/// <summary>
/// Construct a deep belief neural network.
/// </summary>
/// <param name="inputCount">The input count.</param>
/// <param name="hidden">The counts for the hidden layers.</param>
/// <param name="outputCount">The output neuron count.</param>
public DeepBeliefNetwork(int inputCount, int[] hidden, int outputCount)
{
int inputSize;
_layers = new HiddenLayer[hidden.Length];
_rbm = new RestrictedBoltzmannMachine[hidden.Length];
for (int i = 0; i < _rbm.Length; i++)
{
if (i == 0)
{
inputSize = inputCount;
}
else
{
inputSize = hidden[i - 1];
}
_layers[i] = new HiddenLayer(this, inputSize, hidden[i]);
_rbm[i] = new RestrictedBoltzmannMachine(_layers[i]);
}
_outputLayer = new DeepLayer(this, hidden[_layers.Length - 1], outputCount);
Random = new MersenneTwisterGenerateRandom();
}
/// <summary>
/// Construct the backpropagation trainer.
/// </summary>
/// <param name="theNetwork">The network to train.</param>
/// <param name="theTraining">The training data to use.</param>
/// <param name="theLearningRate">The learning rate. Can be changed as training runs.</param>
/// <param name="theMomentum">The momentum. Can be changed as training runs.</param>
public BackPropagation(BasicNetwork theNetwork, IList<BasicData> theTraining, double theLearningRate,
double theMomentum)
{
BatchSize = 500;
Stochastic = new MersenneTwisterGenerateRandom();
NesterovUpdate = true;
_network = theNetwork;
_training = theTraining;
LearningRate = theLearningRate;
Momentum = theMomentum;
_gradients = new GradientCalc(_network, new CrossEntropyErrorFunction(), this);
_lastDelta = new double[theNetwork.Weights.Length];
}
