Container of layers forming a feed-forward neural network, with parameter storage and I/O. More...

#include <neural_network.h>

Inheritance diagram for opennn::NeuralNetwork:

Public Member Functions
	NeuralNetwork ()
	Constructs an empty neural network.

virtual	~NeuralNetwork ()=default

	NeuralNetwork (const filesystem::path &)
	Constructs a neural network and loads its definition from a JSON file.

void	add_layer (unique_ptr< Layer >, const vector< Index > &={})
	Appends a layer to the network.

const Configuration::Resolved &	get_config () const

bool	is_gpu () const

bool	is_cpu () const

Type	get_training_type () const

Type	get_inference_type () const

vector< vector< TensorSpec > >	get_parameter_specs () const
	Returns the tensor specs of trainable parameters for every layer.

vector< vector< TensorSpec > >	get_state_specs () const
	Returns the tensor specs of persistent layer state (e.g. running statistics).

vector< vector< TensorSpec > >	get_forward_specs (Index b) const
	Returns the tensor specs of the forward-propagation workspace for each layer.

vector< vector< TensorSpec > >	get_backward_specs (Index b) const
	Returns the tensor specs of the back-propagation workspace for each layer.

Index	get_states_size () const
	Returns the total byte size required to hold all persistent layer states.

void	compile ()
	Allocates buffers, resolves devices, and wires layer/operator views; call once after all layers are added.

bool	has (const string &) const
	Returns whether the network contains a layer with the given label.

bool	has (LayerType) const
	Returns whether the network contains at least one layer of the given type.

bool	is_empty () const

float *	get_parameters_data ()

const float *	get_parameters_data () const

Index	get_parameters_size () const

const vector< Variable > &	get_input_variables () const

vector< string >	get_input_feature_names () const
	Returns the flat list of input feature names (expanding categorical variables).

const vector< Variable > &	get_output_variables () const

vector< string >	get_output_feature_names () const
	Returns the flat list of output feature names (expanding categorical variables).

const vector< unique_ptr< Layer > > &	get_layers () const

const unique_ptr< Layer > &	get_layer (const Index i) const

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

Index	get_layer_index (const string &) const
	Returns the index of the layer with the given label, or -1 if not found.

const vector< vector< Index > > &	get_source_layers () const

vector< vector< Index > >	get_consumer_layers () const
	Returns the inverse adjacency: for each layer, the indices of layers that consume its output.

Layer *	get_first (const string &)
	Returns the first layer matching the given label, or nullptr if not found.

Layer *	get_first (LayerType)
	Returns the first layer of the given type, or nullptr if not found.

const Layer *	get_first (const string &) const
	Returns the first layer matching the given label, or nullptr if not found.

const Layer *	get_first (LayerType) const
	Returns the first layer of the given type, or nullptr if not found.

void	set_source_layers (const vector< vector< Index > > &new_source_layers)
	Replaces the layer connectivity graph.

void	set_source_layers (const Index layer_index, const vector< Index > &new_sources)
	Replaces the source layers for one specific layer.

void	set_source_layers (const string &, const vector< string > &)
	Sets the source layers of a layer using labels for identification.

void	set_source_layers (const string &, initializer_list< string >)
	Sets the source layers of a layer using labels for identification.

void	set_source_layers (const string &, const string &)
	Convenience overload for a single source layer.

void	set_input_variables (const vector< Variable > &new_input_variables)

void	set_output_variables (const vector< Variable > &new_output_variables)

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

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

void	set_input_shape (const Shape &)
	Sets the shape of the input of the first layer and propagates it through the graph.

void	clear ()
	Removes all layers and resets the network to an empty state.

Index	get_layers_number () const

Index	get_layers_number (const string &) const
	Returns the number of layers whose label contains the given substring.

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

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

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

Index	get_inputs_number () const
	Returns the number of input features expected by the first layer.

Index	get_outputs_number () const
	Returns the number of output features produced by the last layer.

Shape	get_input_shape () const
	Returns the shape of the input of the first layer.

Shape	get_output_shape () const
	Returns the shape of the output of the last layer.

ActivationOp::Function	get_output_activation () const
	Returns the activation function of the output layer.

