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function demtrain(action);
%DEMTRAIN Demonstrate training of MLP network.
%
% Description
% DEMTRAIN brings up a simple GUI to show the training of an MLP
% network on classification and regression problems. The user should
% load in a dataset (which should be in Netlab format: see DATREAD),
% select the output activation function, the number of cycles and
% hidden units and then train the network. The scaled conjugate
% gradient algorithm is used. A graph shows the evolution of the error:
% the value is shown MAX(CEIL(ITERATIONS / 50), 5) cycles.
%
% Once the network is trained, it is saved to the file MLPTRAIN.NET.
% The results can then be viewed as a confusion matrix (for
% classification problems) or a plot of output versus target (for
% regression problems).
%
% See also
% CONFMAT, DATREAD, MLP, NETOPT, SCG
%
% Copyright (c) Ian T Nabney (1996-2001)
% If run without parameters, initialise gui.
if nargin<1,
action='initialise';
end;
% Global variable to reference GUI figure
global DEMTRAIN_FIG
% Global array to reference sub-figures for results plots
global DEMTRAIN_RES_FIGS
global NUM_DEMTRAIN_RES_FIGS
if strcmp(action,'initialise'),
file = '';
path = '.';
% Create FIGURE
fig = figure( ...
'Name', 'Netlab Demo', ...
'NumberTitle', 'off', ...
'Menubar', 'none', ...
'Color', [0.7529 0.7529 0.7529], ...
'Visible', 'on');
% Initialise the globals
DEMTRAIN_FIG = fig;
DEMTRAIN_RES_FIGS = 0;
NUM_DEMTRAIN_RES_FIGS = 0;
% Create GROUP for buttons
uicontrol(fig, ...
'Style', 'frame', ...
'Units', 'normalized', ...
'Position', [0.03 0.08 0.94 0.22], ...
'BackgroundColor', [0.5 0.5 0.5]);
% Create MAIN axis
hMain = axes( ...
'Units', 'normalized', ...
'Position', [0.10 0.5 0.80 0.40], ...
'XColor', [0 0 0], ...
'YColor', [0 0 0], ...
'Visible', 'on');
% Create static text for FILENAME and PATH
hFilename = uicontrol(fig, ...
'Style', 'text', ...
'Units', 'normalized', ...
'BackgroundColor', [0.7529 0.7529 0.7529], ...
'Position', [0.05 0.32 0.90 0.05], ...
'HorizontalAlignment', 'center', ...
'String', 'Please load data file.', ...
'Visible', 'on');
hPath = uicontrol(fig, ...
'Style', 'text', ...
'Units', 'normalized', ...
'BackgroundColor', [0.7529 0.7529 0.7529], ...
'Position', [0.05 0.37 0.90 0.05], ...
'HorizontalAlignment', 'center', ...
'String', '', ...
'Visible', 'on');
% Create NO OF HIDDEN UNITS slider and text
hSliderText = uicontrol(fig, ...
'Style', 'text', ...
'BackgroundColor', [0.5 0.5 0.5], ...
'Units', 'normalized', ...
'Position', [0.27 0.12 0.17 0.04], ...
'HorizontalAlignment', 'right', ...
'String', 'Hidden Units: 5');
hSlider = uicontrol(fig, ...
'Style', 'slider', ...
'Units', 'normalized', ...
'Position', [0.45 0.12 0.26 0.04], ...
'String', 'Slider', ...
'Min', 1, 'Max', 25, ...
'Value', 5, ...
'Callback', 'demtrain slider_moved');
% Create ITERATIONS slider and text
hIterationsText = uicontrol(fig, ...
'Style', 'text', ...
'BackgroundColor', [0.5 0.5 0.5], ...
'Units', 'normalized', ...
'Position', [0.27 0.21 0.17 0.04], ...
'HorizontalAlignment', 'right', ...
'String', 'Iterations: 50');
hIterations = uicontrol(fig, ...
