private void LoadWeightsFromFile()
{
if (string.IsNullOrEmpty(weightPath))
{
weightPath = Application.dataPath + "/CarRacing/weights.txt";
}
StreamReader streamReader = File.OpenText(weightPath);
if (File.Exists(weightPath))
{
string line = streamReader.ReadLine();
ann.LoadWeights(line);
}
}
IEnumerator LoadTrainingSet()
{
string path = Application.dataPath + "/trainingData.txt";
string line;
if (File.Exists(path))
{
int lineCount = File.ReadAllLines(path).Length;
StreamReader tdf = File.OpenText(path);
List <double> calcOutputs = new List <double>();
List <double> inputs = new List <double>();
List <double> outputs = new List <double>();
for (int i = 0; i < epochs; i++)
{
sse = 0;
tdf.BaseStream.Position = 0;
string currentWeights = ann.PrintWeights();
while ((line = tdf.ReadLine()) != null)
{
string[] data = line.Split(',');
float thisError = 0;
if (System.Convert.ToDouble(data[5]) != 0 && System.Convert.ToDouble(data[6]) != 0)
{
inputs.Clear();
outputs.Clear();
inputs.Add(System.Convert.ToDouble(data[0]));
inputs.Add(System.Convert.ToDouble(data[1]));
inputs.Add(System.Convert.ToDouble(data[2]));
inputs.Add(System.Convert.ToDouble(data[3]));
inputs.Add(System.Convert.ToDouble(data[4]));
double o1 = Map(0, 1, -1, 1, System.Convert.ToSingle(data[5]));
outputs.Add(o1);
double o2 = Map(0, 1, -1, 1, System.Convert.ToSingle(data[6]));
outputs.Add(o2);
calcOutputs = ann.Train(inputs, outputs);
thisError = ((Mathf.Pow((float)(outputs[0] - calcOutputs[0]), 2) +
Mathf.Pow((float)(outputs[1] - calcOutputs[1]), 2))) / 2.0f;
}
sse += thisError;
}
trainingProgress = (float)i / (float)epochs;
sse /= lineCount;
if (lastSSE < sse)
{
ann.LoadWeights(currentWeights);
ann.alpha = Mathf.Clamp((float)ann.alpha - 0.001f, 0.01f, 0.9f);
}
else
{
ann.alpha = Mathf.Clamp((float)ann.alpha + 0.001f, 0.01f, 0.9f);
lastSSE = sse;
}
yield return(null);
}
}
trainingDone = true;
}
/// <summary>
/// Adapts alpha value of the Neural Network dynamically.
/// Discards the current iteration's results if no improvement was made.
/// </summary>
/// <param name="currentWeights"> The weights to be re-loaded, if no improvement was made. </param>
private void AdaptLearning(string currentWeights)
{
if (lastSumSquaredError < sumSquaredError)
{
// SumSquaredError hasn't improved over lastSumquaredError.
ann.LoadWeights(currentWeights);
// Decrease alpha.
ann.alpha = Mathf.Clamp((float)ann.alpha - 0.001f, 0.01f, 0.9f);
}
else
{
// SumSquaredError has improved.
// Increase alpha.
ann.alpha = Mathf.Clamp((float)ann.alpha + 0.001f, 0.01f, 0.9f);
lastSumSquaredError = sumSquaredError;
}
}
// Update is called once per frame
void Update()
{
if (Input.GetKey("space"))
{
Debug.Log(ann.PrintWeights());
}
if (Input.GetKey(KeyCode.Return))
{
ann.LoadWeights();
}
}
void LoadWeightsFromFile(ANN ann)
{
string path = Application.dataPath + "/weights.txt";
StreamReader wf = File.OpenText(path);
if (File.Exists(path))
{
string line = wf.ReadLine();
ann.LoadWeights(line);
}
}
public void Start()
{
ann = new ANN(5, 2, 1, 10, 0.5f);
var aux = PlayerPrefs.GetString("Weights");
//if (aux != null)
//ann.LoadWeights(aux);
if (_isAleatoryCircuit)
{
if (manager.GetDictionaryCreate() && manager.GetNameDictionary(currentAleatoryCircuitName))
{
Debug.Log(currentAleatoryCircuitName);
Debug.Log(manager.GetDataDictionary(currentAleatoryCircuitName));
ann.LoadWeights(manager.GetDataDictionary(currentAleatoryCircuitName));
}
/*
* if (SaveAndLoad.SaveExists(currentAleatoryCircuitName+".txt"))
* {
* Debug.Log("Cargar");
* List<string> loadContent = new List<string>();
* loadContent = SaveAndLoad.Load<List<string>>(currentAleatoryCircuitName+".txt");
* Debug.Log(loadContent[0] + "\n" + loadContent[1]);
* ann.LoadWeights(loadContent[1]);
* }*/
}
else
{
