[matlab]MATLAB实现MLP多层感知机minist手写识别预测

news2024/11/8 4:32:52

【测试环境】

matlab2023a

【源码文件截图】

【实现部分代码】

mlp_test.m




%% MLP 2-layer to test XOR
clear;
clc;

Mode = 'MNIST'
%Mode = 'XOR'

if (strcmp(Mode,'MNIST'))
    % Load the digits into workspace (MNIST Test, from
    % http://yann.lecun.com/exdb/mnist/)
    num_train = 1000;
    [train_IMG,train_labels,test_IMG,test_labels] = readMNIST(num_train);
    input =cell(num_train,1);
    output =cell(num_train,1);
    
    test_input=cell(length(test_IMG),1);
    test_output=cell(length(test_labels),1);
    
    for i=1:num_train
        %input_img = double(train_IMG{i});
        %Pre processing - prewitt
        input_img = edge(train_IMG{i},'prewitt');
        
             
        [width height] = size(input_img);
        img_vec = reshape(input_img,1,width*height);
        input{i}=double(img_vec);
        
        labels_arr = zeros(1,10);
        labels_arr(train_labels(i)+1)=1;
        output{i} = labels_arr;
        
    end
    
    for i=1:length(test_input)
        %input_img = double(test_IMG{i});
        %Pre processing - prewitt
        input_img = edge(test_IMG{i},'prewitt');
        
        
        
        [width height] = size(input_img);
        img_vec = reshape(input_img,1,width*height);
        test_input{i} = double(img_vec);
        
        labels_arr = zeros(1,10);
        labels_arr(test_labels(i)+1)=1;
        test_output{i} = labels_arr;
        
        
        
    end
elseif (strcmp(Mode,'XOR'))
    
    num_train = 4;
    input =cell(num_train,1);
    output =cell(num_train,1);
    input{1} = [0 0];
    input{2} = [0 1];
    input{3} = [1 0];
    input{4} = [1 1];
    output{1} = [0];
    output{2} = [1];
    output{3} = [1];
    output{4} = [0];
    
end

if (length(input)~=length(output))
    error('len_input does not equal to len_output');
end



%% Determine # of nodes in hidden layer & output layer
num_node_il = length(input{1});
%num_node_hl = [num_node_il*2];
num_node_hl = [num_node_il];
num_node_ol = length(output{1});

set_node =[num_node_il num_node_hl num_node_ol];

%% Init. template (random)
rand('state',sum(100*clock));

num_layer = length(set_node);
W=cell(num_layer-1,1);
B=cell(num_layer-1);

for i=1:num_layer-1

	%% [Xavier10] shows that the interval ~ from https://deeplearning.net/tutorial/mlp.html
		
    min_W = -4*sqrt(6/(set_node(i)+set_node(i+1)));
    max_W = 4*sqrt(6/(set_node(i)+set_node(i+1)));
	
	W{i} = min_W+(2*max_W).*rand(set_node(i),set_node(i+1));
    B{i} = rand(1,set_node(i+1));
end

%% Learning coeff = 0.7 & Iteration = 10

% 141108, Success rate = 0.725
%lrn_rate = 0.3;
%max_iter = 100;


lrn_rate = 0.3;
max_iter = 50;%最大迭代次数，越大训练时间越长精度越高



tic


Act=cell(num_layer,1);
Err=cell(num_layer-1,1);


err_trace=[];




for index_inter= 1:max_iter
    
    if mod(index_inter,50) ==0
        index_inter
    end
       
    Act_trace=[];
    Train_trace = [];
    for j= 1:num_train
        
        P = randperm(num_train);
        train_input = input{P(j)};
        train_output = output{P(j)};
        
        
        % Forward Propagation
        [Act]   		=   FP(train_input,Act,W,B,num_layer);
        % Backward Propagation & Template update
        [W,B,Err]       =   BP(train_output,Act,W,B,num_layer,lrn_rate,Err);
        
        % Debug
        
        
        
        [row,col]=find(Act{end}==max(Act{end}));
        Act_trace(end+1)=col-1;
        
        [row,col]=find(train_output==max(train_output));
        Train_trace(end+1)=col-1;
        
    end   

    All_arr(index_inter).act = Act_trace;
    All_arr(index_inter).train=Train_trace;
    All_arr(index_inter).err = Act_trace-Train_trace;
end

toc

save
disp('Training Ends')


if (strcmp(Mode,'XOR'))
    grid = [0:0.01:1];
    Z=-1*ones(length(grid),length(grid));
    for i=1:length(grid)
        for j=1:length(grid)
            test = [grid(i) grid(j)];
            Act_new = FP(test,Act,W,B,num_layer);
            Z(i,j) = Act_new{3};
            
        end
    end
	[X,Y] = meshgrid(grid);
	mesh(X,Y,Z)

elseif (strcmp(Mode,'MNIST'))
    Guess_arr = [];
    for i=1:length(test_input)
        
        [guess_result] = FP(test_input{i},Act,W,B,num_layer);
        
        [row,col]=find(guess_result{end}==max(guess_result{end}));
        Guess_arr(end+1)=col-1;
        
    end
end

Z=zeros(10,10);

for i =1:length(test_labels)
    Z(Guess_arr(i)+1,test_labels(i)+1)=Z(Guess_arr(i)+1,test_labels(i)+1)+1;
end



Abs_err = Guess_arr-double(test_labels)';
success_rate = sum(Guess_arr-double(test_labels)'==0)/1000
%plot(abs(Guess_arr-double(test_labels)'))

%figure(1);scatter((Guess_arr*10),test_labels)
%figure(2);plot(err_trace);

运行结果：