Stack of Layers forming a trainable model. More...

#include <neural_network.h>

Inheritance diagram for opennn::NeuralNetwork:

Public Member Functions
	NeuralNetwork ()
	Default-constructs an empty network.

virtual	~NeuralNetwork ()=default
	Defaulted virtual destructor for safe polymorphic deletion.

	NeuralNetwork (const filesystem::path &file_name)
	Constructs a network by loading a serialized model from disk.

void	add_layer (unique_ptr< Layer > new_layer, const vector< Index > &input_indices=vector< Index >())
	Appends a layer to the stack.

const Configuration::Resolved &	get_config () const
	Returns the resolved device/precision configuration.

bool	is_gpu () const
	Reports whether the network is configured to run on CUDA.

bool	is_cpu () const
	Reports whether the network is configured to run on CPU.

Type	get_training_type () const
	Returns the precision used for training.

Type	get_inference_type () const
	Returns the precision used for inference.

vector< vector< Shape > >	get_parameter_shapes () const
	Returns the parameter shapes of every layer.

vector< vector< Shape > >	get_state_shapes () const
	Returns the persistent state shapes of every layer.

vector< vector< Shape > >	get_forward_shapes (Index batch_size) const
	Returns the forward-propagation buffer shapes for a given batch size.

vector< vector< Shape > >	get_backward_shapes (Index batch_size) const
	Returns the backward-propagation buffer shapes for a given batch size.

Index	get_states_size () const
	Returns the total size in floats of all persistent state buffers.

Index	get_forward_size (Index batch_size) const
	Returns the total size in floats of the forward-propagation workspace.

Index	get_backward_size (Index batch_size) const
	Returns the total size in floats of the backward-propagation workspace.

void	compile ()
	Allocates parameters and resolves layer-input indices.

bool	has (const string &layer_name) const
	Reports whether the network contains a layer of the given name.

bool	has (LayerType type) const
	Reports whether the network contains a layer of the given type.

bool	is_empty () const
	Reports whether the network has zero layers.

float *	get_parameters_data ()
	Returns the parameter buffer as a raw float pointer.

const float *	get_parameters_data () const
	Returns the parameter buffer as a raw float pointer (const overload).

Index	get_parameters_size () const
	Returns the parameter count in floats.

const vector< Variable > &	get_input_variables () const
	Returns the input variables describing each input feature.

const vector< string >	get_input_feature_names () const
	Returns the names of the input features.

const vector< Variable > &	get_output_variables () const
	Returns the output variables describing each output target.

const vector< string >	get_output_feature_names () const
	Returns the names of the output features.

const vector< unique_ptr< Layer > > &	get_layers () const
	Returns the layer stack.

const unique_ptr< Layer > &	get_layer (const Index i) const
	Returns the layer at a given index.

const unique_ptr< Layer > &	get_layer (const string &layer_name) const
	Returns the layer with a given name.

Index	get_layer_index (const string &layer_name) const
	Returns the index of the layer with a given name.

const vector< vector< Index > > &	get_layer_input_indices () const
	Returns the per-layer input-layer indices.

vector< vector< Index > >	get_layer_output_indices () const
	Returns the per-layer output-layer indices.

Layer *	get_first (const string &layer_name)
	Returns the first layer of a given type by name.

Layer *	get_first (LayerType type)
	Returns the first layer of a given type.

const Layer *	get_first (const string &layer_name) const
	Returns the first layer of a given type by name (const overload).

const Layer *	get_first (LayerType type) const
	Returns the first layer of a given type (const overload).

void	set_layers_number (const Index new_layers_number)
	Resizes the layer stack.

void	set_layer_input_indices (const vector< vector< Index > > &new_layer_input_indices)
	Replaces the entire layer-input wiring.

void	set_layer_input_indices (const Index layer_index, const vector< Index > &new_input_indices)
	Replaces the input wiring of a single layer by index.

void	set_layer_input_indices (const string &layer_name, const vector< string > &input_layer_names)
	Replaces the input wiring of a single layer by name.

void	set_layer_input_indices (const string &layer_name, initializer_list< string > input_layer_names)
	Replaces the input wiring of a single layer by name (initializer-list overload).

void	set_layer_input_indices (const string &layer_name, const string &input_layer_name)
	Sets a single source layer as input to a destination layer.

void	set_input_variables (const vector< Variable > &new_input_variables)
	Replaces the input variables.

void	set_output_variables (const vector< Variable > &new_output_variables)
	Replaces the output variables.

void	set_input_names (const vector< string > &new_input_names)
	Sets the names of the input features.

void	set_output_names (const vector< string > &new_output_names)
	Sets the names of the output features.

void	set_input_shape (const Shape &new_input_shape)
	Sets the input shape of the network.

void	set_default ()
	Resets non-architectural state to defaults.

