elu_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_ELU_IMPL_HPP
#define MLPACK_METHODS_ANN_LAYER_ELU_IMPL_HPP

// In case it hasn't yet been included.
#include "elu.hpp"

namespace mlpack {
namespace ann  {

// This constructor is called for SELU activation function.  The values of
// alpha and lambda are constant for normalized inputs.
template<typename InputDataType, typename OutputDataType>
ELU<InputDataType, OutputDataType>::ELU() :
    alpha(1.6732632423543774),
    lambda(1.0507009873554802),
    deterministic(false)
{
  // Nothing to do here.
}

// This constructor is called for ELU activation function.  The value of lambda
// is fixed and equal to 1.  'alpha' is a hyperparameter.
template<typename InputDataType, typename OutputDataType>
ELU<InputDataType, OutputDataType>::ELU(const double alpha) :
    alpha(alpha),
    lambda(1),
    deterministic(false)
{
  // Nothing to do here.
}

template<typename InputDataType, typename OutputDataType>
template<typename InputType, typename OutputType>
void ELU<InputDataType, OutputDataType>::Forward(
    const InputType& input, OutputType& output)
{
  output = arma::ones<OutputDataType>(arma::size(input));
  for (size_t i = 0; i < input.n_elem; ++i)
  {
    if (input(i) < DBL_MAX)
    {
      output(i) = (input(i) > 0) ? lambda * input(i) : lambda *
          alpha * (std::exp(input(i)) - 1);
    }
  }

    if (!deterministic)
    {
      derivative.set_size(arma::size(input));
      for (size_t i = 0; i < input.n_elem; ++i)
      {
        derivative(i) = (input(i) > 0) ? lambda : output(i) +
            lambda * alpha;
      }
    }
}

template<typename InputDataType, typename OutputDataType>
template<typename DataType>
void ELU<InputDataType, OutputDataType>::Backward(
    const DataType& /* input */, const DataType& gy, DataType& g)
{
  g = gy % derivative;
}

template<typename InputDataType, typename OutputDataType>
template<typename Archive>
void ELU<InputDataType, OutputDataType>::serialize(
    Archive& ar,
    const uint32_t /* version */)
{
  ar(CEREAL_NVP(alpha));
  ar(CEREAL_NVP(lambda));
}

} // namespace ann
} // namespace mlpack

#endif