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Update to make test with cuda simpler.
author
Francois Fleuret
<francois@fleuret.org>
Sun, 15 Jan 2017 19:59:37 +0000
(20:59 +0100)
committer
Francois Fleuret
<francois@fleuret.org>
Sun, 15 Jan 2017 19:59:37 +0000
(20:59 +0100)
test-dagnn.lua
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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)
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 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)
local output = model:forward(input)
local loss = criterion:forward(output, target)
@@
-109,15
+109,22
@@
model = nn.Sequential()
:add(dag)
:add(nn.CAddTable())
: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
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')
-- 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
))