Index	get_layers_number () const
	Returns the number of layers.

Index	get_layers_number (const string &layer_name) const
	Returns the number of layers of a given type by name.

Index	get_layers_number (LayerType type) const
	Returns the number of layers of a given type.

Index	get_first_trainable_layer_index () const
	Returns the index of the first trainable layer.

Index	get_last_trainable_layer_index () const
	Returns the index of the last trainable layer.

Index	get_inputs_number () const
	Returns the total number of input features.

Index	get_outputs_number () const
	Returns the total number of output features.

Shape	get_input_shape () const
	Returns the network's input shape.

Shape	get_output_shape () const
	Returns the network's output shape.

Activation::Function	get_output_activation () const
	Returns the activation function of the last layer.

Index	get_parameters_number () const
	Returns the total number of trainable parameters.

vector< Index >	get_layer_parameter_numbers () const
	Returns the parameter count of every layer.

void	set_parameters (const VectorR &new_parameters)
	Replaces all trainable parameters.

void	set_parameters_random ()
	Initializes parameters with uniform random values.

void	set_parameters_glorot ()
	Initializes parameters with Glorot (Xavier) uniform values.

MatrixR	calculate_outputs (const vector< TensorView > &inputs)
	Computes outputs from a list of input tensor views.

MatrixR	calculate_outputs (const MatrixR &inputs)
	Computes outputs for tabular inputs.

MatrixR	calculate_outputs (const Tensor3 &inputs)
	Computes outputs for rank-3 inputs (e.g. sequence data).

MatrixR	calculate_outputs (const Tensor4 &inputs)
	Computes outputs for rank-4 inputs (e.g. images).

MatrixR	calculate_directional_inputs (const Index direction, const VectorR &point, float minimum, float maximum, Index points_number=101) const
	Computes outputs along a directional sweep of one input.

Tensor3	calculate_outputs (const Tensor3 &inputs, const Tensor3 &context)
	Computes outputs from two rank-3 inputs (encoder/decoder pair).

Index	calculate_image_output (const filesystem::path &image_path)
	Runs the network on a single image and returns the predicted class.

MatrixR	calculate_text_outputs (const Tensor< string, 1 > &texts)
	Runs the network on a batch of strings and returns class outputs.

void	from_JSON (const JsonDocument &document)
	Restores the network from a JSON document.

void	to_JSON (JsonWriter &writer) const
	Serializes the network to JSON.

void	save (const filesystem::path &file_name) const
	Saves the full network (architecture + parameters) to a JSON file.

void	save_parameters (const filesystem::path &file_name) const
	Saves only the parameters in binary form.

void	load (const filesystem::path &file_name)
	Loads the full network (architecture + parameters) from a JSON file.

void	load_parameters_binary (const filesystem::path &file_name)
	Loads parameters from a binary file produced by save_parameters().

vector< string >	get_names_string () const
	Returns the names of every input and output feature.

void	save_outputs (MatrixR &outputs, const filesystem::path &file_name)
	Saves a tabular outputs tensor to a CSV file.

void	save_outputs (Tensor3 &outputs, const filesystem::path &file_name)
	Saves a rank-3 outputs tensor to a CSV file.

void	forward_propagate (const vector< TensorView > &inputs, ForwardPropagation &forward, bool is_training=false) const
	Runs the forward pass and writes intermediate activations into `forward`.

void	forward_propagate (const vector< TensorView > &inputs, const VectorR &parameters, ForwardPropagation &forward)
	Runs the forward pass with explicitly supplied parameters.

vector< string >	get_layer_labels () const
	Returns a label for every layer (name + key hyperparameters).

