Torch, why is my artificial neural network predicting always zeros?

Question

I'm working with Torch7 on Linux CentOS 7 machine. I'm trying to apply a artificial neural network (ANN) to my dataset, to solve a binary classification problem. I'm using a simple multi-layer perceptron.

I'm using the following Torch packages: optim, torch.

The issue is that my perceptron always predicts zero values (elements classified as zeros), and I cannot understand why...

Here's my dataset ("dataset_file.csv"). There are 34 features and 1 label target (the last column, that might be 0 or 1):

0.55,1,0,1,0,0.29,1,0,1,0.46,1,1,0,0.67,1,0.37,0.41,1,0.08,0.47,0.23,0.13,0.82,0.46,0.25,0.04,0,0,0.52,1,0,0,0,0.33,0
0.65,1,0,1,0,0.64,1,0,0,0.02,1,1,1,1,0,0.52,0.32,0,0.18,0.67,0.47,0.2,0.64,0.38,0.23,1,0.24,0.18,0.04,1,1,1,1,0.41,0
0.34,1,0.13,1,0,0.33,0,0.5,0,0.02,0,0,0,0.67,1,0.25,0.55,1,0.06,0.23,0.18,0.15,0.82,0.51,0.22,0.06,0,0,0.6,1,0,0,0,0.42,1
0.46,1,0,1,0,0.14,1,0,0,0.06,0,1,1,0,1,0.37,0.64,1,0.14,0.22,0.17,0.1,0.94,0.65,0.22,0.06,0.75,0.64,0.3,1,1,0,0,0.2,0
0.55,1,0,1,0,0.14,1,0.5,1,0.03,1,1,0,1,1,0.42,0.18,0,0.16,0.55,0.16,0.12,0.73,0.55,0.2,0.03,0.54,0.44,0.35,1,1,0,0,0.11,0
0.67,1,0,1,0,0.71,0,0.5,0,0.46,1,0,1,1,1,0.74,0.41,0,0.1,0.6,0.15,0.15,0.69,0.42,0.27,0.04,0.61,0.48,0.54,1,1,0,0,0.22,1
0.52,1,0,1,0,0.21,1,0.5,0,0.01,1,1,1,0.67,0,0.27,0.64,0,0.08,0.34,0.14,0.21,0.85,0.51,0.2,0.05,0.51,0.36,0.36,1,1,0,0,0.23,0
0.58,1,0.38,1,0,0.36,1,0.5,1,0.02,0,1,0,1,1,0.38,0.55,1,0.13,0.57,0.21,0.23,0.73,0.52,0.19,0.03,0,0,0.6,1,0,0,0,0.42,0
0.66,1,0,1,0,0.07,1,0,0,0.06,1,0,0,1,1,0.24,0.32,1,0.06,0.45,0.16,0.13,0.92,0.57,0.27,0.06,0,0,0.55,1,0,0,0,0.33,0
0.39,1,0.5,1,0,0.29,1,0,1,0.06,0,0,0,1,1,0.34,0.45,1,0.1,0.31,0.12,0.16,0.81,0.54,0.21,0.02,0.51,0.27,0.5,1,1,0,0,0.32,0
0.26,0,0,1,0,0.21,1,0,0,0.02,1,1,1,0,1,0.17,0.36,0,0.19,0.41,0.24,0.26,0.73,0.55,0.22,0.41,0.46,0.43,0.42,1,1,0,0,0.52,0
