MATCONVNET/cnn_mnist.m at master · zeehuilin/MATCONVNET · GitHub

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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
datadir='E:\Ñ§Ï°\»úÆ÷Ñ§Ï°\matconvnet-1.0-beta20\photos\multi-label';
opts.expDir = fullfile(vl_rootnn, 'data', ['mnist-zyp-' 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=cnn_setup_data(datadir);
  mkdir(opts.expDir) ;
  save(opts.imdbPath, '-struct', 'imdb') ;
end

net.meta.classes.name = arrayfun(@(x)sprintf('%d',x),1:2,'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)) ;
net.meta.data_mean = imdb.images.data_mean;
net.layers{end}.class = [1] ;

% --------------------------------------------------------------------
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} ;