demo code for https://github.com/vlfeat/matconvnet/issues/814

issue_814.m

function issue_814
% demo for @segman

% fix random seed to get repeatable result
rng(1) ;

sample = rand(21, 21, 3, 'single') ;
label = [ 1 1 1 1 1 ;
         1 1 1 1 1 ;
         1 2 2 2 1 ;
         2 2 2 2 1 ;
         3 3 3 3 3 ] ;
net = init() ;
net.initParams() ;
net.eval({'data', sample, 'label', label}) ;
loss = net.vars(net.getVarIndex('loss')).value ;
fprintf('loss value is %.2f \n', loss) ;
I get a loss value: 49.09

% ------------------------
function net = init()
% ------------------------

net = dagnn.DagNN() ;
numClasses = 3 ;

net = addConvLayer(net, 'x1', 'data', [5 5 3 32]) ;
net = addRelu(net, 'x1x', 'x1') ;
net = addConvLayer(net, 'x2', 'x1x', [5 5 32 64]) ;
net = addRelu(net, 'x2x', 'x2') ;
net = addConvLayer(net, 'x3', 'x2x', [5 5 64 96]) ;
net = addRelu(net, 'x3x', 'x3') ;
net = addConvLayer(net, 'x4', 'x3x', [5 5 96 128]) ;
net = addRelu(net, 'x4x', 'x4') ;
net = addConvLayer(net, 'fc5', 'x4x', [1 1 128 128]) ;
net = addDropout(net, 'fc5d', 'fc5') ;
net = addConvLayer(net, 'fc6', 'fc5d', [1 1 128 numClasses]) ;
net = addDropout(net, 'fc6d', 'fc6') ;
net = addLoss(net, 'loss', {'fc6d', 'label'}, 'softmaxlog') ;

% ----------------------------------------------------------
function net = addConvLayer(net, layerName, prev, kernelSize)
% ----------------------------------------------------------

if strcmp(prev, 'data')
    inputs = 'data' ;
else
    inputs = net.layers(net.getLayerIndex(prev)).outputs ;
end

layer = dagnn.Conv('size', kernelSize, 'pad', 0, 'stride', 1, 'hasBias', true) ;
params = {sprintf('%s_1f', layerName), sprintf('%s_1b', layerName)} ;
outputs = layerName ;
net.addLayer(layerName, layer, inputs, outputs, params) ;

% ------------------------------------
function net = addRelu(net, layerName, prev) 
% ------------------------------------
layer = dagnn.ReLU() ;
inputs = net.layers(net.getLayerIndex(prev)).outputs ;
outputs = layerName ;
params = {} ;
net.addLayer(layerName, layer, inputs, outputs, params) ;

% ------------------------------------
function net = addDropout(net, layerName, prev) 
% ------------------------------------
layer = dagnn.DropOut('rate', 0.5) ;
inputs = net.layers(net.getLayerIndex(prev)).outputs ;
outputs = layerName ;
params = {} ;
net.addLayer(layerName, layer, inputs, outputs, params) ;

% ------------------------------------
function net = addLoss(net, layerName, inputs, lossType) 
% ------------------------------------
layer = dagnn.Loss('loss', lossType) ;
outputs = layerName ;
params = {} ;
net.addLayer(layerName, layer, inputs, outputs, params) ;