public double trainNetwork(TrainingTemplate trainingTemplate, AriesNeuroNet.Neurons.Neuron neuron, int maxGenerations, ErrorHistory errorProg)
{
int stableLimit = trainingTemplate.rows.Count;
int stableGenerations = 0;
int currentGeneration = 0;
double error = 0;
List<double> errorHistory = new List<double>();
//We innitialize the flags
bool adjustedWeights = false;
bool stableGenFlag = true;
bool genLimitFlag = true;
bool templateFlag = false;
//Step 1 initialize the neurons to randomize weights
neuron.randomizeWeights();
/*
* Possible breaking mecanism
* if(flag) {
* Console.Writeline(adequate message)
* return error
* which breaks the function
* }
* */
for (int currentRow = 0; currentRow < trainingTemplate.rows.Count; currentRow++)
{
// I extract the current row
TrainingRow row = trainingTemplate.rows[currentRow];
do
{
// I begin a new generation
//Console.WriteLine("========================================================================");
//Console.WriteLine("Begining Generation: " + currentGeneration);
//Console.WriteLine("Current row: " + currentRow);
// I reset the adjutedWeights flag
adjustedWeights = false;
// I set the inputs
neuron.setInputValues(row.inputs);
// I fire the neuron
neuron.fireNeuron();
// I get the expected output out of the template
double expectedOutput = row.outputs[0];
// I get the real output fromt he neuron
double realOutput = neuron.output.weightedReading;
Console.WriteLine("Output is " + realOutput);
// I calculate the error
error = expectedOutput - realOutput;
//Console.WriteLine("Error is " + error);
// I make a decision based on the error
if (error == 0)
{
//Console.WriteLine("I have not found an error");
stableGenerations++;
// I set the flag so that I exit the while
adjustedWeights = false;
}
else
{
//Console.WriteLine("I found an error");
//Console.WriteLine("The error is " + error);
// I reset the stable generations counter
stableGenerations = 0;
// These are for debugging purposes
//List<double> oldWeights = new List<double>();
//List<double> newWeights = new List<double>();
// I mark that I needed to adjust the weights.
adjustedWeights = true;
//Do the heavy duty processing
foreach (NeuronPort input in neuron.inputs)
{
//oldWeights.Add(input.weight);
input.weight += input.reading * learningRate * error; // To do finish this
//newWeights.Add(input.weight);
}
Console.WriteLine("I corrected with " + (learningRate * error));
// I log the error to history
errorHistory.Add(error);
// I publish the old weights
/*
Console.WriteLine("Old weights: ");
foreach (double weight in oldWeights)
{
Console.Write(weight + " ");
}
// I publish the new weights
Console.WriteLine("New weights: " + newWeights);
foreach (double weight in newWeights)
{
Console.Write(weight + " ");
}
* */
}
// I mark that I've finished these generation
currentGeneration++;
//I check the conditions
stableGenFlag = stableGenerations < stableLimit;
genLimitFlag = currentGeneration < maxGenerations;
templateFlag = currentRow < trainingTemplate.rows.Count;
//the breaking conditions
if (!stableGenFlag) {
//Console.WriteLine("Ended due to stable limit gen") ;
return error; }
if (!genLimitFlag) {
//Console.WriteLine("Ended due to limit of gens to train");
return error; }
} while (adjustedWeights);
//maybe not necesary ???
if (!stableGenFlag) { return error; }
if (!genLimitFlag) { return error; }
}
errorProg.errorPoints = errorHistory;
return error;
}
public double trainNetwork2(TrainingTemplate trainingTemplate, AriesNeuroNet.Neurons.Neuron neuron, int maxGenerations)
{
int stableLimit = trainingTemplate.rows.Count;
int stableGenerations = 0;
int currentGeneration = 0;
int currentRow = 0;
List<double> errorHistory = new List<double>();
//Step 1 initialize the neurons to randomize weights
neuron.randomizeWeights();
//We take into account that we might not need to adjust the weights
bool adjustedWeights = false;
bool stableGenFlag = true;
bool genLimitFlag = true;
bool templateFlag = false;
double error = 0;
do
{
//Main loop
Console.WriteLine("========================================================================");
Console.WriteLine("Begining Generation: " + currentGeneration);
//We assume this time arround we don't need to adjust
adjustedWeights = false;
//TODO add some printfs
//Set the imputs
// I need to cycle through the various templates
List<double> inputs = trainingTemplate.rows[currentRow].inputs;
neuron.setInputValues(inputs);
//Fire the neuron
neuron.fireNeuron();
//Get the expected output from the template this works only for training a perceptron
double expectedOutput = trainingTemplate.rows[currentRow].outputs[0];
//Get the real output
double realOutput = neuron.output.weightedReading;
Console.WriteLine("Output is " + realOutput);
//Calculate the error
error = expectedOutput - realOutput;
Console.WriteLine("Error is " + error);
//Process the error
if (error == 0)
{
Console.WriteLine("I have not found an error");
stableGenerations++;
// I move on to the next training row
currentRow = (currentRow + 1) % trainingTemplate.rows.Count;
//Maybe I'm not so sure of this
adjustedWeights = false;
}
else
{
stableGenerations = 0;
Console.WriteLine("I found an error");
Console.WriteLine("The error is " + error);
// Publish the old weights
List<double> oldWeights = new List<double>();
List<double> newWeights = new List<double>();
// I mark that I needed to adjust the weights.
adjustedWeights = true;
//Do the heavy duty processing
foreach (NeuronPort input in neuron.inputs)
{
oldWeights.Add(input.weight);
input.weight += input.reading * learningRate * error; // To do finish this
newWeights.Add(input.weight);
}
Console.WriteLine("I corrected with " + (learningRate * error));
Console.WriteLine("Old weights ");
foreach (double weight in oldWeights) {
Console.Write(weight + " ");
}
Console.WriteLine("New weights " + newWeights);
foreach (double weight in newWeights)
{
Console.Write(weight + " ");
}
}
//This constantly jumps throw the templates maybe not the best choice
// Need to split this up into 2 loops one loops over the rows in the template
// One loops in the rows correcting the errors
// Or see wiki article
currentGeneration++;
//I check the conditions
stableGenFlag = stableGenerations < stableLimit;
genLimitFlag = currentGeneration < maxGenerations;
templateFlag = currentRow < trainingTemplate.rows.Count;
Console.WriteLine("adjustedWeights "+ adjustedWeights +" templateFlag " + templateFlag + " stableGenFlag " + stableGenFlag + " genLimitFlag " + genLimitFlag);
Console.WriteLine("End of Generation: " + (currentGeneration-1));
Console.WriteLine("========================================================================");
Console.ReadKey();
} while (adjustedWeights /*&& stableGenFlag*/ && genLimitFlag && templateFlag);
return error;
}
