我在Linux CentOS 7机器上使用Torch7。 我正在尝试将人工神经网络（ANN）应用于我的数据集，以解决二进制分类问题。我使用的是简单的多层感知器。
我正在使用以下Torch包：optim，torch。
问题是我的感知器总是预测零值（被归类为零的元素），我无法理解为什么......
这是我的数据集（“dataset_file.csv”）。有34个要素和1个标签目标（最后一列，可能是0或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
这是我的Torch Lua代码：
-- 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
有没有人知道为什么我的脚本只预测零元素？
编辑：我将原始数据集替换为我在脚本中使用的规范化版本
答案 0 :(得分：2)
当我运行您的原始代码时，我有时会预测所有零，我有时会获得完美的性能。这表明您的原始模型对参数值的初始化非常敏感。
如果我使用种子值torch.manualSeed(0)（所以我们总是有相同的初始化），我每次都会获得完美的表现。但这不是一般的解决方案。
为了获得更全面的改进，我做了以下更改：
2000单位图层。但是你只有34个输入和
1输出通常你只需要隐藏单位的数量
输入和输出的数量之间。我减少了它
50。MOMENTUM，并将ITERATIONS提高到200。当我运行此模型20次（未播种）时，我获得Excellent次19次。为了进一步改进，您可以进一步调整超参数。并且还应该使用单独的验证集来查看多个初始化，以选择“最佳”模型（尽管这将进一步细分您已经非常小的数据集）。
-- 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
下面粘贴的是20次运行中的1次输出：
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 ! ! !
答案 1 :(得分：0)
你的NN很可能学得太慢，因此没有学到任何东西。 Deeplearning4j在troubleshooting neural net training上有一篇很棒的文章，可能会对各种超参数的影响有所启发。
瞥了一眼你的代码后，我会先尝试以下事项：
LEARN_RATE = 0.001。尝试1e-1和1e-8之间的值。hiddenUnitVect = {2000, 4000, 6000, 8000, 10000}。这对于手头的任务来说似乎有点大。首先尝试使用较小的网，如果不能很好地推广，请增加尺寸。