Bayesopt not printing result

9 vues (au cours des 30 derniers jours)
Ahmad Kamal Bin Mohd Nor
Ahmad Kamal Bin Mohd Nor le 21 Nov 2019
Hi. Im trying to optimize hyperparameters in my LSTM. Im am using Bayesopt. However bayesopt did not print any result for the optimize hyperparameters (I've even put Verbose = 1 . Here is my code
% SPECIFY TRAIN DATA FILE PATH
display = 'specify file name? ';
file = input(display);
filePath = fullfile('data',file);
display = sprintf('Loading File Path for %s',file);
disp(display)
clearvars display
% READ TRAIN DATA FILE
dataTrain = readtable(filePath);
display = sprintf('%s read',file);
disp(display)
clearvars display
% DIVIDE DATA INTO TRAIN,VAL,TEST
rows = height(dataTrain)
idx = randperm(rows);
trainIDX = rows*0.8*0.8;
valIDX = rows*0.8*0.2;
testIDX = rows*0.2;
idx1 = (idx<=trainIDX);
idx2 = (idx>trainIDX & idx<=(trainIDX+valIDX));
idx3 = (idx>(trainIDX+valIDX));
trainData = dataTrain(idx1,:);
valData = dataTrain(idx2,:);
testData = dataTrain(idx3,:);
clearvars rows idx trainIDX valIDX testIDX idx1 idx2 idx3
% CALCULATE NO OF TRAIN FLEET
totalFleet = max(trainData{:,1});
display = sprintf('Total No. of train fleet is %d',totalFleet);
disp(display)
clearvars display
% TRAIN DATA NORMALIZATION
minTrain = min(trainData{:,3});
maxTrain = max(trainData{:,3});
subTable1 = normalize(trainData(:,4:12),'range');
subTable2 = trainData(:,1:3);
clearvars trainData
trainData = [subTable2,subTable1];
clearvars subTable1 subTable2
% DIVIDE TRAINING DATA
Xtrain = cell(totalFleet,1);
Ytrain = cell(totalFleet,1);
for i = 1:totalFleet;
M = rows2vars(trainData(trainData.Fleet==i,[4:12]));
M = M(:,2:end);
Xtrain{i,:} = M;
Xtrain{i,:} = table2array(Xtrain{i,:});
clearvars M
M = rows2vars(trainData(trainData.Fleet==i,[3]));
M = M(:,2:end);
Ytrain{i,:} = M;
Ytrain{i,:} = table2array(Ytrain{i,:});
clearvars M totalFleet
end
disp('Training Data Divided into Xtrain and Ytrain')
clearvars i display
% CALCULATE NO OF VAL FLEET
totalFleet = max(valData{:,1});
display = sprintf('Total No. of val fleet is %d',totalFleet);
disp(display)
clearvars display
% VAL DATA NORMALIZATION
subTable1 = normalize(valData(:,4:12),'range');
subTable2 = valData(:,1:3);
clearvars valData
valData = [subTable2,subTable1];
clearvars subTable1 subTable2
% DIVIDE VAL DATA
Xval = cell(totalFleet,1);
Yval = cell(totalFleet,1);
for i = 1:totalFleet;
M = rows2vars(valData(valData.Fleet==i,[4:12]));
M = M(:,2:end);
Xval{i,:} = M;
Xval{i,:} = table2array(Xval{i,:});
clearvars M
M = rows2vars(valData(valData.Fleet==i,[3]));
M = M(:,2:end);
Yval{i,:} = M;
Yval{i,:} = table2array(Yval{i,:});
clearvars M totalFleet
end
disp('Training Data Divided into Xval and Yval')
clearvars i display
% CALCULATE NO OF TEST FLEET
totalTestFleet = max(testData{:,1});
display = sprintf('Total No. of test fleet is %d',totalTestFleet);
disp(display)
clearvars display
% DATA TEST NORMALIZATION
minTest = min(testData{:,3});
maxTest = max(testData{:,3});
subTable1 = normalize(testData(:,4:11),'range');
subTable2 = testData(:,1:3);
clearvars testData
testData = [subTable2,subTable1];
clearvars subTable1 subTable2
% DIVIDE TEST DATA
Xtest = cell(totalTestFleet,1);
Ytest = cell(totalTestFleet,1);
for i = 1:totalTestFleet;
M = rows2vars(testData(testData.Fleet==i,[3:11]));
M = M(:,2:end);
Xtest{i,:} = M;
Xtest{i,:} = table2array(Xtest{i,:});
clearvars M
M = rows2vars(testData(testData.Fleet==i,[3]));
M = M(:,2:end);
Ytest{i,:} = M;
Ytest{i,:} = table2array(Ytest{i,:});
clearvars M
end
disp('Training Data Divided into Xtest and Ytest')
clearvars i display
% BAYESIAN OPTIMISATION
optimVars = [
optimizableVariable('MaxEpochs',[60 1000],'Type','integer')
optimizableVariable('InitialLearnRate',[1e-3 1],'Transform','log')
optimizableVariable('MiniBatchSize',[26 260], 'Type', 'integer')
optimizableVariable('Size',[10 500], 'Type', 'integer')
optimizableVariable('numHiddenUnits',[10 1000],'Type','integer')];
ObjFcn = makeObjFcn(Xtrain,Ytrain,Xval,Yval);
BayesObject = BayesianOptimization(ObjFcn,optimVars, ...
'MaxTime',50400, ...
'IsObjectiveDeterministic',false, ...
'Verbose',1, ...
'UseParallel',false);
bestIdx = BayesObject.IndexOfMinimumTrace(end);
fileName = BayesObject.UserDataTrace{bestIdx};
savedStruct = load(fileName);
valError = savedStruct.valError
% SCRIPT ObjFcn
function ObjFcn = makeObjFcn(Xtrain,Ytrain,Xval,Yval) %#ok<*INUSD>
ObjFcn = @valErrorFun;
function [valError,cons,fileName] = valErrorFun(optimVars) %#ok<STOUT>
layers = [ ...
sequenceInputLayer(9)
lstmLayer(optimVars.numHiddenUnits,'OutputMode','sequence')
fullyConnectedLayer(optimVars.Size)
dropoutLayer(0.5)
fullyConnectedLayer(1)
regressionLayer];
options = trainingOptions('adam', ...
'MaxEpochs',optimVars.MaxEpochs, ...
'ValidationData',{Xval,Yval},...
'ValidationFrequency',10,...
'MiniBatchSize',optimVars.MiniBatchSize, ...
'InitialLearnRate',optimVars.InitialLearnRate, ...
'GradientThreshold',1, ...
'Shuffle','never', ...
'Plots','training-progress',...
'SequenceLength','longest',...
'Verbose',false);
net = trainNetwork(Xtrain,Ytrain,layers,options);
YPredicted = predict(net,Xval);
valError = 1 - mean(YPredicted == Yval);
fileName = num2str(valError) + ".mat";
save(fileName,'net','valError','options')
cons = [];
end
end

Réponse acceptée

Harsha Priya Daggubati
Harsha Priya Daggubati le 26 Déc 2019

Plus de réponses (0)

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