include/singa/neuralnet/loss_layer.h

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#ifndef SINGA_NEURALNET_LOSS_LAYER_H_
#define SINGA_NEURALNET_LOSS_LAYER_H_

#include <vector>
#include <string>
#include "singa/neuralnet/layer.h"
#include "singa/neuralnet/neuron_layer.h"

namespace singa {
using std::vector;
/**
 * Squared Euclidean loss as @f$0.5 ||p - t||^2@f$, where p is prediction
 * result, t is the ground truth.
 */
class EuclideanLossLayer : public LossLayer {
 public:
  void Setup(const LayerProto& conf, const vector<Layer*>& srclayers) override;
  void ComputeFeature(int flag, const vector<Layer*>& srclayers) override;
  void ComputeGradient(int flag, const vector<Layer*>& srclayers) override;
  const std::string ToString(bool debug, int flag) override;

 private:
  int counter_ = 0;
  float loss_ = 0.0f;
};
/**
 * Cross-entropy loss applied to the probabilities computed from Softmax.
 * @f$ L_i = -log P_{t_i}, t_i\in [0, C] @f$ is the label for the i-th object,
 * C is the total number of classes.
 */
class SoftmaxLossLayer : public LossLayer {
 public:
  void Setup(const LayerProto& conf, const vector<Layer*>& srclayers) override;
  void ComputeFeature(int flag, const vector<Layer*>& srclayers) override;
  void ComputeGradient(int flag, const vector<Layer*>& srclayers) override;
  const std::string ToString(bool debug, int flag) override;

 private:
  int batchsize_, topk_, dim_, counter_ = 0;
  float scale_;
  float loss_ = 0.0f, accuracy_ = 0.0f;
};

#ifdef USE_CUDNN
class CudnnSoftmaxLossLayer : public LossLayer{
 public:
  void Setup(const LayerProto& conf, const vector<Layer*>& srclayers) override;
  void ComputeFeature(int flag, const vector<Layer*>& srclayers) override;
  void ComputeGradient(int flag, const vector<Layer*>& srclayers) override;
  const std::string ToString(bool debug, int flag) override;

 private:
  int batchsize_, dim_;
  int counter_ = 0;
  float loss_ = 0.0f;

  CudnnSoftmaxLayer softmax_;
};
#endif
}  // namespace singa

#endif  // SINGA_NEURALNET_LOSS_LAYER_H_