if (managerCircuits.GetDictionaryCreate() && managerCircuits.GetNameDictionary(circuitName))
{
Debug.Log(managerCircuits.GetDataDictionary(circuitName));
ann.LoadWeights(managerCircuits.GetDataDictionary(circuitName));
}
}
ballStartPos = ball.transform.position;
ballStartRot = ball.transform.rotation;
Time.timeScale = timeScaleValue; //
}
public void LoadWeights()
{
ann = bird.GetANN();
string weights, bias;
System.IO.StreamReader sr = new System.IO.StreamReader(@"C:\Users\Ghost\Desktop\weights.txt");
weights = sr.ReadToEnd();
weights = weights.Replace(",", ".");
sr.Close();
System.IO.StreamReader sr2 = new System.IO.StreamReader(@"C:\Users\Ghost\Desktop\bias.txt");
bias = sr2.ReadToEnd();
bias = bias.Replace(",", ".");
sr2.Close();
//
string[] splitWeights = weights.Split('!');
string[] splitBias = bias.Split('!');
//
List <double> weightsList = new List <double>();
List <double> biasList = new List <double>();
foreach (string str in splitWeights)
{
if (str == "" || string.IsNullOrEmpty(str))
{
continue;
}
// double tmp;
// double.TryParse(str, out tmp);
double value = System.Convert.ToDouble(str, System.Globalization.CultureInfo.InvariantCulture);
weightsList.Add(value);
}
foreach (string str in splitBias)
{
if (str == "" || string.IsNullOrEmpty(str))
{
continue;
}
double value = System.Convert.ToDouble(str, System.Globalization.CultureInfo.InvariantCulture);
biasList.Add(value);
}
ann.LoadWeights(weightsList, biasList);
}
// Method to perform the ANN training using data collected from the player.
IEnumerator LoadTrainingSet()
{
string path = Application.dataPath + "/trainingData.txt";
string line;
if (File.Exists(path))
{
int lineCount = File.ReadAllLines(path).Length;
StreamReader tdf = File.OpenText(path);
List <double> calcOutputs = new List <double>();
List <double> inputs = new List <double>();
List <double> outputs = new List <double>();
//Loop through the epochs
for (int i = 0; i < epochs; i++)
{
//set file pointer to beginning of file
sse = 0;
tdf.BaseStream.Position = 0;
//Get the current weight comma separated string values from the ANN object
string currentWeights = ann.PrintWeights();
// Load the training data, line by line
while ((line = tdf.ReadLine()) != null)
{
string[] data = line.Split(',');
//if nothing to be learned ignore this line
float thisError = 0;
//We are leaving out those data where we have training labels or y (translation and rotation values) with values zero to reduce the data set.
if (System.Convert.ToDouble(data[5]) != 0 && System.Convert.ToDouble(data[6]) != 0) //If translation and rotation outputs in training data are not zero
{
//Clear out lists from previous row
inputs.Clear();
outputs.Clear();
//Add training input data to the inputs to the ANN
inputs.Add(System.Convert.ToDouble(data[0]));
inputs.Add(System.Convert.ToDouble(data[1]));
inputs.Add(System.Convert.ToDouble(data[2]));
inputs.Add(System.Convert.ToDouble(data[3]));
inputs.Add(System.Convert.ToDouble(data[4]));
//Map labels to range (0,1) for efficient training
double o1 = Map(0, 1, -1, 1, System.Convert.ToSingle(data[5]));
outputs.Add(o1);
double o2 = Map(0, 1, -1, 1, System.Convert.ToSingle(data[6]));
outputs.Add(o2);
//Calculated output (y-hat)
calcOutputs = ann.Train(inputs, outputs);
//Sum squared Error value: for both labels
thisError = ((Mathf.Pow((float)(outputs[0] - calcOutputs[0]), 2) +
Mathf.Pow((float)(outputs[1] - calcOutputs[1]), 2))) / 2.0f;
}
//Add this to cumulative SSE for the epoch
sse += thisError;
}
//Percentage training to display on screen
trainingProgress = (float)i / (float)epochs;
// Average SSE
sse /= lineCount;
//If sse isn't better then reload previous set of weights and decrease alpha. This adaptive training to let the ANN move out
// of local optima and hence find global optima.
