static void Main(string[] args)
{
FeedforwardNetwork network = new FeedforwardNetwork();
network.AddLayer(new FeedforwardLayer(2));
network.AddLayer(new FeedforwardLayer(3));
network.AddLayer(new FeedforwardLayer(1));
network.Reset();
// train the neural network
NeuralSimulatedAnnealing train = new NeuralSimulatedAnnealing(
network, XOR_INPUT, XOR_IDEAL, 10, 2, 100);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine("Epoch #" + epoch + " Error:" + train.Error);
epoch++;
} while ((epoch < 5000) && (train.Error > 0.001));
network = train.Network;
// test the neural network
Console.WriteLine("Neural Network Results:");
for (int i = 0; i < XOR_IDEAL.Length; i++)
{
double[] actual = network.ComputeOutputs(XOR_INPUT[i]);
Console.WriteLine(XOR_INPUT[i][0] + "," + XOR_INPUT[i][1]
+ ", actual=" + actual[0] + ",ideal=" + XOR_IDEAL[i][0]);
}
}
static void Main(string[] args)
{
FeedforwardNetwork network = new FeedforwardNetwork();
network.AddLayer(new FeedforwardLayer(2));
network.AddLayer(new FeedforwardLayer(3));
network.AddLayer(new FeedforwardLayer(1));
network.Reset();
// train the neural network
NeuralSimulatedAnnealing train = new NeuralSimulatedAnnealing(
network, XOR_INPUT, XOR_IDEAL, 10, 2, 100);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine("Epoch #" + epoch + " Error:" + train.Error);
epoch++;
} while ((epoch < 5000) && (train.Error > 0.001));
network = train.Network;
// test the neural network
Console.WriteLine("Neural Network Results:");
for (int i = 0; i < XOR_IDEAL.Length; i++)
{
double[] actual = network.ComputeOutputs(XOR_INPUT[i]);
Console.WriteLine(XOR_INPUT[i][0] + "," + XOR_INPUT[i][1]
+ ", actual=" + actual[0] + ",ideal=" + XOR_IDEAL[i][0]);
}
}
private void trainNetworkAnneal()
{
// train the neural network
NeuralSimulatedAnnealing train = new NeuralSimulatedAnnealing(
this.network, this.input, this.ideal, 10, 2, 100);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine("Iteration #" + epoch + " Error:"
+ train.Error);
epoch++;
} while ((train.Error > 0.01));
}
private void trainNetworkAnneal()
{
Console.WriteLine("Training with simulated annealing for 5 iterations");
// train the neural network
NeuralSimulatedAnnealing train = new NeuralSimulatedAnnealing(
this.network, this.input, this.ideal, 10, 2, 100);
int epoch = 1;
for (int i = 1; i <= 5; i++)
{
train.Iteration();
Console.WriteLine("Iteration(Anneal) #" + epoch + " Error:"
+ train.Error);
epoch++;
}
}
private void trainNetworkAnneal()
{
// train the neural network
NeuralSimulatedAnnealing train = new NeuralSimulatedAnnealing(
this.network, this.input, this.ideal, 10, 2, 100);
int epoch = 1;
do
{
train.Iteration();
Console.WriteLine("Iteration #" + epoch + " Error:"
+ train.Error);
epoch++;
} while ((train.Error > 0.01));
}
private void trainNetworkAnneal()
{
Console.WriteLine("Training with simulated annealing for 5 iterations");
// train the neural network
NeuralSimulatedAnnealing train = new NeuralSimulatedAnnealing(
this.network, this.input, this.ideal, 10, 2, 100);
int epoch = 1;
for (int i = 1; i <= 5; i++)
{
train.Iteration();
Console.WriteLine("Iteration(Anneal) #" + epoch + " Error:"
+ train.Error);
epoch++;
