matlab基于BP神经网络手写数字识别（字符串）.zip代码64

%设置参数，利用fmincy函数进行最优化参数的求解,进行梯度下降 function [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5) if exist('options', 'var') && ~isempty(options) && isfield(options, 'MaxIter') length = options.MaxIter; else length = 100; end RHO = 0.01; % a bunch of constants for line searches SIG = 0.5; % RHO and SIG are the constants in the Wolfe-Powell conditions INT = 0.1; % don't reevaluate within 0.1 of the limit of the current bracket EXT = 3.0; % extrapolate maximum 3 times the current bracket MAX = 20; % max 20 function evaluations per line search RATIO = 100; % maximum allowed slope ratio argstr = ['feval(f, X']; % compose string used to call function for i = 1:(nargin - 3) argstr = [argstr, ',P', int2str(i)]; end argstr = [argstr, ')']; if max(size(length)) == 2, red=length(2); length=length(1); else red=1; end S=['Iteration ']; i = 0; % zero the run length counter ls_failed = 0; % no previous line search has failed fX = []; [f1 df1] = eval(argstr); % get function value and gradient i = i + (length<0); % count epochs?! s = -df1; % search direction is steepest d1 = -s'*s; % this is the slope z1 = red/(1-d1); % initial step is red/(|s|+1) while i < abs(length) % while not finished i = i + (length>0); % count iterations?! X0 = X; f0 = f1; df0 = df1; % make a copy of current values X = X + z1*s; % begin line search [f2 df2] = eval(argstr); i = i + (length<0); % count epochs?! d2 = df2'*s; f3 = f1; d3 = d1; z3 = -z1; % initialize point 3 equal to point 1 if length>0, M = MAX; else M = min(MAX, -length-i); end success = 0; limit = -1; % initialize quanteties while 1 while ((f2 > f1+z1*RHO*d1) | (d2 > -SIG*d1)) & (M > 0) limit = z1; % tighten the bracket if f2 > f1 z2 = z3 - (0.5*d3*z3*z3)/(d3*z3+f2-f3); % quadratic fit else A = 6*(f2-f3)/z3+3*(d2+d3); % cubic fit B = 3*(f3-f2)-z3*(d3+2*d2); z2 = (sqrt(B*B-A*d2*z3*z3)-B)/A; % numerical error possible - ok! end if isnan(z2) | isinf(z2) z2 = z3/2; % if we had a numerical problem then bisect end z2 = max(min(z2, INT*z3),(1-INT)*z3); % don't accept too close to limits z1 = z1 + z2; % update the step X = X + z2*s; [f2 df2] = eval(argstr); M = M - 1; i = i + (length<0); % count epochs?! d2 = df2'*s; z3 = z3-z2; % z3 is now relative to the location of z2 end if f2 > f1+z1*RHO*d1 | d2 > -SIG*d1 break; % this is a failure elseif d2 > SIG*d1 success = 1; break; % success elseif M == 0 break; % failure end A = 6*(f2-f3)/z3+3*(d2+d3); % make cubic extrapolation B = 3*(f3-f2)-z3*(d3+2*d2); z2 = -d2*z3*z3/(B+sqrt(B*B-A*d2*z3*z3)); % num. error possible - ok! if ~isreal(z2) | isnan(z2) | isinf(z2) | z2 < 0 % num prob or wrong sign? if limit < -0.5 % if we have no upper limit z2 = z1 * (EXT-1); % the extrapolate the maximum amount else z2 = (limit-z1)/2; % otherwise bisect end elseif (limit > -0.5) & (z2+z1 > limit) % extraplation beyond max? z2 = (limit-z1)/2; % bisect elseif (limit < -0.5) & (z2+z1 > z1*EXT) % extrapolation beyond limit z2 = z1*(EXT-1.0); % set to extrapolation limit elseif z2 < -z3*INT z2 = -z3*INT; elseif (limit > -0.5) & (z2 < (limit-z1)*(1.0-INT)) % too close to limit? z2 = (limit-z1)*(1.0-INT); end f3 = f2; d3 = d2; z3 = -z2; % set point 3 equal to point 2 z1 = z1 + z2; X = X + z2*s; % update current estimates [f2 df2] = eval(argstr); M = M - 1; i = i + (length<0); % count epochs?! d2 = df2'*s; end % end of line search if success % if line search succeeded f1 = f2; fX = [fX' f1]'; fprintf('%s %4i | Cost: %4.6e\r', S, i, f1); s = (df2'*df2-df1'*df2)/(df1'*df1)*s - df2; % Polack-Ribiere direction tmp = df1; df1 = df2; df2 = tmp; % swap derivatives d2 = df1'*s; if d2 > 0 % new slope must be negative s = -df1; % otherwise use steepest direction d2 = -s'*s; end z1 = z1 * min(RATIO, d1/(d2-realmin)); % slope ratio but max RATIO d1 = d2; ls_failed = 0; % this line search did not fail else X = X0; f1 = f0; df1 = df0; % restore point from before failed line search if ls_failed | i > abs(length) % line search failed twice in a row break; % or we ran out of time, so we give up end tmp = df1; df1 = df2; df2 = tmp; % swap derivatives s = -df1; % try steepest d1 = -s'*s; z1 = 1/(1-d1); ls_failed = 1; % this line search failed end if exist('OCTAVE_VERSION') fflush(stdout); end end fprintf('\n');