neural_network.h File Reference

Declares the NeuralNetwork class. More...

#include "layer.h"
#include "tensor_utilities.h"
#include "variable.h"
#include "forward_propagation.h"

Go to the source code of this file.

Classes
struct	opennn::__nv_bfloat16
struct	opennn::__half
struct	opennn::cudnnTensorStruct
class	opennn::opennn::Json
class	opennn::opennn::JsonDocument
class	opennn::opennn::JsonWriter
struct	opennn::opennn::TypeInfo< Type::FP32 >
struct	opennn::opennn::TypeInfo< Type::BF16 >
struct	opennn::opennn::TypeInfo< Type::INT8 >
class	opennn::opennn::Configuration
struct	opennn::opennn::Configuration::Resolved
struct	opennn::opennn::Shape
struct	opennn::opennn::Buffer
struct	opennn::opennn::TensorView
class	opennn::opennn::Backend
struct	opennn::opennn::ForwardPropagation
class	opennn::NeuralNetwork
	Stack of Layers forming a trainable model. More...

Namespaces
namespace	opennn
namespace	opennn::opennn

Macros
#define	EIGEN_MAX_ALIGN_BYTES   64
#define	EIGEN_NO_DEBUG
#define	NOMINMAX
#define	_SILENCE_CXX17_ITERATOR_BASE_CLASS_DEPRECATION_WARNING
#define	_CRT_SECURE_NO_WARNINGS
#define	EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
#define	_SILENCE_EXPERIMENTAL_FILESYSTEM_DEPRECATION_WARNING

Typedefs
using	opennn::cudaStream_t = void*
using	opennn::cudaEvent_t = void*
using	opennn::cublasHandle_t = void*
using	opennn::cublasLtHandle_t = void*
using	opennn::cudnnHandle_t = void*
using	opennn::cudnnTensorDescriptor_t = cudnnTensorStruct*
using	opennn::cudnnFilterDescriptor_t = void*
using	opennn::cudnnConvolutionDescriptor_t = void*
using	opennn::cudnnPoolingDescriptor_t = void*
using	opennn::cudnnActivationDescriptor_t = void*
using	opennn::cudnnDropoutDescriptor_t = void*
using	opennn::cudnnOpTensorDescriptor_t = void*
using	opennn::MatrixR = Matrix<float, Dynamic, Dynamic, Layout>
using	opennn::MatrixI = Matrix<Index, Dynamic, Dynamic, Layout>
using	opennn::MatrixB = Matrix<bool, Dynamic, Dynamic, Layout>
using	opennn::VectorR = Matrix<float, Dynamic, 1>
using	opennn::VectorI = Matrix<Index, Dynamic, 1>
using	opennn::VectorB = Matrix<bool, Dynamic, 1>
using	opennn::VectorMap = Map<VectorR, AlignedMax>
using	opennn::MatrixMap = Map<MatrixR, Layout | AlignedMax>
using	opennn::Tensor0 = Tensor<float, 0, Layout | AlignedMax>
using	opennn::Tensor2 = Tensor<float, 2, Layout | AlignedMax>
using	opennn::Tensor3 = Tensor<float, 3, Layout | AlignedMax>
using	opennn::Tensor4 = Tensor<float, 4, Layout | AlignedMax>
template<int Rank>
using	opennn::TensorR = Tensor<float, Rank, Layout | AlignedMax>
using	opennn::TensorMap2 = TensorMap<Tensor<float, 2, Layout | AlignedMax>, AlignedMax>
using	opennn::TensorMap3 = TensorMap<Tensor<float, 3, Layout | AlignedMax>, AlignedMax>
using	opennn::TensorMap4 = TensorMap<Tensor<float, 4, Layout | AlignedMax>, AlignedMax>
template<int Rank>
using	opennn::TensorMapR = TensorMap<Tensor<float, Rank, Layout | AlignedMax>, AlignedMax>
template<typename T, size_t N>
using	opennn::opennn::array = Eigen::array<T, N>

