                            +--> f ---+
+model = nn.DAG()
 
-a = nn.Linear(10, 10)
+a = nn.Linear(50, 10)
 b = nn.ReLU()
-c = nn.Linear(10, 3)
-d = nn.Linear(10, 3)
-e = nn.CMulTable()
-f = nn.Linear(3, 3)
-g = nn.CAddTable()
+c = nn.Linear(10, 15)
+d = nn.CMulTable()
+e = nn.CAddTable()
 
-model = nn.DAG()
-
-model:addEdge(a, b)
-model:addEdge(b, c)
-model:addEdge(b, d)
-model:addEdge(c, e)
-model:addEdge(d, e)
-model:addEdge(d, f)
-model:addEdge(e, g)
-model:addEdge(f, g)
+model:connect(a, b)
+model:connect(b, nn.Linear(10, 15), nn.ReLU(), d)
+model:connect(d, e)
+model:connect(b, c)
+model:connect(c, d)
+model:connect(c, nn.Mul(-1), e)
 
 model:setInput(a)
-model:setOutput(g)
-
-input = torch.Tensor(3, 10):uniform()
-
-print('******************************************************************')
-print('** updateOutput **************************************************')
-print('******************************************************************')
-
-output = model:updateOutput(input):clone()
+model:setOutput(e)
 
-printTensorTable(output)
-
-print('******************************************************************')
-print('** updateGradInput ***********************************************')
-print('******************************************************************')
-
-gradInput = model:updateGradInput(input, output)
-
-printTensorTable(gradInput)
-
-print('******************************************************************')
-print('** checkGrad *****************************************************')
-print('******************************************************************')
+local input = torch.Tensor(30, 50):uniform()
+local output = model:updateOutput(input):clone()
 
 output:uniform()
 
-checkGrad(model, nn.MSECriterion(), input, output)
+print('Error = ' .. checkGrad(model, nn.MSECriterion(), input, output))
+
+print('Writing /tmp/graph.dot')
+model:dot('/tmp/graph.dot')