Mercurial > hg > camir-aes2014
comparison core/magnatagatune/tests_evals/rbm_subspace/training_rbm_.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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-1:000000000000 | 0:e9a9cd732c1e |
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1 function [W visB hidB] = training_rbm_(conf,W,data) | |
2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
3 % Training RBM % | |
4 % conf: training setting % | |
5 % W: weights of connections % | |
6 % -*-sontran2012-*- % | |
7 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
8 | |
9 assert(~isempty(data),'[KBRBM] Data is empty'); | |
10 %% initialization | |
11 visNum = size(data,2); | |
12 hidNum = conf.hidNum; | |
13 sNum = conf.sNum; | |
14 lr = conf.params(1); | |
15 N = conf.N; % Number of epoch training with lr_1 | |
16 W = 0.1*randn(visNum - size(W,1),size(W,2)); | |
17 W = 0.1*randn(size(W,1),hidNum-size(W,2)); | |
18 | |
19 DW = zeros(size(W)); | |
20 visB = zeros(1,visNum); | |
21 DVB = zeros(1,visNum); | |
22 hidB = zeros(1,hidNum); | |
23 DHB = zeros(1,hidNum); | |
24 visP = zeros(sNum,visNum); | |
25 visN = zeros(sNum,visNum); | |
26 visNs = zeros(sNum,visNum); | |
27 hidP = zeros(sNum,hidNum); | |
28 hidPs = zeros(sNum,hidNum); | |
29 hidN = zeros(sNum,hidNum); | |
30 hidNs = zeros(sNum,hidNum); | |
31 | |
32 plot_ = 0; | |
33 %% Reconstruction error & evaluation error & early stopping | |
34 mse = 0; | |
35 omse = 0; | |
36 inc_count = 0; | |
37 MAX_INC = conf.MAX_INC; % If the error increase MAX_INC times continuously, then stop training | |
38 %% Average best settings | |
39 n_best = 1; | |
40 aW = size(W); | |
41 aVB = size(visB); | |
42 aHB = size(hidB); | |
43 %% Plotting | |
44 if plot_, h = plot(nan); end | |
45 %% ==================== Start training =========================== %% | |
46 for i=1:conf.eNum | |
47 if i== N+1 | |
48 lr = conf.params(2); | |
49 end | |
50 omse = mse; | |
51 mse = 0; | |
52 for j=1:conf.bNum | |
53 visP = data((j-1)*conf.sNum+1:j*conf.sNum,:); | |
54 %up | |
55 hidP = logistic(visP*W + repmat(hidB,sNum,1)); | |
56 hidPs = 1*(hidP >rand(sNum,hidNum)); | |
57 hidNs = hidPs; | |
58 for k=1:conf.gNum | |
59 % down | |
60 visN = logistic(hidNs*W' + repmat(visB,sNum,1)); | |
61 visNs = 1*(visN>rand(sNum,visNum)); | |
62 % up | |
63 hidN = logistic(visNs*W + repmat(hidB,sNum,1)); | |
64 hidNs = 1*(hidN>rand(sNum,hidNum)); | |
65 end | |
66 % Compute MSE for reconstruction | |
67 % rdiff = (visP - visN); | |
68 % mse = mse + sum(sum(rdiff.*rdiff))/(sNum*visNum); | |
69 % Update W,visB,hidB | |
70 diff = (visP'*hidP - visNs'*hidN)/sNum; | |
71 DW = lr*(diff - conf.params(4)*W) + conf.params(3)*DW; | |
72 W = W + DW; | |
73 DVB = lr*sum(visP - visN,1)/sNum + conf.params(3)*DVB; | |
74 visB = visB + DVB; | |
75 DHB = lr*sum(hidP - hidN,1)/sNum + conf.params(3)*DHB; | |
76 hidB = hidB + DHB; | |
77 end | |
78 %% | |
79 if plot_ | |
80 mse_plot(i) = mse; | |
81 axis([0 (conf.eNum+1) 0 5]); | |
82 set(h,'YData',mse_plot); | |
83 drawnow; | |
84 end | |
85 % % save(strcat('C:\Pros\Data\XOR\plot_',num2str(conf.params(2),3),'.mat'),'mse_plot'); | |
86 % % plot(mse_plot,'XDataSource','real(mse_plot)','YDataSource','imag(mse_plot)') | |
87 % % linkdata on; | |
88 | |
89 if mse > omse | |
90 inc_count = inc_count + 1; | |
91 else | |
92 inc_count = 0; | |
93 end | |
94 if inc_count> MAX_INC, break; end; | |
95 % fprintf('Epoch %d : MSE = %f\n',i,mse); | |
96 end | |
97 end |