0.96,0,0.63,1,0,0.86,1,0,1,0.06,1,1,1,0,0,0.41,0.5,1,0.08,0.64,0.23,0.19,0.69,0.45,0.23,0.06,0.72,0.43,0.45,1,1,0,0,0.53,0
0.58,0,0.25,1,0,0.29,1,0,1,0.04,1,0,0,0,1,0.4,0.27,1,0.09,0.65,0.21,0.16,0.8,0.57,0.24,0.02,0.51,0.28,0.5,1,1,1,0,0.63,0
0.6,1,0.5,1,0,0.73,1,0.5,1,0.04,1,0,1,0,1,0.85,0.64,1,0.16,0.71,0.24,0.21,0.72,0.45,0.23,0.1,0.63,0.57,0.13,1,1,1,1,0.65,0
0.72,1,0.25,1,0,0.29,1,0,0,0.06,1,0,0,1,1,0.31,0.41,1,0.17,0.78,0.24,0.16,0.75,0.54,0.27,0.09,0.78,0.68,0.19,1,1,1,1,0.75,0
0.56,0,0.13,1,0,0.4,1,0,0,0.23,1,0,0,1,1,0.42,1,0,0.03,0.14,0.15,0.13,0.85,0.52,0.24,0.06,0,0,0.56,1,0,0,0,0.33,0
0.67,0,0,1,0,0.57,1,0,1,0.02,0,0,0,1,1,0.38,0.36,0,0.08,0.12,0.11,0.14,0.8,0.49,0.22,0.05,0,0,0.6,1,0,0,0,0.22,0
0.67,0,0,1,0,0.36,1,0,0,0.23,0,1,0,0,0,0.32,0.73,0,0.25,0.86,0.26,0.16,0.62,0.35,0.25,0.02,0.46,0.43,0.45,1,1,1,0,0.76,0
0.55,1,0.5,1,0,0.57,0,0.5,1,0.12,1,1,1,0.67,1,1,0.45,0,0.19,0.94,0.19,0.22,0.88,0.41,0.35,0.15,0.47,0.4,0.05,1,1,1,0,0.56,1
0.61,0,0,1,0,0.43,1,0.5,1,0.04,1,0,1,0,0,0.68,0.23,1,0.12,0.68,0.25,0.29,0.68,0.45,0.29,0.13,0.58,0.41,0.11,1,1,1,1,0.74,0
0.59,1,0.25,1,0,0.23,1,0.5,0,0.02,1,1,1,0,1,0.57,0.41,1,0.08,0.05,0.16,0.15,0.87,0.61,0.25,0.04,0.67,0.61,0.45,1,1,0,0,0.65,0
0.74,1,0.5,1,0,0.26,1,0,1,0.01,1,1,1,1,0,0.76,0.36,0,0.14,0.72,0.12,0.13,0.68,0.54,0.54,0.17,0.93,0.82,0.12,1,1,0,0,0.18,0
0.64,0,0,1,0,0.29,0,0,1,0.15,0,0,1,0,1,0.33,0.45,0,0.11,0.55,0.25,0.15,0.75,0.54,0.27,0.05,0.61,0.64,0.43,1,1,0,0,0.23,1
0.36,0,0.38,1,0,0.14,0,0.5,0,0.02,1,1,1,0.33,1,0.18,0.36,0,0.17,0.79,0.21,0.12,0.75,0.54,0.24,0.05,0,0,0.52,1,0,0,0,0.44,1
0.52,0,0.75,1,0,0.14,1,0.5,0,0.04,1,1,1,0,1,0.36,0.68,1,0.08,0.34,0.12,0.13,0.79,0.59,0.22,0.02,0,0,0.5,1,0,0,0,0.23,0
0.59,0,0.75,1,0,0.29,1,0,0,0.06,1,1,0,0,1,0.24,0.27,0,0.12,0.7,0.2,0.16,0.74,0.45,0.26,0.02,0.46,0.32,0.52,1,0,0,0,0.33,0
0.72,1,0.38,1,0,0.43,0,0.5,0,0.06,1,0,1,0.67,1,0.53,0.32,0,0.2,0.68,0.16,0.13,0.79,0.45,0.25,0.09,0.61,0.57,0.15,1,1,0,0,0.22,1

