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AS3 Artificial Neuron Network test code
package
{
import com.lookbackon.AI.ANN.NeuralNetWork;
import flash.display.Sprite;
public class NeuronNetWorkTest extends Sprite
{
private var inputDataArray:Array;
private var outputDataArray:Array;
/// Executes a Neural Network MultiLayer FeedForward BackPropagation
/// Algorithm for Learning or Classification
public function NeuronNetWorkTest()
{
//a two-dimensional array of 14 samples and 6 input Neurons
//14 samples with 6 inputs
inputDataArray = new Array(
[
[1, 0, 0, 0.85, 0.85, 0], [1, 0, 0, 0.80, 0.90, 1], [0, 1, 0, 0.83, 0.86, 0], [0, 0, 1, 0.70, 0.96, 0],
[0, 0, 1, 0.68, 0.80, 0], [0, 0, 1, 0.65, 0.70, 1], [0, 1, 0, 0.64, 0.65, 1], [1, 0, 0, 0.72, 0.95, 0],
[1, 0, 0, 0.69, 0.70, 0], [0, 0, 1, 0.75, 0.80, 0], [1, 0, 0, 0.75, 0.70, 1], [0, 1, 0, 0.72, 0.90, 1],
[0, 1, 0, 0.81, 0.75, 0], [0, 0, 1, 0.71, 0.91, 1]
]);
//a two-dimensional array of 14 samples and 1 output Neuron
//14 samples with 1 target output
outputDataArray = new Array(
[
[0], [0], [1], [1], [1], [0], [1], [0], [1], [1], [1], [1], [1], [0]
]
);
var net:NeuralNetWork = new NeuralNetWork();
var recordCount:int = inputDataArray.length;
// int recordCount = inputDataArray.GetUpperBound(0) + 1;
if(recordCount !=(outputDataArray.length))
{
throw new Error("number of samples in input data is not equal to number of samples in output data!!");
}
/*if (recordCount != (outputDataArray.GetUpperBound(0) + 1))
{
throw new System.ArgumentOutOfRangeException("number of samples in input data is not equal to number of samples in output data");
}*/
net.initialize(inputDataArray,outputDataArray,4);
for(var count:int=0;count<NeuralNetWork.iterations;count++)
{
for(var sample:int=0;sample<recordCount;sample++)
{
net.feedForward(sample);
net.backPropagate();
trace("Count:", count.toFixed(4) , " Output : " , net.outputNeurons.getItemAt(0).output.toFixed(4) , " OutputTraining : " , net.outputNeurons.getItemAt(0).outputTraning.toFixed(4) , " Learning Rate : " , net.learning_rate);
}
//adjust the learning rate after every training sample iteration
net.learning_rate = Number(1 / (Number(count) + 1));
trace("After adjust Count: : " + count.toFixed(4) + " Output : " + net.outputNeurons.getItemAt(0).output.toFixed(4) + " OutputTraining : " + net.outputNeurons.getItemAt(0).outputTraning.toFixed(4) + " Learning Rate : " + net.learning_rate);
}
}
}
}
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