Automatic Differentiation
 
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softmax.hpp
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1#ifndef STAN_MATH_OPENCL_PRIM_SOFTMAX_HPP
2#define STAN_MATH_OPENCL_PRIM_SOFTMAX_HPP
3#ifdef STAN_OPENCL
10
11namespace stan {
12namespace math {
13
21template <typename T,
22 require_all_kernel_expressions_and_none_scalar_t<T>* = nullptr>
23inline matrix_cl<double> softmax(const T& a) {
24 check_vector("softmax (OpenCL)", "a", a);
25 if (a.size() == 0) {
26 return a;
27 }
30 matrix_cl<double> a_max = max_2d(a);
31 theta = exp(a - from_matrix_cl(a_max).maxCoeff());
32 } else {
33 matrix_cl<double> a_eval;
35 results(a_eval, a_max) = expressions(a, max_2d(a));
36 theta = exp(a_eval - from_matrix_cl(a_max).maxCoeff());
37 }
38 return elt_divide(theta, sum(theta));
39}
40
41} // namespace math
42} // namespace stan
43
44#endif
45#endif
Represents an arithmetic matrix on the OpenCL device.
Definition matrix_cl.hpp:47
results_cl< T_results... > results(T_results &&... results)
Deduces types for constructing results_cl object.
elt_divide_< as_operation_cl_t< T_a >, as_operation_cl_t< T_b > > elt_divide(T_a &&a, T_b &&b)
expressions_cl< T_expressions... > expressions(T_expressions &&... expressions)
Deduces types for constructing expressions_cl object.
auto max_2d(T &&a)
Two dimensional max - reduction of a kernel generator expression.
auto from_matrix_cl(const T &src)
Copies the source matrix that is stored on the OpenCL device to the destination Eigen matrix.
Definition copy.hpp:61
auto softmax(const ColVec &alpha)
Definition softmax.hpp:16
void check_vector(const char *function, const char *name, const Mat &x)
Check the input is either a row vector or column vector or a matrix with a single row or column.
fvar< T > sum(const std::vector< fvar< T > > &m)
Return the sum of the entries of the specified standard vector.
Definition sum.hpp:22
fvar< T > exp(const fvar< T > &x)
Definition exp.hpp:13
The lgamma implementation in stan-math is based on either the reentrant safe lgamma_r implementation ...
Definition fvar.hpp:9