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1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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2 % Main script of KRBM-Knowledge-based Restricted Boltzmann Machine %
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3 % -*-sontran2012-*- %
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4 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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5 %% loading background knowledge
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6 % bk_file = 'XOR.bk';
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7 % w = 5;
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8 W = rand(0,0);
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9 mW = rand(0,0);
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10 % [W mW nNames] = encode_bk(bk_file,w)
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11 %%% visualize_system(W,nNames,w,100,100);
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12 %% Loading data
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13 PRO_DIR = 'C://Pros/';
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14 % dat_file = 'XOR.dat';
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15 % dat_file = 'DNA.dat';
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16 % train_file = strcat(PRO_DIR,'/Data/ICDAR/digit_train_data.mat';
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17 % train_label = strcat(PRO_DIR,'/Data/ICDAR/digit_train_label.mat';
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18 % test_file = strcat(PRO_DIR,'/Data/ICDAR/digit_test_data.mat';
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19 % test_label = strcat(PRO_DIR,'/Data/ICDAR/digit_test_label.mat';
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20 %-----------------------------------------------------
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21 % train_file = strcat(PRO_DIR,'/Data/MNIST/mnist_train_data.mat';
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22 % train_label = strcat(PRO_DIR,'/Data/MNIST/mnist_train_label.mat';
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23 % test_file = strcat(PRO_DIR,'/Data/MNIST/mnist_test_data.mat';
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24 % test_label = strcat(PRO_DIR,'/Data/MNIST/mnist_test_label.mat';
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25 %-----------------------------------------------------
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26 % train_file = strcat(PRO_DIR,'/RBMLIB/train_dat.mat');
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27 % train_label = strcat(PRO_DIR,'/RBMLIB/train_lab.mat');
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28 % test_file = strcat(PRO_DIR,'/RBMLIB/test_dat.mat');
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29 % test_label = strcat(PRO_DIR,'/RBMLIB/test_lab.mat');
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30 %-----------------------------------------------------
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31 % train_file = strcat(PRO_DIR,'/RBMLIB/train_dat.mat');
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32 data_file = strcat(PRO_DIR,'/DATA/XOR/xorall.mat');
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33 mod_file = strcat(PRO_DIR,'/DATA/XOR/model.mat');
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34 %% Move weights encoded from rule to right position of input if needed
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35
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36 %% Setting parameters
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37 conf.hidNum = 4;
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38 conf.eNum = 50; % number of epoch
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39 conf.bNum = 1; % number of batches devides the data set
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40 conf.sNum = 4; % number of samples per batch
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41 conf.gNum = 1; % number of iteration for gibb sampling
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42 conf.params = [0.5 0.5 0.1 0.00002]; % [lr1 lr2 momentum cost]
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43 % lr1: learning rate in first 10 epoches
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44 % lr2: learning rate in the other epoches
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45 %% Training system
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46 % training_kbrm(conf,W,mW,dat_file); % unsupevised learning
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47 % [W visB hidB] = gen_training_kbrbm(conf,W,mW,train_file,train_label); % unsupervised learning for generative classification
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48 % dis_training_kbrm(conf,W,mW,dat_file,dat_label); % unsupervised learning for discriminative classification
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49 [W visB hidB] = training_rbm(conf,W,mW,data_file);
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50 save(mod_file,'W','mW','visB','hidB');
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51 nNames = {'X' 'Y' 'Z'};
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52 visualize_system(W,nNames,100,100);
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53 %% Classify (if wanted)
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54 % gen_classify(W, visB, hidB,mW,test_file,test_label);
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55 %% Generate features (if wanted)
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56 % in_f_file = '../Data/ICDAR/images.bin';
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57 % out_f_file = '../Data/ICDAR/feature.mat';
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58 % input_features = mnist2mat(in_f_file);
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59 % output_features = input_features*W;
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60 % save(out_f_file,'output_features');
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61 %% Extract knowledge (if wanted)
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62 % name_file = 'names.txt'; % File contains name of input
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63 % rule_file = 'rules.txt';
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64 % extract_knowledge(rule_file,W);
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65 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% END%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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66 clear; |
