use
options = trainingOptions('sgdm',...
'MaxEpochs',60, ...
'LearnRateDropFactor',0.1,...
'LearnRateDropPeriod',1,...
'LearnRateSchedule','piecewise',...
'ValidationData',{valImages,valLabels},...
'ValidationFrequency',50,...
'Momentum',0.85,...
'ValidationPatience',5,...
'InitialLearnRate',0.1,...
'MiniBatchSize',128,...
'Verbose',true,...
'executionenvironment','cpu',...
'Shuffle','every-epoch',...
'Plots','training-progress');
net = trainNetwork(...,...,...,options);
and choose the parameters accordingly.
For default values, see the docs of trainingOptions.
In terms of the learning rate and momentum, I typically start with a large one just to test the general behaviour and then I drop the learning rate to get more accurate results. Its difficult to give you some concrete advice because the optimal learning rate depends on your specific problem.