if (lastSSE < sse)
{
ann.LoadWeights(currentWeights);
ann.alpha = Mathf.Clamp((float)ann.alpha - 0.001f, 0.01f, 0.9f);
}
else //increase alpha
{
ann.alpha = Mathf.Clamp((float)ann.alpha + 0.001f, 0.01f, 0.9f);
lastSSE = sse;
}
yield return(null); //Allow OnGUI some time to update on-screen values
}
}
//Training done, save weights
trainingDone = true;
SaveWeightsToFile();
}
IEnumerator LoadTrainingSet()
{
string path = ann.GetPath("trainingData");
string line;
if (File.Exists(path))
{
int lineCount = File.ReadAllLines(path).Length;
StreamReader tdf = File.OpenText(path);
List <double> calcOutputs = new List <double>();
List <double> inputs = new List <double>();
List <double> outputs = new List <double>();
for (int i = 0; i < epochs; i++)
{
// set file pointer to beginning of file
sse = 0;
tdf.BaseStream.Position = 0;
string currentWeights = ann.PrintWeights();
while ((line = tdf.ReadLine()) != null)
{
string[] data = line.Split(',');
// if nothing to be learned ignore this line
float thisError = 0;
if (System.Convert.ToDouble(data[5]) != 0 && System.Convert.ToDouble(data[6]) != 0)
{
inputs.Clear();
outputs.Clear();
inputs.Add(System.Convert.ToDouble(data[0]));
inputs.Add(System.Convert.ToDouble(data[1]));
inputs.Add(System.Convert.ToDouble(data[2]));
inputs.Add(System.Convert.ToDouble(data[3]));
inputs.Add(System.Convert.ToDouble(data[4]));
double out1 = Utils.Map(0, 1, -1, 1, System.Convert.ToSingle(data[5]));
double out2 = Utils.Map(0, 1, -1, 1, System.Convert.ToSingle(data[6]));
outputs.Add(out1);
outputs.Add(out2);
calcOutputs = ann.Train(inputs, outputs);
thisError = ((Mathf.Pow((float)(outputs[0] - calcOutputs[0]), 2) +
Mathf.Pow((float)(outputs[1] - calcOutputs[1]), 2))) / 2.0f;
}
sse += thisError;
}
trainingProgress = ((float)i / (float)epochs) * 100;
sse /= lineCount;
if (lastSSE < sse) // if sse isnt better reload old one and decrease alpha
{
ann.LoadWeights(currentWeights);
ann.alpha = Mathf.Clamp((float)ann.alpha - 0.001f, 0.01f, 0.9f);
}
else // increase alpha
{
ann.alpha = Mathf.Clamp((float)ann.alpha + 0.001f, 0.01f, 0.9f);
lastSSE = sse;
}
yield return(null);
}
tdf.Close();
}
trainingDone = true;
ann.SaveWeightsToFile();
}
IEnumerator LoadTrainingSet()
{
string path = Application.dataPath + "/trainingData.txt";
string line;
if (File.Exists(path))
{
int lineCount = File.ReadAllLines(path).Length;
StreamReader tdf = File.OpenText(path);
List <double> calcOutputs = new List <double>();
List <double> inputs = new List <double>();
List <double> outputs = new List <double>();
for (int i = 0; i < epochs; i++)
{
//set file pointer to beginning of file
sse = 0;
tdf.BaseStream.Position = 0;
string currentWeights = ann.PrintWeights();
while ((line = tdf.ReadLine()) != null)
{
string[] data = line.Split(',');
//if nothing to be learned ignore this line
float thisError = 0;
if (Convert.ToDouble(data[5]) != 0 && Convert.ToDouble(data[6]) != 0) //if the data had user input (active keys pressed)
{
inputs.Clear();
outputs.Clear();
inputs.Add(Convert.ToDouble(data[0]));
inputs.Add(Convert.ToDouble(data[1]));
inputs.Add(Convert.ToDouble(data[2]));
inputs.Add(Convert.ToDouble(data[3]));
inputs.Add(Convert.ToDouble(data[4]));
double o1 = Map(0, 1, -1, 1, Convert.ToSingle(data[5]));
outputs.Add(o1);
double o2 = Map(0, 1, -1, 1, Convert.ToSingle(data[6]));
outputs.Add(o2);
calcOutputs = ann.Train(inputs, outputs);
thisError = ((Mathf.Pow((float)(outputs[0] - calcOutputs[0]), 2) + Mathf.Pow((float)(outputs[1] - calcOutputs[1]), 2))) / 2.0f;
}
sse += thisError;
}
trainingProgress = (float)i / (float)epochs;
sse /= lineCount;
//if sse isn't better then reload previous set of weights
//and decrease alpha
if (lastSSE < sse)
{
ann.LoadWeights(currentWeights);
ann.alpha = Mathf.Clamp((float)ann.alpha - 0.001f, 0.01f, 0.9f);
}
else
{
ann.alpha = Mathf.Clamp((float)ann.alpha + 0.001f, 0.01f, 0.9f);
}
yield return(null);
}
}
trainingDone = true;
SaveWeightsToFile();
}
