/**
 * @file methods/ann/layer/elu_impl.hpp
 * @author Vivek Pal
 * @author Dakshit Agrawal
 *
 * Implementation of the ELU activation function as described by Djork-Arne
 * Clevert, Thomas Unterthiner and Sepp Hochreiter.
 *
 * Implementation of the SELU function as introduced by Klambauer et. al. in
 * Self Neural Networks.  The SELU activation function keeps the mean and
 * variance of the input invariant.
 *
 * mlpack is free software; you may redistribute it and/or modify it under the
 * terms of the 3-clause BSD license.  You should have received a copy of the
 * 3-clause BSD license along with mlpack.  If not, see
 * http://www.opensource.org/licenses/BSD-3-Clause for more information.
 */
#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 {

// This constructor is called for SELU activation function.  The values of
// alpha and lambda are constant for normalized inputs.
template<typename MatType>
ELUType<MatType>::ELUType() :
    Layer<MatType>(),
    alpha(1.6732632423543774),
    lambda(1.0507009873554802)
{
  // 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 MatType>
ELUType<MatType>::ELUType(const double alpha) :
    Layer<MatType>(),
    alpha(alpha),
    lambda(1)
{
  // Nothing to do here.
}

template<typename MatType>
ELUType<MatType>::ELUType(const ELUType& other) :
    Layer<MatType>(other),
    alpha(other.alpha),
    lambda(other.lambda)
{
  // Nothing to do.
}

template<typename MatType>
ELUType<MatType>::ELUType(
    ELUType&& other) :
    Layer<MatType>(std::move(other)),
    alpha(std::move(other.alpha)),
    lambda(std::move(other.lambda))
{
  // Nothing to do.
}

template<typename MatType>
ELUType<MatType>&
ELUType<MatType>::operator=(const ELUType& other)
{
  if (&other != this)
  {
    Layer<MatType>::operator=(other);
    alpha = other.alpha;
    lambda = other.lambda;
  }

  return *this;
}

template<typename MatType>
ELUType<MatType>&
ELUType<MatType>::operator=(ELUType&& other)
{
  if (&other != this)
  {
    Layer<MatType>::operator=(std::move(other));
    alpha = std::move(other.alpha);
    lambda = std::move(other.lambda);
  }

  return *this;
}

template<typename MatType>
void ELUType<MatType>::Forward(
    const MatType& input, MatType& output)
{
  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 (this->training)
  {
    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 MatType>
void ELUType<MatType>::Backward(
    const MatType& /* input */, const MatType& gy, MatType& g)
{
  g = gy % derivative;
}

template<typename MatType>
template<typename Archive>
void ELUType<MatType>::serialize(
    Archive& ar, const uint32_t /* version */)
{
  ar(cereal::base_class<Layer<MatType>>(this));

  ar(CEREAL_NVP(alpha));
  ar(CEREAL_NVP(lambda));
}

} // namespace mlpack

#endif
