/// <summary>
/// Adds new connections for the specified node for the parent and child nodes.
/// </summary>
/// <param name="network">Current network.</param>
/// <param name="node">Neuron being added.</param>
/// <param name="parentNodes">Parent nodes that this neuron is connected with.</param>
/// <param name="childNodes">Child nodes that this neuron is connected to.</param>
/// <param name="epsilon">Weight initialization parameter.</param>
/// <returns></returns>
public static Network AddConnections(this Network network, Neuron node, IEnumerable <Neuron> parentNodes, IEnumerable <Neuron> childNodes, double epsilon = double.NaN)
{
if (epsilon == double.NaN)
{
epsilon = Edge.GetEpsilon(node.ActivationFunction.Minimum, node.ActivationFunction.Maximum, parentNodes.Count(), childNodes.Count());
}
if (parentNodes != null)
{
for (int i = 0; i < parentNodes.Count(); i++)
{
network.AddEdge(Edge.Create(parentNodes.ElementAt(i), node, epsilon: epsilon));
}
}
if (childNodes != null)
{
for (int j = 0; j < childNodes.Count(); j++)
{
network.AddEdge(Edge.Create(node, childNodes.ElementAt(j), epsilon: epsilon));
}
}
return(network);
}
/// <summary>
/// Creates a new fully connected deep neural network based on the supplied size and depth parameters.
/// </summary>
/// <param name="network">New network instance.</param>
/// <param name="inputLayer">Neurons in the input layer.</param>
/// <param name="outputLayer">Neurons in the output layer.</param>
/// <param name="activationFunction">Activation function for the hidden and output layers.</param>
/// <param name="outputFunction">(Optional) Output function of the the Nodes in the output layer (overrides the Activation function).</param>
/// <param name="fnNodeInitializer">(Optional) Function to call for initializing new Nodes, where int1: layer, int2: node index, NodeType: node type.</param>
/// <param name="fnWeightInitializer">(Optional) Function to call for initializing the weights of each connection (including bias nodes).
/// <para>Where int1 = Source layer (0 is input layer), int2 = Source Node, int3 = Target node in the next layer.</para></param>
/// <param name="lossFunction">Loss function to apply in computing the error cost.</param>
/// <param name="epsilon">Weight initialization parameter for random weight selection. Weight will be in the range of: -epsilon to +epsilon.</param>
/// <param name="hiddenLayers">An array of hidden neuron dimensions, where each element is the size of each layer (excluding bias nodes).</param>
/// <returns>Returns an untrained neural network model.</returns>
public static Network Create(this Network network, int inputLayer, int outputLayer, IFunction activationFunction, IFunction outputFunction = null, Func <int, int, NodeType, Neuron> fnNodeInitializer = null,
Func <int, int, int, double> fnWeightInitializer = null, ILossFunction lossFunction = null, double epsilon = double.NaN, params int[] hiddenLayers)
{
IFunction ident = new Ident();
if (hiddenLayers == null || hiddenLayers.Length == 0)
{
hiddenLayers = new int[] { (int)System.Math.Ceiling((inputLayer + outputLayer + 1) * (2.0 / 3.0)) }
}
;
List <double> layers = new List <double>();
layers.Add(inputLayer);
foreach (int l in hiddenLayers)
{
layers.Add(l + 1);
}
layers.Add(outputLayer);
if (fnNodeInitializer == null)
{
fnNodeInitializer = new Func <int, int, NodeType, Neuron>((i, j, type) => new Neuron());
}
if (fnWeightInitializer == null)
{
fnWeightInitializer = new Func <int, int, int, double>((l, i, j) => {
double inputs = (l > 0 ? layers[l - 1] : 0);
double outputs = (l < layers.Count - 1 ? layers[l + 1] : 0);
double eps = (double.IsNaN(epsilon) ? Edge.GetEpsilon(activationFunction.Minimum, activationFunction.Maximum, inputs, outputs) : epsilon);
return(Edge.GetWeight(eps));
});
}
// creating input nodes
network.In = new Neuron[inputLayer + 1];
network.In[0] = network.AddNode(new Neuron(true)
{
Label = "B0", ActivationFunction = ident, NodeId = 0, LayerId = 0
});
for (int i = 1; i < inputLayer + 1; i++)
{
network.In[i] = fnNodeInitializer(0, i, NodeType.Input);
network.In[i].Label = (network.In[i].Label ?? string.Format("I{0}", i));
network.In[i].ActivationFunction = (network.In[i].ActivationFunction ?? ident);
network.In[i].LayerId = 0;
network.In[i].NodeId = i;
network.AddNode(network.In[i]);
}
Neuron[] last = null;
for (int layerIdx = 0; layerIdx < hiddenLayers.Length; layerIdx++)
{
// creating hidden nodes
Neuron[] layer = new Neuron[hiddenLayers[layerIdx] + 1];
layer[0] = network.AddNode(new Neuron(true)
{
Label = $"B{layerIdx + 1}", ActivationFunction = ident, LayerId = layerIdx + 1, NodeId = 0
});
for (int i = 1; i < layer.Length; i++)
{
layer[i] = fnNodeInitializer(layerIdx + 1, i, NodeType.Hidden);
layer[i].Label = (layer[i].Label ?? String.Format("H{0}.{1}", layerIdx + 1, i));
layer[i].ActivationFunction = (layer[i].ActivationFunction ?? activationFunction);
layer[i].LayerId = layerIdx + 1;
layer[i].NodeId = i;
network.AddNode(layer[i]);
}
if (layerIdx > 0 && layerIdx < hiddenLayers.Length)
{
// create hidden to hidden (full)
for (int i = 0; i < last.Length; i++)
{
for (int x = 1; x < layer.Length; x++)
{
network.AddEdge(Edge.Create(last[i], layer[x], weight: fnWeightInitializer(layerIdx, i, x), epsilon: epsilon));
}
}
}
else if (layerIdx == 0)
{
// create input to hidden (full)
for (int i = 0; i < network.In.Length; i++)
{
for (int j = 1; j < layer.Length; j++)
{
network.AddEdge(Edge.Create(network.In[i], layer[j], weight: fnWeightInitializer(layerIdx, i, j), epsilon: epsilon));
}
}
}
last = layer;
}
// creating output nodes
network.Out = new Neuron[outputLayer];
for (int i = 0; i < outputLayer; i++)
{
network.Out[i] = fnNodeInitializer(hiddenLayers.Length + 1, i, NodeType.Output);
network.Out[i].Label = (network.Out[i].Label ?? String.Format("O{0}", i));
network.Out[i].ActivationFunction = (network.Out[i].ActivationFunction ?? activationFunction);
network.Out[i].LayerId = hiddenLayers.Length + 1;
network.Out[i].NodeId = i;
network.AddNode(network.Out[i]);
}
// link from (last) hidden to output (full)
for (int i = 0; i < network.Out.Length; i++)
{
for (int j = 0; j < last.Length; j++)
{
network.AddEdge(Edge.Create(last[j], network.Out[i], weight: fnWeightInitializer(hiddenLayers.Length, j, i), epsilon: epsilon));
}
}
network.OutputFunction = outputFunction;
network.LossFunction = (lossFunction ?? network.LossFunction);
return(network);
}