And here's my Torch Lua code:

-- add comma to separate thousands
function comma_value(amount)
  local formatted = amount
  while true do  
    formatted, k = string.gsub(formatted, "^(-?%d+)(%d%d%d)", '%1,%2')
    if (k==0) then
      break
    end
  end
  return formatted
end

-- function that computes the confusion matrix
function confusion_matrix(predictionTestVect, truthVect, threshold, printValues)

  local tp = 0
  local tn = 0
  local fp = 0
  local fn = 0
  local MatthewsCC = -2
  local accuracy = -2
  local arrayFPindices = {}
  local arrayFPvalues = {}
  local arrayTPvalues = {}
  local areaRoc = 0

  local fpRateVett = {}
  local tpRateVett = {}
  local precisionVett = {}
  local recallVett = {}

  for i=1,#predictionTestVect do

    if printValues == true then
      io.write("predictionTestVect["..i.."] = ".. round(predictionTestVect[i],4).."\ttruthVect["..i.."] = "..truthVect[i].." ");
      io.flush();
    end

    if predictionTestVect[i] >= threshold and truthVect[i] >= threshold then
      tp = tp + 1
      arrayTPvalues[#arrayTPvalues+1] = predictionTestVect[i]
      if printValues == true then print(" TP ") end
    elseif  predictionTestVect[i] < threshold and truthVect[i] >= threshold then
      fn = fn + 1
      if printValues == true then print(" FN ") end
    elseif  predictionTestVect[i] >= threshold and truthVect[i] < threshold then
      fp = fp + 1
      if printValues == true then print(" FP ") end
      arrayFPindices[#arrayFPindices+1] = i;
      arrayFPvalues[#arrayFPvalues+1] = predictionTestVect[i]  
    elseif  predictionTestVect[i] < threshold and truthVect[i] < threshold then
      tn = tn + 1
      if printValues == true then print(" TN ") end
    end
  end

    print("TOTAL:")
    print(" FN = "..comma_value(fn).." / "..comma_value(tonumber(fn+tp)).."\t (truth == 1) & (prediction < threshold)");
    print(" TP = "..comma_value(tp).." / "..comma_value(tonumber(fn+tp)).."\t (truth == 1) & (prediction >= threshold)\n");

    print(" FP = "..comma_value(fp).." / "..comma_value(tonumber(fp+tn)).."\t (truth == 0) & (prediction >= threshold)");
    print(" TN = "..comma_value(tn).." / "..comma_value(tonumber(fp+tn)).."\t (truth == 0) & (prediction < threshold)\n");

  local continueLabel = true

    if continueLabel then
      upperMCC = (tp*tn) - (fp*fn)
      innerSquare = (tp+fp)*(tp+fn)*(tn+fp)*(tn+fn)
      lowerMCC = math.sqrt(innerSquare)

      MatthewsCC = -2
      if lowerMCC>0 then MatthewsCC = upperMCC/lowerMCC end
      local signedMCC = MatthewsCC
      print("signedMCC = "..signedMCC)

      if MatthewsCC > -2 then print("\n::::\tMatthews correlation coefficient = "..signedMCC.."\t::::\n");
      else print("Matthews correlation coefficient = NOT computable");  end

      accuracy = (tp + tn)/(tp + tn +fn + fp)
      print("accuracy = "..round(accuracy,2).. " = (tp + tn) / (tp + tn +fn + fp) \t  \t [worst = -1, best =  +1]");

      local f1_score = -2
      if (tp+fp+fn)>0 then   
    f1_score = (2*tp) / (2*tp+fp+fn)
    print("f1_score = "..round(f1_score,2).." = (2*tp) / (2*tp+fp+fn) \t [worst = 0, best = 1]");
      else
    print("f1_score CANNOT be computed because (tp+fp+fn)==0")    
      end

      local totalRate = 0
      if MatthewsCC > -2 and f1_score > -2 then 
    totalRate = MatthewsCC + accuracy + f1_score 
    print("total rate = "..round(totalRate,2).." in [-1, +3] that is "..round((totalRate+1)*100/4,2).."% of possible correctness");
      end

      local numberOfPredictedOnes = tp + fp;
      print("numberOfPredictedOnes = (TP + FP) = "..comma_value(numberOfPredictedOnes).." = "..round(numberOfPredictedOnes*100/(tp + tn + fn + fp),2).."%");

      io.write("\nDiagnosis: ");
      if (fn >= tp and (fn+tp)>0) then print("too many FN false negatives"); end
      if (fp >= tn and (fp+tn)>0) then print("too many FP false positives"); end


      if (tn > (10*fp) and tp > (10*fn)) then print("Excellent ! ! !");
      elseif (tn > (5*fp) and tp > (5*fn)) then print("Very good ! !"); 
      elseif (tn > (2*fp) and tp > (2*fn)) then print("Good !"); 
      elseif (tn >= fp and tp >= fn) then print("Alright"); 
      else print("Baaaad"); end
    end

    return {accuracy, arrayFPindices, arrayFPvalues, MatthewsCC};
end


-- Permutations
-- tab = {1,2,3,4,5,6,7,8,9,10}
-- permute(tab, 10, 10)
function permute(tab, n, count)
      n = n or #tab
      for i = 1, count or n do
        local j = math.random(i, n)
        tab[i], tab[j] = tab[j], tab[i]
      end
      return tab
end

-- round a real value
function round(num, idp)
  local mult = 10^(idp or 0)
  return math.floor(num * mult + 0.5) / mult
end



