Shua Dissen
105 lines
2.8 KiB
Lua
105 lines
2.8 KiB
Lua
require 'torch' -- torch
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require 'optim'
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require 'nn' -- provides a normalization operator
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local train_file_path = 'train.th7'
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local test_file_path = 'test.th7'
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local train_data = torch.load(train_file_path)
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local test_data = torch.load(test_file_path)
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local Y = train_data[{{},{2,5}}]
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local X = train_data[{{},{6,-1}}]
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local test_labels = test_data[{{},{2,5}}]
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local test_X = test_data[{{},{6,-1}}]
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local batch_size = 30
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epochs = 3
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model = nn.Sequential() -- define the container
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ninputs = 350; noutputs = 4 ; nhiddens1 = 1024; nhiddens2 = 512; nhiddens3 = 256
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model:add(nn.Linear(ninputs,nhiddens1))
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model:add(nn.Sigmoid())
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model:add(nn.Linear(nhiddens1,nhiddens2))
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model:add(nn.Sigmoid())
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model:add(nn.Linear(nhiddens2,nhiddens3))
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model:add(nn.Sigmoid())
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model:add(nn.Linear(nhiddens3,noutputs))
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criterion = nn.AbsCriterion()--MSECriterion()
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x, dl_dx = model:getParameters()
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sgd_params = {
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learningRate = 0.01,
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learningRateDecay = 1e-08,
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weightDecay = 0,
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momentum = 0
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}
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function train(X,Y)
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current_loss = 0
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for batch = 1,(#train_data)[1], batch_size do
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local inputs = {}
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local targets = {}
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local x_start = batch
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local x_end = math.min(batch + batch_size-1, (#train_data)[1])
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for i = x_start,x_end do
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local target = Y[i]
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local input = X[i]
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table.insert(inputs, input)
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table.insert(targets, target)
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end
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local feval = function(x_new)
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if x ~= x_new then
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x:copy(x_new)
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end
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dl_dx:zero()
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local f=0
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for i = 1, #inputs do
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local loss_x = criterion:forward(model:forward(inputs[i]), targets[i])
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model:backward(inputs[i], criterion:backward(model.output, targets[i]))
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f = f+loss_x
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end
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return f/#inputs, dl_dx:div(#inputs)
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end
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_,fs = optim.adagrad(feval,x,sgd_params)
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current_loss = current_loss + fs[1]
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end
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current_loss = current_loss/( (#train_data)[1]/batch_size)
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print('train loss = ' .. current_loss)
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return current_loss
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end
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time = sys.clock()
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local cumm_loss = 0.
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for j = 1, epochs do
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print(j)
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cumm_loss = train( X, Y )
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print( 'Final loss = ' .. cumm_loss )
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if j%10 == 0 then
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print('id approx text')
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local loss1 = 0.0
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local loss2 = 0.0
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local loss3 = 0.0
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local loss4 = 0.0
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for i = 1,(#test_data)[1] do
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local myPrediction = model:forward(test_X[i])
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loss1 = loss1+math.abs(myPrediction[1] - test_labels[i][1])
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loss2 = loss2+math.abs(myPrediction[2] - test_labels[i][2])
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loss3 = loss3+math.abs(myPrediction[3] - test_labels[i][3])
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loss4 = loss4+math.abs(myPrediction[4] - test_labels[i][4])
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end
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loss1 = loss1/(#test_data)[1]
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loss2 = loss2/(#test_data)[1]
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loss3 = loss3/(#test_data)[1]
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loss4 = loss4/(#test_data)[1]
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end
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end
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-- time taken
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time = sys.clock() - time
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print( "Time per epoch = " .. (time / epochs) .. '[s]')
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print(loss1,loss2,loss3,loss4)
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torch.save('estimation_model.dat',model)
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