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

void	set_parameters (const VectorR &new_parameters)
	Copies the contents of `new_parameters` into the network's parameter buffer.

void	set_parameters_random ()
	Initializes every parameter with random values.

void	set_parameters_glorot ()
	Initializes every parameter using Glorot (Xavier) initialization.

void	link_parameters ()
	Wires the contiguous parameter buffer to per-layer / per-operator views.

void	link_states ()
	Wires the contiguous state buffer to per-layer / per-operator views.

MatrixR	calculate_outputs (const vector< TensorView > &)
	Computes outputs for the given input tensor views.

MatrixR	calculate_outputs (const MatrixR &)
	Computes outputs for a 2D input matrix.

MatrixR	calculate_outputs (const Tensor3 &)
	Computes outputs for a 3D input tensor.

MatrixR	calculate_outputs (const Tensor4 &)
	Computes outputs for a 4D input tensor.

MatrixR	calculate_directional_inputs (const Index, const VectorR &, float, float, Index=101) const
	Generates samples by sweeping one input dimension across a range while keeping the others fixed.

Tensor3	calculate_outputs (const Tensor3 &, const Tensor3 &)
	Computes outputs for an encoder/decoder model.

Index	calculate_image_output (const filesystem::path &)
	Reads an image file and returns the predicted class index.

MatrixR	calculate_text_outputs (const Tensor< string, 1 > &)
	Tokenizes the given strings and returns the network's outputs.

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

void	to_JSON (JsonWriter &) const
	Serializes the network architecture and parameters to a JSON writer.

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

void	save_parameters (const filesystem::path &) const
	Saves only the parameter values to a JSON file.

void	save_parameters_binary (const filesystem::path &) const
	Saves only the parameter values to a binary file.

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

void	load_parameters_binary (const filesystem::path &)
	Loads parameter values from a binary file produced by save_parameters_binary().

vector< string >	get_names_string () const
	Returns the labels of all layers as a vector of strings.

void	save_outputs (MatrixR &, const filesystem::path &)
	Writes the output matrix to a CSV file.

void	save_outputs (Tensor3 &, const filesystem::path &)
	Writes the 3D output tensor to a CSV file.

void	forward_propagate (const vector< TensorView > &, ForwardPropagation &, bool=false) const
	Runs a forward pass over all layers.

void	forward_propagate (const vector< TensorView > &, ForwardPropagation &, bool is_training, Index first_layer_index, Index last_layer_index) const
	Runs a forward pass over a contiguous sub-range of layers.

void	forward_propagate (const vector< TensorView > &, const VectorR &, ForwardPropagation &)
	Runs a forward pass after temporarily overwriting the parameter buffer.

vector< string >	get_layer_labels () const
	Returns the labels of all layers in order.

Protected Attributes
vector< Variable >	input_variables

vector< Variable >	output_variables

vector< unique_ptr< Layer > >	layers

vector< vector< Index > >	source_layers

Buffer	parameters

Buffer	parameters_bf16 {Device::CUDA}

Buffer	states

Configuration::Resolved	config

Index	first_trainable_cache_ = -1

Index	last_trainable_cache_ = -1

Detailed Description

Container of layers forming a feed-forward neural network, with parameter storage and I/O.

Constructor & Destructor Documentation

◆ NeuralNetwork() [1/2]

opennn::NeuralNetwork::NeuralNetwork ( )

Constructs an empty neural network.

◆ ~NeuralNetwork()

virtual opennn::NeuralNetwork::~NeuralNetwork ( )

virtualdefault

◆ NeuralNetwork() [2/2]

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

Constructs a neural network and loads its definition from a JSON file.

Parameters

path	Path to the JSON file describing the network.

Member Function Documentation

◆ add_layer()

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

Appends a layer to the network.

Parameters

layer	Owning pointer to the layer; the network takes ownership.
source_indices	Indices of layers feeding into this one (empty means the previous layer).

◆ calculate_directional_inputs()

MatrixR opennn::NeuralNetwork::calculate_directional_inputs	(	const Index	,
		const VectorR &	,
		float	,
		float	,
		Index	= 101 ) const

nodiscard

Generates samples by sweeping one input dimension across a range while keeping the others fixed.

Parameters

direction	Index of the input to vary.
point	Baseline values for the remaining inputs.
minimum	Lower bound of the sweep range.
maximum	Upper bound of the sweep range.
points_number	Number of points sampled in the range.

