/// <summary>
/// Create a network with random weights and biases
/// </summary>
/// <param name="layerConfig">The layer configuration containing the number of neurons in each layer in this order: [input layer][1st hidden layer][2nd hidden layer]...[nth hidden layer][output layer]</param>
/// <param name="activationFunction">The activation function to use</param>
/// <param name="weightInitializer">The weight and bias initializer to use</param>
/// <returns></returns>
public static Network CreateNetworkInitRandom(int[] layerConfig, IActivationFunction activationFunction, IWeightInitializer weightInitializer = null)
{
if (weightInitializer == null)
{
weightInitializer = new DefaultWeightInitializer();
}
List <List <Tuple <List <float>, float> > > inputLayers = new List <List <Tuple <List <float>, float> > >();
for (int layId = 1; layId < layerConfig.Length; ++layId)
{
int prevLayerSize = layerConfig[layId - 1];
int layerSize = layerConfig[layId];
List <Tuple <List <float>, float> > neuronList = new List <Tuple <List <float>, float> >();
for (int i = 0; i < layerSize; i++)
{
List <float> weights = new List <float>();
for (int j = 0; j < prevLayerSize; ++j)
{
weights.Add(weightInitializer.GetRandomWeight(prevLayerSize));
}
neuronList.Add(new Tuple <List <float>, float>(weights, weightInitializer.GetRandomBias()));
}
inputLayers.Add(neuronList);
}
return(CreateNetwork(inputLayers, activationFunction));
}
/// <summary>
/// Create a network with random weights and biases
/// </summary>
/// <param name="input_layer">The neurons in the input layer</param>
/// <param name="hidden_and_output_layers">The layer configuration containing the activation function, and the number of neurons in each layer in this order: [1st hidden layer][2nd hidden layer]...[nth hidden layer][output layer]</param>
/// <param name="weightInitializer">The weight and bias initializer to use</param>
/// <returns></returns>
public static Network CreateNetworkInitRandom(int input_layer, List <Tuple <IActivationFunction, int> > hidden_and_output_layers, IWeightInitializer weightInitializer = null)
{
if (weightInitializer == null)
{
weightInitializer = new DefaultWeightInitializer();
}
var inputLayers = new List <Tuple <IActivationFunction, List <Tuple <List <float>, float> > > >();
for (int layId = 0; layId < hidden_and_output_layers.Count; ++layId)
{
int prevLayerSize = layId == 0 ? input_layer : hidden_and_output_layers[layId - 1].Item2;
int layerSize = hidden_and_output_layers[layId].Item2;
IActivationFunction activationFunction = hidden_and_output_layers[layId].Item1;
List <Tuple <List <float>, float> > neuronList = new List <Tuple <List <float>, float> >();
for (int i = 0; i < layerSize; i++)
{
List <float> weights = new List <float>();
for (int j = 0; j < prevLayerSize; ++j)
{
weights.Add(weightInitializer.GetRandomWeight(prevLayerSize));
}
neuronList.Add(new Tuple <List <float>, float>(weights, weightInitializer.GetRandomBias()));
}
inputLayers.Add(new Tuple <IActivationFunction, List <Tuple <List <float>, float> > >(activationFunction, neuronList));
}
return(CreateNetwork(inputLayers));
}
