#include <Perceptron.h>

Inheritance diagram for panann::Perceptron:

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Collaboration diagram for panann::Perceptron:

Public Types
enum	ErrorCostFunction : uint8_t { MeanSquareError = 1, MeanAbsoluteError }

Public Member Functions
	Perceptron (const Perceptron &)=delete

Perceptron &	operator= (const Perceptron &)=delete

void	SetErrorCostFunction (ErrorCostFunction mode)

ErrorCostFunction	GetErrorCostFunction () const

void	SetHiddenNeuronActivationFunctionType (ActivationFunctionType type)

ActivationFunctionType	GetHiddenNeuronActivationFunctionType () const

void	SetOutputNeuronActivationFunctionType (ActivationFunctionType type)

ActivationFunctionType	GetOutputNeuronActivationFunctionType () const

void	InitializeWeightsRandom (double min=-1.0, double max=1.0)

void	InitializeWeights (const TrainingData &training_data)

virtual void	RunForward (const std::vector< double > &input)

void	RunBackward (const std::vector< double > &output)

double	GetError (const std::vector< double > &output)

double	GetError (const TrainingData &training_data)

std::vector< double > &	GetWeights ()

void	SetWeights (const std::vector< double > &weights)

void	GetOutput (std::vector< double > *output) const

virtual void	Construct ()

bool	IsConstructed () const

void	EnableErrorShaping ()

void	DisableErrorShaping ()

Public Member Functions inherited from panann::MultiLayerNeuralTopology
	MultiLayerNeuralTopology (const MultiLayerNeuralTopology &)=delete

MultiLayerNeuralTopology &	operator= (const MultiLayerNeuralTopology &)=delete

void	AddHiddenLayer (size_t neuron_count)

void	EnableShortcutConnections ()

void	DisableShortcutConnections ()

void	ConstructTopology ()

bool	IsTopologyConstructed () const

size_t	GetHiddenLayerCount () const

const Layer &	GetHiddenLayer (size_t layer_index) const

size_t	GetInputConnectionCount () const

size_t	GetOutputConnectionCount () const

const InputConnection &	GetInputConnection (size_t index) const

const OutputConnection &	GetOutputConnection (size_t index) const

Public Member Functions inherited from panann::NeuronContainer
	NeuronContainer (const NeuronContainer &)=delete

NeuronContainer &	operator= (const NeuronContainer &)=delete

void	SetInputNeuronCount (size_t input_neuron_count)

size_t	GetInputNeuronCount () const

void	SetOutputNeuronCount (size_t output_neuron_count)

size_t	GetOutputNeuronCount () const

size_t	GetNeuronCount () const

const Neuron &	GetNeuron (size_t neuron_index) const

Protected Member Functions
void	AllocateWeights ()

bool	AreWeightsAllocated () const

void	InitializeNeurons ()

virtual void	InitializeHiddenNeurons ()

void	ComputeNeuronValue (size_t neuron_index)

void	ComputeNeuronValueRange (size_t neuron_start_index, size_t neuron_count)

void	ComputeNeuronError (size_t neuron_index)

void	ResetOutputLayerError ()

void	CalculateOutputLayerError (const std::vector< double > &output)

double	GetError () const

RandomWrapper &	GetRandom ()

size_t	GetWeightCount () const

double &	GetWeight (size_t index)

Protected Member Functions inherited from panann::MultiLayerNeuralTopology
Layer &	GetHiddenLayer (size_t layer_index)

InputConnection &	GetInputConnection (size_t index)

OutputConnection &	GetOutputConnection (size_t index)

size_t	AddInputConnections (size_t count)

size_t	AddOutputConnections (size_t count)

virtual void	FixNeuronConnectionIndices ()

void	AllocateConnections ()

bool	AreConnectionsAllocated () const

virtual void	ConnectFully ()

void	ConnectLayerToNeuron (size_t from_neuron_index, size_t from_neuron_count, size_t to_neuron_index)

void	ConnectLayers (size_t from_neuron_index, size_t from_neuron_count, size_t to_neuron_index, size_t to_neuron_count)

void	ConnectBiasNeuron (size_t bias_neuron_index, size_t to_neuron_index, size_t to_neuron_count)

void	ConnectNeurons (size_t from_neuron_index, size_t to_neuron_index)

Protected Member Functions inherited from panann::NeuronContainer
size_t	GetHiddenNeuronStartIndex () const

size_t	GetInputNeuronStartIndex () const

size_t	GetOutputNeuronStartIndex () const

size_t	GetBiasNeuronStartIndex () const

size_t	GetHiddenNeuronCount () const

size_t	GetBiasNeuronCount () const

size_t	AddHiddenNeurons (size_t count)

void	AddBiasNeurons (size_t count)

void	AllocateNeurons ()

bool	AreNeuronsAllocated () const

void	SetNeuronActivationFunction (size_t neuron_index, ActivationFunctionType type)

Neuron &	GetNeuron (size_t neuron_index)

Neuron &	GetInputNeuron (size_t input_neuron_index)

Neuron &	GetOutputNeuron (size_t output_neuron_index)

Neuron &	GetBiasNeuron (size_t bias_neuron_index)

Neuron &	GetHiddenNeuron (size_t hidden_neuron_index)

const Neuron &	GetOutputNeuron (size_t output_neuron_index) const

Detailed Description

Simple feed-forward, multi-layer perceptron.

