X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=test-dagnn.lua;h=5d8a309ce9769b547de0fa104b4b5f0b99157fe1;hb=5b8a4fcd29e68b14964d0c3e6b3f67f6ad66efaa;hp=f7de819fb6d170afa0e0b5ce85d865a85514ecda;hpb=1e0c4363ad088061af7bee3504f391d0717b1ae8;p=dagnn.git

diff --git a/test-dagnn.lua b/test-dagnn.lua
index f7de819..5d8a309 100755
--- a/test-dagnn.lua
+++ b/test-dagnn.lua
@@ -57,7 +57,7 @@ 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.abs(num))
       end
    end
 
@@ -99,7 +99,11 @@ dag:connect(c, e)
 dag:setInput(a)
 dag:setOutput({ d, e })
 
--- We check it works when we put it into a nn.Sequential
+-- 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)
@@ -109,7 +113,11 @@ 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('Writing /tmp/graph.dot')
-dag:saveDot('/tmp/graph.dot')
+-- 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))