unsupported/Eigen/CXX11/src/NeuralNetworks/Activations.h

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CXX11_NEURAL_NETWORKS_ACTIVATIONS_H
#define EIGEN_CXX11_NEURAL_NETWORKS_ACTIVATIONS_H

namespace Eigen {

/** scalar_sigmoid_fast_derivative_op
  * \ingroup CXX11_NeuralNetworks_Module
  * \brief Template functor to compute the fast derivative of a sigmoid
  *
  * Input should be the backpropagated gradient.
  *
  * \sa class CwiseUnaryOp, Cwise::sigmoid_fast_derivative()
  */
template <typename T>
struct scalar_sigmoid_fast_derivative_op {
  EIGEN_EMPTY_STRUCT_CTOR(scalar_sigmoid_fast_derivative_op)
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& y) const {
    const T one = T(1);
    return (one - y) * y;
  }

  template <typename Packet>
  inline Packet packetOp(const Packet& y) const {
    const Packet one = internal::pset1<Packet>(1);
    return internal::pmul(internal::psub(one, y), y);
  }
};

namespace internal {
template <typename T>
struct functor_traits<scalar_sigmoid_fast_derivative_op<T> > {
  enum {
    Cost = NumTraits<T>::AddCost * 2 + NumTraits<T>::MulCost,
    PacketAccess = packet_traits<T>::HasAdd && packet_traits<T>::HasMul &&
                   packet_traits<T>::HasNegate
  };
};
}  // namespace internal

/** scalar_tanh_fast_derivative_op
  * \ingroup CXX11_NeuralNetworks_Module
  * \brief Template functor to compute the fast derivative of a tanh
  *
  * Input should be the backpropagated gradient.
  *
  * \sa class CwiseUnaryOp, Cwise::tanh_fast_derivative()
  */
template <typename T>
struct scalar_tanh_fast_derivative_op {
  EIGEN_EMPTY_STRUCT_CTOR(scalar_tanh_fast_derivative_op)
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& y) const {
    const T one = T(1);
    return one - (y * y);
  }

  template <typename Packet>
  inline Packet packetOp(const Packet& y) const {
    const Packet one = internal::pset1<Packet>(1);
    return internal::psub(one, internal::pmul(y, y));
  }
};

namespace internal {
template <typename T>
struct functor_traits<scalar_tanh_fast_derivative_op<T> > {
  enum {
    Cost = NumTraits<T>::AddCost * 2 + NumTraits<T>::MulCost * 1,
    PacketAccess = packet_traits<T>::HasAdd && packet_traits<T>::HasMul &&
                   packet_traits<T>::HasNegate
  };
};
}  // namespace internal

/**
  * \ingroup CXX11_NeuralNetworks_Module
  * \brief Template functor to clip the the magnitude of the first scalar.
  *
  * \sa class CwiseBinaryOp, MatrixBase::Clip
  */
template <typename Scalar>
struct scalar_clip_op {
  EIGEN_EMPTY_STRUCT_CTOR(scalar_clip_op)
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar
  operator()(const Scalar& a, const Scalar& b) const {
    return numext::mini(numext::maxi(a, -b), b);
  }
  template <typename Packet>
  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet
  packetOp(const Packet& a, const Packet& b) const {
    return internal::pmin(internal::pmax(a, internal::pnegate(b)), b);
  }
};

namespace internal {
template <typename Scalar>
struct functor_traits<scalar_clip_op<Scalar> > {
  enum {
    Cost = NumTraits<Scalar>::AddCost * 3,
    PacketAccess = packet_traits<Scalar>::HasMax &&
                   packet_traits<Scalar>::HasMin &&
                   packet_traits<Scalar>::HasNegate
  };
};
}  // namespace internal

}  // end namespace Eigen

#endif  // EIGEN_CXX11_NEURAL_NETWORKS_ACTIVATIONS_H