/// <summary>
///
/// </summary>
/// <param name="sizes">First is the input size, others are the layers output sizes.</param>
/// <param name="types">The type of each layer. Length of types must be one less then length of sizes.</param>
public NeuralNetwork(int[] sizes, LayerActivation[] types = null)
{
if (types == null)
{
types = new LayerActivation[sizes.Length - 1];
for (int i = 0; i < types.Length; i++)
{
types[i] = LayerActivation.Relu;
}
}
else if (types.Length != sizes.Length - 1)
{
throw new Exception("Length of types must be 1 less then length of sizes!");
}
if (sizes.Length < 2)
{
throw new Exception("There must be at least one layer");
}
NeuralNetworkLayer previous = null;
int inputSize = sizes[0];
Layers = new NeuralNetworkLayer[sizes.Length - 1];
for (int i = 1; i < sizes.Length; i++)
{
int outputSize = sizes[i];
NeuralNetworkLayer layer = new NeuralNetworkLayer(inputSize, outputSize, types[i - 1], previous);
Layers[i - 1] = layer;
previous = layer;
inputSize = outputSize;
}
InputSize = Layers[0].InputSize;
OutputSize = Layers.Last().OutputSize;
}
public NeuralTrainLayer(NeuralNetworkLayer layer, float[] input)
{
float[] result = layer.Feed(input, false);
Input = input;
Output = result;
Layer = layer;
DCDB = new float[Output.Length];
DCDW = new float[input.Length * Output.Length];
}
public NeuralNetworkLayer(int inputSize, int outputSize, NeuralNetwork.LayerActivation layerType = NeuralNetwork.LayerActivation.Tanh, NeuralNetworkLayer previousLayer = null)
{
InputSize = inputSize;
OutputSize = outputSize;
Size = inputSize * outputSize;
Bias = new float[outputSize];
Weight = new float[Size];
if (previousLayer != null)
{
previousLayer.nextLayer = this;
}
switch (layerType)
{
case NeuralNetwork.LayerActivation.Tanh:
FlatFunction = Tanh;
FlatDeriv = TanhD;
break;
case NeuralNetwork.LayerActivation.Sigmoid:
FlatFunction = Sigmoid;
FlatDeriv = SigmoidD;
break;
case NeuralNetwork.LayerActivation.Relu:
FlatFunction = Relu;
FlatDeriv = ReluD;
break;
case NeuralNetwork.LayerActivation.LeakyRelu:
FlatFunction = LeakyRelu;
FlatDeriv = LeakyReluD;
break;
default:
FlatFunction = Relu;
FlatDeriv = ReluD;
break;
}
LayerActivation = layerType;
InitializeWeights();
InitializeBiases();
}
