Perceptron: Difference between revisions
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(Added Pascal (a non-graphical implementation)) |
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}</lang> |
}</lang> |
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[[File:perceptron_java.gif]] |
[[File:perceptron_java.gif]] |
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=={{header|Pascal}}== |
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This is a text-based implementation, using a 20x20 grid (just like the original Mark 1 Perceptron had). The rate of improvement drops quite markedly as you increase the number of training runs. |
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<lang pascal>program Perceptron; |
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(* |
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* implements a version of the algorithm set out at |
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* http://natureofcode.com/book/chapter-10-neural-networks/ , |
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* but without graphics |
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*) |
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function targetOutput( a, b : integer) : integer; |
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(* the function the perceptron will be learning is f(x) = 2x + 1 *) |
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begin |
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if a * 2 + 1 < b then |
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targetOutput := 1 |
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else |
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targetOutput := -1 |
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end; |
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procedure showTargetOutput; |
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var x, y : integer; |
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begin |
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for y := 10 downto -9 do |
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begin |
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for x := -9 to 10 do |
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if targetOutput( x, y ) = 1 then |
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write( '#' ) |
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else |
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write( 'O' ); |
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writeln( '' ) |
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end; |
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writeln( '' ) |
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end; |
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procedure randomWeights( var ws : array of real ); |
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(* start with random weights -- NB pass by reference *) |
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var i : integer; |
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begin |
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randomize; (* seed random-number generator *) |
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for i := 0 to 2 do |
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ws[i] := random * 2 - 1 |
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end; |
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function feedForward( ins : array of integer; ws : array of real ) : integer; |
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(* the perceptron outputs 1 if the sum of its inputs multiplied by |
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its input weights is positive, otherwise -1 *) |
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var sum : real; |
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i : integer; |
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begin |
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sum := 0; |
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for i := 0 to 2 do |
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sum := sum + ins[i] * ws[i]; |
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if sum > 0 then |
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feedForward := 1 |
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else |
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feedForward := -1 |
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end; |
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procedure showOutput( ws : array of real ); |
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var inputs : array[0..2] of integer; |
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x, y : integer; |
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begin |
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inputs[2] := 1; (* bias *) |
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for y := 10 downto -9 do |
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begin |
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for x := -9 to 10 do |
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begin |
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inputs[0] := x; |
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inputs[1] := y; |
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if feedForward( inputs, ws ) = 1 then |
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write( '#' ) |
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else |
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write( 'O' ) |
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end; |
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writeln( '' ) |
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end; |
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writeln( '' ) |
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end; |
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procedure train( var ws : array of real; runs : integer ); |
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(* pass the array of weights by reference so it can be modified *) |
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var inputs : array[0..2] of integer; |
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error : real; |
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x, y, i, j : integer; |
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begin |
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inputs[2] := 1; (* bias *) |
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for i := 1 to runs do |
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begin |
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for y := 10 downto -9 do |
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begin |
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for x := -9 to 10 do |
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begin |
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inputs[0] := x; |
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inputs[1] := y; |
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error := targetOutput( x, y ) - feedForward( inputs, ws ); |
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for j := 0 to 2 do |
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ws[j] := ws[j] + error * inputs[j] * 0.01; |
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(* 0.01 is the learning constant *) |
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end; |
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end; |
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end; |
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end; |
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var weights : array[0..2] of real; |
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begin |
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writeln( 'Target output for the function f(x) = 2x + 1:' ); |
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showTargetOutput; |
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randomWeights( weights ); |
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writeln( 'Output from untrained perceptron:' ); |
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showOutput( weights ); |
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train( weights, 1 ); |
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writeln( 'Output from perceptron after 1 training run:' ); |
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showOutput( weights ); |
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train( weights, 4 ); |
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writeln( 'Output from perceptron after 5 training runs:' ); |
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showOutput( weights ) |
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end.</lang> |
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{{out}} |
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<pre>Target output for the function f(x) = 2x + 1: |
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##############OOOOOO |
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#############OOOOOOO |
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#############OOOOOOO |
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############OOOOOOOO |
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############OOOOOOOO |
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###########OOOOOOOOO |
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###########OOOOOOOOO |
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##########OOOOOOOOOO |
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##########OOOOOOOOOO |
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#########OOOOOOOOOOO |
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#########OOOOOOOOOOO |
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########OOOOOOOOOOOO |
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########OOOOOOOOOOOO |
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#######OOOOOOOOOOOOO |
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#######OOOOOOOOOOOOO |
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######OOOOOOOOOOOOOO |
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######OOOOOOOOOOOOOO |
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#####OOOOOOOOOOOOOOO |
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#####OOOOOOOOOOOOOOO |
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####OOOOOOOOOOOOOOOO |
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Output from untrained perceptron: |
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OOO################# |
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OOOO################ |
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OOOOO############### |
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OOOOO############### |
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OOOOOO############## |
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OOOOOO############## |
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OOOOOOO############# |
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OOOOOOOO############ |
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OOOOOOOO############ |
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OOOOOOOOO########### |
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OOOOOOOOO########### |
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OOOOOOOOOO########## |
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OOOOOOOOOOO######### |
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OOOOOOOOOOO######### |
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OOOOOOOOOOOO######## |
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OOOOOOOOOOOOO####### |
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OOOOOOOOOOOOO####### |
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OOOOOOOOOOOOOO###### |
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OOOOOOOOOOOOOO###### |
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OOOOOOOOOOOOOOO##### |
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Output from perceptron after 1 training run: |
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###############OOOOO |
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###############OOOOO |
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##############OOOOOO |
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#############OOOOOOO |
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#############OOOOOOO |
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############OOOOOOOO |
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############OOOOOOOO |
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###########OOOOOOOOO |
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##########OOOOOOOOOO |
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##########OOOOOOOOOO |
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#########OOOOOOOOOOO |
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#########OOOOOOOOOOO |
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########OOOOOOOOOOOO |
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#######OOOOOOOOOOOOO |
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#######OOOOOOOOOOOOO |
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######OOOOOOOOOOOOOO |
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######OOOOOOOOOOOOOO |
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#####OOOOOOOOOOOOOOO |
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####OOOOOOOOOOOOOOOO |
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####OOOOOOOOOOOOOOOO |
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Output from perceptron after 5 training runs: |
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##############OOOOOO |
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#############OOOOOOO |
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#############OOOOOOO |
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############OOOOOOOO |
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############OOOOOOOO |
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###########OOOOOOOOO |
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###########OOOOOOOOO |
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##########OOOOOOOOOO |
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##########OOOOOOOOOO |
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#########OOOOOOOOOOO |
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#########OOOOOOOOOOO |
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########OOOOOOOOOOOO |
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########OOOOOOOOOOOO |
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#######OOOOOOOOOOOOO |
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#######OOOOOOOOOOOOO |
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######OOOOOOOOOOOOOO |
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######OOOOOOOOOOOOOO |
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#####OOOOOOOOOOOOOOO |
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#####OOOOOOOOOOOOOOO |
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####OOOOOOOOOOOOOOOO</pre> |
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=={{header|zkl}}== |
=={{header|zkl}}== |
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