Class Hierarchy

Go to the graphical class hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:

[detail level 12]

C__half
Copennn::__half
C__nv_bfloat16
Copennn::__nv_bfloat16
Copennn::AugmentationSettings	Optional data-augmentation transforms applied at training time
Copennn::Backend
Copennn::opennn::Backend
Copennn::BackPropagation
Copennn::BackPropagationLM	Scratch state used by LevenbergMarquardtAlgorithm
Copennn::BackwardEdge
Copennn::Batch	Owns the host-side and (optional) device-side buffers for one mini-batch of dataset samples
Copennn::TestingAnalysis::BinaryClassificationRates	Sample indices grouped by binary-classification outcome
Copennn::BoxPlot
Copennn::Buffer
Copennn::opennn::Buffer
Copennn::ResponseOptimization::Condition
Copennn::Configuration
Copennn::opennn::Configuration
Copennn::Correlation
CcudnnTensorStruct
Copennn::cudnnTensorStruct
►Copennn::Dataset	Base data container with samples, variables and per-variable metadata
Copennn::ImageDataset	Dataset specialization for image data
Copennn::LanguageDataset	Dataset specialization for tokenized text
Copennn::TabularDataset
Copennn::TimeSeriesDataset	Dataset specialization for time series with explicit past / future windows
Copennn::Descriptives
Copennn::ResponseOptimization::Domain
Copennn::EnumMap< Enum >
Copennn::EpochStats	Aggregate metrics produced for a single training or evaluation epoch
Copennn::Loss::EvaluationResult	Output of calculate_error()
Copennn::ForwardPropagation
Copennn::opennn::ForwardPropagation
Copennn::TestingAnalysis::GoodnessOfFitAnalysis	Per-output regression goodness-of-fit summary
Copennn::Histogram
►Copennn::InputsSelection	Abstract base class for input feature selection methods
Copennn::GeneticAlgorithm	Genetic-algorithm based input feature selection
Copennn::GrowingInputs	Forward-selection of input features driven by feature-target correlation
Copennn::InputsSelectionResults	Outcome of an InputsSelection run
Copennn::Json
Copennn::opennn::Json
Copennn::JsonDocument
Copennn::opennn::JsonDocument
Copennn::JsonWriter
Copennn::opennn::JsonWriter
Copennn::KMeans
Copennn::TestingAnalysis::KolmogorovSmirnovResults	Output of perform_Kolmogorov_Smirnov_analysis()
►Copennn::Layer	Abstract base class for every layer in an OpenNN NeuralNetwork
Copennn::Addition	Elementwise tensor addition layer (residual / skip connections)
Copennn::Bounding	Per-feature output-clamping layer
Copennn::Convolutional	2D convolutional layer: y = activation(BN(conv(x, kernels) + bias))
Copennn::ConvolutionalRelu	2D convolution + ReLU fused into a single forward op on GPU
Copennn::Dense	Fully-connected layer: y = activation(BN(x * W + b)) with optional dropout
Copennn::DenseRelu	Dense + ReLU fused into a single forward op
Copennn::Embedding	Token-id-to-vector lookup layer used in language models
Copennn::Flatten	Reshape layer that collapses every input axis into a single feature axis
Copennn::MultiHeadAttention	Scaled dot-product attention with multiple heads and learned linear projections
Copennn::Normalization3d	Layer normalization across the embedding dimension of rank-2 inputs
Copennn::Pooling	2D pooling layer (max or average)
Copennn::Pooling3d	Sequence-pooling layer for rank-2 inputs (sequence_length, features)
Copennn::Recurrent	Plain (Elman-style) recurrent layer over fixed-length sequences
Copennn::Scaling	Per-feature input normalization layer
Copennn::Unscaling	Per-output inverse normalization layer
Copennn::Loss	Trainable loss function attached to a NeuralNetwork and a Dataset
Copennn::ModelExpression
Copennn::ModelSelection	Searches for the best generalizing architecture for a model
►Copennn::NeuralNetwork	Stack of Layers forming a trainable model
Copennn::ApproximationNetwork	Standard regression (function approximation) MLP
Copennn::AutoAssociationNetwork	Standard auto-encoder for outlier and novelty detection
Copennn::ClassificationNetwork	Standard tabular classification MLP
Copennn::ForecastingNetwork	Standard time-series forecasting MLP
Copennn::ImageClassificationNetwork	Standard convolutional image classifier
Copennn::SimpleResNet	Compact residual network for image classification
Copennn::TextClassificationNetwork	Standard text classification model
Copennn::Transformer	Encoder-decoder Transformer (Vaswani et al., 2017) for sequence-to-sequence modeling
Copennn::VGG16	VGG-16 architecture (Simonyan & Zisserman, 2014) for image classification
►Copennn::NeuronSelection	Abstract base class for hidden-layer-size selection methods
Copennn::GrowingNeurons	Forward-selection of hidden-layer size
Copennn::NeuronsSelectionResults	Outcome of a NeuronSelection run
Copennn::ResponseOptimization::Objectives
►Copennn::Operator
Copennn::Activation
Copennn::Add
Copennn::Attention
Copennn::BatchNorm
Copennn::Bound
Copennn::Combination
Copennn::Convolution
Copennn::Dropout
Copennn::EmbeddingLookup
Copennn::Flat
Copennn::LayerNorm
Copennn::MultiHeadProjection
Copennn::Pool
Copennn::Pool3d
Copennn::Scale
Copennn::Unscale
►Copennn::Optimizer	Abstract base class for every training algorithm in OpenNN
Copennn::AdaptiveMomentEstimation	Adam optimizer (Kingma & Ba, 2014)
Copennn::LevenbergMarquardtAlgorithm	Levenberg-Marquardt optimizer with adaptive damping
Copennn::QuasiNewtonMethod	BFGS quasi-Newton optimizer with line search
Copennn::StochasticGradientDescent	Mini-batch SGD with optional momentum, Nesterov acceleration and learning-rate decay
Copennn::OptimizerData	Per-optimizer scratch state shared across iterations
Copennn::Registry< T >
Copennn::Configuration::Resolved
Copennn::opennn::Configuration::Resolved
Copennn::ResponseOptimization
Copennn::TestingAnalysis::RocAnalysis	Output of perform_roc_analysis()
Copennn::profiler::ScopedTimer
Copennn::opennn::Shape
Copennn::Shape
Copennn::profiler::Stats
Copennn::opennn::TensorView
Copennn::TensorView
Copennn::TestingAnalysis	Computes diagnostic metrics for a trained network on testing data
Copennn::ThreadSafeQueue< T >
Copennn::TrainingResults	Per-epoch error history and final summary produced by Optimizer::train()
Copennn::TrainingStrategy	Coordinates the training of a NeuralNetwork on a Dataset
Copennn::opennn::TypeInfo< T >
Copennn::TypeInfo< T >
Copennn::opennn::TypeInfo< Type::BF16 >
Copennn::TypeInfo< Type::BF16 >
Copennn::opennn::TypeInfo< Type::FP32 >
Copennn::TypeInfo< Type::FP32 >
Copennn::opennn::TypeInfo< Type::INT8 >
Copennn::TypeInfo< Type::INT8 >
Copennn::Variable