Enumerations
enum	opennn::cudaDataType_t {   opennn::CUDA_R_32F = 0 , opennn::CUDA_R_16F = 2 , opennn::CUDA_R_8I = 3 , opennn::CUDA_R_32I = 10 ,   opennn::CUDA_R_16BF = 14 }
enum	opennn::cublasComputeType_t { opennn::CUBLAS_COMPUTE_32F = 0 , opennn::CUBLAS_COMPUTE_32F_FAST_16BF = 65 , opennn::CUBLAS_COMPUTE_32F_FAST_TF32 = 68 }
enum	opennn::cublasOperation_t { opennn::CUBLAS_OP_N = 0 , opennn::CUBLAS_OP_T = 1 }
enum	opennn::cublasLtEpilogue_t { opennn::CUBLASLT_EPILOGUE_DEFAULT = 1 , opennn::CUBLASLT_EPILOGUE_BIAS = 4 , opennn::CUBLASLT_EPILOGUE_RELU_BIAS = 132 }
enum	opennn::cudnnDataType_t {   opennn::CUDNN_DATA_FLOAT = 0 , opennn::CUDNN_DATA_HALF = 2 , opennn::CUDNN_DATA_INT8 = 3 , opennn::CUDNN_DATA_INT32 = 4 ,   opennn::CUDNN_DATA_BFLOAT16 = 14 }
enum	opennn::cudnnActivationMode_t {   opennn::CUDNN_ACTIVATION_IDENTITY = 0 , opennn::CUDNN_ACTIVATION_SIGMOID = 1 , opennn::CUDNN_ACTIVATION_RELU = 2 , opennn::CUDNN_ACTIVATION_TANH = 3 ,   opennn::CUDNN_ACTIVATION_ELU = 4 }
enum	opennn::cudnnPoolingMode_t { opennn::CUDNN_POOLING_MAX = 0 }
enum	opennn::cudnnBatchNormMode_t { opennn::CUDNN_BATCHNORM_PER_ACTIVATION = 0 }
enum	opennn::cudnnConvolutionFwdAlgo_t { opennn::CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM = 0 }
enum	opennn::cudnnConvolutionBwdDataAlgo_t { opennn::CUDNN_CONVOLUTION_BWD_DATA_ALGO_0 = 0 }
enum	opennn::cudnnConvolutionBwdFilterAlgo_t { opennn::CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0 = 0 }
enum class	opennn::opennn::Device { opennn::opennn::Auto , opennn::opennn::CPU , opennn::opennn::CUDA }
enum class	opennn::opennn::Type { opennn::opennn::Auto , opennn::opennn::FP32 , opennn::opennn::BF16 , opennn::opennn::INT8 }

