#ifndef CAFFE_DROPOUT_LAYER_HPP_
#define CAFFE_DROPOUT_LAYER_HPP_

#include <vector>

#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"

#include "caffe/layers/neuron_layer.hpp"

namespace caffe {

// dropout层
// 在训练过程中随机的将一部分输入x之置为0

/**
 * @brief During training only, sets a random portion of @f$x@f$ to 0, adjusting
 *        the rest of the vector magnitude accordingly.
 *
 * @param bottom input Blob vector (length 1)
 *   -# @f$ (N \times C \times H \times W) @f$
 *      the inputs @f$ x @f$
 * @param top output Blob vector (length 1)
 *   -# @f$ (N \times C \times H \times W) @f$
 *      the computed outputs @f$ y = |x| @f$
 */
template <typename Dtype>
class DropoutLayer : public NeuronLayer<Dtype> {
 public:
  /**
   * @param param provides DropoutParameter dropout_param,
   *     with DropoutLayer options:
   *   - dropout_ratio (\b optional, default 0.5).
   *     Sets the probability @f$ p @f$ that any given unit is dropped.
   */
  explicit DropoutLayer(const LayerParameter& param)
      : NeuronLayer<Dtype>(param) {}
  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);

  virtual inline const char* type() const { return "Dropout"; }

 protected:
  /**
   * @param bottom input Blob vector (length 1)
   *   -# @f$ (N \times C \times H \times W) @f$
   *      the inputs @f$ x @f$
   * @param top output Blob vector (length 1)
   *   -# @f$ (N \times C \times H \times W) @f$
   *      the computed outputs. At training time, we have @f$
   *      y_{\mbox{train}} = \left\{
   *         \begin{array}{ll}
   *            \frac{x}{1 - p} & \mbox{if } u > p \\
   *            0 & \mbox{otherwise}
   *         \end{array} \right.
   *      @f$, where @f$ u \sim U(0, 1)@f$ is generated independently for each
   *      input at each iteration. At test time, we simply have
   *      @f$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x @f$.
   */
  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);

  /// when divided by UINT_MAX, the randomly generated values @f$u\sim U(0,1)@f$
  // 随机矩阵，提供随机数
  // 保存是为了在反向计算的时候还需要用到
  Blob<unsigned int> rand_vec_;

  // if u > p then
  //   y = scale_ * x 
  // else
  //   y = 0
  // 其中u是一个随机数

  /// the probability @f$ p @f$ of dropping any input
  // drop概率
  Dtype threshold_;
  /// the scale for undropped inputs at train time @f$ 1 / (1 - p) @f$
  // 输入的乘积倍率
  // scale_ = 1 / (1 - p)
  Dtype scale_;
  unsigned int uint_thres_;
};

}  // namespace caffe

#endif  // CAFFE_DROPOUT_LAYER_HPP_
