The MATLAB realization of BP

%2015.04.26 Kang yongxin----v 2% completion of the operation of the BP algorithm, the batch method to update the weight%%% input data format%x Matrix: Sample number * Feature dimension%y Matrix: Number of Samples * category number (in 01000 form) Close All;c Lear ALL;CLC; load data.mat;%x_test=x (1:3:30,:);% leave part of the original data as a test sample y_test=y (1:3:30,:); x_train=[x (2:3:30,:); X ( 3:3:30,:)];%x (1:2:30,:);%[x (11:25,:); x (26:30,:); x (1:10,:)];y_train=[y (2:3:30,:); y (3:3:30,:)];%%[y (11:25,:); Y ( 26:30,:); y (1:10,:)];%%% defines the variable name, initializes the network D=size (x_train,2), the% feature dimension, and the number of input layer nodes num_trains=size (x_train,1);% Training Samples n_class= Size (y_train,2);% sample category Node_layer=[d 4 n_class];% number of nodes per layer%[d 3 5 n_class];% Build more layers of network num_layer=size (node_layer,2) -1;% The network layer for i=1:1:num_layer-1 f_name{i}= ' sigmoid ';% corresponds to the activation function of each layer endf_name{num_layer}= ' tanh ';% ' sigmoid '; the last layer of activation function eta= 0.08;% Learning rate theta=10e-4;% termination condition W=cell (num_layer,1);% initialize weight matrix, all set 1,for I=1:1:num_layer W{i}=rand (Node_layer (i), Node_        Layer (i+1)); endw_init=w;% start loop item=1;while item>0 && item<1500 percent% initialization weight increment for Layer=1:1:num_layer   Delta_sum{layer}=zeros (Size (W{layer})); End percent for k=1:1:num_trains% per sampleCyclic%%%%%%%%%%%%%%%%%%%% forward calculation x_in=x_train (k,:); For layer=1:1:num_layer% forward calculation for each layer, and save output value Y_out Y_out{layer}=forward (X_in,w{layer},f_name{layer});        % batch update time w to follow the outer loop X_in=y_out{layer}; End%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% reverse propagation, the last layer to be counted, because there is only one output delta_out{layer}=y_train (k,:)-y_ou The difference between t{layer};% output and True Value J (k) =0.5*sum (delta_out{layer}.^2);% mean square error delta_error{layer}=delta_out{layer}.*d_function (y_o        Ut{layer},f_name{num_layer});% delta_w{layer}=eta* (y_out{layer-1}) ' *delta_error{layer};% the weight variation of this layer from the point of error collected from the pointing node           While layer>1% reverse propagation error, save Delta_w layer=layer-1; delta_error{layer}=delta_error{layer+1}* (W{layer+1}) '. *d_function (Y_out{layer},f_name{layer});% The error that is collected from the pointing node is weighted if layer~=1% if it is not to the first layer, and the output of the LAYER-1 layer is used as the input delta_w{layer}=eta* (y _out{layer-1}) ' *delta_error{layer};% The weight change of this layer else% if it is the first level, use this trainingPractice x as input delta_w{layer}=eta* (X_train (k,:)) ' *delta_error{layer};% The weight change of this layer end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% batch update, to all kind of contribution to add and for Layer=1:1:num_layer Delta_sum{layer}       =delta_sum{layer}+delta_w{layer};    End End%k The contribution of a sample to the amount of weight variance figure (1);    JW (item) =sum (J);        If Item>10 delta_jw=abs (JW (item)-JW (item-1));    If Delta_jw<theta break;% loop termination condition end end Plot (ITEM,JW (item), '. ')    Hold on;      item=item+1;    % update weight for layer=1:1:num_layer% update weights for each layer, the location of this update determines whether to make a batch update or a w{layer}=w{layer}+delta_sum{layer};% batch update per update endend%%% calculation accuracy x_in=x_test;for layer=1:1:num_layer% for each layer forward calculation, and save the output value Y_out Y_out{layer}=forward (x_in,w{layer},f_        Name{layer});% bulk Update time w to follow the outer loop X_in=y_out{layer};end[c,i]=max (y_out{layer},[],2); [C,i_true]=max (y_test,[],2); Trues=find ((i-i_true) ==0); Precision=size (trues,1)/size (y_test,1)

function [Y] = forward (x,w,f_name)%forward forward calculation get output value%   input x: Input vector 1*m%        w: Weight matrix m*n%        f_name: function name (temporarily supported ' sigmoid ' Tanh ')%   output y: Output vector 1*n formula is  y=f (x*w) if strcmp (f_name, ' sigmoid ')    y=sigmoid (x*w); else if strcmp (F_name, ' Tanh ')        Y=tanh (x*w);%matlab comes with    else       disp (' wrong function name ');    EndEnd
End

function [D_f] = d_function (y,f_name)%d_founction the derivative of the correlation function the   input is the output value of the layer y if strcmp (f_name, ' sigmoid ')    d_f=y . * (1-y); else if strcmp (f_name, ' Tanh ')        D_f=1-y.^2;%matlab comes with    else       disp (' wrong function name at D_ function ');    Endendend

function [y] = sigmoid (x)%my_sigmoid%   input vector x, Output y=1./(1+exp (x)); end

The MATLAB realization of BP