网络结构（6c-2s-12c-2s）：
初始化：
\begin{align}\notag W \sim U(- \frac{\sqrt{6}}{\sqrt{n_j+n_{j+1}}} , \frac{\sqrt{6}}{\sqrt{n_j+n_{j+1}}}) \end{align}
\begin{align}\notag Var(W_i) = \frac{1}{n_i} ; Var(W_i) = \frac{1}{n_{i+1}} ; Var(W_i) = \frac{1}{n_i + n_{i+1}} \end{align}
偏置
\(b\)
统一初始化为$0$，权重
$W$设置为
$random(-1,1)\sqrt{\frac{6}{fan\_in + fan\_out}} \sim U(- \frac{\sqrt{6}}{\sqrt{n_j+n_{j+1}}} , \frac{\sqrt{6}}{\sqrt{n_j+n_{j+1}}})$，
$n_j$表示神经网络的大小，
$fan\_in = $输入通道数
$\times$卷积核
$size$，
$fan\_out = $输出通道数
$\times$卷积核
$size$。
for l = 1 : numel(net.layers) % layer
if strcmp(net.layers{l}.type, 's')
mapsize = mapsize / net.layers{l}.scale;
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;
end
end
if strcmp(net.layers{l}.type, 'c')
mapsize = mapsize - net.layers{l}.kernelsize + 1;
fan_out = net.layers{l}.outputmaps * net.layers{l}.kernelsize ^ 2;
for j = 1 : net.layers{l}.outputmaps % output map
fan_in = inputmaps * net.layers{l}.kernelsize ^ 2;
for i = 1 : inputmaps % input map
net.layers{l}.k{i}{j} = (rand(net.layers{l}.kernelsize) - 0.5) * 2 * sqrt(6 / (fan_in + fan_out));
end
net.layers{l}.b{j} = 0;
end
inputmaps = net.layers{l}.outputmaps;
end
end % 'onum' is the number of labels, that's why it is calculated using size(y, 1). If you have 20 labels so the output of the network will be 20 neurons.
% 'fvnum' is the number of output neurons at the last layer, the layer just before the output layer.
% 'ffb' is the biases of the output neurons.
% 'ffW' is the weights between the last layer and the output neurons. Note that the last layer is fully connected to the output layer, that's why the size of the weights is (onum * fvnum)
fvnum = prod(mapsize) * inputmaps;
onum = size(y, 1);
net.ffb = zeros(onum, 1);
net.ffW = (rand(onum, fvnum) - 0.5) * 2 * sqrt(6 / (onum + fvnum));前向传播：
x_j^l = f(\sum_ {i\in M_j} x_i^{l-1} * k_{ij}^l + b_j^l) % !!below can probably be handled by insane matrix operations
for j = 1 : net.layers{l}.outputmaps % for each output map
% create temp output map
z = zeros(size(net.layers{l - 1}.a{1}) - [net.layers{l}.kernelsize - 1 net.layers{l}.kernelsize - 1 0]);
for i = 1 : inputmaps % for each input map
% convolve with corresponding kernel and add to temp output map
z = z + convn(net.layers{l - 1}.a{i}, net.layers{l}.k{i}{j}, 'valid');
end
% add bias, pass through nonlinearity
net.layers{l}.a{j} = sigm(z + net.layers{l}.b{j});
end
% set number of input maps to this layers number of outputmaps
inputmaps = net.layers{l}.outputmaps;前向传播：
x_j^l = f(\beta_j^l down(x_j^{l-1}) + b_j^l) % 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, :);
end前向传播：
% concatenate all end layer feature maps into vector
net.fv = [];
for j = 1 : numel(net.layers{n}.a)
sa = size(net.layers{n}.a{j});
net.fv = [net.fv; reshape(net.layers{n}.a{j}, sa(1) * sa(2), sa(3))];
end
% feedforward into output perceptrons
net.o = sigm(net.ffW * net.fv + repmat(net.ffb, 1, size(net.fv, 2)));sigmoid函数求导：
f(x) = \frac{1}{1+e^{-x}} ; f^\prime(x) = \frac{e^{-x}}{(1+e^{-x})^2} = f(x) \cdot [1 - f(x)]对网络的最后一层输出层，计算输出值和样本值得残差：
\delta^n = -(y-a^n)\cdot f^\prime(z^n) % error
net.e = net.o - y;
%% backprop deltas
net.od = net.e .* (net.o .* (1 - net.o)); % output delta对于隐层 $ l = n-1,n-2,n-3,...,2$，计算各节点残差：
\delta^l = ({(W^l)}^T \delta^{l+1}) \cdot f^\prime(z^l) % concatenate all end layer feature maps into vector
net.fv = [];
for j = 1 : numel(net.layers{n}.a)
sa = size(net.layers{n}.a{j});
net.fv = [net.fv; reshape(net.layers{n}.a{j}, sa(1) * sa(2), sa(3))];
end net.fvd = (net.ffW' * net.od); % feature vector delta
if strcmp(net.layers{n}.type, 'c') % only conv layers has sigm function
net.fvd = net.fvd .* (net.fv .* (1 - net.fv));
end反向传播：
\delta_j^l = f^\prime(u_j^l)\circ conv2(\delta_j^{l+1},rot180(k_j^{l+1}),'full') for i = 1 : numel(net.layers{l}.a)
z = zeros(size(net.layers{l}.a{1}));
for j = 1 : numel(net.layers{l + 1}.a)
z = z + convn(net.layers{l + 1}.d{j}, rot180(net.layers{l + 1}.k{i}{j}), 'full');
end
net.layers{l}.d{i} = z;
end反向传播：
\delta_j^l = \beta_j^{l+1}(f^\prime(u_j^l) \circ up(\delta_j^{l+1})) 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);
end计算最终需要的偏导数值：
\nabla_{W^l}J(W,b;x,y) = \delta^{l+1}(a^l)^T \nabla_{b^l}J(W,b;x,y) = \delta^{l+1}\nabla_{W^l}J(W,b) = [\frac{1}{m}\sum_{i=1}^m\nabla_{W^l}J(W,b;x,y)]+\lambda W_{ij}^l\nabla_{b^l}J(W,b) = \frac{1}{m}\sum_{i=1}^m\nabla_{b^l}J(W,b;x,y)\frac{\partial E}{\partial k_{ij}^l} = rot180(conv2(x_i^{l-1},rot180(\delta_j^l),'valid')\frac{\partial E}{\partial b_j} = \sum_{u,v}(\delta_j^l)_{uv} for l = 2 : n
if strcmp(net.layers{l}.type, 'c')
for j = 1 : numel(net.layers{l}.a)
for i = 1 : numel(net.layers{l - 1}.a)
net.layers{l}.dk{i}{j} = convn(flipall(net.layers{l - 1}.a{i}), net.layers{l}.d{j}, 'valid') / size(net.layers{l}.d{j}, 3);
end
net.layers{l}.db{j} = sum(net.layers{l}.d{j}(:)) / size(net.layers{l}.d{j}, 3);
end
end
end net.dffW = net.od * (net.fv)' / size(net.od, 2);
net.dffb = mean(net.od, 2);