Protected Attributes
string	name = "neural_network"
	Network identifier; used as a JSON tag.

vector< Variable >	input_variables
	Description of every input feature (role, scaler, type).

vector< Variable >	output_variables
	Description of every output feature (role, scaler, type).

vector< unique_ptr< Layer > >	layers
	Owned layers in execution order.

vector< vector< Index > >	layer_input_indices
	Per-destination-layer list of source-layer indices.

Buffer	parameters
	Flat parameter buffer shared by all layers.

Buffer	parameters_bf16 {Device::CUDA}
	BF16 mirror of `parameters` for mixed-precision CUDA training.

vector< vector< vector< TensorView > > >	parameter_views
	Per-layer per-parameter-group views into `parameters`.

Buffer	states
	Flat persistent-state buffer shared by stateful layers (e.g. Recurrent).

Configuration::Resolved	config
	Resolved device/precision configuration applied at compile time.

Detailed Description

Stack of Layers forming a trainable model.

The class is designed to be subclassed; the standard architectures (ApproximationNetwork, ClassificationNetwork, ForecastingNetwork, ImageClassificationNetwork, etc.) preconfigure the layer stack for common tasks. Direct users can also call add_layer() and compile() to build custom topologies.

Holds a single parameter Buffer that all layers index into, plus a parallel states Buffer for layers that maintain hidden state. CUDA mirrors are available when compiled with OPENNN_HAS_CUDA.

Constructor & Destructor Documentation

◆ NeuralNetwork() [1/2]

opennn::NeuralNetwork::NeuralNetwork ( )

Default-constructs an empty network.

No layers, no input/output variables, default device/precision config.

◆ ~NeuralNetwork()

virtual opennn::NeuralNetwork::~NeuralNetwork ( )

virtualdefault

Defaulted virtual destructor for safe polymorphic deletion.

◆ NeuralNetwork() [2/2]

opennn::NeuralNetwork::NeuralNetwork ( const filesystem::path & file_name )

Constructs a network by loading a serialized model from disk.

Parameters

file_name Path to a JSON file produced by save().

Member Function Documentation

◆ add_layer()

void opennn::NeuralNetwork::add_layer	(	unique_ptr< Layer >	new_layer,
		const vector< Index > &	input_indices = vector< Index >() )

Appends a layer to the stack.

Parameters

new_layer	Owning pointer to the layer; ownership is transferred.
input_indices	Indices of the layers whose outputs feed this layer. Empty means "the previous layer".

◆ calculate_directional_inputs()

MatrixR opennn::NeuralNetwork::calculate_directional_inputs	(	const Index	direction,
		const VectorR &	point,
		float	minimum,
		float	maximum,
		Index	points_number = 101 ) const

Computes outputs along a directional sweep of one input.

Holds the other inputs at point and varies input direction across points_number values in [minimum, maximum].

Parameters

direction	Index of the input to sweep.
point	Reference point for all other inputs.
minimum	Minimum value for the swept input.
maximum	Maximum value for the swept input.
points_number	Number of evaluation points along the sweep.

Returns: Matrix [points_number x outputs] with the network response.

◆ calculate_image_output()

Index opennn::NeuralNetwork::calculate_image_output ( const filesystem::path & image_path )

Runs the network on a single image and returns the predicted class.

Parameters

image_path Path to the image file.

Returns: Predicted class index.

◆ calculate_outputs() [1/5]

MatrixR opennn::NeuralNetwork::calculate_outputs ( const MatrixR & inputs )

Computes outputs for tabular inputs.

Parameters

inputs Input matrix [samples x features].

Returns: Network outputs as a matrix [samples x outputs].

◆ calculate_outputs() [2/5]

MatrixR opennn::NeuralNetwork::calculate_outputs ( const Tensor3 & inputs )

Computes outputs for rank-3 inputs (e.g. sequence data).

Parameters

inputs Rank-3 input tensor.

Returns: Network outputs as a matrix [samples x outputs].

◆ calculate_outputs() [3/5]

Tensor3 opennn::NeuralNetwork::calculate_outputs	(	const Tensor3 &	inputs,
		const Tensor3 &	context )

Computes outputs from two rank-3 inputs (encoder/decoder pair).

Parameters

inputs	Rank-3 input tensor.
context	Rank-3 context tensor.

Returns: Rank-3 output tensor.