Returns: Matrix with one row per sampled point.

◆ calculate_image_output()

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

nodiscard

Reads an image file and returns the predicted class index.

◆ calculate_outputs() [1/5]

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

nodiscard

Computes outputs for a 2D input matrix.

◆ calculate_outputs() [2/5]

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

nodiscard

Computes outputs for a 3D input tensor.

◆ calculate_outputs() [3/5]

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

nodiscard

Computes outputs for an encoder/decoder model.

Parameters

encoder_input	Encoder side input tensor.
decoder_input	Decoder side input tensor.

◆ calculate_outputs() [4/5]

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

nodiscard

Computes outputs for a 4D input tensor.

◆ calculate_outputs() [5/5]

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

nodiscard

Computes outputs for the given input tensor views.

Parameters

inputs Tensor views of the inputs (one per input variable).

Returns: Matrix of outputs with one row per sample.

◆ calculate_text_outputs()

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

nodiscard

Tokenizes the given strings and returns the network's outputs.

◆ clear()

void opennn::NeuralNetwork::clear ( )

Removes all layers and resets the network to an empty state.

◆ compile()

void opennn::NeuralNetwork::compile ( )

Allocates buffers, resolves devices, and wires layer/operator views; call once after all layers are added.

◆ forward_propagate() [1/3]

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

Runs a forward pass after temporarily overwriting the parameter buffer.

Parameters

inputs	Tensor views of the inputs.
parameters	Replacement parameter values used for this pass.
forward_propagation	Workspace receiving per-layer activations.

◆ forward_propagate() [2/3]

void opennn::NeuralNetwork::forward_propagate	(	const vector< TensorView > &	,
		ForwardPropagation &	,
		bool	is_training,
		Index	first_layer_index,
		Index	last_layer_index ) const

Runs a forward pass over a contiguous sub-range of layers.

Parameters

inputs	Tensor views of the inputs.
forward_propagation	Workspace receiving per-layer activations.
is_training	Enables training-only behavior.
first_layer_index	First layer index (inclusive) to evaluate.
last_layer_index	Last layer index (inclusive) to evaluate.

◆ forward_propagate() [3/3]

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

Runs a forward pass over all layers.

Parameters

inputs	Tensor views of the inputs (one per input variable).
forward_propagation	Workspace receiving per-layer activations.
is_training	If true, enables training-only behavior (dropout, batch-norm stats).

◆ from_JSON()

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

Restores the network architecture and parameters from a JSON document.

◆ get_backward_specs()

vector< vector< TensorSpec > > opennn::NeuralNetwork::get_backward_specs ( Index b ) const

inlinenodiscard

Returns the tensor specs of the back-propagation workspace for each layer.

Parameters

b	Batch size used to size the per-layer gradient buffers.

◆ get_config()

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

inlinenodiscard

◆ get_consumer_layers()

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

nodiscard

Returns the inverse adjacency: for each layer, the indices of layers that consume its output.

◆ get_first() [1/4]

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

nodiscard

Returns the first layer matching the given label, or nullptr if not found.

◆ get_first() [2/4]

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

nodiscard

Returns the first layer matching the given label, or nullptr if not found.

◆ get_first() [3/4]

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

nodiscard

Returns the first layer of the given type, or nullptr if not found.

◆ get_first() [4/4]

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

nodiscard

Returns the first layer of the given type, or nullptr if not found.

◆ get_first_trainable_layer_index()

Index opennn::NeuralNetwork::get_first_trainable_layer_index ( ) const

nodiscard

Returns the index of the first trainable layer (cached).

◆ get_forward_specs()

vector< vector< TensorSpec > > opennn::NeuralNetwork::get_forward_specs ( Index b ) const

inlinenodiscard

Returns the tensor specs of the forward-propagation workspace for each layer.

Parameters

b	Batch size used to size the per-layer activations.

◆ get_inference_type()

Type opennn::NeuralNetwork::get_inference_type ( ) const

inlinenodiscard

◆ get_input_feature_names()

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

nodiscard

Returns the flat list of input feature names (expanding categorical variables).

◆ get_input_shape()

Shape opennn::NeuralNetwork::get_input_shape ( ) const

nodiscard

Returns the shape of the input of the first layer.