Member Function Documentation

◆ Construct()

void panann::Perceptron::Construct ( )

virtual

Build the neural network.
After construction, the network topology may not be modified.

Reimplemented in panann::RecurrentNeuralNetwork.

◆ EnableErrorShaping()

void panann::Perceptron::EnableErrorShaping ( )

Enable to perform shaping of the error curve. Default: Disabled

◆ GetError() [1/2]

double panann::Perceptron::GetError ( const std::vector< double > & output )

Get the total network error by calculating the average of the difference between each output neuron value and each value in the output parameter.
The difference between the expected value and the output neuron value will be modified according to the error cost function.

Parameters

output Expected output values. Difference between this and the value of each output neuron is our total network error. The number of values in output must be the same as the number of output neurons in this network.

See also: ErrorCostFunction; SetErrorCostFunction

◆ GetError() [2/2]

double panann::Perceptron::GetError ( const TrainingData & training_data )

Get the total network error against a set of examples.
For each example, we run the network forward on the input and compute the difference between the output and the values stored in each output neuron.
The total network error is computed as the average of all of those computed differences.
The difference between the expected value and the output neuron value will be modified according to the error cost function.

See also: ErrorCostFunction; SetErrorCostFunction

◆ GetOutput()

void panann::Perceptron::GetOutput ( std::vector< double > * output ) const

Writes all of the output neuron values into |output|.
Existing values in |output| will be discarded.

◆ GetWeights()

std::vector< double > & panann::Perceptron::GetWeights ( )

Get a writable vector containing all the weight values for the network.

◆ InitializeHiddenNeurons()

void panann::Perceptron::InitializeHiddenNeurons ( )

protectedvirtual

Set the default activation function etc for all hidden neurons in the network.

Reimplemented in panann::RecurrentNeuralNetwork.

◆ InitializeNeurons()

void panann::Perceptron::InitializeNeurons ( )

protected

Set the initial value, activation function, etc for all neurons in the network.

◆ InitializeWeights()

void panann::Perceptron::InitializeWeights ( const TrainingData & training_data )

Initialize the weight of each connection via Widrow-Nguyen's algorithm.

◆ InitializeWeightsRandom()

void panann::Perceptron::InitializeWeightsRandom	(	double	min = `-1.0`,
		double	max = `1.0`
	)

Reset every weight in the network to a random value between min and max.

◆ IsConstructed()

bool panann::Perceptron::IsConstructed ( ) const

Returns true if the network has been constructed and false otherwise.
Note: Once constructed, the network topology is fixed and cannot be changed.

◆ RunBackward()

void panann::Perceptron::RunBackward ( const std::vector< double > & output )

Compute the error attributed to each neuron in the network.
Begins by calculating the difference between each output neuron value and each element in the output parameter.
Next we pull that neuron error backwards through the network, computing the partial error contributed by each neuron in the network.

Parameters

output Must have the same number of elements as this network has output neurons.

◆ RunForward()

void panann::Perceptron::RunForward ( const std::vector< double > & input )

virtual

Run the network forward on a set of inputs.
The values computed by running the network will be stored in the output neurons.
Works by assigning the values in the input parameter into each input neuron and pulling those values through the hidden layers, calculating the output of each neuron as the result of executing the activation function on the sum of the incoming values multiplied by their connection weights.

Parameters

input Must have the same number of elements as this network has input neurons.

Reimplemented in panann::RecurrentNeuralNetwork.

◆ SetErrorCostFunction()

void panann::Perceptron::SetErrorCostFunction ( ErrorCostFunction mode )

Set the cost function we will use to calculate the total network error.
Default: MeanSquareError

See also: GetError; ErrorCostFunction

◆ SetHiddenNeuronActivationFunctionType()

void panann::Perceptron::SetHiddenNeuronActivationFunctionType ( ActivationFunctionType type )

Set the default activation function we will use for hidden layer neurons.
Default: Sigmoid

◆ SetOutputNeuronActivationFunctionType()

void panann::Perceptron::SetOutputNeuronActivationFunctionType ( ActivationFunctionType type )

Set the default activation function we will use for output layer neurons.
Default: Sigmoid

◆ SetWeights()

void panann::Perceptron::SetWeights ( const std::vector< double > & weights )

Set the weight values for the network based on an input vector |weights|.
The weight values will be copied from the provided vector which must contain exactly the number of values for which this network has weights.

The documentation for this class was generated from the following files:

src/Perceptron.h
src/Perceptron.cc

Public Types

Public Member Functions

Protected Member Functions

Detailed Description

Member Function Documentation

◆ Construct()

◆ EnableErrorShaping()

◆ GetError() [1/2]

◆ GetError() [2/2]

◆ GetOutput()

◆ GetWeights()

◆ InitializeHiddenNeurons()

◆ InitializeNeurons()

◆ InitializeWeights()

◆ InitializeWeightsRandom()

◆ IsConstructed()

◆ RunBackward()

◆ RunForward()

◆ SetErrorCostFunction()

◆ SetHiddenNeuronActivationFunctionType()

◆ SetOutputNeuronActivationFunctionType()

◆ SetWeights()