◆ calculate_outputs() [4/5]

MatrixR opennn::NeuralNetwork::calculate_outputs ( const Tensor4 & inputs )

Computes outputs for rank-4 inputs (e.g. images).

Parameters

inputs Rank-4 input tensor.

Returns: Network outputs as a matrix [samples x outputs].

◆ calculate_outputs() [5/5]

MatrixR opennn::NeuralNetwork::calculate_outputs ( const vector< TensorView > & inputs )

Computes outputs from a list of input tensor views.

Parameters

inputs Input tensor views indexed by input slot.

Returns: Network outputs as a matrix [samples x outputs].

◆ calculate_text_outputs()

MatrixR opennn::NeuralNetwork::calculate_text_outputs ( const Tensor< string, 1 > & texts )

Runs the network on a batch of strings and returns class outputs.

Parameters

texts One string per sample.

Returns: Output matrix [samples x outputs].

◆ compile()

void opennn::NeuralNetwork::compile ( )

Allocates parameters and resolves layer-input indices.

Should be called after the layer stack is fully assembled. Validates input/output shape consistency and sets up the shared parameter buffer.

◆ forward_propagate() [1/2]

void opennn::NeuralNetwork::forward_propagate	(	const vector< TensorView > &	inputs,
		const VectorR &	parameters,
		ForwardPropagation &	forward )

Runs the forward pass with explicitly supplied parameters.

Used by line-search style optimizers that probe along a direction.

Parameters

inputs	Input tensor views.
parameters	Parameter vector used for this evaluation.
forward	Forward-propagation workspace; populated in place.

◆ forward_propagate() [2/2]

void opennn::NeuralNetwork::forward_propagate	(	const vector< TensorView > &	inputs,
		ForwardPropagation &	forward,
		bool	is_training = false ) const

Runs the forward pass and writes intermediate activations into forward.

Parameters

inputs	Input tensor views.
forward	Forward-propagation workspace; populated in place.
is_training	Whether to enable training-time behavior (e.g. dropout).

◆ from_JSON()

void opennn::NeuralNetwork::from_JSON ( const JsonDocument & document )

Restores the network from a JSON document.

Parameters

document Parsed JSON produced by to_JSON().

◆ get_backward_shapes()

vector< vector< Shape > > opennn::NeuralNetwork::get_backward_shapes ( Index batch_size ) const

inline

Returns the backward-propagation buffer shapes for a given batch size.

Parameters

batch_size Number of samples per backward pass.

Returns: Outer vector indexed by layer, inner by buffer group.

◆ get_backward_size()

Index opennn::NeuralNetwork::get_backward_size ( Index batch_size ) const

inline

Returns the total size in floats of the backward-propagation workspace.

Parameters

batch_size Number of samples per backward pass.

Returns: Element count summed across layers, accounting for alignment.

◆ get_config()

const Configuration::Resolved & opennn::NeuralNetwork::get_config ( ) const

inline

Returns the resolved device/precision configuration.

Returns: Const reference to the configuration.

◆ get_first() [1/4]

Layer * opennn::NeuralNetwork::get_first ( const string & layer_name )

Returns the first layer of a given type by name.

Parameters

layer_name Layer type name (e.g. "Scaling", "Dense").

Returns: Pointer to the layer, or nullptr if none exists.

◆ get_first() [2/4]

const Layer * opennn::NeuralNetwork::get_first ( const string & layer_name ) const

Returns the first layer of a given type by name (const overload).

Parameters

layer_name Layer type name.

Returns: Const pointer to the layer, or nullptr.

◆ get_first() [3/4]

Layer * opennn::NeuralNetwork::get_first ( LayerType type )

Returns the first layer of a given type.

Parameters

type	Layer type enum.

Returns: Pointer to the layer, or nullptr if none exists.

◆ get_first() [4/4]

const Layer * opennn::NeuralNetwork::get_first ( LayerType type ) const

Returns the first layer of a given type (const overload).

Parameters

type	Layer type enum.

Returns: Const pointer to the layer, or nullptr.

◆ get_first_trainable_layer_index()

Index opennn::NeuralNetwork::get_first_trainable_layer_index ( ) const

Returns the index of the first trainable layer.

Returns: Layer index, or -1 if no trainable layer exists.

