void Start()
{
rigidbody2D = GetComponent <Rigidbody2D>();
anim = GetComponentInChildren <Animator>();
nn = new DeepNeuralNetwork(2, new int[] { 6 }, 2);
if (weights != null)
{
nn.SetWeights(weights);
}
maxY = GameController._instance.bgSize.y - 2f;
spawnPos = GameController._instance.spawnPos;
}
/// <summary>
/// This method creates a 1D array containing random weight and bias values.
/// A 1D Array makes storing the weights easier
/// The SetWeights method will then take the created array and initialize all arrays with the values
/// Order: ihweights - hhWeights[] - hoWeights - hBiases[] - oBiases
/// </summary>
public void InitializeWeights()
{
// make wts
double lo = -0.75f;
double hi = +0.75f;
int numWts = DeepNeuralNetwork.NumWeights(this.nInput, this.nHidden, this.nOutput);
double[] wts = new double[numWts];
for (int i = 0; i < numWts; ++i)
{
wts[i] = (hi - lo) * rnd.NextDouble() + lo;
}
this.SetWeights(wts);
}
private void LoadNetworkButton_Click(object sender, RoutedEventArgs e)
{
try
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.DefaultExt = ".nn";
ofd.Filter = "Neural Network definition files (.nn)|*.nn";
if (ofd.ShowDialog().GetValueOrDefault())
{
loadedNetwork = new DeepNeuralNetwork(ofd.FileName);
string layerString = string.Empty;
foreach (int ls in loadedNetwork.GetLayerCounts())
{
layerString += ls.ToString() + " ";
}
model.LoadedNNName = System.IO.Path.GetFileName(ofd.FileName) + " ( " + layerString + ")";
}
}
catch
{
MessageBox.Show("Error loading file.");
}
}
} // SetWeights
public void updateWeights(double[] wts, double chance, double mutationRate)
{
// scale the weights with the factor
double[] weights = this.GetWeights();
double[] newWeights = new double[DeepNeuralNetwork.NumWeights(this.nInput, this.nHidden, this.nOutput)];
for (int i = 0; i < wts.Length; i++)
{
if (rnd.Next(1, 101) <= mutationRate)
{
int x = rnd.Next(1, 101);
// 20% chance for one of these cases to occure
if (x <= 20)
{
newWeights[i] = (0.75f - -0.75f) * rnd.NextDouble() + -0.75f;
}
else if (x <= 40)
{
newWeights[i] += (0.375f - -0.375f) * rnd.NextDouble() + -0.375f;
}
else if (x <= 60)
{
newWeights[i] -= (0.375f - -0.375f) * rnd.NextDouble() + -0.375f;
}
else if (x <= 80)
{
newWeights[i] += (0.75f - -0.75f) * rnd.NextDouble() + -0.75f;
}
else
{
newWeights[i] -= (0.75f - -0.75f) * rnd.NextDouble() + -0.75f;
}
}
else
{
if (rnd.Next(1, 101) <= chance)
{
newWeights[i] = wts[i];
}
else
{
newWeights[i] = weights[i];
}
}
}
// order: ihweights - hhWeights[] - hoWeights - hBiases[] - oBiases
int nw = NumWeights(this.nInput, this.nHidden, this.nOutput); // total num wts + biases
if (wts.Length != nw)
{
return;
}
int ptr = 0; // pointer into wts[]
for (int i = 0; i < nInput; ++i) // input node
{
for (int j = 0; j < hNodes[0].Length; ++j) // 1st hidden layer nodes
{
ihWeights[i][j] = newWeights[ptr++];
}
}
for (int h = 0; h < nLayers - 1; ++h) // not last h layer
{
for (int j = 0; j < nHidden[h]; ++j) // from node
{
for (int jj = 0; jj < nHidden[h + 1]; ++jj) // to node
{
hhWeights[h][j][jj] = newWeights[ptr++];
}
}
}
int hi = this.nLayers - 1; // if 3 hidden layers (0,1,2) last is 3-1 = [2]
for (int j = 0; j < this.nHidden[hi]; ++j)
{
for (int k = 0; k < this.nOutput; ++k)
{
hoWeights[j][k] = newWeights[ptr++];
}
}
for (int h = 0; h < nLayers; ++h) // hidden node biases
{
for (int j = 0; j < this.nHidden[h]; ++j)
{
hBiases[h][j] = newWeights[ptr++];
}
}
for (int k = 0; k < nOutput; ++k)
{
oBiases[k] = newWeights[ptr++];
}
} // updateWeights
private async void StartButton_Click(object sender, RoutedEventArgs e)
{
if (model.TrainingActive)
{
MessageBox.Show("Training is already active.");
return;
}
if (dataContainer == null)
{
MessageBox.Show("No data loaded.");
return;
}
model.TrainingActive = true;
//prepare network
if (model.UseLoadedNN && loadedNetwork != null &&
loadedNetwork.GetLayerCounts()[0] == dataContainer.GetInputDataSize() && loadedNetwork.GetLayerCounts().Last() == dataContainer.GetOutputDataSize())
{
neuralNetwork = loadedNetwork;
}
else
{
string[] tokens = model.HiddenLayers.Split(' ').Where(s => !string.IsNullOrWhiteSpace(s)).ToArray();
List <int> hlList = new List <int>();
foreach (string token in tokens)
{
if (int.TryParse(token, out int hl))
{
hlList.Add(hl);
}
}
neuralNetwork = new DeepNeuralNetwork(dataContainer.GetInputDataSize(), dataContainer.GetOutputDataSize(), hlList.ToArray());
}
//hyper-params
int epochs = 0, batchSize = 0;
double learningRate = 0;
bool okToStart = true;
okToStart = okToStart && int.TryParse(model.Epochs, out epochs);
okToStart = okToStart && int.TryParse(model.BatchSize, out batchSize);
okToStart = okToStart && double.TryParse(model.LearningRate, out learningRate);
if (!okToStart)
{
return;
}
_epochs = epochs;
string layerString = string.Empty;
foreach (int ls in neuralNetwork.GetLayerCounts())
{
layerString += ls.ToString() + " ";
}
model.ConsoleString += $"{layerString} - epochs = {epochs}, batch size = {batchSize}, learning rate = {learningRate}\n\n\n";
//clear temp csv file
File.Delete(_tempCsvFileName);
//train
await Task.Run(() =>
Trainer.Sgd(neuralNetwork, dataContainer.TrainingSetAsLabeledDataArray(), batchSize, epochs, learningRate,
UpdateTrainingStatus, dataContainer.TestingSetAsLabeledDataArray(), dataContainer.CheckIfOutputIsCorrect));
MessageBox.Show("Training done.");
model.TrainingActive = false;
}
