Deep Learning Algorithm Implementations 1.0.0
C++ implementations of fundamental deep learning algorithms
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tensor.hpp File Reference
#include <initializer_list>
#include <iostream>
#include <tuple>
#include <vector>
#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>
#include <xtensor/core/xshape.hpp>
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Classes

class  utils::Tensor< T >
 

Namespaces

namespace  utils
 

Typedefs

Type Aliases
using utils::TensorF = Tensor< float >
 Single-precision floating point tensor.
 
using utils::TensorD = Tensor< double >
 Double-precision floating point tensor.
 
using utils::MatrixF = Tensor< float >
 Single-precision floating point matrix (backward compatibility)
 
using utils::MatrixD = Tensor< double >
 Double-precision floating point matrix (backward compatibility)
 

Functions

Non-member Functions
template<typename T >
std::ostream & utils::operator<< (std::ostream &os, const Tensor< T > &tensor)
 Output stream operator for tensor visualization.
 
template<typename T >
Tensor< T > utils::dot (const Tensor< T > &a, const Tensor< T > &b)
 Compute dot product of two tensors.
 
template<typename T >
utils::sum (const Tensor< T > &tensor)
 Calculate sum of all tensor elements.
 
template<typename T >
utils::mean (const Tensor< T > &tensor)
 Calculate mean of all tensor elements.