matlab function for building resnet with nn toolbox

resnet.m

function net = resnet(input_shape)
%RESNET Creates a resnet model with imput_size
n = imageInputLayer(input_shape,'Name','input');
n = layerGraph(n);
n = bottleneck(256, ['bottleneck_' char(string(1))], n)

end

function graph = bottleneck(depth, name, graph)
head = graph.Layers(end).Name;

layers = [
convlayer(1, depth/2, [name '_s1'])
convlayer(3, depth/2, [name '_s2'])
noactconvlayer(1, depth, [name '_exp'])
additionLayer(2, 'Name', [name '_add'])
reluLayer('Name', [name, '_relu'])
];

graph = addLayers(graph, layers);
graph = connectLayers(graph, head, layers(1).Name);
graph = connectLayers(graph, head, [name '_add' '/in2']);

% figure;
% plot(graph);
% title('Bottleneck Block');

end

function layers = convlayer(kernel, filters, name)
layers = [
    convolution2dLayer(kernel, filters, 'Padding', 'same', 'Name', [name, '_conv'])
    batchNormalizationLayer('Name', [name, '_BN'])
    reluLayer('Name', [name, '_relu'])
    ];
end

function layers = noactconvlayer(kernel, filters, name)
layers = [
    convolution2dLayer(kernel, filters, 'Padding', 'same', 'Name', [name, '_conv'])
    batchNormalizationLayer('Name', [name, '_BN'])
    ];
end