From: Francois Fleuret Date: Sun, 15 Jan 2017 19:59:37 +0000 (+0100) Subject: Update to make test with cuda simpler. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=d77b7e4eedd6fefefaefe0b2656247d563287817;p=dagnn.git Update to make test with cuda simpler. --- diff --git a/test-dagnn.lua b/test-dagnn.lua index 5d8a309..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) @@ -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))