x = linspace(0,1,10000)';
inputSize = 1;
layers = [
featureInputLayer(inputSize,Normalization="none")
fullyConnectedLayer(10)
sigmoidLayer
fullyConnectedLayer(1)
sigmoidLayer];
dlnet = dlnetwork(layers);
numEpochs = 15;
miniBatchSize =100 ;
initialLearnRate = 0.5;
learnRateDropFactor = 0.5;
learnRateDropPeriod = 5;
momentum = 0.9;
icCoeff = 7;
ads = arrayDatastore(x,IterationDimension=1);
mbq = minibatchqueue(ads,MiniBatchSize=miniBatchSize,MiniBatchFormat="BC");
figure
set(gca,YScale="log")
lineLossTrain = animatedline(Color=[0.85 0.325 0.098]);
ylim([0 inf])
xlabel("Iteration")
ylabel("Loss (log scale)")
grid on
velocity = [];
iteration = 0;
learnRate = initialLearnRate;
start = tic;
% Loop over epochs.
for epoch = 1:numEpochs
% Shuffle data.
mbq.shuffle
% Loop over mini-batches.
while hasdata(mbq)
iteration = iteration + 1;
% Read mini-batch of data.
dlX = next(mbq);
% Evaluate the model gradients and loss using dlfeval and the modelGradients function.
[gradients,loss] = dlfeval(@modelGradients, dlnet, dlX, icCoeff);
% Update network parameters using the SGDM optimizer.
[dlnet,velocity] = sgdmupdate(dlnet,gradients,velocity,learnRate,momentum);
% To plot, convert the loss to double.
loss = double(gather(extractdata(loss)));
% Display the training progress.
D = duration(0,0,toc(start),Format="mm:ss.SS");
addpoints(lineLossTrain,iteration,loss)
title("Epoch: " + epoch + " of " + numEpochs + ", Elapsed: " + string(D))
drawnow
end
% Reduce the learning rate.
if mod(epoch,learnRateDropPeriod)==0
learnRate = learnRate*learnRateDropFactor;
end
end
