public override PatternResult Train(List <Pattern> patterns, int iterations)
{
double error = 0.0;
if (patterns.Count == 0)
{
return(null);
}
foreach (string c in charsSet)
{
DoubleVectorList inputs = new DoubleVectorList();
DoubleVectorList outputs = new DoubleVectorList();
foreach (Pattern p in patterns)
{
// Add every item. The solution is either "yes this is a letter X" or "no this is not a letter X"
inputs.Add(p.Inputs.Scale());
if (GetOutputCharacter(p.Outputs) == c)
{
DoubleVector v = new DoubleVector(1);
v[0] = 0.99;
outputs.Add(v);
}
else
{
DoubleVector v = new DoubleVector(1);
v[0] = 0.01;
outputs.Add(v);
}
}
// Train only if we have patters for this letter in our character set
if (inputs.Count > 0)
{
// Get the specialized neural network for this specific letter
error += sann.Where(nn => nn.Solution == c).ToList()[0].NeuralNet.Train(inputs, outputs, iterations);
}
}
return(new PatternResult(error));
}
public override PatternResult Train(List <Pattern> patterns, int iterations)
{
if (patterns.Count == 0)
{
return(null);
}
DoubleVectorList inputs = new DoubleVectorList();
DoubleVectorList outputs = new DoubleVectorList();
foreach (Pattern p in patterns)
{
inputs.Add(p.Inputs.Scale(0.02, 0.98));
outputs.Add(p.Outputs);
}
double error = sann.Train(inputs, outputs, iterations);
return(new PatternResult(error));
}
/// <summary>
/// Train the network on a single input vector. Returns the sum of the mean square error of the sets.
/// </summary>
/// <param name="inputs">The input vectors to train on</param>
/// <param name="targets">The target output vectors</param>
/// <param name="iterations">The number of iterations to train on these inputs</param>
public double Train(DoubleVectorList inputs, DoubleVectorList targets, int iterations)
{
// Make sure all input vectors have a matching output vector
if (inputs.Count != targets.Count)
{
throw new Exception("You must provide a desired output vector for each input vector!");
}
double error = 0.0;
int progress;
Random r = new Random();
for (int i = 0; i < iterations; i++)
{
// The error for this iteration
error = 0.0;
// Create a list of indexes and mix it up so that we don't train on the sets sequentially
List<int> indices = new List<int>();
for (int n = 0; n < inputs.Count; n++)
{
indices.Insert(r.Next(n), n);
}
// Train for one iteration on this set
for (int s = 0; s < inputs.Count; s++)
{
error += Train(inputs[indices[s]], targets[indices[s]], 1);
// Report Progress
progress = (int)(((double)i * 100 / (double)iterations) + (((double)s * 100) / (double)inputs.Count) / (double)iterations);
if (OnTrainingProgressChange != null)
{
OnTrainingProgressChange(this, new OnTrainingProgressChangeEventArgs(progress, (error / (double)s) * (double)inputs.Count));
}
}
// Lower the learning rate
learningRate *= _learningRateDescent;
}
// Return error of most recent iteration
return error;
}
public override PatternResult Train(List<Pattern> patterns, int iterations)
{
double error = 0.0;
if (patterns.Count == 0)
{
return null;
}
foreach (string c in charsSet)
{
DoubleVectorList inputs = new DoubleVectorList();
DoubleVectorList outputs = new DoubleVectorList();
foreach (Pattern p in patterns)
{
// Add every item. The solution is either "yes this is a letter X" or "no this is not a letter X"
inputs.Add(p.Inputs.Scale());
if (GetOutputCharacter(p.Outputs) == c)
{
DoubleVector v = new DoubleVector(1);
v[0] = 0.99;
outputs.Add(v);
}
else
{
DoubleVector v = new DoubleVector(1);
v[0] = 0.01;
outputs.Add(v);
}
}
// Train only if we have patters for this letter in our character set
if (inputs.Count > 0)
{
// Get the specialized neural network for this specific letter
error += sann.Where(nn => nn.Solution == c).ToList()[0].NeuralNet.Train(inputs, outputs, iterations);
}
}
return new PatternResult(error);
}
public override PatternResult Train(List<Pattern> patterns, int iterations)
{
if (patterns.Count == 0)
{
return null;
}
DoubleVectorList inputs = new DoubleVectorList();
DoubleVectorList outputs = new DoubleVectorList();
foreach (Pattern p in patterns)
{
inputs.Add(p.Inputs.Scale(0.02, 0.98));
outputs.Add(p.Outputs);
}
double error = sann.Train(inputs, outputs, iterations);
return new PatternResult(error);
}
