recurrent-time-series.lua

-- Multi-variate time-series example

require 'rnn'

cmd = torch.CmdLine()
cmd:text()
cmd:text('Train a multivariate time-series model using RNN')
cmd:option('--seqlen', 5, 'maximum number of time steps for back-propagate through time (BPTT)')
cmd:option('--multiSize', 6, 'number of random variables as input and output')
cmd:option('--hiddenSize', 10, 'number of hidden units used at output of the recurrent layer')
cmd:option('--dataSize', 100, 'total number of time-steps in dataset')
cmd:option('--batchSize', 8, 'number of training samples per batch')
cmd:option('--nIterations', 1000, 'max number of training iterations')
cmd:option('--learningRate', 0.001, 'learning rate')
cmd:option('--plot', false, 'plot the errors during training?')
cmd:text()
local opt = cmd:parse(arg or {})

if opt.plot then
   require 'optim'
   logger = optim.Logger(paths.concat('outputs', 'rects_log.txt'))
end
-- For simplicity, the multi-variate dataset in this example is independently distributed.
-- Toy dataset (task is to predict next vector, given previous vectors) following the normal distribution .
-- Generated by sampling a separate normal distribution for each random variable.
-- note: vX is used as both input X and output Y to save memory
local function evalPDF(vMean, vSigma, vX)
   for i=1,vMean:size(1) do
      local b = (vX[i]-vMean[i])/vSigma[i]
      vX[i] = math.exp(-b*b/2)/(vSigma[i]*math.sqrt(2*math.pi))
   end
   return vX
end

assert(opt.multiSize > 1, "Multi-variate time-series")

vBias = torch.randn(opt.multiSize)
vMean = torch.Tensor(opt.multiSize):fill(5)
vSigma = torch.linspace(1,opt.multiSize,opt.multiSize)
sequence = torch.Tensor(opt.dataSize, opt.multiSize)

j = 0
for i=1,opt.dataSize do
  sequence[{i,{}}]:fill(j)
  evalPDF(vMean, vSigma, sequence[{i,{}}])
  sequence[{i,{}}]:add(vBias)
  j = j + 1
  if j>10 then j = 0 end
end
print('Sequence:'); print(sequence)

-- batch mode

offsets = torch.LongTensor(opt.batchSize):random(1,opt.dataSize)

-- Simple RNN
rnn = nn.Sequential()
   :add(nn.LinearRNN(opt.multiSize, opt.hiddenSize))
   :add(nn.Linear(opt.hiddenSize, opt.multiSize))

criterion = nn.MSECriterion()

-- use Sequencer for better data handling
rnn = nn.Sequencer(rnn)

criterion = nn.SequencerCriterion(criterion)
print("Model :")
print(rnn)

-- train rnn model
minErr = opt.multiSize -- report min error
minK = 0
avgErrs = torch.Tensor(opt.nIterations):fill(0)
for k = 1, opt.nIterations do

   -- 1. create a sequence of seqlen time-steps

   local inputs, targets = {}, {}
   for step = 1, opt.seqlen do
      -- batch of inputs
      inputs[step] = inputs[step] or sequence.new()
      inputs[step]:index(sequence, 1, offsets)
      -- batch of targets
      offsets:add(1) -- increase indices by 1
      offsets[offsets:gt(opt.dataSize)] = 1
      targets[step] = targets[step] or sequence.new()
      targets[step]:index(sequence, 1, offsets)
   end

   -- 2. forward sequence through rnn

   local outputs = rnn:forward(inputs)
   local err = criterion:forward(outputs, targets)

   -- report errors

   print('Iter: ' .. k .. '   Err: ' .. err)
   if opt.plot then
      logger:add{['Err'] = err}
      logger:style{['Err'] = '-'}
      logger:plot()
   end

   avgErrs[k] = err
   if avgErrs[k] < minErr then
      minErr = avgErrs[k]
      minK = k
   end

   -- 3. backward sequence through rnn (i.e. backprop through time)

   rnn:zeroGradParameters()

   local gradOutputs = criterion:backward(outputs, targets)
   local gradInputs = rnn:backward(inputs, gradOutputs)

   -- 4. updates parameters

   rnn:updateParameters(opt.learningRate)
end

print('min err: ' .. minErr .. ' on iteration ' .. minK)