dropout_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_DROPOUT_IMPL_HPP
#define MLPACK_METHODS_ANN_LAYER_DROPOUT_IMPL_HPP

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

namespace mlpack {
namespace ann  {

template<typename InputDataType, typename OutputDataType>
Dropout<InputDataType, OutputDataType>::Dropout(
    const double ratio) :
    ratio(ratio),
    scale(1.0 / (1.0 - ratio)),
    deterministic(false)
{
  // Nothing to do here.
}

template<typename InputDataType, typename OutputDataType>
Dropout<InputDataType, OutputDataType>::Dropout(
    const Dropout& layer) :
    ratio(layer.ratio),
    scale(layer.scale),
    deterministic(layer.deterministic)
{
  // Nothing to do here.
}

template<typename InputDataType, typename OutputDataType>
Dropout<InputDataType, OutputDataType>::Dropout(
    const Dropout&& layer) :
    ratio(std::move(layer.ratio)),
    scale(std::move(scale)),
    deterministic(std::move(deterministic))
{
  // Nothing to do here.
}

template<typename InputDataType, typename OutputDataType>
Dropout<InputDataType, OutputDataType>&
Dropout<InputDataType, OutputDataType>::
operator=(const Dropout& layer)
{
  if (this != &layer)
  {
    ratio = layer.ratio;
    scale = layer.scale;
    deterministic = layer.deterministic;
  }
  return *this;
}

template<typename InputDataType, typename OutputDataType>
Dropout<InputDataType, OutputDataType>&
Dropout<InputDataType, OutputDataType>::
operator=(Dropout&& layer)
{
  if (this != &layer)
  {
    ratio = std::move(layer.ratio);
    scale = std::move(layer.scale);
    deterministic = std::move(layer.deterministic);
  }
  return *this;
}

template<typename InputDataType, typename OutputDataType>
template<typename eT>
void Dropout<InputDataType, OutputDataType>::Forward(
    const arma::Mat<eT>& input,
    arma::Mat<eT>& output)
{
  // The dropout mask will not be multiplied in the deterministic mode
  // (during testing).
  if (deterministic)
  {
    output = input;
  }
  else
  {
    // Scale with input / (1 - ratio) and set values to zero with probability
    // 'ratio'.
    mask = arma::randu<arma::Mat<eT> >(input.n_rows, input.n_cols);
    mask.transform([&](double val) { return (val > ratio); });
    output = input % mask * scale;
  }
}

template<typename InputDataType, typename OutputDataType>
template<typename eT>
void Dropout<InputDataType, OutputDataType>::Backward(
    const arma::Mat<eT>& /* input */,
    const arma::Mat<eT>& gy,
    arma::Mat<eT>& g)
{
  g = gy % mask * scale;
}

template<typename InputDataType, typename OutputDataType>
template<typename Archive>
void Dropout<InputDataType, OutputDataType>::serialize(
    Archive& ar,
    const uint32_t /* version */)
{
  ar(CEREAL_NVP(ratio));

  // Reset scale.
  scale = 1.0 / (1.0 - ratio);
}

} // namespace ann
} // namespace mlpack

#endif