Stan Math Library  2.15.0
reverse mode automatic differentiation
log_softmax.hpp
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1 #ifndef STAN_MATH_FWD_MAT_FUN_LOG_SOFTMAX_HPP
2 #define STAN_MATH_FWD_MAT_FUN_LOG_SOFTMAX_HPP
3 
4 #include <stan/math/fwd/core.hpp>
9 
10 namespace stan {
11  namespace math {
12 
13  template <typename T>
14  inline
15  Eigen::Matrix<fvar<T>, Eigen::Dynamic, 1>
16  log_softmax(const Eigen::Matrix<fvar<T>, Eigen::Dynamic, 1>& alpha) {
17  using Eigen::Matrix;
18  using Eigen::Dynamic;
19 
20  Matrix<T, Dynamic, 1> alpha_t(alpha.size());
21  for (int k = 0; k < alpha.size(); ++k)
22  alpha_t(k) = alpha(k).val_;
23 
24  Matrix<T, Dynamic, 1> softmax_alpha_t = softmax(alpha_t);
25  Matrix<T, Dynamic, 1> log_softmax_alpha_t = log_softmax(alpha_t);
26 
27  Matrix<fvar<T>, Dynamic, 1> log_softmax_alpha(alpha.size());
28  for (int k = 0; k < alpha.size(); ++k) {
29  log_softmax_alpha(k).val_ = log_softmax_alpha_t(k);
30  log_softmax_alpha(k).d_ = 0;
31  }
32 
33  for (int m = 0; m < alpha.size(); ++m) {
34  T negative_alpha_m_d_times_softmax_alpha_t_m
35  = - alpha(m).d_ * softmax_alpha_t(m);
36  for (int k = 0; k < alpha.size(); ++k) {
37  if (m == k)
38  log_softmax_alpha(k).d_
39  += alpha(m).d_
40  + negative_alpha_m_d_times_softmax_alpha_t_m;
41  else
42  log_softmax_alpha(k).d_
43  += negative_alpha_m_d_times_softmax_alpha_t_m;
44  }
45  }
46 
47  return log_softmax_alpha;
48  }
49 
50  }
51 }
52 #endif
Eigen::Matrix< fvar< T >, Eigen::Dynamic, 1 > softmax(const Eigen::Matrix< fvar< T >, Eigen::Dynamic, 1 > &alpha)
Definition: softmax.hpp:14
Eigen::Matrix< fvar< T >, Eigen::Dynamic, 1 > log_softmax(const Eigen::Matrix< fvar< T >, Eigen::Dynamic, 1 > &alpha)
Definition: log_softmax.hpp:16

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