Error_surface_of_a_linear_neuron_for_a_single_training_case.png
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Summary
| Description |
English:
On the x-axis is the output, y, of a linear neuron and on the y-axis is the error, E, of the output compared to a target output of zero. The error is calculated by squaring the difference between the actual and target output. This demonstrates that training of a neural network can be thought of as an optimization problem where the correct output involves finding the minima of the parabola.
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| Date | |
| Source | Created using MatLab |
| Author | AI456 |
Source code
function [output] = Plot_Error_Surface(x, expected)
% Plots the error surface for a single output unit with the given
% expected output.
hold all
for e = expected
y = (e - x).^2;
plot(x, y)
grid on
title ('Error Surface for a Single Training Case');
xlabel('y')
ylabel('E')
end
hold off
Plot_Error_Surface(-5:0.5:5, 0)
Licensing
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Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License , Version 1.2 or any later version published by the Free Software Foundation ; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled GNU Free Documentation License . |
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