X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=dagnn.git;a=blobdiff_plain;f=test-dagnn.lua;h=b390a29a5b7b412c6ff019e44f51ef5ef5e2a6de;hp=9f343a981c67ae389acfee10b69df51de15c79f1;hb=HEAD;hpb=f1e78b9584e64728aed5482249380879dc7646a0

diff --git a/test-dagnn.lua b/test-dagnn.lua
index 9f343a9..b390a29 100755
--- a/test-dagnn.lua
+++ b/test-dagnn.lua
@@ -21,15 +21,18 @@
 
 require 'torch'
 require 'nn'
+
+-- require 'cunn'
+
 require 'dagnn'
 
 torch.setdefaulttensortype('torch.DoubleTensor')
 torch.manualSeed(1)
 
-function checkGrad(model, criterion, input, target)
+function checkGrad(model, criterion, input, target, epsilon)
    local params, gradParams = model:getParameters()
 
-   local epsilon = 1e-5
+   local epsilon = epsilon or 1e-5
 
    local output = model:forward(input)
    local loss = criterion:forward(output, target)
@@ -57,7 +60,7 @@ function checkGrad(model, criterion, input, target)
       local num = (loss1 - loss0) / (2 * epsilon)
 
       if num ~= ana then
-         err = math.max(err, torch.abs(num - ana) / torch.abs(num))
+         err = math.max(err, math.abs(num - ana) / math.max(epsilon, math.abs(num)))
       end
    end
 
@@ -96,12 +99,17 @@ dag:connect(b, nn.Linear(10, 15), nn.ReLU(), d)
 dag:connect(c, d)
 dag:connect(c, e)
 
+dag:setLabel(a, 'first module')
+
 dag:setInput(a)
 dag:setOutput({ d, e })
 
--- Check the output of the dot file
-print('Writing /tmp/graph.dot')
-dag:saveDot('/tmp/graph.dot')
+-- Check the output of the dot file. Generate a pdf with:
+--
+-- dot ./graph.dot -Lg -T pdf -o ./graph.pdf
+--
+print('Writing ./graph.dot')
+dag:saveDot('./graph.dot')
 
 -- Let's make a model where the dag is inside another nn.Container.
 model = nn.Sequential()
@@ -109,15 +117,27 @@ model = nn.Sequential()
    :add(dag)
    :add(nn.CAddTable())
 
+criterion = nn.MSECriterion()
+
+if cunn then
+   print("Using CUDA")
+   model:cuda()
+   criterion:cuda()
+   torch.setdefaulttensortype('torch.CudaTensor')
+   epsilon = 1e-3
+end
+
 local input = torch.Tensor(30, 50):uniform()
 local output = model:updateOutput(input):clone()
 output:uniform()
 
 -- Check that DAG:accGradParameters and friends work okay
-print('Gradient estimate error ' .. checkGrad(model, nn.MSECriterion(), input, output))
+print('Gradient estimate error ' .. checkGrad(model, criterion, input, output, epsilon))
 
 -- 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))
+torch.save('./test.t7', model)
+local otherModel = torch.load('./test.t7')
+print('Gradient estimate error ' .. checkGrad(otherModel, criterion, input, output, epsilon))
+
+dag:print()