Improving the autoencoder in Matlab

I am trying to reconstruct the handwritten letters using Matlab. I get the output but the images are too blurry. The same data set in Keras produce a much better result. One important to thing to mention is that in Keras I used cross-entropy for losses. In Matlab I cannot figure out how to change it for autoencoder (found how to do it for softmax).

% Implementation of autoencoder

% Import dataset
% Train images and lables
train_images = loadMNISTImages('train-images-idx3-ubyte');
train_labels = loadMNISTLabels('train-labels-idx1-ubyte');

% Test images and lables
test_images = loadMNISTImages('t10k-images-idx3-ubyte');
test_labels = loadMNISTLabels('t10k-labels-idx1-ubyte');

rng('default')
% rng ('shuffle')
hiddenSize1 = 16;
autoenc1 = trainAutoencoder(train_images,hiddenSize1, ...
    'MaxEpochs',100, ...
    'L2WeightRegularization',0.004, ...
    'SparsityRegularization',4, ...
    'SparsityProportion',0.15, ...
    'ScaleData', false);
feat1 = encode(autoenc1,train_images);

hiddenSize2 = 8;
autoenc2 = trainAutoencoder(feat1,hiddenSize2, ...
    'MaxEpochs',100, ...
    'L2WeightRegularization',0.002, ...
    'SparsityRegularization',4, ...
    'SparsityProportion',0.1, ...
    'ScaleData', false);
feat2 = encode(autoenc2,feat1);

softnet = trainSoftmaxLayer(feat2,train_labels','MaxEpochs',100,'LossFunction','crossentropy');
deepnet = stack(autoenc1,autoenc2,softnet);
deepnet = train(deepnet, train_images,train_labels');

recon = predict (autoenc1,test_images);
recon = reshape (recon, 28,28,10000);

% Reconstructed Images
figure (1);
for i = 1:10
    subplot(2,5,i)
    imshow(recon(:,:,i))
end