EVOLUTION-MANAGER

Edit File: squared-loss.h

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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_SQUARED_LOSS_H_
#define TENSORFLOW_CORE_KERNELS_SQUARED_LOSS_H_

#include "tensorflow/core/kernels/loss.h"

namespace tensorflow {

class SquaredLossUpdater : public DualLossUpdater {
 public:
  // Closed form solution that decreases the dual squared loss.
  // See page 23 of http://arxiv.org/pdf/1309.2375v2.pdf for the derivation of
  // the update rule when the example weights are equal to 1.0.
  // Note: There is a typo in the formula in the paper: the denominator should
  // be 1 + ||x_i||^2/(\lambda n) (without the 2 multiplier).
  //
  // The CoCoA+ modification is detailed in readme.md.
  double ComputeUpdatedDual(const int num_loss_partitions, const double label,
                            const double example_weight,
                            const double current_dual, const double wx,
                            const double weighted_example_norm) const final {
    const double delta_numerator = label - current_dual - wx;
    const double delta_denominator =
        1 + num_loss_partitions * weighted_example_norm * example_weight;
    return current_dual + delta_numerator / delta_denominator;
  }

// Dual of squared loss function.
  // https://en.wikipedia.org/wiki/Convex_conjugate
  double ComputeDualLoss(const double current_dual, const double example_label,
                         const double example_weight) const final {
    // Dual of the squared loss function = b * (y + b/2), where b is the
    // dual variable and y is the label.  This is Dual(-b).
    return current_dual * (0.5 * current_dual - example_label) * example_weight;
  }

// Squared loss for linear regression.
  double ComputePrimalLoss(const double wx, const double example_label,
                           const double example_weight) const final {
    const double error = wx - example_label;
    return error * error * example_weight * 0.5;
  }

inline double PrimalLossDerivative(const double wx, const double label,
                                     const double example_weight) const final {
    return (wx - label) * example_weight;
  }

inline double SmoothnessConstant() const final { return 1.0; }

// Labels don't require conversion for linear regression.
  Status ConvertLabel(float* const example_label) const final {
    return Status::OK();
  }
};

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_SQUARED_LOSS_H_