usps_cnn.lua

require "torch"
require "nn"
require "math"

-- global variables
learningRate = 0.01
maxIterations = 100000


-- here we set up the architecture of the neural network
function create_network(nb_outputs)

   local ann = nn.Sequential();  -- make a multi-layer structure
   
   -- 16x16x1                        
   ann:add(nn.SpatialConvolution(1,6,5,5))   -- becomes 12x12x6
   ann:add(nn.SpatialSubSampling(6,2,2,2,2)) -- becomes  6x6x6 
      
   ann:add(nn.Reshape(6*6*6))
   ann:add(nn.Tanh())
   ann:add(nn.Linear(6*6*6,nb_outputs))
   ann:add(nn.LogSoftMax())
   
   return ann
end

-- train a Neural Netowrk
function train_network( network, dataset)
               
   print( "Training the network" )
   local criterion = nn.ClassNLLCriterion()
   
   for iteration=1,maxIterations do
      local index = math.random(dataset:size()) -- pick example at random
      local input = dataset[index][1]               
      local output = dataset[index][2]
      
      criterion:forward(network:forward(input), output)
      
      network:zeroGradParameters()
      network:backward(input, criterion:backward(network.output, output))
      network:updateParameters(learningRate)
   end

end

function test_predictor(predictor, test_dataset, classes, classes_names)

        local mistakes = 0
        local tested_samples = 0
        
        print( "----------------------" )
        print( "Index Label Prediction" )
        for i=1,test_dataset:size() do

               local input  = test_dataset[i][1]
               local class_id = test_dataset[i][2]
        
               local responses_per_class  =  predictor:forward(input) 
               local probabilites_per_class = torch.exp(responses_per_class)
               local probability, prediction = torch.max(probabilites_per_class, 1) 

                      
               if prediction[1] ~= class_id then
                      mistakes = mistakes + 1
                      local label = classes_names[ classes[class_id] ]
                      local predicted_label = classes_names[ classes[prediction[1] ] ]
                      print(i , label , predicted_label )
               end

               tested_samples = tested_samples + 1
        end

        local test_err = mistakes/tested_samples
        print ( "Test error " .. test_err .. " ( " .. mistakes .. " out of " .. tested_samples .. " )")

end


-- main routine
function main()

        local training_dataset, testing_dataset, classes, classes_names = dofile('usps_dataset.lua')

        local network = create_network(#classes)

        train_network(network, training_dataset)
        
        test_predictor(network, testing_dataset, classes, classes_names)

end


main()