Deep Learning Algorithm Implementations 1.0.0
C++ implementations of fundamental deep learning algorithms
|
Matrix utility class for deep learning operations. More...
#include <initializer_list>
#include <iostream>
#include <tuple>
#include <xtensor/containers/xarray.hpp>
#include <xtensor/core/xmath.hpp>
#include <xtensor/generators/xrandom.hpp>
#include <xtensor/io/xio.hpp>
#include <xtensor/reducers/xreducer.hpp>
#include <xtensor/views/xview.hpp>
Go to the source code of this file.
Classes | |
class | utils::Matrix< T > |
Namespaces | |
namespace | utils |
Typedefs | |
Type Aliases | |
using | utils::MatrixF = Matrix< float > |
Single-precision floating point matrix. | |
using | utils::MatrixD = Matrix< double > |
Double-precision floating point matrix. | |
Functions | |
Non-member Functions | |
template<typename T > | |
std::ostream & | utils::operator<< (std::ostream &os, const Matrix< T > &matrix) |
Output stream operator for matrix visualization. | |
template<typename T > | |
Matrix< T > | utils::dot (const Matrix< T > &a, const Matrix< T > &b) |
Compute dot product of two matrices. | |
template<typename T > | |
T | utils::sum (const Matrix< T > &matrix) |
Calculate sum of all matrix elements. | |
template<typename T > | |
T | utils::mean (const Matrix< T > &matrix) |
Calculate mean of all matrix elements. | |