◆ get_forward_shapes()

vector< vector< Shape > > opennn::NeuralNetwork::get_forward_shapes ( Index batch_size ) const

inline

Returns the forward-propagation buffer shapes for a given batch size.

Parameters

batch_size Number of samples per forward pass.

Returns: Outer vector indexed by layer, inner by buffer group.

◆ get_forward_size()

Index opennn::NeuralNetwork::get_forward_size ( Index batch_size ) const

inline

Returns the total size in floats of the forward-propagation workspace.

Parameters

batch_size Number of samples per forward pass.

Returns: Element count summed across layers, accounting for alignment.

◆ get_inference_type()

Type opennn::NeuralNetwork::get_inference_type ( ) const

inline

Returns the precision used for inference.

Returns: Configured inference Type (e.g. FP32, BF16, FP16).

◆ get_input_feature_names()

const vector< string > opennn::NeuralNetwork::get_input_feature_names ( ) const

Returns the names of the input features.

Returns: Vector of input feature names, one per input variable.

◆ get_input_shape()

Shape opennn::NeuralNetwork::get_input_shape ( ) const

Returns the network's input shape.

Returns: Shape of the input tensor expected by the first layer.

◆ get_input_variables()

const vector< Variable > & opennn::NeuralNetwork::get_input_variables ( ) const

inline

Returns the input variables describing each input feature.

Returns: Const reference to the input variables vector.

◆ get_inputs_number()

Index opennn::NeuralNetwork::get_inputs_number ( ) const

Returns the total number of input features.

Returns: Inputs count.

◆ get_last_trainable_layer_index()

Index opennn::NeuralNetwork::get_last_trainable_layer_index ( ) const

Returns the index of the last trainable layer.

Returns: Layer index, or -1 if no trainable layer exists.

◆ get_layer() [1/2]

const unique_ptr< Layer > & opennn::NeuralNetwork::get_layer ( const Index i ) const

inline

Returns the layer at a given index.

Parameters

i	Zero-based index into the layer stack.

Returns: Const reference to the owned Layer pointer.

◆ get_layer() [2/2]

const unique_ptr< Layer > & opennn::NeuralNetwork::get_layer ( const string & layer_name ) const

Returns the layer with a given name.

Parameters

layer_name Layer name.

Returns: Const reference to the owned Layer pointer.

◆ get_layer_index()

Index opennn::NeuralNetwork::get_layer_index ( const string & layer_name ) const

Returns the index of the layer with a given name.

Parameters

layer_name Layer name.

Returns: Zero-based index into the layer stack.

◆ get_layer_input_indices()

const vector< vector< Index > > & opennn::NeuralNetwork::get_layer_input_indices ( ) const

inline

Returns the per-layer input-layer indices.

Returns: Outer vector indexed by destination layer, inner by source layer.

◆ get_layer_labels()

vector< string > opennn::NeuralNetwork::get_layer_labels ( ) const

Returns a label for every layer (name + key hyperparameters).

Returns: Layer labels suitable for printing.

◆ get_layer_output_indices()

vector< vector< Index > > opennn::NeuralNetwork::get_layer_output_indices ( ) const

Returns the per-layer output-layer indices.

Returns: Outer vector indexed by source layer, inner by destination layer.

◆ get_layer_parameter_numbers()

vector< Index > opennn::NeuralNetwork::get_layer_parameter_numbers ( ) const

Returns the parameter count of every layer.

Returns: Vector of parameter counts, one per layer.

◆ get_layers()

const vector< unique_ptr< Layer > > & opennn::NeuralNetwork::get_layers ( ) const

inline

Returns the layer stack.

Returns: Const reference to the vector of owned Layer pointers.

◆ get_layers_number() [1/3]

Index opennn::NeuralNetwork::get_layers_number ( ) const

inline

Returns the number of layers.

Returns: Layer count.

◆ get_layers_number() [2/3]

Index opennn::NeuralNetwork::get_layers_number ( const string & layer_name ) const

Returns the number of layers of a given type by name.

Parameters

layer_name Layer type name.

Returns: Number of matching layers.

◆ get_layers_number() [3/3]

Index opennn::NeuralNetwork::get_layers_number ( LayerType type ) const

Returns the number of layers of a given type.

Parameters

type	Layer type enum.

Returns: Number of matching layers.