-- ##############################3

local profile_vett = {}
local csv = require("csv")
local fileName = "dataset_file.csv" 

print("Readin' "..tostring(fileName))
local f = csv.open(fileName)
local column_names = {}

local j = 0
for fields in f:lines() do

  if j>0 then
    profile_vett[j] = {}
      for i, v in ipairs(fields) do 
    profile_vett[j][i] = tonumber(v);
      end
    j = j + 1
  else
    for i, v in ipairs(fields) do 
    column_names[i] = v
     end
    j = j + 1
  end
end

OPTIM_PACKAGE = true
local output_number = 1
THRESHOLD = 0.5 -- ORIGINAL
DROPOUT_FLAG = false
MOMENTUM = false
MOMENTUM_ALPHA = 0.5
MAX_MSE = 4
LEARN_RATE = 0.001
ITERATIONS = 100

local hidden_units = 2000
local hidden_layers = 1

local hiddenUnitVect = {2000, 4000, 6000, 8000, 10000}
-- local hiddenLayerVect = {1,2,3,4,5}
local hiddenLayerVect = {1}

local profile_vett_data = {}
local label_vett = {}

for i=1,#profile_vett do
  profile_vett_data[i] = {}

  for j=1,#(profile_vett[1]) do  
    if j<#(profile_vett[1]) then
      profile_vett_data[i][j] = profile_vett[i][j]
    else
      label_vett[i] = profile_vett[i][j]
    end    
  end
end

print("Number of value profiles (rows) = "..#profile_vett_data);
print("Number features (columns) = "..#(profile_vett_data[1]));
print("Number of targets (rows) = "..#label_vett);

local table_row_outcome = label_vett
local table_rows_vett = profile_vett

-- ########################################################

-- START

local indexVect = {}; 
for i=1, #table_rows_vett do indexVect[i] = i;  end
permutedIndexVect = permute(indexVect, #indexVect, #indexVect);

TEST_SET_PERC = 20
local test_set_size = round((TEST_SET_PERC*#table_rows_vett)/100)

print("training_set_size = "..(#table_rows_vett-test_set_size).." elements");
print("test_set_size = "..test_set_size.." elements\n");

local train_table_row_profile = {}
local test_table_row_profile = {}
local original_test_indexes = {}

for i=1,#table_rows_vett do
  if i<=(tonumber(#table_rows_vett)-test_set_size) then
    train_table_row_profile[#train_table_row_profile+1] = {torch.Tensor(table_rows_vett[permutedIndexVect[i]]), torch.Tensor{table_row_outcome[permutedIndexVect[i]]}}
  else

    original_test_indexes[#original_test_indexes+1] = permutedIndexVect[i];

    test_table_row_profile[#test_table_row_profile+1] = {torch.Tensor(table_rows_vett[permutedIndexVect[i]]), torch.Tensor{table_row_outcome[permutedIndexVect[i]]}}
  end
end

require 'nn'
perceptron = nn.Sequential()
input_number = #table_rows_vett[1]

perceptron:add(nn.Linear(input_number, hidden_units))
perceptron:add(nn.Sigmoid())
if DROPOUT_FLAG==true then perceptron:add(nn.Dropout()) end

for w=1,hidden_layers do
  perceptron:add(nn.Linear(hidden_units, hidden_units))
  perceptron:add(nn.Sigmoid())
  if DROPOUT_FLAG==true then perceptron:add(nn.Dropout()) end
end
perceptron:add(nn.Linear(hidden_units, output_number))


function train_table_row_profile:size() return #train_table_row_profile end 
function test_table_row_profile:size() return #test_table_row_profile end 


-- OPTIMIZATION LOOPS  
local MCC_vect = {}  

for a=1,#hiddenUnitVect do
  for b=1,#hiddenLayerVect do

    local hidden_units = hiddenUnitVect[a]
    local hidden_layers = hiddenLayerVect[b]
    print("hidden_units = "..hidden_units.."\t output_number = "..output_number.." hidden_layers = "..hidden_layers)


    local criterion = nn.MSECriterion()  
    local lossSum = 0
    local error_progress = 0

      require 'optim'
      local params, gradParams = perceptron:getParameters()     
      local optimState = nil

      if MOMENTUM==true then 
    optimState = {learningRate = LEARN_RATE}
      else 
    optimState = {learningRate = LEARN_RATE,
              momentum = MOMENTUM_ALPHA }
      end

      local total_runs = ITERATIONS*#train_table_row_profile

      local loopIterations = 1
      for epoch=1,ITERATIONS do
    for k=1,#train_table_row_profile do

