src/model/layer/lrn.h

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#ifndef SINGA_MODEL_LAYER_LRN_H_
#define SINGA_MODEL_LAYER_LRN_H_
#include "singa/model/layer.h"
#include <stack>

namespace singa {
class LRN : public Layer {
 public:
  /// \copydoc Layer::layer_type()
  // const std::string layer_type() const override { return "LRN"; }

  /// \copydoc Layer::Setup(const LayerConf&);
  void Setup(const Shape& in_sample, const LayerConf& conf) override;
  const Shape GetOutputSampleShape() const override {
    CHECK(out_sample_shape_.size()) << "You may haven't call Setup()";
    return out_sample_shape_;
  }

  /**
   * Local Response Normalization edge
   *
   * @f$ b_i=a_i/x_i^beta @f$
   * @f$x_i=k+alpha*\sum_{j=max(0,i-n/2)}^{min(N,i+n/2)}(a_j)^2 @f$
   * n is size of local response area.
   * @f$a_i@f$, the activation (after ReLU) of a neuron convolved with the i-th kernel.
   * @f$b_i@f$, the neuron after normalization, N is the total num of kernels
   */
  const Tensor Forward(int flag, const Tensor& input)
    override;

  /// \copydoc Layer::Backward(int, const Tensor&, const Tensor&);
  const std::pair<Tensor, vector<Tensor>> Backward(
      int flag, const Tensor& grad) override;

  int local_size() const { return local_size_; }
  float alpha() const { return alpha_; }
  float beta() const { return beta_; }
  float k() const { return k_; }

 protected:
  //!< hyper-parameter: size local response (neighbor) area
  int local_size_;
  //!< other hyper-parameters
  float alpha_, beta_, k_;
  // store intermediate data, i.e., input tensor
  std::stack<Tensor> buf_;
  Shape out_sample_shape_;

}; // class LRN
} // namespace

#endif  // SINGA_MODEL_LAYER_LRN_H_