'Style', 'slider', ...
'Units', 'normalized', ...
'Position', [0.45 0.21 0.26 0.04], ...
'String', 'Slider', ...
'Min', 10, 'Max', 500, ...
'Value', 50, ...
'Callback', 'demtrain iterations_moved');
% Create ACTIVATION FUNCTION popup and text
uicontrol(fig, ...
'Style', 'text', ...
'BackgroundColor', [0.5 0.5 0.5], ...
'Units', 'normalized', ...
'Position', [0.05 0.20 0.20 0.04], ...
'HorizontalAlignment', 'center', ...
'String', 'Activation Function:');
hPopup = uicontrol(fig, ...
'Style', 'popup', ...
'Units', 'normalized', ...
'Position' , [0.05 0.10 0.20 0.08], ...
'String', 'Linear|Logistic|Softmax', ...
'Callback', '');
% Create MENU
hMenu1 = uimenu('Label', 'Load Data file...', 'Callback', '');
uimenu(hMenu1, 'Label', 'Select training data file', ...
'Callback', 'demtrain get_ip_file');
hMenu2 = uimenu('Label', 'Show Results...', 'Callback', '');
uimenu(hMenu2, 'Label', 'Show classification results', ...
'Callback', 'demtrain classify');
uimenu(hMenu2, 'Label', 'Show regression results', ...
'Callback', 'demtrain predict');
% Create START button
hStart = uicontrol(fig, ...
'Units', 'normalized', ...
'Position' , [0.75 0.2 0.20 0.08], ...
'String', 'Start Training', ...
'Enable', 'off',...
'Callback', 'demtrain start');
% Create CLOSE button
uicontrol(fig, ...
'Units', 'normalized', ...
'Position' , [0.75 0.1 0.20 0.08], ...
'String', 'Close', ...
'Callback', 'demtrain close');
% Save handles of important UI objects
hndlList = [hSlider hSliderText hFilename hPath hPopup ...
hIterations hIterationsText hStart];
set(fig, 'UserData', hndlList);
% Hide window from command line
set(fig, 'HandleVisibility', 'callback');
elseif strcmp(action, 'slider_moved'),
% Slider has been moved.
hndlList = get(gcf, 'UserData');
hSlider = hndlList(1);
hSliderText = hndlList(2);
val = get(hSlider, 'Value');
if rem(val, 1) < 0.5, % Force up and down arrows to work!
val = ceil(val);
else
val = floor(val);
end;
set(hSlider, 'Value', val);
set(hSliderText, 'String', ['Hidden Units: ' int2str(val)]);
elseif strcmp(action, 'iterations_moved'),
% Slider has been moved.
hndlList = get(gcf, 'UserData');
hSlider = hndlList(6);
hSliderText = hndlList(7);
val = get(hSlider, 'Value');
set(hSliderText, 'String', ['Iterations: ' int2str(val)]);
elseif strcmp(action, 'get_ip_file'),
% Get data file button pressed.
hndlList = get(gcf, 'UserData');
[file, path] = uigetfile('*.dat', 'Get Data File', 50, 50);
if strcmp(file, '') | file == 0,
set(hndlList(3), 'String', 'No data file loaded.');
set(hndlList(4), 'String', '');
else
set(hndlList(3), 'String', file);
set(hndlList(4), 'String', path);
end;
% Enable training button
set(hndlList(8), 'Enable', 'on');
set(gcf, 'UserData', hndlList);
elseif strcmp(action, 'start'),
% Start training
% Get handles of and values from UI objects
hndlList = get(gcf, 'UserData');
hSlider = hndlList(1); % No of hidden units
hIterations = hndlList(6);
iterations = get(hIterations, 'Value');
hFilename = hndlList(3); % Data file name
filename = get(hFilename, 'String');
hPath = hndlList(4); % Data file path
path = get(hPath, 'String');
hPopup = hndlList(5); % Activation function
if get(hPopup, 'Value') == 1,
act_fn = 'linear';
elseif get(hPopup, 'Value') == 2,
act_fn = 'logistic';
else
act_fn = 'softmax';
end;
nhidden = get(hSlider, 'Value');
% Check data file exists
if fopen([path '/' filename]) == -1,
errordlg('Training data file has not been selected.', 'Error');
else
% Load data file
[x,t,nin,nout,ndata] = datread([path filename]);
% Call MLPTRAIN function repeatedly, while drawing training graph.