        -- Function feval 
        local function feval(params)
        gradParams:zero()

        local thisProfile = train_table_row_profile[k][1]
        local thisLabel = train_table_row_profile[k][2]

        local thisPrediction = perceptron:forward(thisProfile)
        local loss = criterion:forward(thisPrediction, thisLabel)

        -- print("thisPrediction = "..round(thisPrediction[1],2).." thisLabel = "..thisLabel[1])

        lossSum = lossSum + loss
        error_progress = lossSum*100 / (loopIterations*MAX_MSE)

        if ((loopIterations*100/total_runs)*10)%10==0 then
          io.write("completion: ", round((loopIterations*100/total_runs),2).."%" )
          io.write(" (epoch="..epoch..")(element="..k..") loss = "..round(loss,2).." ")      
          io.write("\terror progress = "..round(error_progress,5).."%\n")
        end

        local dloss_doutput = criterion:backward(thisPrediction, thisLabel)

        perceptron:backward(thisProfile, dloss_doutput)

        return loss,gradParams
        end
      optim.sgd(feval, params, optimState)
      loopIterations = loopIterations+1
    end     
      end


    local correctPredictions = 0
    local atleastOneTrue = false
    local atleastOneFalse = false
    local predictionTestVect = {}
    local truthVect = {}

    for i=1,#test_table_row_profile do
      local current_label = test_table_row_profile[i][2][1]
      local prediction = perceptron:forward(test_table_row_profile[i][1])[1]

      predictionTestVect[i] = prediction
      truthVect[i] = current_label

      local labelResult = false

      if current_label >= THRESHOLD and prediction >= THRESHOLD  then
    labelResult = true
      elseif current_label < THRESHOLD and prediction < THRESHOLD  then
    labelResult = true
      end

      if labelResult==true then correctPredictions = correctPredictions + 1; end

    print("\nCorrect predictions = "..round(correctPredictions*100/#test_table_row_profile,2).."%")

     local printValues = false
     local output_confusion_matrix = confusion_matrix(predictionTestVect, truthVect, THRESHOLD, printValues)
  end
end

Does anyone have an idea about why my script is predicting only zero elements?

EDIT: I replaced the original dataset with its normalized version, that I use in my script

Answer 1

When I run your original code I sometimes get all zeros predicted, and I sometimes get perfect performance. This suggests your original model is very sensitive to the initialization of the parameter values.

If I use a seed value of torch.manualSeed(0) (so we always have the same initialization) I get perfect performance every time. But this isn't really a general solution.

To get a more general improvement I have made the following changes:

1. Reduced the number of hidden units. In the original code you have a single hidden layer of 2000 units. But you only have 34 inputs and 1 output Often you only need the number of hidden units to be between the number of inputs and outputs. I have reduced it to 50.
2. The labels are asymmetric, only 5/27 (19%) of the labels are ones, so you should really divide the train|test sets in such a way that preserves the ratio of ones to zeros. For now I have simply increased the test set size to '50'%.
3. I have also increased the learning rate to '0.01', turned MOMENTUM on, and increased ITERATIONS to 200.

When I run this model 20 times (unseeded) I got Excellent performance 19 times. To improve further you could tweak the hyper-parameters further. And should also look at multiple initializations with a separate validation set to choose the "best" model (although this will further sub-divide your already very small data-set).

-- add comma to separate thousands
function comma_value(amount)
  local formatted = amount
  while true do  
    formatted, k = string.gsub(formatted, "^(-?%d+)(%d%d%d)", '%1,%2')
    if (k==0) then
      break
    end
  end
  return formatted
end

