LeNet5的一個MATLAB實現的代碼解析（4）

function cnet = adapt_dw(cnet,dW)%更改各層的權值
%ADAPT_DW apply calculated weights and biases gradient
%
% Syntax
%
% cnet = adapt_dw(cnet,dW)
%
% Description
% Input:
% cnet - Convolutional neural network class object
% dW - delta weights and biases as a single vector
% Output:
% cnet - convolutional neural network with updated weights and biases
% Usage:
% Inorder to update weights and biases
%© Sirotenko Mikhail, 2009

%Weight pointer in reverse order (1 means the bias of the last neuron of last layer)
wPtr = 1;
%==========First all F-layers
for k=cnet.numLayers👎(cnet.numLayers-cnet.numFLayers+1)
%Calculate total number of weights for the layer
sz = numel(cnet.FLayer{k}.W);
%Update weights
cnet.FLayer{k}.W = cnet.FLayer{k}.W-reshape(dW(wPtr:(wPtr+sz-1)),size(cnet.FLayer{k}.W));
%Increment pointer
wPtr = wPtr+sz;

%Calculate total number of biases for the layer
sz = numel(cnet.FLayer{k}.B);
%Update biases
cnet.FLayer{k}.B = cnet.FLayer{k}.B-dW(wPtr:(wPtr+sz-1))';
%Increment pointer
wPtr = wPtr+sz;

end

%Main loop
for k=(cnet.numLayers-cnet.numFLayers)👎2 %(all except first layer, its dummy)
if(rem(k,2)) %Parity check
%S-Layer
%Calculate total number of weights for the layer
sz = numel(cnet.SLayer{k}.WS)*numel(cnet.SLayer{k}.WS{1});
%Size of feature map
mW = cnet.SLayer{k}.FMapWidth;
mH = cnet.SLayer{k}.FMapHeight;
%Updating weights
%Initialize dimension vectors to convert to cell array
vVert=ones(cnet.SLayer{k}.numFMaps,1)*mH;
vHoriz = mW;
%Convert cell to matrix and update weights
Wnew = cell2mat(cnet.SLayer{k}.WS’) - reshape(dW(wPtr:(wPtr+sz-1)),cnet.SLayer{k}.numFMaps,[]);
%Convert back to cell array
cnet.SLayer{k}.WS = num2cell(Wnew)’;
%Increment pointer
wPtr = wPtr+sz;

    %Calculate total number of biases for the layer
    sz = numel(cnet.SLayer{k}.BS)*numel(cnet.SLayer{k}.BS{1});
    %Size of feature map
    mW = cnet.SLayer{k}.FMapWidth;
    mH = cnet.SLayer{k}.FMapHeight;        
    %Updating biases
    %Initialize dimension vectors to convert to cell array
    vVert=ones(cnet.SLayer{k}.numFMaps,1)*mH;
    vHoriz = mW;
    %Convert cell to matrix and update weights
    Bnew = cell2mat(cnet.SLayer{k}.BS') - reshape(dW(wPtr:(wPtr+sz-1)),cnet.SLayer{k}.numFMaps,[]);        
    %Convert back to cell array
    cnet.SLayer{k}.BS = num2cell(Bnew)';
    %Increment pointer
    wPtr = wPtr+sz;
else
%C-Layer
    %Calculate total number of weights for the layer
    sz = numel(cnet.CLayer{k}.WC)*numel(cnet.CLayer{k}.WC{1});
    %Size of convolution kernel
    mW = cnet.CLayer{k}.KernWidth;
    mH = cnet.CLayer{k}.KernHeight;        
    %Updating weights
    %Initialize dimension vectors to convert to cell array
    vVert=ones(cnet.CLayer{k}.numKernels,1)*mH;
    vHoriz = mW;
    %Convert cell to matrix and update weights
    Wnew = cell2mat(cnet.CLayer{k}.WC) - reshape(dW(wPtr:(wPtr+sz-1)),[],mW*cnet.CLayer{k}.numKernels);        
    %Convert back to cell array
    cnet.CLayer{k}.WC = mat2cell(Wnew,vHoriz,vVert);
    %Increment pointer
    wPtr = wPtr+sz;

    %Calculate total number of biases for the layer
    sz = numel(cnet.CLayer{k}.BC)*numel(cnet.CLayer{k}.BC{1});
    %Updating biases
    %Convert cell to matrix and update weights
    Bnew = cell2mat(cnet.CLayer{k}.BC) - dW(wPtr:(wPtr+sz-1))';        
    %Convert back to cell array
    cnet.CLayer{k}.BC = num2cell(Bnew);
    %Increment pointer
    wPtr = wPtr+sz;

end

end

end