Inheritance diagram for ConvolutionalLayer:

Public Types
enum class	ActivationFunction { Threshold , SymmetricThreshold , Logistic , HyperbolicTangent , Linear , RectifiedLinear , ExponentialLinear , ScaledExponentialLinear , SoftPlus , SoftSign , HardSigmoid }
	Enumeration of available activation functions for the convolutional layer. More...

enum class	ConvolutionType { Valid , Same }

Public Types inherited from Layer
enum class	Type { Scaling , Convolutional , Perceptron , Pooling , Probabilistic , LongShortTermMemory , Recurrent , Unscaling , Bounding }
	This enumeration represents the possible types of layers. More...

Public Member Functions
	ConvolutionalLayer ()

	ConvolutionalLayer (const Tensor< Index, 1 > &, const Tensor< Index, 1 > &)

bool	is_empty () const
	Returns a boolean, true if convolutional layer is empty and false otherwise. More...

const Tensor< type, 1 > &	get_biases () const
	Returns the layer's biases. More...

const Tensor< type, 4 > &	get_synaptic_weights () const
	Returns the layer's synaptic weights. More...

Index	get_synaptic_weights_number () const
	Returns the number of layer's synaptic weights. More...

ActivationFunction	get_activation_function () const
	Returns the convolutional layer's activation function. More...

Tensor< Index, 1 >	get_outputs_dimensions () const
	Returns a vector containing the number of channels, rows and columns of the result of applying the layer's kernels to an image. More...

Tensor< Index, 1 >	get_input_variables_dimensions () const
	Returns the dimension of the input variables. More...

Index	get_outputs_rows_number () const
	Returns the number of rows the result of applying the layer's kernels to an image will have. More...

Index	get_outputs_columns_number () const
	Returns the number of columns the result of applying the layer's kernels to an image will have. More...

ConvolutionType	get_convolution_type () const
	Returns the padding option. More...

Index	get_column_stride () const
	Returns the column stride. More...

Index	get_row_stride () const
	Returns the row stride. More...

Index	get_kernels_number () const
	Returns the number of kernels of the layer. More...

Index	get_kernels_channels_number () const
	Returns the number of channels of the layer's kernels. More...

Index	get_kernels_rows_number () const
	Returns the number of rows of the layer's kernels. More...

Index	get_kernels_columns_number () const
	Returns the number of columns of the layer's kernels. More...

Index	get_padding_width () const
	Returns the total number of columns of zeroes to be added to an image before applying a kernel, which depends on the padding option set. More...

Index	get_padding_height () const
	Returns the total number of rows of zeroes to be added to an image before applying a kernel, which depends on the padding option set. More...

Index	get_inputs_channels_number () const
	Returns the number of channels of the input. More...

Index	get_inputs_rows_number () const
	Returns the number of rows of the input. More...

Index	get_inputs_columns_number () const
	Returns the number of columns of the input. More...

Index	get_inputs_number () const
	Returns the number of inputs. More...

Index	get_neurons_number () const
	Returns the number of neurons. More...

Tensor< type, 1 >	get_parameters () const
	Returns the layer's parameters in the form of a vector. More...

Index	get_parameters_number () const
	Returns the number of parameters of the layer. More...

void	set (const Tensor< Index, 1 > &, const Tensor< Index, 1 > &)

void	set (const Tensor< type, 4 > &, const Tensor< type, 4 > &, const Tensor< type, 1 > &)

void	set_activation_function (const ActivationFunction &)

void	set_biases (const Tensor< type, 1 > &)

void	set_synaptic_weights (const Tensor< type, 4 > &)

void	set_convolution_type (const ConvolutionType &)

void	set_parameters (const Tensor< type, 1 > &, const Index &index)

void	set_row_stride (const Index &)

void	set_column_stride (const Index &)

void	set_biases_constant (const type &)

void	set_synaptic_weights_constant (const type &)

void	set_parameters_constant (const type &)

void	set_parameters_random ()

void	insert_padding (const Tensor< type, 4 > &, Tensor< type, 4 > &)

void	calculate_convolutions (const Tensor< type, 4 > &, Tensor< type, 4 > &) const
	Calculate convolutions. More...

void	calculate_convolutions (const Tensor< type, 4 > &, const Tensor< type, 2 > &, const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	calculate_activations (const Tensor< type, 4 > &, Tensor< type, 4 > &) const
	Calculates activations. More...