◆ get_input_variables()

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

inlinenodiscard

◆ get_inputs_number()

Index opennn::NeuralNetwork::get_inputs_number ( ) const

nodiscard

Returns the number of input features expected by the first layer.

◆ get_last_trainable_layer_index()

Index opennn::NeuralNetwork::get_last_trainable_layer_index ( ) const

nodiscard

Returns the index of the last trainable layer (cached).

◆ get_layer() [1/2]

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

inlinenodiscard

◆ get_layer() [2/2]

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

nodiscard

Returns the layer with the given label.

Parameters

label Label assigned to the layer (e.g. via Layer::set_label).

◆ get_layer_index()

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

nodiscard

Returns the index of the layer with the given label, or -1 if not found.

◆ get_layer_labels()

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

nodiscard

Returns the labels of all layers in order.

◆ get_layers()

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

inlinenodiscard

◆ get_layers_number() [1/3]

Index opennn::NeuralNetwork::get_layers_number ( ) const

inlinenodiscard

◆ get_layers_number() [2/3]

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

nodiscard

Returns the number of layers whose label contains the given substring.

◆ get_layers_number() [3/3]

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

nodiscard

Returns the number of layers of the given type.

◆ get_names_string()

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

nodiscard

Returns the labels of all layers as a vector of strings.

◆ get_output_activation()

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

nodiscard

Returns the activation function of the output layer.

◆ get_output_feature_names()

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

nodiscard

Returns the flat list of output feature names (expanding categorical variables).

◆ get_output_shape()

Shape opennn::NeuralNetwork::get_output_shape ( ) const

nodiscard

Returns the shape of the output of the last layer.

◆ get_output_variables()

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

inlinenodiscard

◆ get_outputs_number()

Index opennn::NeuralNetwork::get_outputs_number ( ) const

nodiscard

Returns the number of output features produced by the last layer.

◆ get_parameter_specs()

vector< vector< TensorSpec > > opennn::NeuralNetwork::get_parameter_specs ( ) const

inlinenodiscard

Returns the tensor specs of trainable parameters for every layer.

◆ get_parameters_data() [1/2]

float * opennn::NeuralNetwork::get_parameters_data ( )

inlinenodiscard

◆ get_parameters_data() [2/2]

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

inlinenodiscard

◆ get_parameters_number()

Index opennn::NeuralNetwork::get_parameters_number ( ) const

nodiscard

Returns the total number of trainable parameters across all layers.

◆ get_parameters_size()

Index opennn::NeuralNetwork::get_parameters_size ( ) const

inlinenodiscard

◆ get_source_layers()

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

inlinenodiscard

◆ get_state_specs()

vector< vector< TensorSpec > > opennn::NeuralNetwork::get_state_specs ( ) const

inlinenodiscard

Returns the tensor specs of persistent layer state (e.g. running statistics).

◆ get_states_size()

Index opennn::NeuralNetwork::get_states_size ( ) const

inlinenodiscard

Returns the total byte size required to hold all persistent layer states.

◆ get_training_type()

Type opennn::NeuralNetwork::get_training_type ( ) const

inlinenodiscard

◆ has() [1/2]

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

nodiscard

Returns whether the network contains a layer with the given label.

◆ has() [2/2]

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

nodiscard

Returns whether the network contains at least one layer of the given type.

◆ is_cpu()

bool opennn::NeuralNetwork::is_cpu ( ) const

inlinenodiscard

◆ is_empty()

bool opennn::NeuralNetwork::is_empty ( ) const

inlinenodiscard

◆ is_gpu()

bool opennn::NeuralNetwork::is_gpu ( ) const

inlinenodiscard

◆ link_parameters()

void opennn::NeuralNetwork::link_parameters ( )

Wires the contiguous parameter buffer to per-layer / per-operator views.

◆ link_states()

void opennn::NeuralNetwork::link_states ( )

Wires the contiguous state buffer to per-layer / per-operator views.

◆ load()

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

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

◆ load_parameters_binary()

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

Loads parameter values from a binary file produced by save_parameters_binary().

◆ save()

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

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

◆ save_outputs() [1/2]

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

Writes the output matrix to a CSV file.

◆ save_outputs() [2/2]

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

Writes the 3D output tensor to a CSV file.

◆ save_parameters()

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

Saves only the parameter values to a JSON file.

