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require 'torch'
require 'nn'
if opt.type == 'cuda' then
require 'cunn'
end
----------------------------------------------------------------------
print(sys.COLORS.red .. '==> construct CNN')
-- This gets around ????% on test starting around epoc ??
-- ????% on training
--
-- th run.lua
local CNN = nn.Sequential()
CNN:add(nn.SpatialConvolution(3, 64, 3, 3, 1, 1, 1, 1))
CNN:add(nn.SpatialBatchNormalization(64))
CNN:add(nn.SpatialDropout(0.2))
CNN:add(nn.ReLU())
CNN:add(nn.SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1))
CNN:add(nn.SpatialBatchNormalization(64))
CNN:add(nn.SpatialDropout(0.2))
CNN:add(nn.ReLU())
CNN:add(nn.SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1))
CNN:add(nn.SpatialBatchNormalization(64))
CNN:add(nn.SpatialDropout(0.2))
CNN:add(nn.ReLU())
CNN:add(nn.SpatialMaxPooling(2,2,2,2))
CNN:add(nn.SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1))
CNN:add(nn.SpatialBatchNormalization(64))
CNN:add(nn.SpatialDropout(0.2))
CNN:add(nn.ReLU())
CNN:add(nn.SpatialMaxPooling(2,2,2,2))
CNN:add(nn.SpatialConvolution(64, 64, 3, 3, 1, 1, 1, 1))
CNN:add(nn.SpatialBatchNormalization(64))
CNN:add(nn.SpatialDropout(0.25))
CNN:add(nn.ReLU())
CNN:add(nn.View(64*8*8))
local classifier = nn.Sequential()
classifier:add(nn.Linear(64*8*8, 500))
classifier:add(nn.BatchNormalization(500))
classifier:add(nn.ReLU())
classifier:add(nn.Dropout(0.5))
classifier:add(nn.Linear(500, 10))
classifier:add(nn.LogSoftMax())
local model = nn.Sequential()
model:add(CNN)
model:add(classifier)
-- Loss: NLL
local loss = nn.ClassNLLCriterion()
----------------------------------------------------------------------
print(sys.COLORS.red .. '==> here is the CNN:')
print(model)
if opt.type == 'cuda' then
model:cuda()
loss:cuda()
end
-- return package:
return {
model = model,
loss = loss,
}
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