fisherff_snn.m

来自「神经网络的工具箱, 神经网络的工具箱,」· M 代码 · 共 137 行

function [F, dE_dX] = fisherff_snn(X, net, data)
%FISHERFF_SNN derivatives for costfunction with respect to connection
%             weights and biases (Fisher matrix).
%
%  Syntax
%
%   [F, dE_dX] = fisherff_snn(net, data)
%   [F, dE_dX] = fisherff_snn(X, net, data)
%
%  Description
%
%   FISHERFF_SNN takes
%    net    - a net_struct
%    data   - the data for net.costFcn.name
%    X      - a vector containing connection weights and biases (optional).
%   and returns
%    F      - dy_dX' * d^2E_dy^2 * dy_dX 
%    dE_dX  - dE_dy' * dy_dX
%   (E is cost function)
%

%#function lintf_snn exptf_snn logsigtf_snn radbastf_snn tansigtf_snn 
%#function dlintf_snn dexptf_snn dlogsigtf_snn dradbastf_snn dtansigtf_snn 

if (nargin == 2)         % fisherff_snn(net, data)
   [F, dE_dX] = fisherff_all(X, net);
elseif (nargin == 3)     % fisherff_snn(X, net, data)
   net = setx_snn(net,X);
   [F, dE_dX] = fisherff_all(net, data);
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [F, dE_dX] = fisherff_all(net, data)

M = net.numLayers;
q = size(data.P,2);

indb = 1;
inde = 0;
for m = 1:M
    indb = inde + 1;
    inde = inde + prod(size(net.weights{m}));
    Xindices{m}.w = [indb:inde];
    indb = inde + 1;
    inde = inde + size(net.biases{m},1);
    Xindices{m}.b = [indb:inde];
end

N{1} = net.weights{1}*data.P + repmat(net.biases{1}, 1, size(data.P,2));
V{1} = feval(net.transferFcn{1}, N{1});
n0 = size(net.weights{1},2);
nl{1} = size(net.biases{1},1);

if (M > 1)
    for m = 2:M
       N{m} = net.weights{m}*V{m-1} + repmat(net.biases{m}, 1, q);
       V{m} = feval(net.transferFcn{m}, N{m});
       nl{m} = size(net.biases{m},1);
    end
end

gamma{M} = repmat(...
             reshape(...
	       feval(feval(net.transferFcn{M}, 'deriv'), N{M}, V{M}),...
	       nl{M}*q, 1), ...
	     1,nl{M}) ...
           .* ...
	   reshape(shiftdim(repdim(diag(ones(nl{M},1)), q),1), nl{M}*q, nl{M});

for m = [(M-1):-1:1]
    gamma{m} = (gamma{m+1}*net.weights{m+1}) .* ...
                reshape(...  
                  shiftdim(...
                    repdim(...
		     feval(feval(net.transferFcn{m}, 'deriv'), N{m}, V{m})', ...
	            nl{M}),...
                  2),...
                nl{M}*q, nl{m});
end


dY_dX(:,Xindices{1}.w) = repmat(gamma{1},1,n0) .* ...
                         reshape(...
                           shiftdim(...
                             repdim(...
                               shiftdim(...
                                 repdim(data.P', nl{M}), ...
                                 1), ...
                               nl{1}), ...
                             1), ...
                           nl{M}*q, nl{1}*n0);
dY_dX(:,Xindices{1}.b) = gamma{1};

for m = 2:M
    dY_dX(:,Xindices{m}.w) = repmat(gamma{m},1,nl{m-1}) .* ...
                             reshape(...
                               shiftdim(...
                                 repdim(...  
                                   shiftdim(... 
                                     repdim(V{m-1}', nl{M}),...
                                     1), ...
                                   nl{m}), ...
                                 1), ...
                               nl{M}*q, nl{m}*nl{m-1});
    dY_dX(:,Xindices{m}.b) = gamma{m};
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%#function wcf_snn dwcf_snn 
D = feval(feval(net.costFcn.name, 'deriv'), net, data, V{M});

%#function wcf_snn d2wcf_snn 
H = feval(feval(net.costFcn.name, '2deriv'), net, data, V{M}, V{M});

    %SOME OPTIMALIZATION CODE (begin)%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    if strcmp(net.costFcn.name, 'wcf_snn')
       F = (dY_dX') * ((repmat(diag(H), 1, size(dY_dX,2))).*(dY_dX));
       dE_dX = dY_dX' * reshape(D, nl{M}*q, 1);
       return;
    end
    %SOME OPTIMALIZATION CODE (end)%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

F = dY_dX' * H * dY_dX;
dE_dX = dY_dX' * reshape(D, nl{M}*q, 1);


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function B = repdim(A, q)

sizeA = size(A);
B = reshape(repmat(reshape(A, prod(sizeA), 1),1,q),[sizeA q]);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%