4_train.lua

----------------------------------------------------------------------
-- This script demonstrates how to define a training procedure,
-- irrespective of the model/loss functions chosen.
--
-- It shows how to:
--   + construct mini-batches on the fly
--   + define a closure to estimate (a noisy) loss
--     function, as well as its derivatives wrt the parameters of the
--     model to be trained
--   + optimize the function, according to several optmization
--     methods: SGD, L-BFGS.
--
-- Clement Farabet
----------------------------------------------------------------------

require 'torch'   -- torch
require 'xlua'    -- xlua provides useful tools, like progress bars
require 'optim'   -- an optimization package, for online and batch methods

----------------------------------------------------------------------
-- parse command line arguments
if not opt then
   print '==> processing options'
   cmd = torch.CmdLine()
   cmd:text()
   cmd:text()
   cmd:text('Options:')
   cmd:option('-save', 'results', 'subdirectory to save/log experiments in')
   cmd:option('-visualize', false, 'visualize input data and weights during training')
   cmd:option('-plot', false, 'live plot')
   cmd:option('-optimization', 'SGD', 'optimization method: SGD | ASGD | CG | LBFGS')
   cmd:option('-learningRate', 1e-3, 'learning rate at t=0')
   cmd:option('-batchSize', 1, 'mini-batch size (1 = pure stochastic)')
   cmd:option('-weightDecay', 0, 'weight decay (SGD only)')
   cmd:option('-momentum', 0, 'momentum (SGD only)')
   cmd:option('-t0', 1, 'start averaging at t0 (ASGD only), in nb of epochs')
   cmd:option('-maxIter', 2, 'maximum nb of iterations for CG and LBFGS')
   cmd:text()
   opt = cmd:parse(arg or {})
end

----------------------------------------------------------------------
print '==> defining some tools'

-- classes
classes = {'1','2','3','4','5','6','7','8','9','0'}

-- This matrix records the current confusion across classes
confusion = optim.ConfusionMatrix(classes)

-- Log results to files
trainLogger = optim.Logger(paths.concat(opt.save, 'train.log'))
testLogger = optim.Logger(paths.concat(opt.save, 'test.log'))

-- Retrieve parameters and gradients:
-- this extracts and flattens all the trainable parameters of the mode
-- into a 1-dim vector
if model then
   parameters,gradParameters = model:getParameters()
   modelsave = model:clone('weight','bias')  -- network to save (cloned so it saves a compact file)
end

----------------------------------------------------------------------
print '==> defining training procedure'

function train()

   -- epoch tracker
   epoch = epoch or 1

   -- local vars
   local time = sys.clock()

   -- shuffle at each epoch
   shuffle = torch.randperm(trsize)

   -- do one epoch
   print('==> doing epoch on training data:')
   print("==> online epoch # " .. epoch .. ' [batchSize = ' .. opt.batchSize .. ']')
   for t = 1,trainData:size(),opt.batchSize do
      -- disp progress
      xlua.progress(t, trainData:size())

      -- create mini batch
      local inputs = {}
      local targets = {}
      for i = t,math.min(t+opt.batchSize-1,trainData:size()) do
         -- load new sample
         local input = trainData.data[shuffle[i]]
         local target = trainData.labels[shuffle[i]]
         table.insert(inputs, input)
         table.insert(targets, target)
      end

      -- create closure to evaluate f(X) and df/dX
      local feval = function(x)
                       -- get new parameters
                       if x ~= parameters then
                          parameters:copy(x)
                       end

                       -- reset gradients
                       gradParameters:zero()

                       -- f is the average of all criterions
                       local f = 0

                       -- evaluate function for complete mini batch
                       for i = 1,#inputs do
                          -- estimate f
                          local output = model:forward(inputs[i])
                          local err = criterion:forward(output, targets[i])
                          f = f + err

                          -- estimate df/dW
                          local df_do = criterion:backward(output, targets[i])
                          model:backward(inputs[i], df_do)

                          -- update confusion
                          confusion:add(output, targets[i])
                       end

                       -- normalize gradients and f(X)
                       gradParameters:div(#inputs)
                       f = f/#inputs

                       -- return f and df/dX
                       return f,gradParameters
                    end

     -- optimization is SGD
      config = config or {learningRate = opt.learningRate,
                           weightDecay = opt.weightDecay,
                           momentum = opt.momentum,
                           learningRateDecay = 5e-7}
      optim.sgd(feval, parameters, config)

   end

   -- time taken
   time = sys.clock() - time
   time = time / trainData:size()
   print("\n==> time to learn 1 sample = " .. (time*1000) .. 'ms')

   -- print confusion matrix
   print(confusion)

   -- update logger/plot
   trainLogger:add{['% mean class accuracy (train set)'] = confusion.totalValid * 100}
   if opt.plot then
      trainLogger:style{['% mean class accuracy (train set)'] = '-'}
      trainLogger:plot()
   end

   -- save/log current net
   local filename = paths.concat(opt.save, 'model.net')
   os.execute('mkdir -p ' .. sys.dirname(filename))
   --print('==> saving model to '..filename)
   --torch.save(filename, modelsave)

   -- next epoch
   confusion:zero()
   epoch = epoch + 1
end