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initializeCnn.m
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initializeCnn.m
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function net = initializeCnn()
net.meta.inputSize = [64 64 3 10] ;
net.layers = {} ;
net.layers{end+1} = struct(...
'name', 'conv1_1', ...
'type', 'conv', ...
'weights', {xavier(3,3,3,64)}, ...
'pad', 1, ...
'learningRate', [2 1], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu1_1', ...
'type', 'relu') ;
net.layers{end+1} = struct(...
'name', 'conv1_2', ...
'type', 'conv', ...
'weights', {xavier(3,3,64,64)}, ...
'pad', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu1_2', ...
'type', 'relu') ;
%net.layers{end+1} = struct(...
% 'name', 'pool1', ...
% 'type', 'pool', ...
% 'method', 'max', ...
% 'pool', [2 2], ...
% 'stride', [2 2], ...
% 'pad', [0 1 0 1]) ;
net.layers{end+1} = struct(...
'name', 'conv2_1', ...
'type', 'conv', ...
'weights', {xavier(3,3,64,128)}, ...
'pad', 1, ...
'stride', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu2_1', ...
'type', 'relu') ;
net.layers{end+1} = struct(...
'name', 'conv2_2', ...
'type', 'conv', ...
'weights', {xavier(3,3,128,128)}, ...
'pad', 1, ...
'stride', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu2_2', ...
'type', 'relu') ;
%net.layers{end+1} = struct(...
% 'name', 'pool2', ...
% 'type', 'pool', ...
% 'method', 'max', ...
% 'pool', [2 2], ...
% 'stride', [2 2], ...
% 'pad', [0 1 0 1]) ;
net.layers{end+1} = struct(...
'name', 'conv3_1', ...
'type', 'conv', ...
'weights', {xavier(3,3,128,256)}, ...
'pad', 1, ...
'stride', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu3_1', ...
'type', 'relu') ;
net.layers{end+1} = struct(...
'name', 'conv3_2', ...
'type', 'conv', ...
'weights', {xavier(3,3,256,256)}, ...
'pad', 1, ...
'stride', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu3_2', ...
'type', 'relu') ;
net.layers{end+1} = struct(...
'name', 'conv3_3', ...
'type', 'conv', ...
'weights', {xavier(3,3,256,256)}, ...
'pad', 1, ...
'stride', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu3_3', ...
'type', 'relu') ;
%net.layers{end+1} = struct(...
% 'name', 'pool3', ...
% 'type', 'pool', ...
% 'method', 'max', ...
% 'pool', [2 2], ...
% 'stride', [2 2], ...
% 'pad', [0 1 0 1]) ;
net.layers{end+1} = struct(...
'name', 'conv4_1', ...
'type', 'conv', ...
'weights', {xavier(3,3,256,512)}, ...
'pad', 1, ...
'stride', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu4_1', ...
'type', 'relu') ;
net.layers{end+1} = struct(...
'name', 'conv4_2', ...
'type', 'conv', ...
'weights', {xavier(3,3,512,512)}, ...
'pad', 1, ...
'stride', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu4_2', ...
'type', 'relu') ;
net.layers{end+1} = struct(...
'name', 'conv4_3', ...
'type', 'conv', ...
'weights', {xavier(3,3,512,512)}, ...
'pad', 1, ...
'stride', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu4_3', ...
'type', 'relu') ;
%net.layers{end+1} = struct(...
% 'name', 'pool4', ...
% 'type', 'pool', ...
% 'method', 'max', ...
% 'pool', [2 2], ...
% 'stride', [2 2], ...
% 'pad', [0 1 0 1]) ;
net.layers{end+1} = struct(...
'name', 'conv5_1', ...
'type', 'conv', ...
'weights', {xavier(3,3,512,512)}, ...
'pad', 1, ...
'stride', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu5_1', ...
'type', 'relu') ;
net.layers{end+1} = struct(...
'name', 'conv5_2', ...
'type', 'conv', ...
'weights', {xavier(3,3,512,512)}, ...
'pad', 1, ...
'stride', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu5_2', ...
'type', 'relu') ;
net.layers{end+1} = struct(...
'name', 'conv5_3', ...
'type', 'conv', ...
'weights', {xavier(3,3,512,512)}, ...
'pad', 1, ...
'stride', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu5_3', ...
'type', 'relu') ;
%net.layers{end+1} = struct(...
% 'name', 'pool5', ...
% 'type', 'pool', ...
% 'method', 'max', ...
% 'pool', [2 2], ...
% 'stride', [2 2], ...
% 'pad', [0 1 0 1]) ;
net.layers{end+1} = struct(...
'name', 'fc6', ...
'type', 'conv', ...
'weights', {xavier(3,3,512,4096)}, ...
'pad', 0, ...
'stride', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu6', ...
'type', 'relu') ;
net.layers{end+1} = struct(...
'name', 'drop6', ...
'type', 'dropout', ...
'rate', 0.5) ;
net.layers{end+1} = struct(...
'name', 'fc7', ...
'type', 'conv', ...
'weights', {xavier(3,3,4096,4096)}, ...
'pad', 0, ...
'stride', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'relu7', ...
'type', 'relu') ;
net.layers{end+1} = struct(...
'name', 'drop7', ...
'type', 'dropout', ...
'rate', 0.5) ;
net.layers{end+1} = struct(...
'name', 'fc8', ...
'type', 'conv', ...
'weights', {xavier(3,3,4096,200)}, ...
'pad', 0, ...
'stride', 1, ...
'learningRate', [1 2], ...
'weightDecay', [1 0]) ;
net.layers{end+1} = struct(...
'name', 'prob', ...
'type', 'softmax') ;
net = vl_simplenn_tidy(net) ;