/// <summary>
/// Creates a new deep neural network based on the supplied inputs and layers.
/// </summary>
/// <param name="d">Descriptor object.</param>
/// <param name="X">Training examples</param>
/// <param name="y">Training labels</param>
/// <param name="activationFunction">Activation Function for each output layer.</param>
/// <param name="outputFunction">Ouput Function for each output layer.</param>
/// <param name="hiddenLayers">The intermediary (hidden) layers / ensembles in the network.</param>
/// <returns>A Deep Neural Network</returns>
public static Network Create(this Network network, Descriptor d, Matrix X, Vector y, IFunction activationFunction, IFunction outputFunction = null, params NetworkLayer[] hiddenLayers)
{
// set output to number of choices of available
// 1 if only two choices
int distinct = y.Distinct().Count();
int output = distinct > 2 ? distinct : 1;
// identity function for bias nodes
IFunction ident = new Ident();
// creating input nodes
network.In = new Neuron[X.Cols + 1];
network.In[0] = new Neuron {
Label = "B0", ActivationFunction = ident
};
for (int i = 1; i < X.Cols + 1; i++)
{
network.In[i] = new Neuron {
Label = d.ColumnAt(i - 1), ActivationFunction = ident
}
}
;
// creating output nodes
network.Out = new Neuron[output];
for (int i = 0; i < output; i++)
{
network.Out[i] = new Neuron {
Label = Network.GetLabel(i, d), ActivationFunction = activationFunction, OutputFunction = outputFunction
}
}
;
for (int layer = 0; layer < hiddenLayers.Count(); layer++)
{
if (layer == 0 && hiddenLayers[layer].IsAutoencoder)
{
// init and train it.
}
// connect input with previous layer or input layer
// connect last layer with output layer
}
// link input to hidden. Note: there are
// no inputs to the hidden bias node
//for (int i = 1; i < h.Length; i++)
// for (int j = 0; j < nn.In.Length; j++)
// Edge.Create(nn.In[j], h[i]);
//// link from hidden to output (full)
//for (int i = 0; i < nn.Out.Length; i++)
// for (int j = 0; j < h.Length; j++)
// Edge.Create(h[j], nn.Out[i]);
return(network);
}
/// <summary>Defaults.</summary>
/// <param name="network">The network.</param>
/// <param name="d">The Descriptor to process.</param>
/// <param name="x">The Vector to process.</param>
/// <param name="y">The Vector to process.</param>
/// <param name="activationFunction">The activation.</param>
/// <param name="outputFunction">The ouput function for hidden nodes (Optional).</param>
/// <param name="epsilon">epsilon</param>
/// <returns>A Network.</returns>
public static Network Create(
this Network network,
Descriptor d,
Matrix x,
Vector y,
IFunction activationFunction,
IFunction outputFunction = null,
double epsilon = double.NaN)
{
// set output to number of choices of available
// 1 if only two choices
var distinct = y.Distinct().Count();
var output = distinct > 2 ? distinct : 1;
// identity funciton for bias nodes
IFunction ident = new Ident();
// set number of hidden units to (Input + Hidden) * 2/3 as basic best guess.
var hidden = (int)System.Math.Ceiling((x.Cols + output) * 2.0 / 3.0);
return(network.Create(
x.Cols,
output,
activationFunction,
outputFunction,
fnNodeInitializer: (l, i) =>
{
if (l == 0)
{
return new Neuron(false)
{
Label = d.ColumnAt(i - 1),
ActivationFunction = activationFunction,
NodeId = i,
LayerId = l
}
}
;
if (l == 2)
{
return new Neuron(false)
{
Label = Network.GetLabel(i, d),
ActivationFunction = activationFunction,
NodeId = i,
LayerId = l
}
}
;
return new Neuron(false)
{
ActivationFunction = activationFunction, NodeId = i, LayerId = l
};
},
hiddenLayers: hidden));
}
/// <summary>Defaults.</summary>
/// <param name="d">The Descriptor to process.</param>
/// <param name="x">The Vector to process.</param>
/// <param name="y">The Vector to process.</param>
/// <param name="activationFunction">The activation.</param>
/// <param name="outputFunction">The ouput function for hidden nodes (Optional).</param>
/// <param name="epsilon">epsilon</param>
/// <returns>A Network.</returns>
public static Network Create(this Network network, Descriptor d, Matrix x, Vector y, IFunction activationFunction, IFunction outputFunction = null,
double epsilon = double.NaN, ILossFunction lossFunction = null)
{
// set output to number of choices of available
// 1 if only two choices
int distinct = y.Distinct().Count();
int output = distinct > 2 ? distinct : 1;
// identity funciton for bias nodes
IFunction ident = new Ident();
// set number of hidden units to (Input + Hidden) * 2/3 as basic best guess.
int hidden = (int)System.Math.Ceiling((double)(x.Cols + output) * 2.0 / 3.0);
return(network.Create(x.Cols, output, activationFunction, outputFunction,
fnNodeInitializer: new Func <int, int, NodeType, Neuron>((l, i, type) =>
{
if (type == NodeType.Input)
{
return new Neuron(false)
{
Label = d.ColumnAt(i - 1), ActivationFunction = activationFunction, NodeId = i, LayerId = l
}
}
;
else if (type == NodeType.Output)
{
return new Neuron(false)
{
Label = Network.GetLabel(i, d), ActivationFunction = activationFunction, NodeId = i, LayerId = l
}
}
;
else
{
return new Neuron(false)
{
ActivationFunction = activationFunction, NodeId = i, LayerId = l
}
};
}), lossFunction: lossFunction, hiddenLayers: hidden));
}