X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=dagnn.git;a=blobdiff_plain;f=test-dagnn.lua;h=e34ee0211336aae7f7f5c033f8d4ed9379289bd4;hp=f7de819fb6d170afa0e0b5ce85d865a85514ecda;hb=d77b7e4eedd6fefefaefe0b2656247d563287817;hpb=1e0c4363ad088061af7bee3504f391d0717b1ae8

diff --git a/test-dagnn.lua b/test-dagnn.lua
index f7de819..e34ee02 100755
--- a/test-dagnn.lua
+++ b/test-dagnn.lua
@@ -26,10 +26,10 @@ 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 +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
 
@@ -99,17 +99,32 @@ 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)
    :add(nn.CAddTable())
 
+criterion = nn.MSECriterion()
+
+-- model:cuda()
+-- criterion:cuda()
+-- torch.setdefaulttensortype('torch.CudaTensor')
+-- epsilon = 1e-4
+
 local input = torch.Tensor(30, 50):uniform()
 local output = model:updateOutput(input):clone()
 output:uniform()
 
-print('Gradient estimate error ' .. checkGrad(model, nn.MSECriterion(), input, output))
+-- Check that DAG:accGradParameters and friends work okay
+print('Gradient estimate error ' .. checkGrad(model, criterion, input, output, epsilon))
 
-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, criterion, input, output, epsilon))