What is the sequence of functions to run in a Matlab code?

Question

I have an example of Matlab code from this source. There are only functions and there is no initial starting point like python language. Here is an overview of the code:

What is the sequence of functions to run? in other words, which function run first and which second etc.?

And here is the complete code:

function [net, info] = cnn_mnist(varargin)
%CNN_MNIST  Demonstrates MatConvNet on MNIST

run(fullfile(fileparts(mfilename('fullpath')),...
  '..', '..', 'matlab', 'vl_setupnn.m')) ;

opts.batchNormalization = false ;
opts.networkType = 'simplenn' ;
[opts, varargin] = vl_argparse(opts, varargin) ;

sfx = opts.networkType ;
if opts.batchNormalization, sfx = [sfx '-bnorm'] ; end
opts.expDir = fullfile(vl_rootnn, 'data', ['mnist-baseline-' sfx]) ;
[opts, varargin] = vl_argparse(opts, varargin) ;

opts.dataDir = fullfile(vl_rootnn, 'data', 'mnist') ;
opts.imdbPath = fullfile(opts.expDir, 'imdb.mat');
opts.train = struct() ;
opts = vl_argparse(opts, varargin) ;
if ~isfield(opts.train, 'gpus'), opts.train.gpus = []; end;

% --------------------------------------------------------------------
%                                                         Prepare data
% --------------------------------------------------------------------

net = cnn_mnist_init('batchNormalization', opts.batchNormalization, ...
                     'networkType', opts.networkType) ;

if exist(opts.imdbPath, 'file')
  imdb = load(opts.imdbPath) ;
else
  imdb = getMnistImdb(opts) ;
  mkdir(opts.expDir) ;
  save(opts.imdbPath, '-struct', 'imdb') ;
end

net.meta.classes.name = arrayfun(@(x)sprintf('%d',x),1:10,'UniformOutput',false) ;

% --------------------------------------------------------------------
%                                                                Train
% --------------------------------------------------------------------

switch opts.networkType
  case 'simplenn', trainfn = @cnn_train ;
  case 'dagnn', trainfn = @cnn_train_dag ;
end

[net, info] = trainfn(net, imdb, getBatch(opts), ...
  'expDir', opts.expDir, ...
  net.meta.trainOpts, ...
  opts.train, ...
  'val', find(imdb.images.set == 3)) ;

% --------------------------------------------------------------------
function fn = getBatch(opts)
% --------------------------------------------------------------------
switch lower(opts.networkType)
  case 'simplenn'
    fn = @(x,y) getSimpleNNBatch(x,y) ;
  case 'dagnn'
    bopts = struct('numGpus', numel(opts.train.gpus)) ;
    fn = @(x,y) getDagNNBatch(bopts,x,y) ;
end

% --------------------------------------------------------------------
function [images, labels] = getSimpleNNBatch(imdb, batch)
% --------------------------------------------------------------------
images = imdb.images.data(:,:,:,batch) ;
labels = imdb.images.labels(1,batch) ;

% --------------------------------------------------------------------
function inputs = getDagNNBatch(opts, imdb, batch)
% --------------------------------------------------------------------
images = imdb.images.data(:,:,:,batch) ;
labels = imdb.images.labels(1,batch) ;
if opts.numGpus > 0
  images = gpuArray(images) ;
end
inputs = {'input', images, 'label', labels} ;

% --------------------------------------------------------------------
function imdb = getMnistImdb(opts)
% --------------------------------------------------------------------
% Preapre the imdb structure, returns image data with mean image subtracted
files = {'train-images-idx3-ubyte', ...
         'train-labels-idx1-ubyte', ...
         't10k-images-idx3-ubyte', ...
         't10k-labels-idx1-ubyte'} ;

if ~exist(opts.dataDir, 'dir')
  mkdir(opts.dataDir) ;
end

for i=1:4
  if ~exist(fullfile(opts.dataDir, files{i}), 'file')
    url = sprintf('http://yann.lecun.com/exdb/mnist/%s.gz',files{i}) ;
    fprintf('downloading %s\n', url) ;
    gunzip(url, opts.dataDir) ;
  end
end

f=fopen(fullfile(opts.dataDir, 'train-images-idx3-ubyte'),'r') ;
x1=fread(f,inf,'uint8');
fclose(f) ;
x1=permute(reshape(x1(17:end),28,28,60e3),[2 1 3]) ;

f=fopen(fullfile(opts.dataDir, 't10k-images-idx3-ubyte'),'r') ;
x2=fread(f,inf,'uint8');
fclose(f) ;
x2=permute(reshape(x2(17:end),28,28,10e3),[2 1 3]) ;

f=fopen(fullfile(opts.dataDir, 'train-labels-idx1-ubyte'),'r') ;
y1=fread(f,inf,'uint8');
fclose(f) ;
y1=double(y1(9:end)')+1 ;

f=fopen(fullfile(opts.dataDir, 't10k-labels-idx1-ubyte'),'r') ;
y2=fread(f,inf,'uint8');
fclose(f) ;
y2=double(y2(9:end)')+1 ;

set = [ones(1,numel(y1)) 3*ones(1,numel(y2))];
data = single(reshape(cat(3, x1, x2),28,28,1,[]));
dataMean = mean(data(:,:,:,set == 1), 4);
data = bsxfun(@minus, data, dataMean) ;

imdb.images.data = data ;
imdb.images.data_mean = dataMean;
imdb.images.labels = cat(2, y1, y2) ;
imdb.images.set = set ;
imdb.meta.sets = {'train', 'val', 'test'} ;
imdb.meta.classes = arrayfun(@(x)sprintf('%d',x),0:9,'uniformoutput',false);

Answer 1

In MATLAB, when multiple functions are defined within the same file, the first function definition is the one that is executed when the file is executed using the name of the containing .m file. All of the other subfunctions within that file will not be executed unless they are called from the first function (or another sub function). In MATLAB, you cannot access the subfunctions without first going through the main function.

Also note that even if the name of the first function doesn't match the name of the .m file, that function will still be the one that is executed. If the name of your function doesn't match the filename, you should receive an mlint warning indicating this.

For your specific code, cnn_mnist is called when you execute your code. This function calls the subfunctions getMnistImdb and getBatch which then call getSimpleNNBatch and getDagNNBatch