Update to make test with cuda simpler.

diff --git a/test-dagnn.lua b/test-dagnn.lua
index 5d8a309..e34ee02 100755 (executable)
--- 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)
@@ -109,15 +109,22 @@ model = nn.Sequential()
    :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()
 
 -- 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))
+print('Gradient estimate error ' .. checkGrad(otherModel, criterion, input, output, epsilon))