Brian Broll
822a4080e5
WIP #40 Updated overfeat devTest Fixed setting values to null. WIP #40 WIP #40 Fixed attr name typos WIP #40 Updated tests
53 linhas
1.7 KiB
Lua
53 linhas
1.7 KiB
Lua
-- thanks to https://github.com/soumith/imagenet-multiGPU.torch for this example
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require 'nn'
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local nClasses = 10;
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local nGPU = 1;
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local features = nn.Concat(2)
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local fb1 = nn.Sequential() -- branch 1
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fb1:add(nn.SpatialConvolution(3,48,11,11,4,4,2,2)) -- 224 -> 55
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fb1:add(nn.ReLU(true))
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fb1:add(nn.SpatialMaxPooling(3,3,2,2)) -- 55 -> 27
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fb1:add(nn.SpatialConvolution(48,128,5,5,1,1,2,2)) -- 27 -> 27
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fb1:add(nn.ReLU(true))
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fb1:add(nn.SpatialMaxPooling(3,3,2,2)) -- 27 -> 13
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fb1:add(nn.SpatialConvolution(128,192,3,3,1,1,1,1)) -- 13 -> 13
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fb1:add(nn.ReLU(true))
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fb1:add(nn.SpatialConvolution(192,192,3,3,1,1,1,1)) -- 13 -> 13
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fb1:add(nn.ReLU(true))
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fb1:add(nn.SpatialConvolution(192,128,3,3,1,1,1,1)) -- 13 -> 13
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fb1:add(nn.ReLU(true))
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fb1:add(nn.SpatialMaxPooling(3,3,2,2)) -- 13 -> 6
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local fb2 = fb1:clone() -- branch 2
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-- add support for `findModules`
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for k,v in ipairs(fb2:findModules('nn.SpatialConvolution')) do
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v:reset() -- reset branch 2's weights
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end
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features:add(fb1)
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features:add(fb2)
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-- features:cuda()
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-- features = makeDataParallel(features, nGPU) -- defined in util.lua
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-- 1.3. Create Classifier (fully connected layers)
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local classifier = nn.Sequential()
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classifier:add(nn.View(256*6*6))
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classifier:add(nn.Dropout(0.5))
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classifier:add(nn.Linear(256*6*6, 4096))
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classifier:add(nn.Threshold(0, 1e-6))
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classifier:add(nn.Dropout(0.5))
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classifier:add(nn.Linear(4096, 4096))
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classifier:add(nn.Threshold(0, 1e-6))
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classifier:add(nn.Linear(4096, nClasses))
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classifier:add(nn.LogSoftMax())
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--classifier:cuda()
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-- 1.4. Combine 1.1 and 1.3 to produce final model
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local model = nn.Sequential():add(features):add(classifier)
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model.imageSize = 256
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model.imageCrop = 224
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return model
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