/// <summary>
/// Runs the defines activation function over the given vector.
/// </summary>
/// <param name="activation">The values for the input layer.</param>
/// <param name="activationFunction">The activation function to use.</param>
/// <param name="derivative">Whether we want the derivative of the values.</param>
/// <returns>A vector to give to the next layer of the net.</returns>
internal Vector <double> RunActivation(Vector <double> activation, NonLinearFunction activationFunction, bool derivative = false)
{
switch (activationFunction)
{
case NonLinearFunction.Sigmoid:
return(NonLinearTransformations.Sigmoid(activation, derivative));
case NonLinearFunction.Tanh:
return(NonLinearTransformations.Tanh(activation, derivative));
case NonLinearFunction.ReLU:
return(NonLinearTransformations.ReLU(activation, derivative));
case NonLinearFunction.LReLU:
return(NonLinearTransformations.LReLU(activation, derivative));
default:
return(null);
}
}
/// <summary>
/// Runs through a non-linear layer of the network.
/// </summary>
/// <param name="layerInfo">The layer info used for this layer.</param>
/// <param name="inputImages">
/// A matrix of all the images that will be ran through the non-linear function. Each row is
/// an image.
/// </param>
/// <returns>A matrix of all the resulting images. Each row is an image.</returns>
internal Matrix <double> NonLinear(NonLinearLayerInformation layerInfo, Matrix <double> inputImages)
{
switch (layerInfo.NonLinearFunction)
{
case NonLinearFunction.Sigmoid:
return(NonLinearTransformations.Sigmoid(inputImages));
case NonLinearFunction.Tanh:
return(NonLinearTransformations.Tanh(inputImages));
case NonLinearFunction.ReLU:
return(NonLinearTransformations.ReLU(inputImages));
case NonLinearFunction.LReLU:
return(NonLinearTransformations.LReLU(inputImages));
default:
return(null);
}
}