◆ get_names_string()

vector< string > opennn::NeuralNetwork::get_names_string ( ) const

Returns the names of every input and output feature.

Returns: Concatenated vector of input feature names followed by output feature names.

◆ get_output_activation()

Activation::Function opennn::NeuralNetwork::get_output_activation ( ) const

Returns the activation function of the last layer.

Returns: Activation::Function enum value.

◆ get_output_feature_names()

const vector< string > opennn::NeuralNetwork::get_output_feature_names ( ) const

Returns the names of the output features.

Returns: Vector of output feature names, one per output variable.

◆ get_output_shape()

Shape opennn::NeuralNetwork::get_output_shape ( ) const

Returns the network's output shape.

Returns: Shape of the output tensor produced by the last layer.

◆ get_output_variables()

const vector< Variable > & opennn::NeuralNetwork::get_output_variables ( ) const

inline

Returns the output variables describing each output target.

Returns: Const reference to the output variables vector.

◆ get_outputs_number()

Index opennn::NeuralNetwork::get_outputs_number ( ) const

Returns the total number of output features.

Returns: Outputs count.

◆ get_parameter_shapes()

vector< vector< Shape > > opennn::NeuralNetwork::get_parameter_shapes ( ) const

inline

Returns the parameter shapes of every layer.

Returns: Outer vector indexed by layer, inner by parameter group.

◆ get_parameters_data() [1/2]

float * opennn::NeuralNetwork::get_parameters_data ( )

inline

Returns the parameter buffer as a raw float pointer.

Returns: Pointer to the underlying float storage.

◆ get_parameters_data() [2/2]

const float * opennn::NeuralNetwork::get_parameters_data ( ) const

inline

Returns the parameter buffer as a raw float pointer (const overload).

Returns: Const pointer to the underlying float storage.

◆ get_parameters_number()

Index opennn::NeuralNetwork::get_parameters_number ( ) const

Returns the total number of trainable parameters.

Returns: Parameter count summed across layers.

◆ get_parameters_size()

Index opennn::NeuralNetwork::get_parameters_size ( ) const

inline

Returns the parameter count in floats.

Returns: Number of float entries in the parameter buffer.

◆ get_state_shapes()

vector< vector< Shape > > opennn::NeuralNetwork::get_state_shapes ( ) const

inline

Returns the persistent state shapes of every layer.

Returns: Outer vector indexed by layer, inner by state group.

◆ get_states_size()

Index opennn::NeuralNetwork::get_states_size ( ) const

inline

Returns the total size in floats of all persistent state buffers.

Returns: Element count summed across layers, accounting for alignment.

◆ get_training_type()

Type opennn::NeuralNetwork::get_training_type ( ) const

inline

Returns the precision used for training.

Returns: Configured training Type (e.g. FP32, BF16).

◆ has() [1/2]

bool opennn::NeuralNetwork::has ( const string & layer_name ) const

Reports whether the network contains a layer of the given name.

Parameters

layer_name Layer type name (e.g. "Dense", "Recurrent").

Returns: true if any layer matches the name.

◆ has() [2/2]

bool opennn::NeuralNetwork::has ( LayerType type ) const

Reports whether the network contains a layer of the given type.

Parameters

type	Layer type enum.

Returns: true if any layer matches the type.

◆ is_cpu()

bool opennn::NeuralNetwork::is_cpu ( ) const

inline

Reports whether the network is configured to run on CPU.

Returns: true when the configured device is Device::CPU.

◆ is_empty()

bool opennn::NeuralNetwork::is_empty ( ) const

inline

Reports whether the network has zero layers.

Returns: true when the layer stack is empty.

◆ is_gpu()

bool opennn::NeuralNetwork::is_gpu ( ) const

inline

Reports whether the network is configured to run on CUDA.

Returns: true when the configured device is Device::CUDA.

◆ load()

void opennn::NeuralNetwork::load ( const filesystem::path & file_name )

Loads the full network (architecture + parameters) from a JSON file.

Parameters

file_name Source path.

◆ load_parameters_binary()

void opennn::NeuralNetwork::load_parameters_binary ( const filesystem::path & file_name )

Loads parameters from a binary file produced by save_parameters().

The architecture must already match the saved network.

Parameters

file_name Source path.

