1#ifndef STAN_MATH_REV_FUN_SOFTMAX_HPP
2#define STAN_MATH_REV_FUN_SOFTMAX_HPP
23template <
typename T, require_rev_matrix_t<T>* =
nullptr>
25 auto x_arena =
to_arena(std::forward<T>(x));
28 if (x_arena.size() == 0) {
31 arena_t<return_t> res =
softmax(x_arena.val());
34 += res.val().array() * (res.adj().array() - res.val().dot(res.adj()));
46template <
typename T, require_std_vector_st<is_var, T>* =
nullptr>
48 return apply_vector_unary<T>::apply(std::forward<T>(x), [](
auto&& v) {
49 return softmax(std::forward<
decltype(v)>(v));
void reverse_pass_callback(F &&functor)
Puts a callback on the autodiff stack to be called in reverse pass.
auto softmax(T &&x)
Return the softmax of each vector in a container of fvar values.
arena_t< T > to_arena(const T &a)
Converts given argument into a type that either has any dynamic allocation on AD stack or schedules i...
typename plain_type< std::decay_t< T > >::type plain_type_t
std::conditional_t< is_any_var_matrix< ReturnType, Types... >::value, stan::math::var_value< stan::math::promote_scalar_t< double, plain_type_t< ReturnType > > >, stan::math::promote_scalar_t< stan::math::var_value< double >, plain_type_t< ReturnType > > > return_var_matrix_t
Given an Eigen type and several inputs, determine if a matrix should be var<Matrix> or Matrix<var>.
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...