mavenlin's 3-layer NIN

nin3layer_model.lua

require 'cunn'
require './lib/SpatialAveragePooling'

-- reference: https://gist.github.com/mavenlin/e56253735ef32c3c296d

local function normal_init(m, u, s, bias)
   m.weight:normal(u, s)
   if bias == nil then
      m.bias:normal(u, s)
   else
      m.bias:fill(bias)
   end
   return m
end
-- 3-layer NIN for 32x32
function nin3layer_model()
   local model = nn.Sequential() 
   local final_mlpconv_layer = nil

   model:add(normal_init(nn.SpatialConvolutionMM(3, 192, 5, 5, 1, 1, 2), 0, 0.05))
   model:add(nn.ReLU())
   model:add(normal_init(nn.SpatialConvolutionMM(192, 160, 1, 1), 0, 0.05, 0))
   model:add(nn.ReLU())
   model:add(normal_init(nn.SpatialConvolutionMM(160, 96, 1, 1), 0, 0.05, 0))
   model:add(nn.ReLU())
   model:add(nn.SpatialMaxPooling(2, 2, 2, 2))
   model:add(nn.Dropout(0.5))
   
   model:add(normal_init(nn.SpatialConvolutionMM(96, 192, 5, 5, 1, 1, 2), 0, 0.05))
   model:add(nn.ReLU())
   model:add(normal_init(nn.SpatialConvolutionMM(192, 192, 1, 1), 0, 0.05, 0))
   model:add(nn.ReLU())
   model:add(normal_init(nn.SpatialConvolutionMM(192, 192, 1, 1), 0, 0.05, 0))
   model:add(nn.ReLU())
   -- model:add(nn.SpatialMaxPooling(2, 2, 2, 2)) -- cuda-convnet version uses maxpool
   model:add(nn.MySpatialAveragePooling(192, 2, 2, 2, 2))
   model:add(nn.Dropout(0.5))

   model:add(normal_init(nn.SpatialConvolutionMM(192, 192, 3, 3, 1, 1, 1), 0, 0.05, 0))
   model:add(nn.ReLU())
   model:add(normal_init(nn.SpatialConvolutionMM(192, 192, 1, 1), 0, 0.05, 0))
   model:add(nn.ReLU())
   final_mlpconv_layer = normal_init(nn.SpatialConvolutionMM(192, 10, 1, 1, 1, 1), 0, 0.01, 0)
   model:add(final_mlpconv_layer)
   model:add(nn.ReLU())

   model:add(nn.MySpatialAveragePooling(10, 8, 8, 8, 8))
   model:add(nn.Reshape(10))
   model:add(nn.SoftMax())

   final_mlpconv_layer.weight:abs()
   final_mlpconv_layer.bias:abs()
   
   return model
end

This code did not get good result.