alpha_dropout_impl.hpp

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#ifndef MLPACK_METHODS_ANN_LAYER_ALPHA_DROPOUT_IMPL_HPP
#define MLPACK_METHODS_ANN_LAYER_ALPHA_DROPOUT_IMPL_HPP

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

namespace mlpack {
namespace ann  {

template<typename InputDataType, typename OutputDataType>
AlphaDropout<InputDataType, OutputDataType>::AlphaDropout(
    const double ratio,
    const double alphaDash) :
    ratio(ratio),
    alphaDash(alphaDash),
    deterministic(false)
{
  Ratio(ratio);
}

template<typename InputDataType, typename OutputDataType>
template<typename eT>
void AlphaDropout<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
  {
    // Set values to alphaDash with probability ratio.  Then apply affine
    // transformation so as to keep mean and variance of outputs to their
    // original values.
    mask = arma::randu< arma::Mat<eT> >(input.n_rows, input.n_cols);
    mask.transform( [&](double val) { return (val > ratio); } );
    output = (input % mask + alphaDash * (1 - mask)) * a + b;
  }
}

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

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

} // namespace ann
} // namespace mlpack

#endif