Hi Amir,
To add a feature map in a neural network you can typically add more convolutional layers, which are responsible for detecting features in an image. In the context of your MATLAB code, you can add additional "convolution2dLayer" followed by "batchNormalizationLayer" and "reluLayer" to expand the feature map. Here's how you can modify your code:
% example height width and channels
width = 28;
height = 28;
channels = 1;
%% To calculate the output size of each layer, you can use the formula:
%% output feature size = floor( (Input size + 2*(padding) - filter size) / stride) + 1
%% layer = convolution2dLayer(filterSize,numFilters,Name=Value)
layers = [
imageInputLayer([width, height, channels])
convolution2dLayer(2, 16, 'Padding', 1)
batchNormalizationLayer
reluLayer
convolution2dLayer(3, 32, 'Padding', 1)
batchNormalizationLayer
reluLayer
convolution2dLayer(3, 64, 'Padding', 1)
batchNormalizationLayer
reluLayer
fullyConnectedLayer(12)
softmaxLayer
];
Additionally you can refer to the following MathWorks documentation for more information:
- 'convolution2dLayer': https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.convolution2dlayer.html
- 'batchNormalizationLayer': https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.batchnormalizationlayer.html
Hope this helps!
