/// <summary>Generate model based on a set of examples.</summary>
/// <param name="x">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix x, Vector y)
{
// because I said so...
if (this.MaxIterations == -1)
{
this.MaxIterations = x.Rows * 1000;
}
var network = Network.Default(this.Descriptor, x, y, this.Activation);
var model = new NeuralNetworkModel {
Descriptor = this.Descriptor, Network = network
};
this.OnModelChanged(this, ModelEventArgs.Make(model, "Initialized"));
for (int i = 0; i < this.MaxIterations; i++)
{
int idx = i % x.Rows;
network.Forward(x[idx, VectorType.Row]);
//OnModelChanged(this, ModelEventArgs.Make(model, "Forward"));
network.Back(y[idx], this.LearningRate);
var output = String.Format("Run ({0}/{1})", i, this.MaxIterations);
this.OnModelChanged(this, ModelEventArgs.Make(model, output));
}
return(model);
}
/// <summary>Generate model based on a set of examples.</summary>
/// <param name="x">The Matrix to process.</param>
/// <param name="y">The Vector to process.</param>
/// <returns>Model.</returns>
public override IModel Generate(Matrix x, Vector y)
{
// because I said so...
if (this.MaxIterations == -1)
{
this.MaxIterations = x.Rows * 1000;
}
var network = Network.Default(this.Descriptor, x, y, this.Activation);
var model = new NeuralNetworkModel { Descriptor = this.Descriptor, Network = network };
this.OnModelChanged(this, ModelEventArgs.Make(model, "Initialized"));
for (var i = 0; i < this.MaxIterations; i++)
{
var idx = i % x.Rows;
network.Forward(x[idx, VectorType.Row]);
// OnModelChanged(this, ModelEventArgs.Make(model, "Forward"));
network.Back(y[idx], this.LearningRate);
var output = string.Format("Run ({0}/{1})", i, this.MaxIterations);
this.OnModelChanged(this, ModelEventArgs.Make(model, output));
}
return model;
}
public virtual ISequenceModel Generate(Matrix X, Matrix Y)
{
this.Preprocess(X);
// because I said so...
if (MaxIterations == -1)
{
MaxIterations = 500;
}
var network = Network.New().Create(X.Cols, Y.Cols, Activation, OutputFunction, epsilon: Epsilon);
INetworkTrainer trainer = new GradientDescentTrainer();
var model = new NeuralNetworkModel
{
Descriptor = Descriptor,
NormalizeFeatures = base.NormalizeFeatures,
FeatureNormalizer = base.FeatureNormalizer,
FeatureProperties = base.FeatureProperties,
Network = network
};
OnModelChanged(this, ModelEventArgs.Make(model, "Initialized"));
NetworkTrainingProperties properties = NetworkTrainingProperties.Create(network, X.Rows, X.Cols, this.LearningRate, this.Lambda, this.MaxIterations);
Vector loss = Vector.Zeros(this.MaxIterations);
for (int i = 0; i < MaxIterations; i++)
{
properties.Iteration = i;
network.ResetStates(properties);
for (int x = 0; x < X.Rows; x++)
{
network.Forward(X[x, VectorType.Row]);
//OnModelChanged(this, ModelEventArgs.Make(model, "Forward"));
network.Back(Y[x, VectorType.Row], properties, trainer);
loss[i] += network.Cost;
}
var output = String.Format("Run ({0}/{1}): {2}", i, MaxIterations, network.Cost);
OnModelChanged(this, ModelEventArgs.Make(model, output));
if (this.LossMinimized(loss, i))
{
break;
}
}
return(model);
}
public virtual ISequenceModel Generate(Matrix X, Matrix Y)
{
Preprocess(X);
// because I said so...
if (MaxIterations == -1)
{
MaxIterations = 500;
}
var network = Network.New().Create(X.Cols, Y.Cols, Activation, OutputFunction, epsilon: Epsilon);
var model = new NeuralNetworkModel
{
Descriptor = Descriptor,
NormalizeFeatures = NormalizeFeatures,
FeatureNormalizer = FeatureNormalizer,
FeatureProperties = FeatureProperties,
Network = network
};
OnModelChanged(this, ModelEventArgs.Make(model, "Initialized"));
var properties = NetworkTrainingProperties.Create(network, X.Rows, X.Cols, LearningRate, Lambda, MaxIterations);
for (var i = 0; i < MaxIterations; i++)
{
properties.Iteration = i;
for (var x = 0; x < X.Rows; x++)
{
network.Forward(X[x, VectorType.Row]);
//OnModelChanged(this, ModelEventArgs.Make(model, "Forward"));
network.Back(Y[x, VectorType.Row], properties);
}
var output = string.Format("Run ({0}/{1}): {2}", i, MaxIterations, network.Cost);
OnModelChanged(this, ModelEventArgs.Make(model, output));
}
return(model);
}
public virtual ISequenceModel Generate(Matrix X, Matrix Y)
{
this.Preprocess(X);
// because I said so...
if (MaxIterations == -1) MaxIterations = 500;
var network = Network.New().Create(X.Cols, Y.Cols, Activation, OutputFunction, epsilon: Epsilon);
var model = new NeuralNetworkModel
{
Descriptor = Descriptor,
NormalizeFeatures = base.NormalizeFeatures,
FeatureNormalizer = base.FeatureNormalizer,
FeatureProperties = base.FeatureProperties,
Network = network
};
OnModelChanged(this, ModelEventArgs.Make(model, "Initialized"));
NetworkTrainingProperties properties = NetworkTrainingProperties.Create(network, X.Rows, X.Cols, this.LearningRate, this.Lambda, this.MaxIterations);
for (int i = 0; i < MaxIterations; i++)
{
properties.Iteration = i;
for (int x = 0; x < X.Rows; x++)
{
network.Forward(X[x, VectorType.Row]);
//OnModelChanged(this, ModelEventArgs.Make(model, "Forward"));
network.Back(Y[x, VectorType.Row], properties);
}
var output = String.Format("Run ({0}/{1}): {2}", i, MaxIterations, network.Cost);
OnModelChanged(this, ModelEventArgs.Make(model, output));
}
return model;
}
