MATLAB에서 TensorFlow 모델 가져오기

p = gcp('nocreate'); % 풀이 없으면 새로 생성하지 않음.
if isempty(p)
   parpool()
else
    poolsize = p.NumWorkers
end

filename = "driving_log.csv";
drivinglog = import_driving_log(filename);
drivinglog = drivinglog(2:end, :);

figure;
histogram(drivinglog.steering);
title("Original steering angle distribution");

steeringLimits = [-1 -0.5 -0.05 0 0.05 0.5 1];
steeringClasses = discretize(drivinglog.steering, steeringLimits, 'categorical');
classNames = categories(steeringClasses);

steeringClasses = mergecats(steeringClasses,["[-0.05, 0)","[0, 0.05)"], "0.0");
histogramClasses = histogram(steeringClasses);
title("Angle distribution discretized (5 categories)");

imds = imageDatastore("sim_data/"+drivinglog.center,"Labels", steeringClasses);
countEachLabel(imds)

maxSamples = 800;
countLabel = countEachLabel(imds);
[~, unbalancedLabelIdx] = max(countLabel.Count);
unbalanced = imds.Labels == countLabel.Label(unbalancedLabelIdx);
idx = find(unbalanced);
randomIdx = randperm(numel(idx));
downsampled = idx(randomIdx(1:maxSamples));
retained = [find(~unbalanced) ; downsampled];
imds = subset(imds, retained');
histogram(imds.Labels)

[imdsTrain, imdsValid,imdsTest] = splitEachLabel(imds, 0.9, 0.05, 0.05);

trainData = transform(imdsTrain, @imagePreprocess, "IncludeInfo", true);
testData = transform(imdsTest, @imagePreprocess, "IncludeInfo", true);
valData = transform(imdsValid, @imagePreprocess, "IncludeInfo", true);
imds_origI = imdsTrain.read;
imds_newI = trainData.read{1};
subplot(211), imshow(imds_origI), title("imds의 원본 이미지")
subplot(212), imshow(imds_newI), title("imds의 전처리된 이미지")

layers = importKerasLayers("tf_model.h5")

deepNetworkDesigner(layers)

netGraph = layerGraph(layers);
clf; plot(netGraph)

classificationLayers = [fullyConnectedLayer(5,"Name","dense_3"), ...
                        softmaxLayer("Name","softmax"), ...
                        classificationLayer("Name","classoutput")];
netGraph = removeLayers(netGraph, {'dense_3', 'RegressionLayer_dense_3'});
netGraph = addLayers(netGraph,classificationLayers);
layers_1 = netGraph.Layers

initialLearnRate = 0.001;
maxEpochs = 30;
miniBatchSize = 100;
options = trainingOptions("adam", ...
    "MaxEpochs",maxEpochs, ...
    "InitialLearnRate",initialLearnRate, ...
    "Plots","training-progress",  ...
    "ValidationData",valData, ...
    "ValidationFrequency",10, ...
    "LearnRateSchedule","piecewise", ...
    "LearnRateDropPeriod",10, ...
    "LearnRateDropFactor",0.5, ...
    "ExecutionEnvironment","parallel",...
    "Shuffle","every-epoch");
net = trainNetwork(trainData, layers_1, options);

model_name = "net-class-30-1e-4-drop10-0_5"; % classification-epochs-learning_rate-drop_period-drop_factor
save(model_name + ".mat", "net")

exportONNXNetwork(net, model_name + ".onnx")

model_name = "net-class-30-1e-4-drop10-0_5"; % classification-epochs-learning_rate-drop_period-drop_factor
load(model_name+".mat","net")
predSteering = classify(net, testData);
figure
startTest = 80;
endTest = 100;
plot(predSteering(startTest:endTest), 'r*', "MarkerSize",10)
hold on
plot(imdsTest.Labels(startTest:endTest), 'b*')
legend("Predictions", "Actual")
hold off

confMat = confusionmat(imdsTest.Labels, predSteering);
confusionchart(confMat)

numberImages = length(imdsTest.Labels);
i = 42;
img = readimage(imdsTest, i);
imshow(img), title(char(imdsTest.Labels(i)) + "/" + char(predSteering(i)));

function drivinglog = import_driving_log(filename, dataLines)
%IMPORTFILE Import data from a text file
%  DRIVINGLOG = IMPORTFILE(FILENAME) reads data from text file FILENAME
%  for the default selection.  Returns the data as a table.
%
%  DRIVINGLOG = IMPORTFILE(FILE, DATALINES) reads data for the specified
%  row interval(s) of text file FILENAME. Specify DATALINES as a
%  positive scalar integer or a N-by-2 array of positive scalar integers
%  for dis-contiguous row intervals.
%
%  See also READTABLE.
%
% Auto-generated by MATLAB on 28-May-2021 21:31:34
%% Input handling
% If dataLines is not specified, define defaults
if nargin < 2
    dataLines = [1, Inf];
end
%% Set up the Import Options and import the data
opts = delimitedTextImportOptions("NumVariables", 8);
% Specify range and delimiter
opts.DataLines = dataLines;
opts.Delimiter = ",";
% Specify column names and types
opts.VariableNames = ["VarName1", "center", "left", "right", "steering", "throttle", "reverse", "speed"];
opts.VariableTypes = ["double", "string", "string", "string", "double", "double", "double", "double"];
% Specify file level properties
opts.ExtraColumnsRule = "ignore";
opts.EmptyLineRule = "read";
% Specify variable properties
opts = setvaropts(opts, ["center", "left", "right"], "WhitespaceRule", "preserve");
opts = setvaropts(opts, ["center", "left", "right"], "EmptyFieldRule", "auto");
opts = setvaropts(opts, ["VarName1", "steering", "throttle", "reverse", "speed"], "ThousandsSeparator", ",");
% Import the data
drivinglog = readtable(filename, opts);
end

function [dataOut, info] = imagePreprocess(dataIn, info)
imgOut = dataIn(60:135, :, :);
imgOut = rgb2ycbcr(imgOut);
imgOut = imresize(imgOut, [66 200]);
dataOut = {imgOut, info.Label};
end