hard_tanh_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_HARD_TANH_IMPL_HPP
#define MLPACK_METHODS_ANN_LAYER_HARD_TANH_IMPL_HPP

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

namespace mlpack {
namespace ann  {

template<typename InputDataType, typename OutputDataType>
HardTanH<InputDataType, OutputDataType>::HardTanH(
    const double maxValue,
    const double minValue) :
    maxValue(maxValue),
    minValue(minValue)
{
  // Nothing to do here.
}

template<typename InputDataType, typename OutputDataType>
template<typename InputType, typename OutputType>
void HardTanH<InputDataType, OutputDataType>::Forward(
    const InputType& input, OutputType& output)
{
  output = input;
  for (size_t i = 0; i < input.n_elem; ++i)
  {
    output(i) = (output(i) > maxValue ? maxValue :
        (output(i) < minValue ? minValue : output(i)));
  }
}

template<typename InputDataType, typename OutputDataType>
template<typename DataType>
void HardTanH<InputDataType, OutputDataType>::Backward(
    const DataType& input, const DataType& gy, DataType& g)
{
  g = gy;
  for (size_t i = 0; i < input.n_elem; ++i)
  {
    if (input(i) < minValue || input(i) > maxValue)
    {
      g(i) = 0;
    }
  }
}

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

} // namespace ann
} // namespace mlpack

#endif