public override ISequenceModel Generate(Matrix X, Matrix Y)
{
// dense autoencoders learn the approximation identity function so ignore labels.
// the output layer is the number of columns in X
// default hidden layer to 2/3 of the input
this.Preprocess(X);
if (this.Density <= 0) this.Density = (int) System.Math.Ceiling(X.Cols * (2.0 / 3.0));
if (this.MaxIterations <= 0) MaxIterations = 400; // because Seth said so...
Network network = Network.New().Create(X.Cols, X.Cols, this.Activation, this.OutputFunction,
(i, j) => new AutoencoderNeuron(), epsilon: this.Epsilon, hiddenLayers: new int[] { this.Density });
var model = new AutoencoderModel
{
Descriptor = Descriptor,
NormalizeFeatures = base.NormalizeFeatures,
FeatureNormalizer = base.FeatureNormalizer,
FeatureProperties = base.FeatureProperties,
Network = network,
OutputFunction = this.OutputFunction
};
OnModelChanged(this, ModelEventArgs.Make(model, "Initialized"));
NetworkTrainingProperties properties = NetworkTrainingProperties.Create(network, X.Rows, X.Cols, this.LearningRate,
this.Lambda, this.MaxIterations, new { this.Density, this.Sparsity, this.SparsityWeight });
for (int i = 0; i < this.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(X[x, VectorType.Row], properties);
}
var result = String.Format("Run ({0}/{1}): {2}", i, MaxIterations, network.Cost);
OnModelChanged(this, ModelEventArgs.Make(model, result));
}
return model;
}