-- function that computes the confusion matrix
function confusion_matrix(predictionTestVect, truthVect, threshold, printValues)

  local tp = 0
  local tn = 0
  local fp = 0
  local fn = 0
  local MatthewsCC = -2
  local accuracy = -2
  local arrayFPindices = {}
  local arrayFPvalues = {}
  local arrayTPvalues = {}
  local areaRoc = 0

  local fpRateVett = {}
  local tpRateVett = {}
  local precisionVett = {}
  local recallVett = {}

  for i=1,#predictionTestVect do

    if printValues == true then
      io.write("predictionTestVect["..i.."] = ".. round(predictionTestVect[i],4).."\ttruthVect["..i.."] = "..truthVect[i].." ");
      io.flush();
    end

    if predictionTestVect[i] >= threshold and truthVect[i] >= threshold then
      tp = tp + 1
      arrayTPvalues[#arrayTPvalues+1] = predictionTestVect[i]
      if printValues == true then print(" TP ") end
    elseif  predictionTestVect[i] < threshold and truthVect[i] >= threshold then
      fn = fn + 1
      if printValues == true then print(" FN ") end
    elseif  predictionTestVect[i] >= threshold and truthVect[i] < threshold then
      fp = fp + 1
      if printValues == true then print(" FP ") end
      arrayFPindices[#arrayFPindices+1] = i;
      arrayFPvalues[#arrayFPvalues+1] = predictionTestVect[i]  
    elseif  predictionTestVect[i] < threshold and truthVect[i] < threshold then
      tn = tn + 1
      if printValues == true then print(" TN ") end
    end
  end

    print("TOTAL:")
    print(" FN = "..comma_value(fn).." / "..comma_value(tonumber(fn+tp)).."\t (truth == 1) & (prediction < threshold)");
    print(" TP = "..comma_value(tp).." / "..comma_value(tonumber(fn+tp)).."\t (truth == 1) & (prediction >= threshold)\n");

    print(" FP = "..comma_value(fp).." / "..comma_value(tonumber(fp+tn)).."\t (truth == 0) & (prediction >= threshold)");
    print(" TN = "..comma_value(tn).." / "..comma_value(tonumber(fp+tn)).."\t (truth == 0) & (prediction < threshold)\n");

  local continueLabel = true

    if continueLabel then
      upperMCC = (tp*tn) - (fp*fn)
      innerSquare = (tp+fp)*(tp+fn)*(tn+fp)*(tn+fn)
      lowerMCC = math.sqrt(innerSquare)

      MatthewsCC = -2
      if lowerMCC>0 then MatthewsCC = upperMCC/lowerMCC end
      local signedMCC = MatthewsCC
      print("signedMCC = "..signedMCC)

      if MatthewsCC > -2 then print("\n::::\tMatthews correlation coefficient = "..signedMCC.."\t::::\n");
      else print("Matthews correlation coefficient = NOT computable");  end

      accuracy = (tp + tn)/(tp + tn +fn + fp)
      print("accuracy = "..round(accuracy,2).. " = (tp + tn) / (tp + tn +fn + fp) \t  \t [worst = -1, best =  +1]");

      local f1_score = -2
      if (tp+fp+fn)>0 then   
    f1_score = (2*tp) / (2*tp+fp+fn)
    print("f1_score = "..round(f1_score,2).." = (2*tp) / (2*tp+fp+fn) \t [worst = 0, best = 1]");
      else
    print("f1_score CANNOT be computed because (tp+fp+fn)==0")    
      end

      local totalRate = 0
      if MatthewsCC > -2 and f1_score > -2 then 
    totalRate = MatthewsCC + accuracy + f1_score 
    print("total rate = "..round(totalRate,2).." in [-1, +3] that is "..round((totalRate+1)*100/4,2).."% of possible correctness");
      end

      local numberOfPredictedOnes = tp + fp;
      print("numberOfPredictedOnes = (TP + FP) = "..comma_value(numberOfPredictedOnes).." = "..round(numberOfPredictedOnes*100/(tp + tn + fn + fp),2).."%");

      io.write("\nDiagnosis: ");
      if (fn >= tp and (fn+tp)>0) then print("too many FN false negatives"); end
      if (fp >= tn and (fp+tn)>0) then print("too many FP false positives"); end


      if (tn > (10*fp) and tp > (10*fn)) then print("Excellent ! ! !");
      elseif (tn > (5*fp) and tp > (5*fn)) then print("Very good ! !"); 
      elseif (tn > (2*fp) and tp > (2*fn)) then print("Good !"); 
      elseif (tn >= fp and tp >= fn) then print("Alright"); 
      else print("Baaaad"); end
    end

    return {accuracy, arrayFPindices, arrayFPvalues, MatthewsCC};
end


-- Permutations
-- tab = {1,2,3,4,5,6,7,8,9,10}
-- permute(tab, 10, 10)
function permute(tab, n, count)
      n = n or #tab
      for i = 1, count or n do
        local j = math.random(i, n)
        tab[i], tab[j] = tab[j], tab[i]
      end
      return tab
end

