63 lines
1.8 KiB
Matlab
63 lines
1.8 KiB
Matlab
function nnupdatefigures(nn,fhandle,L,opts,i)
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%NNUPDATEFIGURES updates figures during training
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if i > 1 %dont plot first point, its only a point
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x_ax = 1:i;
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% create legend
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if opts.validation == 1
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M = {'Training','Validation'};
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else
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M = {'Training'};
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end
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%create data for plots
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if strcmp(nn.output,'softmax')
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plot_x = x_ax';
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plot_ye = L.train.e';
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plot_yfrac = L.train.e_frac';
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else
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plot_x = x_ax';
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plot_ye = L.train.e';
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end
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%add error on validation data if present
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if opts.validation == 1
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plot_x = [plot_x, x_ax'];
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plot_ye = [plot_ye,L.val.e'];
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end
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%add classification error on validation data if present
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if opts.validation == 1 && strcmp(nn.output,'softmax')
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plot_yfrac = [plot_yfrac, L.val.e_frac'];
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end
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% plotting
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figure(fhandle);
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if strcmp(nn.output,'softmax') %also plot classification error
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p1 = subplot(1,2,1);
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plot(plot_x,plot_ye);
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xlabel('Number of epochs'); ylabel('Error');title('Error');
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title('Error')
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legend(p1, M,'Location','NorthEast');
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set(p1, 'Xlim',[0,opts.numepochs + 1])
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p2 = subplot(1,2,2);
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plot(plot_x,plot_yfrac);
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xlabel('Number of epochs'); ylabel('Misclassification rate');
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title('Misclassification rate')
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legend(p2, M,'Location','NorthEast');
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set(p2, 'Xlim',[0,opts.numepochs + 1])
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else
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p = plot(plot_x,plot_ye);
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xlabel('Number of epochs'); ylabel('Error');title('Error');
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legend(p, M,'Location','NorthEast');
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set(gca, 'Xlim',[0,opts.numepochs + 1])
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end
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drawnow;
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end
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end
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