◆ save_parameters_binary()

void opennn::NeuralNetwork::save_parameters_binary ( const filesystem::path & ) const

Saves only the parameter values to a binary file.

◆ set_input_names()

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

Sets the names of every input feature.

◆ set_input_shape()

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

Sets the shape of the input of the first layer and propagates it through the graph.

◆ set_input_variables()

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

inline

◆ set_output_names()

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

Sets the names of every output feature.

◆ set_output_variables()

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

inline

◆ set_parameters()

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

Copies the contents of new_parameters into the network's parameter buffer.

◆ set_parameters_glorot()

void opennn::NeuralNetwork::set_parameters_glorot ( )

Initializes every parameter using Glorot (Xavier) initialization.

◆ set_parameters_random()

void opennn::NeuralNetwork::set_parameters_random ( )

Initializes every parameter with random values.

◆ set_source_layers() [1/5]

void opennn::NeuralNetwork::set_source_layers	(	const Index	layer_index,
		const vector< Index > &	new_sources )

inline

Replaces the source layers for one specific layer.

◆ set_source_layers() [2/5]

void opennn::NeuralNetwork::set_source_layers	(	const string &	,
		const string &	)

Convenience overload for a single source layer.

Parameters

target	Label of the destination layer.
source	Label of the source layer feeding into the target.

◆ set_source_layers() [3/5]

void opennn::NeuralNetwork::set_source_layers	(	const string &	,
		const vector< string > &	)

Sets the source layers of a layer using labels for identification.

Parameters

target	Label of the destination layer.
sources	Labels of the source layers feeding into the target.

◆ set_source_layers() [4/5]

void opennn::NeuralNetwork::set_source_layers	(	const string &	,
		initializer_list< string >	)

Sets the source layers of a layer using labels for identification.

Parameters

target	Label of the destination layer.
sources	Labels of the source layers feeding into the target.

◆ set_source_layers() [5/5]

void opennn::NeuralNetwork::set_source_layers ( const vector< vector< Index > > & new_source_layers )

inline

Replaces the layer connectivity graph.

Parameters

new_source_layers For each layer index, the indices of its source layers.

◆ to_JSON()

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

Serializes the network architecture and parameters to a JSON writer.

Member Data Documentation

◆ config

Configuration::Resolved opennn::NeuralNetwork::config

protected

◆ first_trainable_cache_

Index opennn::NeuralNetwork::first_trainable_cache_ = -1

mutableprotected

◆ input_variables

vector<Variable> opennn::NeuralNetwork::input_variables

protected

◆ last_trainable_cache_

Index opennn::NeuralNetwork::last_trainable_cache_ = -1

mutableprotected

◆ layers

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

protected

◆ output_variables

vector<Variable> opennn::NeuralNetwork::output_variables

protected

◆ parameters

Buffer opennn::NeuralNetwork::parameters

protected

◆ parameters_bf16

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

protected

◆ source_layers

vector<vector<Index> > opennn::NeuralNetwork::source_layers

protected

◆ states

Buffer opennn::NeuralNetwork::states

protected

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()

◆ clear()

◆ compile()

◆ forward_propagate() [1/3]

◆ forward_propagate() [2/3]

◆ forward_propagate() [3/3]

◆ from_JSON()

◆ get_backward_specs()

◆ get_config()

◆ get_consumer_layers()

◆ get_first() [1/4]

◆ get_first() [2/4]

◆ get_first() [3/4]

◆ get_first() [4/4]

◆ get_first_trainable_layer_index()

◆ get_forward_specs()

◆ 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_labels()

◆ 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_specs()

◆ get_parameters_data() [1/2]

◆ get_parameters_data() [2/2]

◆ get_parameters_number()

◆ get_parameters_size()

◆ get_source_layers()

◆ get_state_specs()

◆ get_states_size()

◆ get_training_type()

◆ has() [1/2]

◆ has() [2/2]

◆ is_cpu()

◆ is_empty()

◆ is_gpu()

◆ link_parameters()

◆ link_states()

◆ load()

◆ load_parameters_binary()

◆ save()

◆ save_outputs() [1/2]

◆ save_outputs() [2/2]

◆ save_parameters()

◆ save_parameters_binary()

◆ set_input_names()

◆ set_input_shape()

◆ set_input_variables()

◆ set_output_names()

◆ set_output_variables()