-- round a real value
function round(num, idp)
  local mult = 10^(idp or 0)
  return math.floor(num * mult + 0.5) / mult
end



-- ##############################3

local profile_vett = {}
local csv = require("csv")
local fileName = "dataset_file.csv" 

print("Readin' "..tostring(fileName))
local f = csv.open(fileName)
local column_names = {}

local j = 0
for fields in f:lines() do

  if j>0 then
    profile_vett[j] = {}
      for i, v in ipairs(fields) do 
    profile_vett[j][i] = tonumber(v);
      end
    j = j + 1
  else
    for i, v in ipairs(fields) do 
    column_names[i] = v
     end
    j = j + 1
  end
end

OPTIM_PACKAGE = true
local output_number = 1
THRESHOLD = 0.5 -- ORIGINAL
DROPOUT_FLAG = false
MOMENTUM_ALPHA = 0.5
MAX_MSE = 4

-- CHANGE: increased learn_rate to 0.01, reduced hidden units to 50, turned momentum on, increased iterations to 200
LEARN_RATE = 0.01
local hidden_units = 50
MOMENTUM = true
ITERATIONS = 200
-------------------------------------

local hidden_layers = 1

local hiddenUnitVect = {2000, 4000, 6000, 8000, 10000}
-- local hiddenLayerVect = {1,2,3,4,5}
local hiddenLayerVect = {1}

local profile_vett_data = {}
local label_vett = {}

for i=1,#profile_vett do
  profile_vett_data[i] = {}

  for j=1,#(profile_vett[1]) do  
    if j<#(profile_vett[1]) then
      profile_vett_data[i][j] = profile_vett[i][j]
    else
      label_vett[i] = profile_vett[i][j]
    end    
  end
end

print("Number of value profiles (rows) = "..#profile_vett_data);
print("Number features (columns) = "..#(profile_vett_data[1]));
print("Number of targets (rows) = "..#label_vett);

local table_row_outcome = label_vett
local table_rows_vett = profile_vett

-- ########################################################

-- START

-- Seed random number generator
-- torch.manualSeed(0)

local indexVect = {}; 
for i=1, #table_rows_vett do indexVect[i] = i;  end
permutedIndexVect = permute(indexVect, #indexVect, #indexVect);

-- CHANGE: increase test_set to 50%
TEST_SET_PERC = 50
---------------------------

local test_set_size = round((TEST_SET_PERC*#table_rows_vett)/100)

print("training_set_size = "..(#table_rows_vett-test_set_size).." elements");
print("test_set_size = "..test_set_size.." elements\n");

local train_table_row_profile = {}
local test_table_row_profile = {}
local original_test_indexes = {}

for i=1,#table_rows_vett do
  if i<=(tonumber(#table_rows_vett)-test_set_size) then
    train_table_row_profile[#train_table_row_profile+1] = {torch.Tensor(table_rows_vett[permutedIndexVect[i]]), torch.Tensor{table_row_outcome[permutedIndexVect[i]]}}
  else

    original_test_indexes[#original_test_indexes+1] = permutedIndexVect[i];

    test_table_row_profile[#test_table_row_profile+1] = {torch.Tensor(table_rows_vett[permutedIndexVect[i]]), torch.Tensor{table_row_outcome[permutedIndexVect[i]]}}
  end
end

require 'nn'
perceptron = nn.Sequential()
input_number = #table_rows_vett[1]

perceptron:add(nn.Linear(input_number, hidden_units))
perceptron:add(nn.Sigmoid())
if DROPOUT_FLAG==true then perceptron:add(nn.Dropout()) end

for w=1,hidden_layers do
  perceptron:add(nn.Linear(hidden_units, hidden_units))
  perceptron:add(nn.Sigmoid())
  if DROPOUT_FLAG==true then perceptron:add(nn.Dropout()) end
end
perceptron:add(nn.Linear(hidden_units, output_number))


function train_table_row_profile:size() return #train_table_row_profile end 
function test_table_row_profile:size() return #test_table_row_profile end 