}
}
/// <summary>
/// Trains the network
/// </summary>
public virtual void trainNetwork()
{
INeuralDataSet trainingSet = new BasicNeuralDataSet(networkInput, networkIdealOutput);
//ITrain trainBackProp = new Backpropagation(network, trainingSet, BACKPROP_LEARN_RATE, BACKPROP_MOMENTUM);
ITrain trainBackProp = new ScaledConjugateGradient(network, trainingSet);
double error = Double.MaxValue;
double lastError = Double.MaxValue;
int epoch = 1;
int lastAnneal = 0;
int errorExit = 0;
double errorOnLastAnnealStart = double.MaxValue;
double sameErrorOnLastAnnealStartCount = 0;
double currentAnnealInterval = MIN_BACKPROP_ITERATIONS_ANNEAL_START;
double annealStartError = 0;
do
{
trainBackProp.Iteration();
error = trainBackProp.Error;
if (lastError - error < MAX_RMS_ITERATION_NETWORK_ERROR)
errorExit++;
else
errorExit = 0;
Console.WriteLine("Iteration(SC) #{0} Error: {1}", epoch, error.ToString("0.00000000"));
if (error > ANNEAL_MIN_ERROR)
{
if ((lastAnneal > currentAnnealInterval) && (lastError - error < MAX_ANNEAL_START_ERROR))
{
if (error == errorOnLastAnnealStart)
sameErrorOnLastAnnealStartCount++;
else if (error < errorOnLastAnnealStart)
{
sameErrorOnLastAnnealStartCount = 0;
errorOnLastAnnealStart = error;
}
ICalculateScore score = new TrainingSetScore(trainingSet);
NeuralSimulatedAnnealing trainAnneal = new NeuralSimulatedAnnealing(network, score, ANNEAL_STARTTEMP, ANNEAL_ENDTEMP, ANNEAL_ITERATIONS);
for (int i = 1; i <= ANNEAL_ATTEMPTS; i++)
{
trainAnneal.Iteration();
if (i == 1)
annealStartError = trainAnneal.Error;
Console.WriteLine("Iteration(Anneal) #{0}-{1} Error: {2}", epoch, i, trainAnneal.Error.ToString("0.00000000"));
//WebLogging.AddLog("WinRatioNeural", WebLogging.LogCategory.WinRatioNeural, "Iteration(Anneal) #" + i + " Error: " + trainAnneal.Error.ToString("0.00000000"));
}
if (annealStartError == trainAnneal.Error)
{
if (currentAnnealInterval < 200)
{
currentAnnealInterval *= 1.5;
Console.WriteLine("Iteration(Anneal) # No improvment. Increasing anneal interval to " + currentAnnealInterval);
}
else
Console.WriteLine("Iteration(Anneal) # No improvment. Anneal interval at max.");
}
lastAnneal = 0;
trainBackProp = new ScaledConjugateGradient(network, trainingSet);
trainBackProp.Iteration();
error = trainBackProp.Error;
//saveNetwork(correctPredictions.ToString("##0.0")+ "_" + epoch.ToString() + "_nerualPokerAI_LA.nnDAT");
}
}
//Every 50 epochs we can test the network accuracy
//#if DEBUG
//if (epoch % 50 == 0)
//{
// //We want to switch to the testing set if we are not using all data for training
// if (TRAIN_DATA_PERCENT < 1.0) createTestingSets();
// Console.WriteLine(" Network accuracy is currently {0}%",getNetworkAccuracy());
// //Wait for 1 second so that we can read the output
// Thread.Sleep(1000);
// //Likewise we want to switch back before continuing
// if (TRAIN_DATA_PERCENT < 1.0) createTrainingSets();
//}
//#endif
lastError = trainBackProp.Error;
epoch++;
lastAnneal++;
//} while (error > MAX_RMS_TOTAL_NETWORK_ERROR && errorExit < 10 && epoch < MAX_ITERATIONS);
} while (trainBackProp.Error > MAX_RMS_TOTAL_NETWORK_ERROR && epoch < MAX_ITERATIONS && sameErrorOnLastAnnealStartCount < 2);
}