/// <summary>
/// Initializes a new instance of the <see cref="StochasticNeuron"/> class.
/// </summary>
///
/// <param name="inputs">Number of inputs for the neuron.</param>
/// <param name="function">Activation function for the neuron.</param>
///
public StochasticNeuron(int inputs, IStochasticFunction function)
: base(inputs, function)
{
this.ActivationFunction = function;
this.Threshold = 0; // Ruslan Salakhutdinov and Geoff Hinton
// also start with zero thresholds
}
/// <summary>
/// Initializes a new instance of the <see cref="StochasticLayer"/> class.
/// </summary>
///
/// <param name="function">The activation function for the neurons in the layer.</param>
/// <param name="neuronsCount">The neurons count.</param>
/// <param name="inputsCount">The inputs count.</param>
///
public StochasticLayer(IStochasticFunction function, int neuronsCount, int inputsCount)
: base(neuronsCount, inputsCount, function)
{
neurons = new StochasticNeuron[neuronsCount];
for (int i = 0; i < neurons.Length; i++)
base.neurons[i] = this.neurons[i] =
new StochasticNeuron(inputsCount, function);
}
/// <summary>
/// Creates a new <see cref="DeepBeliefNetwork"/>.
/// </summary>
///
/// <param name="function">The activation function to be used in the network neurons.</param>
/// <param name="inputsCount">The number of inputs for the network.</param>
/// <param name="hiddenNeurons">The number of hidden neurons in each layer.</param>
///
public DeepBeliefNetwork(IStochasticFunction function, int inputsCount, params int[] hiddenNeurons)
: base(function, inputsCount, hiddenNeurons)
{
machines = new List<RestrictedBoltzmannMachine>();
// Create first layer
machines.Add(new RestrictedBoltzmannMachine(
hidden: new StochasticLayer(function, hiddenNeurons[0], inputsCount),
visible: new StochasticLayer(function, inputsCount, hiddenNeurons[0]))
);
// Create other layers
for (int i = 1; i < hiddenNeurons.Length; i++)
{
machines.Add(new RestrictedBoltzmannMachine(
hidden: new StochasticLayer(function, hiddenNeurons[i], hiddenNeurons[i - 1]),
visible: new StochasticLayer(function, hiddenNeurons[i - 1], hiddenNeurons[i])));
}
// Override AForge layers
layers = new Layer[machines.Count];
for (int i = 0; i < layers.Length; i++)
layers[i] = machines[i].Hidden;
}
/// <summary>
/// Creates a Mixed-Bernoulli network.
/// </summary>
///
/// <param name="visible">The <see cref="IStochasticFunction"/> to be used in the first visible layer.</param>
/// <param name="hidden">The <see cref="IStochasticFunction"/> to be used in all other layers.</param>
///
/// <param name="inputsCount">The number of inputs for the network.</param>
/// <param name="hiddenNeurons">The number of hidden neurons in each layer.</param>
///
public static DeepBeliefNetwork CreateMixedNetwork(IStochasticFunction visible,
IStochasticFunction hidden, int inputsCount, params int[] hiddenNeurons)
{
DeepBeliefNetwork network = new DeepBeliefNetwork(hidden, inputsCount, hiddenNeurons);
foreach (StochasticNeuron neuron in network.machines[0].Visible.Neurons)
neuron.ActivationFunction = visible;
return network;
}
/// <summary>
/// Inserts a new layer at the end of this network.
/// </summary>
///
/// <param name="neurons">The number of neurons in the layer.</param>
/// <param name="visibleFunction">The activation function which should be used by the visible neurons.</param>
/// <param name="hiddenFunction">The activation function which should be used by the hidden neurons.</param>
///
public void Push(int neurons, IStochasticFunction visibleFunction, IStochasticFunction hiddenFunction)
{
int lastLayerNeurons;
if (machines.Count > 0)
lastLayerNeurons = machines[machines.Count - 1].Hidden.Neurons.Length;
else lastLayerNeurons = inputsCount;
machines.Add(new RestrictedBoltzmannMachine(
hidden: new StochasticLayer(hiddenFunction, neurons, lastLayerNeurons),
visible: new StochasticLayer(visibleFunction, lastLayerNeurons, neurons)));
// Override AForge layers
layers = new Layer[machines.Count];
for (int i = 0; i < layers.Length; i++)
layers[i] = machines[i].Hidden;
}
/// <summary>
/// Inserts a new layer at the end of this network.
/// </summary>
///
/// <param name="neurons">The number of neurons in the new layer.</param>
/// <param name="function">The activation function which should be used by the neurons.</param>
///
public void Push(int neurons, IStochasticFunction function)
{
Push(neurons, function, function);
}
/// <summary>
/// Creates a new <see cref="RestrictedBoltzmannMachine"/>.
/// </summary>
///
/// <param name="function">The activation function to use in the network neurons.</param>
/// <param name="inputsCount">The number of inputs for the machine.</param>
/// <param name="hiddenNeurons">The number of hidden neurons in the machine.</param>
///
public RestrictedBoltzmannMachine(IStochasticFunction function, int inputsCount, int hiddenNeurons)
: base(function, inputsCount, 1)
{
this.visible = new StochasticLayer(function, inputsCount, hiddenNeurons);
this.hidden = new StochasticLayer(function, hiddenNeurons, inputsCount);
base.layers[0] = hidden;
}
