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Dimensions #75

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Dimensions #75

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75329a2
Rebase.
albertoandreottiATgmail Nov 21, 2013
a7eb6b7
After rebase.
albertoandreottiATgmail Nov 21, 2013
0d34794
Merge branch 'master' of https://github.com/albertoandreottiATgmail/D…
albertoandreottiATgmail Dec 2, 2013
3fed6e0
After rebasing upstream/master.
albertoandreottiATgmail Dec 2, 2013
5d43610
Changes in dimensions so TDNN can be built.
albertoandreottiATgmail Dec 2, 2013
eabe5b7
Merge remote-tracking branch 'upstream/master' into dimensions
albertoandreottiATgmail Dec 2, 2013
9879c3b
Changed deploy script to please CI system.
albertoandreottiATgmail Dec 2, 2013
13abd70
Small change in CNN.
albertoandreottiATgmail Dec 3, 2013
7e5d7ce
Work in progress for nonsquared kernels.
albertoandreottiATgmail Dec 7, 2013
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2 changes: 1 addition & 1 deletion .travis.yml
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
before_script:
- sudo apt-add-repository ppa:octave/stable --yes
- sudo apt-get update -y
- sudo apt-get install octave -y
- sudo apt-get install octave --force-yes
- sudo apt-get install liboctave-dev -y
script:
- sh -c "octave tests/runalltests.m"
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4 changes: 3 additions & 1 deletion CNN/cnnbp.m
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Expand Up @@ -23,7 +23,9 @@
for l = (n - 1) : -1 : 1
if strcmp(net.layers{l}.type, 'c')
for j = 1 : numel(net.layers{l}.a)
net.layers{l}.d{j} = net.layers{l}.a{j} .* (1 - net.layers{l}.a{j}) .* (expand(net.layers{l + 1}.d{j}, [net.layers{l + 1}.scale net.layers{l + 1}.scale 1]) / net.layers{l + 1}.scale ^ 2);
xscale = net.layers{l + 1}.xscale;
yscale = net.layers{l + 1}.yscale;
net.layers{l}.d{j} = net.layers{l}.a{j} .* (1 - net.layers{l}.a{j}) .* (expand(net.layers{l + 1}.d{j}, [xscale yscale 1]) / (xscale*yscale));
end
elseif strcmp(net.layers{l}.type, 's')
for i = 1 : numel(net.layers{l}.a)
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7 changes: 5 additions & 2 deletions CNN/cnnff.m
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Expand Up @@ -21,8 +21,11 @@
elseif strcmp(net.layers{l}.type, 's')
% downsample
for j = 1 : inputmaps
z = convn(net.layers{l - 1}.a{j}, ones(net.layers{l}.scale) / (net.layers{l}.scale ^ 2), 'valid'); % !! replace with variable
net.layers{l}.a{j} = z(1 : net.layers{l}.scale : end, 1 : net.layers{l}.scale : end, :);
xscale = net.layers{l}.xscale;
yscale = net.layers{l}.yscale;

z = convn(net.layers{l - 1}.a{j}, ones(xscale, yscale) / (xscale*yscale), 'valid'); % !! replace with variable
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please calculate ones(xscale, yscale) / (xscale*yscale) in cnnsetup, put it in the net somewhere (net._meanFilter or similar), use that and remove the comment to replace with var

net.layers{l}.a{j} = z(1 : xscale : end, 1 : yscale : end, :);
end
end
end
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2 changes: 1 addition & 1 deletion CNN/cnnsetup.m
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Expand Up @@ -4,7 +4,7 @@

for l = 1 : numel(net.layers) % layer
if strcmp(net.layers{l}.type, 's')
mapsize = mapsize / net.layers{l}.scale;
mapsize = [mapsize(1)/net.layers{l}.xscale, mapsize(2)/net.layers{l}.yscale];
assert(all(floor(mapsize)==mapsize), ['Layer ' num2str(l) ' size must be integer. Actual: ' num2str(mapsize)]);
for j = 1 : inputmaps
net.layers{l}.b{j} = 0;
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4 changes: 2 additions & 2 deletions tests/test_cnn_gradients_are_numerically_correct.m
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Expand Up @@ -4,9 +4,9 @@
cnn.layers = {
struct('type', 'i') %input layer
struct('type', 'c', 'outputmaps', 2, 'kernelsize', 5) %convolution layer
struct('type', 's', 'scale', 2) %sub sampling layer
struct('type', 's', 'xscale', 2, 'yscale', 2) %sub sampling layer
struct('type', 'c', 'outputmaps', 2, 'kernelsize', 5) %convolution layer
struct('type', 's', 'scale', 2) %subsampling layer
struct('type', 's', 'xscale', 2, 'yscale', 2) %subsampling layer
};
cnn = cnnsetup(cnn, batch_x, batch_y);

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5 changes: 2 additions & 3 deletions tests/test_example_CNN.m
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Expand Up @@ -15,12 +15,11 @@
cnn.layers = {
struct('type', 'i') %input layer
struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer
struct('type', 's', 'scale', 2) %sub sampling layer
struct('type', 's', 'xscale', 2, 'yscale', 2) %sub sampling layer
struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer
struct('type', 's', 'scale', 2) %subsampling layer
struct('type', 's', 'xscale', 2, 'yscale', 2) %subsampling layer
};


opts.alpha = 1;
opts.batchsize = 200;
opts.numepochs = 7;
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