◆ save()

void opennn::NeuralNetwork::save ( const filesystem::path & file_name ) const

Saves the full network (architecture + parameters) to a JSON file.

Parameters

file_name Destination path.

◆ save_outputs() [1/2]

void opennn::NeuralNetwork::save_outputs	(	MatrixR &	outputs,
		const filesystem::path &	file_name )

Saves a tabular outputs tensor to a CSV file.

Parameters

outputs	Outputs matrix [samples x outputs].
file_name	Destination path.

◆ save_outputs() [2/2]

void opennn::NeuralNetwork::save_outputs	(	Tensor3 &	outputs,
		const filesystem::path &	file_name )

Saves a rank-3 outputs tensor to a CSV file.

Parameters

outputs	Rank-3 outputs tensor.
file_name	Destination path.

◆ save_parameters()

void opennn::NeuralNetwork::save_parameters ( const filesystem::path & file_name ) const

Saves only the parameters in binary form.

Parameters

file_name Destination path.

◆ set_default()

void opennn::NeuralNetwork::set_default ( )

Resets non-architectural state to defaults.

◆ set_input_names()

void opennn::NeuralNetwork::set_input_names ( const vector< string > & new_input_names )

Sets the names of the input features.

Parameters

new_input_names One entry per input variable.

◆ set_input_shape()

void opennn::NeuralNetwork::set_input_shape ( const Shape & new_input_shape )

Sets the input shape of the network.

Reshapes the first layer accordingly. Useful when reusing an architecture for a dataset with different feature dimensions.

Parameters

new_input_shape New input shape.

◆ set_input_variables()

void opennn::NeuralNetwork::set_input_variables ( const vector< Variable > & new_input_variables )

inline

Replaces the input variables.

Parameters

new_input_variables New input variables vector.

◆ set_layer_input_indices() [1/5]

void opennn::NeuralNetwork::set_layer_input_indices	(	const Index	layer_index,
		const vector< Index > &	new_input_indices )

inline

Replaces the input wiring of a single layer by index.

Parameters

layer_index	Index of the destination layer.
new_input_indices	Indices of source layers feeding it.

◆ set_layer_input_indices() [2/5]

void opennn::NeuralNetwork::set_layer_input_indices	(	const string &	layer_name,
		const string &	input_layer_name )

Sets a single source layer as input to a destination layer.

Parameters

layer_name	Name of the destination layer.
input_layer_name	Name of the source layer.

◆ set_layer_input_indices() [3/5]

void opennn::NeuralNetwork::set_layer_input_indices	(	const string &	layer_name,
		const vector< string > &	input_layer_names )

Replaces the input wiring of a single layer by name.

Parameters

layer_name	Name of the destination layer.
input_layer_names	Names of layers feeding it.

◆ set_layer_input_indices() [4/5]

void opennn::NeuralNetwork::set_layer_input_indices	(	const string &	layer_name,
		initializer_list< string >	input_layer_names )

Replaces the input wiring of a single layer by name (initializer-list overload).

Parameters

layer_name	Name of the destination layer.
input_layer_names	Names of layers feeding it.

◆ set_layer_input_indices() [5/5]

void opennn::NeuralNetwork::set_layer_input_indices ( const vector< vector< Index > > & new_layer_input_indices )

inline

Replaces the entire layer-input wiring.

Parameters

new_layer_input_indices Outer vector indexed by destination layer.

◆ set_layers_number()

void opennn::NeuralNetwork::set_layers_number ( const Index new_layers_number )

inline

Resizes the layer stack.

Parameters

new_layers_number New layer count; existing entries are preserved.

◆ set_output_names()

void opennn::NeuralNetwork::set_output_names ( const vector< string > & new_output_names )

Sets the names of the output features.

Parameters

new_output_names One entry per output variable.

◆ set_output_variables()

void opennn::NeuralNetwork::set_output_variables ( const vector< Variable > & new_output_variables )

inline

Replaces the output variables.

Parameters

new_output_variables New output variables vector.

◆ set_parameters()

void opennn::NeuralNetwork::set_parameters ( const VectorR & new_parameters )

Replaces all trainable parameters.

Parameters

new_parameters Flat vector with as many entries as get_parameters_number().

◆ set_parameters_glorot()

void opennn::NeuralNetwork::set_parameters_glorot ( )

Initializes parameters with Glorot (Xavier) uniform values.