figure(DEMTRAIN_FIG);
hold on;
title('Training - please wait.');
% Create net and find initial error
net = mlp(size(x, 2), nhidden, size(t, 2), act_fn);
% Initialise network with inverse variance of 10
net = mlpinit(net, 10);
error = mlperr(net, x, t);
% Work out reporting step: should be sufficiently big to let training
% algorithm have a chance
step = max(ceil(iterations / 50), 5);
% Refresh and rescale axis.
cla;
max = error;
min = max/10;
set(gca, 'YScale', 'log');
ylabel('log Error');
xlabel('No. iterations');
axis([0 iterations min max+1]);
iold = 0;
errold = error;
% Plot circle to show error of last iteration
% Setting erase mode to none prevents screen flashing during
% training
plot(0, error, 'ro', 'EraseMode', 'none');
hold on
drawnow; % Force redraw
for i = step-1:step:iterations,
[net, error] = mlptrain(net, x, t, step);
% Plot line from last point to new point.
line([iold i], [errold error], 'Color', 'r', 'EraseMode', 'none');
iold = i;
errold = error;
% If new point off scale, redraw axes.
if error > max,
max = error;
axis([0 iterations min max+1]);
end;
if error < min
min = error/10;
axis([0 iterations min max+1]);
end
% Plot circle to show error of last iteration
plot(i, error, 'ro', 'EraseMode', 'none');
drawnow; % Force redraw
end;
save mlptrain.net net
zoom on;
title(['Training complete. Final error=', num2str(error)]);
end;
elseif strcmp(action, 'close'),
% Close all the figures we have created
close(DEMTRAIN_FIG);
for n = 1:NUM_DEMTRAIN_RES_FIGS
if ishandle(DEMTRAIN_RES_FIGS(n))
close(DEMTRAIN_RES_FIGS(n));
end
end
elseif strcmp(action, 'classify'),
if fopen('mlptrain.net') == -1,
errordlg('You have not yet trained the network.', 'Error');
else
hndlList = get(gcf, 'UserData');
filename = get(hndlList(3), 'String');
path = get(hndlList(4), 'String');
[x,t,nin,nout,ndata] = datread([path filename]);
load mlptrain.net net -mat
y = mlpfwd(net, x);
% Save results figure so that it can be closed later
NUM_DEMTRAIN_RES_FIGS = NUM_DEMTRAIN_RES_FIGS + 1;
DEMTRAIN_RES_FIGS(NUM_DEMTRAIN_RES_FIGS)=conffig(y,t);
end;
elseif strcmp(action, 'predict'),
if fopen('mlptrain.net') == -1,
errordlg('You have not yet trained the network.', 'Error');
else
hndlList = get(gcf, 'UserData');
filename = get(hndlList(3), 'String');
path = get(hndlList(4), 'String');
[x,t,nin,nout,ndata] = datread([path filename]);
load mlptrain.net net -mat
y = mlpfwd(net, x);
for i = 1:size(y,2),
% Save results figure so that it can be closed later
NUM_DEMTRAIN_RES_FIGS = NUM_DEMTRAIN_RES_FIGS + 1;
DEMTRAIN_RES_FIGS(NUM_DEMTRAIN_RES_FIGS) = figure;
hold on;
title(['Output no ' num2str(i)]);
plot([0 1], [0 1], 'r:');
plot(y(:,i),t(:,i), 'o');
hold off;
end;
end;
end;
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