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training_for_git/recurrent_training.lua

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+require 'rnn'
+require 'optim'
+
+batchSize = 30
+rho = 20
+hiddenSize = 512
+hiddenSize1 = 256
+inputSize = 400
+outputSize = 4
+epochs = 10000
+xStart = 6
+yStart = 2
+yEnd = 5
+
+
+local train_file_path = 'recurrent_train.th7' 
+local train_data = torch.load(train_file_path)
+local Y = train_data[{{},{yStart,yEnd}}]
+local X = train_data[{{},{xStart,-1}}]
+local place = train_data[{{},{1}}]
+seriesSize = (#train_data)[1]
+print(seriesSize)
+local test_file_path = 'recurrent_test.th7'
+local test_data = torch.load(test_file_path)
+local test_labels = test_data[{{},{yStart,yEnd}}]
+local test_X = test_data[{{},{xStart,-1}}]
+
+model = nn.Sequential()
+model:add(nn.Sequencer(nn.FastLSTM(inputSize, hiddenSize, rho)))
+model:add(nn.Sequencer(nn.FastLSTM(hiddenSize, hiddenSize1, rho)))
+model:add(nn.Sequencer(nn.Linear(hiddenSize1, outputSize)))
+
+criterion = nn.SequencerCriterion(nn.AbsCriterion())
+--local method = 'xavier'
+--local model_new = require('weight-init')(model, method)
+
+-- define the index of the batch elements
+offsets = {}
+function offset_(seed)
+offsets = {}
+math.randomseed(seed)
+for i= 1, batchSize do
+   table.insert(offsets, math.ceil(math.random() * batchSize))
+end
+offsets = torch.LongTensor(offsets)
+end
+function nextBatch()
+   local inputs, targets = {}, {}
+   local nums = {}
+   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[{{},{1,4}}]:index(1,offsets))
+      table.insert(nums,place: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
+
+adagrad_params = {
+   learningRate = 0.01,
+   learningRateDecay = 1e-08,
+   weightDecay = 0,
+   momentum = 0
+}
+seed = 1
+offset_(seed)
+time = sys.clock()
+for j = 1, epochs do
+	if j%1000 == 0 then 
+		seed = seed + 1
+		offset_(seed)
+	end
+   -- train a mini_batch of batchSize in parallel
+	 _, fs = optim.adagrad(feval,x, adagrad_params)
+	print('error for iteration ' .. adagrad_params.evalCounter  .. ' is ' .. fs[1]/rho)
+
+end
+
+
+print('id  approx   text')
+local loss1 = 0.0
+local loss2 = 0.0
+local loss3 = 0.0
+local loss4 = 0.0
+predict_batch = 100
+for i = 1,(#test_data)[1], predict_batch do
+   local inputs = {}
+   for step = 0, predict_batch-1 do
+      --get a batch of inputs
+      table.insert(inputs, test_X[i+step])
+   end
+   local myPrediction = model:forward(inputs)
+   for step = 1, predict_batch do
+   loss1 = loss1+math.abs(myPrediction[step][1] - test_labels[i+step-1][1])
+   loss2 = loss2+math.abs(myPrediction[step][2] - test_labels[i+step-1][2])
+   loss3 = loss3+math.abs(myPrediction[step][3] - test_labels[i+step-1][3])
+   loss4 = loss4+math.abs(myPrediction[4] - test_labels[i][4])
+   end
+
+end
+
+loss1 = loss1/(#test_data)[1]
+loss2 = loss2/(#test_data)[1]
+loss3 = loss3/(#test_data)[1]
+loss4 = loss4/(#test_data)[1]
+
+-- time taken
+time = sys.clock() - time
+print( "Time per epoch = " .. (time / epochs) .. '[s]')
+
+print(loss1,loss2,loss3,loss4)
+torch.save('recurrent3.dat',model)