Sparse Autoencoder with Adam optimization

Hello!

I have a data set that contains 4 parts 1- Train Attribute( 121x125973 double ) , 2- Train Label (1x125973 double ), 3- Test Attribute(121x22544 double ) , 4- Test Label (1x22544 double) for NSL KDD dataset and it is ready to implement algorithem.

I applied sparse autoencoder and works with out any problem

options.Method = 'lbfgs' ; 
options.maxIter = maxIter ;	
options.useMex = 0 ;
[opttheta, cost] = minFunc( @(p)sparseAutoencoderCost(p, inputSize, ...
				hs, l1, sp, beta, trainAttr), theta, options) ;
	  
trainFeatures = feedForwardAutoencoder(opttheta, hs, inputSize, ...
                                       trainAttr) ;
testFeatures = feedForwardAutoencoder(opttheta, hs, inputSize, ...
                                      testAttr)  ;

But when I try to optimize the result using Adam optimizer I faced this problem " Unrecognized property 'GRADIENTDECAYFACTOR' for class 'nnet.cnn.TrainingOptionsADAM'.

this is my code

options = trainingOptions('adam', ...
    'InitialLearnRate',3e-4, ...
    'SquaredGradientDecayFactor',0.99, ...
    'MaxEpochs',20, ...
    'MiniBatchSize',64, ...
    'Plots','training-progress');
[opttheta, cost] = minFunc( @(p)sparseAutoencoderCost(p, inputSize, ...
				hs, l1, sp, beta, trainAttr), theta, options) ;
	  
trainFeatures = feedForwardAutoencoder(opttheta, hs, inputSize, ...
                                       trainAttr) ;
testFeatures = feedForwardAutoencoder(opttheta, hs, inputSize, ...
                                      testAttr)  ;

I wonder how can apply sparse autoencoder with adam optimization ?