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training_for_git/no_validation.lua

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+require 'torch'   -- torch
+require 'optim'
+require 'nn'      -- provides a normalization operator
+local train_file_path = 'train.th7' 
+local test_file_path = 'test.th7'
+local train_data = torch.load(train_file_path)
+local test_data = torch.load(test_file_path)
+local train_labels = train_data[{{},{2,5}}]
+local train_X = train_data[{{},{6,-1}}]
+local test_labels = test_data[{{},{2,5}}]
+local test_X = test_data[{{},{6,-1}}]
+local batch_size = 30
+model = nn.Sequential()                 -- define the container
+ninputs = 350; noutputs = 4 ; nhiddens1 = 1024; nhiddens2 = 512; nhiddens3 = 256
+--model:add(nn.Linear(ninputs, noutputs)) -- define the only module
+model:add(nn.Linear(ninputs,nhiddens1))
+model:add(nn.Sigmoid())
+model:add(nn.Linear(nhiddens1,nhiddens2))
+model:add(nn.Sigmoid())
+model:add(nn.Linear(nhiddens2,nhiddens3))
+model:add(nn.Sigmoid())
+model:add(nn.Linear(nhiddens3,noutputs))
+criterion = nn.AbsCriterion()--MSECriterion()
+x, dl_dx = model:getParameters()
+
+feval = function(x_new)
+   if x ~= x_new then
+      x:copy(x_new)
+   end
+   -- select a new training sample
+   _nidx_ = (_nidx_ or 0) + 1
+   if _nidx_ > (#train_data)[1] then _nidx_ = 1 end
+   --local sample = data[_nidx_]
+   local target = train_labels[_nidx_]      -- this funny looking syntax allows
+   local inputs = train_X[_nidx_]    -- slicing of arrays.
+   -- reset gradients (gradients are always accumulated, to accommodate 
+   -- batch methods)
+   dl_dx:zero()
+   -- evaluate the loss function and its derivative wrt x, for that sample
+   --print(inputs)
+   --print(target)
+   for i=1, 350 do
+  if type(inputs[i]) ~= 'number' then
+  print(i)
+  print(inputs[i])
+  print(type(inputs[i])) end
+  end
+   --io.write("continue with this operation (y/n)?")
+   --answer=io.read()
+   local loss_x = criterion:forward(model:forward(inputs), target)
+   model:backward(inputs, criterion:backward(model.output, target))
+   -- return loss(x) and dloss/dx
+   return loss_x, dl_dx
+end
+-- Given the function above, we can now easily train the model using SGD.
+-- For that, we need to define four key parameters:
+--   + a learning rate: the size of the step taken at each stochastic 
+--     estimate of the gradient
+--   + a weight decay, to regularize the solution (L2 regularization)
+--   + a momentum term, to average steps over time
+--   + a learning rate decay, to let the algorithm converge more precisely
+sgd_params = {
+   learningRate = 0.01,
+   learningRateDecay = 1e-08,
+   weightDecay = 0,
+   momentum = 0
+}
+-- We're now good to go... all we have left to do is run over the dataset
+-- for a certain number of iterations, and perform a stochastic update 
+-- at each iteration. The number of iterations is found empirically here,
+-- but should typically be determinined using cross-validation.
+-- we cycle 1e4 times over our training data
+for i = 1,1 do
+   print(i)
+   -- this variable is used to estimate the average loss
+   current_loss = 0
+   -- an epoch is a full loop over our training data
+   for i = 1,(#train_data)[1] do
+      -- optim contains several optimization algorithms. 
+      -- All of these algorithms assume the same parameters:
+      --   + a closure that computes the loss, and its gradient wrt to x, 
+      --     given a point x
+      --   + a point x
+      --   + some parameters, which are algorithm-specific      
+      _,fs = optim.adagrad(feval,x,sgd_params)
+      -- Functions in optim all return two things:
+      --   + the new x, found by the optimization method (here SGD)
+      --   + the value of the loss functions at all points that were used by
+      --     the algorithm. SGD only estimates the function once, so
+      --     that list just contains one value.
+      current_loss = current_loss + fs[1]
+   end
+   -- report average error on epoch
+   current_loss = current_loss / (#train_data)[1]
+   print('train loss = ' .. current_loss)
+   
+end
+----------------------------------------------------------------------
+-- 5. Test the trained model.
+
+-- Now that the model is trained, one can test it by evaluating it
+-- on new samples.
+
+-- The text solves the model exactly using matrix techniques and determines
+-- that 
+--   corn = 31.98 + 0.65 * fertilizer + 1.11 * insecticides
+
+-- We compare our approximate results with the text's results.
+
+print('id  approx   text')
+local loss1 = 0.0
+local loss2 = 0.0
+local loss3 = 0.0
+local loss4 = 0.0
+for i = 1,(#test_data)[1] do
+   local myPrediction = model:forward(test_X[i])
+   loss1 = loss1+math.abs(myPrediction[1] - test_labels[i][1])
+   loss2 = loss2+math.abs(myPrediction[2] - test_labels[i][2])
+   loss3 = loss3+math.abs(myPrediction[3] - test_labels[i][3])
+   loss4 = loss4+math.abs(myPrediction[4] - test_labels[i][4])
+end
+
+loss1 = loss1/(#test_data)[1]
+loss2 = loss2/(#test_data)[1]
+loss3 = loss3/(#test_data)[1]
+loss4 = loss4/(#test_data)[1]
+
+print(loss1,loss2,loss3,loss4)
+torch.save('save.dat',model)