Functions
template<typename T>
ostream &	opennn::opennn::operator<< (ostream &os, const vector< T > &vec)
void	opennn::opennn::add_json_field (JsonWriter &writer, const std::string &name, const std::string &value)
void	opennn::opennn::write_json (JsonWriter &writer, std::initializer_list< std::pair< const char *, std::string > > props)
float	opennn::opennn::read_json_type (const Json *root, const std::string &field)
long	opennn::opennn::read_json_index (const Json *root, const std::string &field)
bool	opennn::opennn::read_json_bool (const Json *root, const std::string &field)
std::string	opennn::opennn::read_json_string (const Json *root, const std::string &field)
std::string	opennn::opennn::read_json_string_fallback (const Json *root, std::initializer_list< std::string > names)
const Json *	opennn::opennn::require_json_field (const Json *root, const std::string &field)
template<typename Func>
void	opennn::opennn::for_json_items (const Json *parent, const char *tag, long count, Func func)
JsonDocument	opennn::opennn::load_json_file (const std::filesystem::path &file_name)
const Json *	opennn::opennn::get_json_root (const JsonDocument &document, const std::string &tag)
template<Type... Supported, typename F>
void	opennn::opennn::visit_type (Type t, F &&f)
template<Type... Supported, typename F>
void	opennn::opennn::visit_type_pair (Type t_in, Type t_out, F &&f)
cudnnDataType_t	opennn::opennn::to_cudnn (Type type) noexcept
cudaDataType_t	opennn::opennn::to_cuda (Type type) noexcept
Index	opennn::opennn::type_bytes (Type type) noexcept
int	opennn::opennn::to_int (Index value)
float	opennn::opennn::to_type (Index value)
Index	opennn::opennn::align_up (Index value, Index alignment)
Index	opennn::opennn::get_aligned_size (Index size)
Index	opennn::opennn::get_aligned_bytes (Index n_bytes)
template<typename Container>
Index	opennn::opennn::ssize (const Container &container) noexcept
bool	opennn::opennn::is_aligned (const void *ptr)
Index	opennn::opennn::aligned_total_elements (const vector< Shape > &shapes)
Index	opennn::opennn::aligned_total_elements (const vector< vector< Shape > > &nested)
Index	opennn::opennn::aligned_total_bytes (const vector< Shape > &shapes, const vector< Type > &dtypes)
Index	opennn::opennn::aligned_total_bytes (const vector< vector< Shape > > &nested, const vector< vector< Type > > &dtypes)
Index	opennn::opennn::aligned_total_bytes (const vector< Shape > &shapes, Type dtype)
string	opennn::opennn::shape_to_string (const Shape &, const string &=" ")
Shape	opennn::opennn::string_to_shape (const string &, const string &=" ")
template<typename... Vs>
size_t	opennn::opennn::hash_combine (const Vs &... values)
ThreadPoolDevice &	opennn::opennn::get_device ()

Variables
constexpr float	opennn::opennn::EPSILON = numeric_limits<float>::epsilon()
constexpr float	opennn::opennn::MAX = numeric_limits<float>::max()
constexpr float	opennn::opennn::NEG_INFINITY = -numeric_limits<float>::infinity()
constexpr float	opennn::opennn::QUIET_NAN = numeric_limits<float>::quiet_NaN()
constexpr float	opennn::opennn::SOFTMAX_MASK_VALUE = float(-1e9f)
constexpr int	opennn::Layout = Eigen::RowMajor
static constexpr Index	opennn::opennn::ALIGN_BYTES = EIGEN_MAX_ALIGN_BYTES
static constexpr Index	opennn::opennn::ALIGN_ELEMENTS = ALIGN_BYTES / sizeof(float)
constexpr cudaDataType_t	opennn::opennn::CUDA_REDUCTION_DTYPE = CUDA_R_32F
constexpr cublasComputeType_t	opennn::opennn::CUBLAS_COMPUTE_DTYPE = CUBLAS_COMPUTE_32F_FAST_TF32

Detailed Description

Declares the NeuralNetwork class.

NeuralNetwork is the base class for every model in OpenNN. It owns an ordered stack of Layer instances, the parameter buffer they share, and the device/precision configuration used for forward and backward passes.

Macro Definition Documentation

_CRT_SECURE_NO_WARNINGS

#define _CRT_SECURE_NO_WARNINGS

_SILENCE_CXX17_ITERATOR_BASE_CLASS_DEPRECATION_WARNING

#define _SILENCE_CXX17_ITERATOR_BASE_CLASS_DEPRECATION_WARNING

_SILENCE_EXPERIMENTAL_FILESYSTEM_DEPRECATION_WARNING

#define _SILENCE_EXPERIMENTAL_FILESYSTEM_DEPRECATION_WARNING

EIGEN_MAX_ALIGN_BYTES

#define EIGEN_MAX_ALIGN_BYTES   64

EIGEN_NO_DEBUG

#define EIGEN_NO_DEBUG

EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS

#define EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS

NOMINMAX

#define NOMINMAX