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Create source code examples with only one neuron #111
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@joaopauloschuler
joaopauloschuler committed May 28, 2023
1 parent 529479d commit 76fe56e
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165 changes: 165 additions & 0 deletions examples/OnlyOneNeuron/OnlyOneNeuronOrOperation.lpi
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111 changes: 111 additions & 0 deletions examples/OnlyOneNeuron/OnlyOneNeuronOrOperation.lpr
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program OnlyOneNeuronOrOperation;
(*
OnlyOneNeuronOrOperation: learns how to calculate boolean X OR Y.
Copyright (C) 2023 Joao Paulo Schwarz Schuler
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
*)


{$mode objfpc}{$H+}

uses {$IFDEF UNIX} {$IFDEF UseCThreads}
cthreads, {$ENDIF} {$ENDIF}
Classes,
neuralnetwork,
neuralvolume,
neuralfit;

type TBackInput = array[0..3] of array[0..1] of TNeuralFloat;
type TBackOutput = array[0..3] of array[0..0] of TNeuralFloat;

const
cs_false = 0.1;
cs_true = 0.8;
cs_threshold = (cs_false+cs_true) / 2;

const cs_inputs : TBackInput =
( // x1, x2
( cs_false, cs_false),
( cs_false, cs_true),
( cs_true, cs_false),
( cs_true, cs_true)
);

const cs_outputs : TBackOutput =
( // OR result
(cs_false),
(cs_true),
(cs_true),
(cs_true)
);

procedure RunAlgo();
var
NN: TNNet;
EpochCnt: integer;
Cnt: integer;
pOutPut: TNNetVolume;
vInputs: TBackInput;
vOutput: TBackOutput;
begin
NN := TNNet.Create();
NN.AddLayer( TNNetInput.Create(2) );
NN.AddLayer( TNNetFullConnectLinear.Create(1) );
NN.SetLearningRate(0.01, 0.9);

vInputs := cs_inputs;
vOutput := cs_outputs;
pOutPut := TNNetVolume.Create(1, 1, 1, 1);

WriteLn('Value encoding FALSE is: ', cs_false:4:2);
WriteLn('Value encoding TRUE is: ', cs_true:4:2);
WriteLn('Threshold is: ', cs_threshold:4:2);
WriteLn;

for EpochCnt := 1 to 600 do
begin
for Cnt := Low(cs_inputs) to High(cs_inputs) do
begin
NN.Compute(vInputs[Cnt]);
NN.GetOutput(pOutPut);
NN.Backpropagate(vOutput[Cnt]);
if EpochCnt mod 100 = 0 then
WriteLn
(
EpochCnt:7,'x',Cnt,
' Output:',
pOutPut.Raw[0]:5:2,' ',
' - Training/Desired Output:',
vOutput[cnt][0]:5:2,' '
);
end;
if EpochCnt mod 100 = 0 then WriteLn();
end;
NN.DebugWeights();
pOutPut.Free;
NN.Free;
Write('Press ENTER to exit.');
ReadLn;
end;

var
// Stops Lazarus errors
Application: record Title:string; end;

begin
Application.Title:='Only One Neuron - OR Operation';
RunAlgo();
end.

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