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training_for_git/recurrent.lua

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+require 'rnn'
+require 'optim'
+
+function range(from, to, step)
+  step = step or 1
+  return function(_, lastvalue)
+    local nextvalue = lastvalue + step
+    if step > 0 and nextvalue <= to or step < 0 and nextvalue >= to or
+       step == 0
+    then
+      return nextvalue
+    end
+  end, nil, from - step
+end
+
+local train_file_path = 'recurrent_train.th7' 
+local test_file_path = 'recurrent_test.th7'
+local train_data = torch.load(train_file_path)
+local test_data = torch.load(test_file_path)
+local Y = train_data[{{},{2,5}}]
+local X = train_data[{{},{6,-1}}]
+local test_labels = test_data[{{},{2,5}}]
+local test_X = test_data[{{},{6,-1}}]
+
+batchSize = 5
+rho = 10
+hiddenSize1 = 1024
+hiddenSize2 = 512
+hiddenSize3 = 256
+inputSize = 1
+outputSize = 1
+seriesSize = 100
+
+model = nn.Sequential()
+model:add(nn.Sequencer(nn.FastLSTM(inputSize, hiddenSize2, rho)))
+model:add(nn.Sequencer(nn.FastLSTM(hiddenSize2, hiddenSize3, rho)))
+--model:add(nn.Sequencer(nn.Linear(hiddenSize2, hiddenSize3, rho)))
+--model:add(nn.Sequencer(nn.Sigmoid()))
+model:add(nn.Sequencer(nn.Linear(hiddenSize3, outputSize)))
+
+criterion = nn.SequencerCriterion(nn.MSECriterion())
+
+-- dummy dataset (task predict the next item)
+--dataset = torch.randn(seriesSize, inputSize)
+
+-- define the index of the batch elements
+offsets = {}
+for i= 1, batchSize do
+   table.insert(offsets,i)
+end
+offsets = torch.LongTensor(offsets)
+print(offsets)
+function nextBatch()
+   local inputs, targets = {}, {}
+   for step = 1, rho do
+      --get a batch of inputs
+      table.insert(inputs, X:index(1, offsets))
+      -- shift of one batch indexes
+      offsets:add(1)
+      for j=1,batchSize do
+         if offsets[j] > seriesSize then
+            offsets[j] = 1
+         end
+      end
+      -- a batch of targets
+      table.insert(targets, Y:index(1,offsets))
+   end
+   return inputs, targets
+end
+
+-- get weights and loss wrt weights from the model
+x, dl_dx = model:getParameters()
+
+feval = function(x_new)
+  -- copy the weight if are changed
+  if x ~= x_new then
+    x:copy(x_new)
+  end
+
+  -- select a training batch
+  local inputs, targets = nextBatch()
+
+  -- reset gradients (gradients are always accumulated, to accommodate
+  -- batch methods)
+  dl_dx:zero()
+
+  -- evaluate the loss function and its derivative wrt x, given mini batch
+  local prediction = model:forward(inputs)
+  local loss_x = criterion:forward(prediction, targets)
+  model:backward(inputs, criterion:backward(prediction, targets))
+
+  return loss_x, dl_dx
+end
+
+sgd_params = {
+   learningRate = 0.01,
+   learningRateDecay = 1e-08,
+   weightDecay = 0,
+   momentum = 0
+}
+
+for i = 1, 2 do
+   -- train a mini_batch of batchSize in parallel
+   _, fs = optim.adagrad(feval,x, sgd_params)
+
+  if sgd_params.evalCounter % 100 == 0 then
+    print('error for iteration ' .. sgd_params.evalCounter  .. ' is ' .. fs[1] / rho)
+  end
+end