-- OPTIMIZATION LOOPS  
local MCC_vect = {}  

for a=1,#hiddenUnitVect do
  for b=1,#hiddenLayerVect do

    local hidden_units = hiddenUnitVect[a]
    local hidden_layers = hiddenLayerVect[b]
    print("hidden_units = "..hidden_units.."\t output_number = "..output_number.." hidden_layers = "..hidden_layers)


    local criterion = nn.MSECriterion()  
    local lossSum = 0
    local error_progress = 0

      require 'optim'
      local params, gradParams = perceptron:getParameters()     
      local optimState = nil

      if MOMENTUM==true then 
    optimState = {learningRate = LEARN_RATE}
      else 
    optimState = {learningRate = LEARN_RATE,
              momentum = MOMENTUM_ALPHA }
      end

      local total_runs = ITERATIONS*#train_table_row_profile

      local loopIterations = 1
      for epoch=1,ITERATIONS do
    for k=1,#train_table_row_profile do

        -- Function feval 
        local function feval(params)
        gradParams:zero()

        local thisProfile = train_table_row_profile[k][1]
        local thisLabel = train_table_row_profile[k][2]

        local thisPrediction = perceptron:forward(thisProfile)
        local loss = criterion:forward(thisPrediction, thisLabel)

        -- print("thisPrediction = "..round(thisPrediction[1],2).." thisLabel = "..thisLabel[1])

        lossSum = lossSum + loss
        error_progress = lossSum*100 / (loopIterations*MAX_MSE)

        if ((loopIterations*100/total_runs)*10)%10==0 then
          io.write("completion: ", round((loopIterations*100/total_runs),2).."%" )
          io.write(" (epoch="..epoch..")(element="..k..") loss = "..round(loss,2).." ")      
          io.write("\terror progress = "..round(error_progress,5).."%\n")
        end

        local dloss_doutput = criterion:backward(thisPrediction, thisLabel)

        perceptron:backward(thisProfile, dloss_doutput)

        return loss,gradParams
        end
      optim.sgd(feval, params, optimState)
      loopIterations = loopIterations+1
    end     
      end


    local correctPredictions = 0
    local atleastOneTrue = false
    local atleastOneFalse = false
    local predictionTestVect = {}
    local truthVect = {}

    for i=1,#test_table_row_profile do
      local current_label = test_table_row_profile[i][2][1]
      local prediction = perceptron:forward(test_table_row_profile[i][1])[1]

      predictionTestVect[i] = prediction
      truthVect[i] = current_label

      local labelResult = false

      if current_label >= THRESHOLD and prediction >= THRESHOLD  then
    labelResult = true
      elseif current_label < THRESHOLD and prediction < THRESHOLD  then
    labelResult = true
      end

      if labelResult==true then correctPredictions = correctPredictions + 1; end

    print("\nCorrect predictions = "..round(correctPredictions*100/#test_table_row_profile,2).."%")

     local printValues = false
     local output_confusion_matrix = confusion_matrix(predictionTestVect, truthVect, THRESHOLD, printValues)
  end
end
end

Pasted below is the output of 1 of the 20 runs:

Correct predictions = 100%  
TOTAL:  
 FN = 0 / 4  (truth == 1) & (prediction < threshold)    
 TP = 4 / 4  (truth == 1) & (prediction >= threshold)

 FP = 0 / 9  (truth == 0) & (prediction >= threshold)   
 TN = 9 / 9  (truth == 0) & (prediction < threshold)

signedMCC = 1   

::::    Matthews correlation coefficient = 1    ::::

accuracy = 1 = (tp + tn) / (tp + tn +fn + fp)        [worst = -1, best =  +1]   
f1_score = 1 = (2*tp) / (2*tp+fp+fn)     [worst = 0, best = 1]  
total rate = 3 in [-1, +3] that is 100% of possible correctness 
numberOfPredictedOnes = (TP + FP) = 4 = 30.77%  

Diagnosis: Excellent ! ! !  

Answer 2

Chances are your NN is learning too slow and thus not learning anything. Deeplearning4j has a great article on troubleshooting neural net training, which may shed some light on the effect the various hyperparameters have.

Having had a glance at your code, I would first try the following things:

• Adjust learning rate:
  You have fixed the learn rate to: LEARN_RATE = 0.001. Try values between 1e-1 and 1e-8.
• Tweak your hidden layer:
  You may have to tweak your hidden layer: hiddenUnitVect = {2000, 4000, 6000, 8000, 10000}. This seems a little big for the task at hand. Try a smaller net first and increase the size if it does not generalize well.