network_weights_picture_snn.m

来自「神经网络的工具箱, 神经网络的工具箱,」· M 代码 · 共 77 行

function network_weights_picture_snn(net);
%NETWORK_WEIGHTS_PICTURE_SNN Draw a picture of a net_struct neural
%                            network.
%
% See also
%
%   NET_STRUCT_SNN
%

L = net.numLayers+1;
units = zeros(1, L);
for l = 1:L-1
  units(l) = size(net.weights{l}, 2);
end
units(L) = size(net.weights{L-1},1);

maxw = max(max(abs(net.weights{1})));
for l = 2:L-1
    tmpmax = max(max(abs(net.weights{l})));
    if (tmpmax > maxw)
       maxw = tmpmax;
    end
end
limw = [0 maxw];
cm = summer;
m = size(cm,1);



cla;
set(gca, 'Units', 'normalized');
hold on;

%L = size(units, 2);
dx = 0.9/(L-1);

nmax = max(max(units),2);
for l = 1:L
    dy = 0.9/(sqrt(nmax*units(l))-1);
    x{l} = 0.5 + dx*(l - (L+1)/2);  
    y{l} = 0.5 + dy*([1:units(l)] - (units(l)+1)/2);  
end

nmax = max(max(units(1), units(L)), 2);
y{1} = 0.5 + (0.9/(nmax - 1))*([1:units(1)] - (units(1)+1)/2);
y{L} = 0.5 + (0.9/(nmax - 1))*([1:units(L)] - (units(L)+1)/2);

for l = 2:L
    for u = 1:units(l)
        for i = 1:units(l-1)
	    if (abs(net.weights{l-1}(u,i)) < 1e-10)
               cl = cm(1,:);
	    else
	       w = net.weights{l-1}(u,i);
	       cl = cm(ceil(abs(w/(limw(2)-limw(1))*m)),:);
	    end
	    if (w > 0)
               line([x{l}, x{l-1}], [y{l}(u), y{l-1}(i)], 'Color', cl);
	    else
               line([x{l}, x{l-1}], [y{l}(u), y{l-1}(i)], 'Color', cl, ...
	            'LineStyle', '--');
	    end
	end
        draw_circle(x{l}, y{l}(u), 0.01, 0.01, 'r-');
    end
end

for u = 1:units(1)
    draw_circle(x{1}, y{1}(u), 0.01, 0.01, 'r-');
end

axis([0 1 0 1]);
axis('off');

hold off;