◆ set_parameters_random()

void opennn::NeuralNetwork::set_parameters_random ( )

Initializes parameters with uniform random values.

◆ to_JSON()

void opennn::NeuralNetwork::to_JSON ( JsonWriter & writer ) const

Serializes the network to JSON.

Parameters

writer JSON writer that receives the network tree.

Member Data Documentation

◆ config

Configuration::Resolved opennn::NeuralNetwork::config

protected

Resolved device/precision configuration applied at compile time.

◆ input_variables

vector<Variable> opennn::NeuralNetwork::input_variables

protected

Description of every input feature (role, scaler, type).

◆ layer_input_indices

vector<vector<Index> > opennn::NeuralNetwork::layer_input_indices

protected

Per-destination-layer list of source-layer indices.

◆ layers

vector<unique_ptr<Layer> > opennn::NeuralNetwork::layers

protected

Owned layers in execution order.

◆ name

string opennn::NeuralNetwork::name = "neural_network"

protected

Network identifier; used as a JSON tag.

◆ output_variables

vector<Variable> opennn::NeuralNetwork::output_variables

protected

Description of every output feature (role, scaler, type).

◆ parameter_views

vector<vector<vector<TensorView> > > opennn::NeuralNetwork::parameter_views

protected

Per-layer per-parameter-group views into parameters.

◆ parameters

Buffer opennn::NeuralNetwork::parameters

protected

Flat parameter buffer shared by all layers.

◆ parameters_bf16

Buffer opennn::NeuralNetwork::parameters_bf16 {Device::CUDA}

protected

BF16 mirror of parameters for mixed-precision CUDA training.

◆ states

Buffer opennn::NeuralNetwork::states

protected

Flat persistent-state buffer shared by stateful layers (e.g. Recurrent).

Public Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ NeuralNetwork() [1/2]

◆ ~NeuralNetwork()

◆ NeuralNetwork() [2/2]

Member Function Documentation

◆ add_layer()

◆ calculate_directional_inputs()

◆ calculate_image_output()

◆ calculate_outputs() [1/5]

◆ calculate_outputs() [2/5]

◆ calculate_outputs() [3/5]

◆ calculate_outputs() [4/5]

◆ calculate_outputs() [5/5]

◆ calculate_text_outputs()

◆ compile()

◆ forward_propagate() [1/2]

◆ forward_propagate() [2/2]

◆ from_JSON()

◆ get_backward_shapes()

◆ get_backward_size()

◆ get_config()

◆ get_first() [1/4]

◆ get_first() [2/4]

◆ get_first() [3/4]

◆ get_first() [4/4]

◆ get_first_trainable_layer_index()

◆ get_forward_shapes()

◆ get_forward_size()

◆ get_inference_type()

◆ get_input_feature_names()

◆ get_input_shape()

◆ get_input_variables()

◆ get_inputs_number()

◆ get_last_trainable_layer_index()

◆ get_layer() [1/2]

◆ get_layer() [2/2]

◆ get_layer_index()

◆ get_layer_input_indices()

◆ get_layer_labels()

◆ get_layer_output_indices()

◆ get_layer_parameter_numbers()

◆ get_layers()

◆ get_layers_number() [1/3]

◆ get_layers_number() [2/3]

◆ get_layers_number() [3/3]

◆ get_names_string()

◆ get_output_activation()

◆ get_output_feature_names()

◆ get_output_shape()

◆ get_output_variables()

◆ get_outputs_number()

◆ get_parameter_shapes()

◆ get_parameters_data() [1/2]

◆ get_parameters_data() [2/2]

◆ get_parameters_number()

◆ get_parameters_size()

◆ get_state_shapes()

◆ get_states_size()

◆ get_training_type()

◆ has() [1/2]

◆ has() [2/2]

◆ is_cpu()

◆ is_empty()

◆ is_gpu()

◆ load()

◆ load_parameters_binary()

◆ save()

◆ save_outputs() [1/2]

◆ save_outputs() [2/2]

◆ save_parameters()

◆ set_default()

◆ set_input_names()

◆ set_input_shape()

◆ set_input_variables()

◆ set_layer_input_indices() [1/5]

◆ set_layer_input_indices() [2/5]

◆ set_layer_input_indices() [3/5]