void	calculate_activations_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	calculate_outputs (const Tensor< type, 4 > &, Tensor< type, 4 > &)

void	forward_propagate (const Tensor< type, 4 > &, LayerForwardPropagation *)

void	forward_propagate (const Tensor< type, 2 > &, LayerForwardPropagation *)

void	forward_propagate (const Tensor< type, 4 > &, Tensor< type, 1 >, LayerForwardPropagation *)

void	forward_propagate (const Tensor< type, 2 > &, Tensor< type, 1 >, LayerForwardPropagation *)

void	calculate_hidden_delta (Layer , LayerForwardPropagation , const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	calculate_hidden_delta_convolutional (ConvolutionalLayer *, const Tensor< type, 4 > &, const Tensor< type, 4 > &, const Tensor< type, 4 > &, Tensor< type, 2 > &) const

void	calculate_hidden_delta_pooling (PoolingLayer *, const Tensor< type, 4 > &, const Tensor< type, 4 > &, const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	calculate_hidden_delta_perceptron (const PerceptronLayer *, const Tensor< type, 4 > &, const Tensor< type, 2 > &, const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	calculate_hidden_delta_probabilistic (ProbabilisticLayer *, const Tensor< type, 4 > &, const Tensor< type, 4 > &, const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	calculate_error_gradient (const Tensor< type, 4 > &, LayerForwardPropagation *, LayerBackPropagation &) const

void	calculate_error_gradient (const Tensor< type, 2 > &, LayerForwardPropagation *, LayerBackPropagation &) const

void	insert_gradient (LayerBackPropagation *, const Index &, Tensor< type, 1 > &) const

void	to_2d (const Tensor< type, 4 > &, Tensor< type, 2 > &) const

Public Member Functions inherited from Layer
string	get_name () const

void	set_threads_number (const int &)

virtual Tensor< type, 2 >	calculate_outputs (const Tensor< type, 2 > &)

virtual Tensor< type, 2 >	calculate_outputs_from4D (const Tensor< type, 4 > &)

virtual Tensor< type, 4 >	calculate_outputs_4D (const Tensor< type, 4 > &)

virtual void	calculate_hidden_delta (LayerForwardPropagation , LayerBackPropagation , LayerBackPropagation *) const

virtual void	calculate_hidden_delta_lm (LayerForwardPropagation , LayerBackPropagationLM , LayerBackPropagationLM *) const

virtual void	calculate_error_gradient (const Tensor< type, 2 > &, LayerForwardPropagation , LayerBackPropagation ) const

virtual void	calculate_error_gradient (const Tensor< type, 4 > &, LayerForwardPropagation , LayerBackPropagation ) const

virtual void	calculate_squared_errors_Jacobian_lm (const Tensor< type, 2 > &, LayerForwardPropagation , LayerBackPropagationLM )

virtual void	insert_squared_errors_Jacobian_lm (LayerBackPropagationLM *, const Index &, Tensor< type, 2 > &) const

virtual void	set_inputs_number (const Index &)

virtual void	set_neurons_number (const Index &)

Type	get_type () const

string	get_type_string () const
	Takes the type of layer used by the model. More...

virtual void	from_XML (const tinyxml2::XMLDocument &)

virtual void	write_XML (tinyxml2::XMLPrinter &) const

virtual string	write_expression (const Tensor< string, 1 > &, const Tensor< string, 1 > &) const

virtual string	write_expression_c () const

virtual string	write_expression_python () const

Protected Attributes
Tensor< type, 4 >	synaptic_weights
	This tensor containing conection strengths from a layer's inputs to its neurons. More...

Tensor< type, 1 >	biases

Index	row_stride = 1

Index	column_stride = 1

Tensor< Index, 1 >	input_variables_dimensions

ConvolutionType	convolution_type = ConvolutionType::Valid

ActivationFunction	activation_function = ActivationFunction::RectifiedLinear

Protected Attributes inherited from Layer
NonBlockingThreadPool *	non_blocking_thread_pool = nullptr

ThreadPoolDevice *	thread_pool_device = nullptr

string	layer_name = "layer"
	Layer name. More...

Type	layer_type = Type::Perceptron
	Layer type. More...

const Eigen::array< IndexPair< Index >, 1 >	A_BT = {IndexPair<Index>(1, 1)}

const Eigen::array< IndexPair< Index >, 1 >	AT_B = {IndexPair<Index>(0, 0)}

const Eigen::array< IndexPair< Index >, 1 >	A_B = {IndexPair<Index>(1, 0)}

Additional Inherited Members
Protected Member Functions inherited from Layer
void	hard_sigmoid (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	hyperbolic_tangent (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	logistic (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	threshold (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	symmetric_threshold (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	rectified_linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	scaled_exponential_linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	soft_plus (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	soft_sign (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	exponential_linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	softmax (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	binary (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	competitive (const Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	hard_sigmoid_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	hyperbolic_tangent_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	logistic_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	threshold_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	symmetric_threshold_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	rectified_linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	scaled_exponential_linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	soft_plus_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	soft_sign_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	exponential_linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const

void	hard_sigmoid (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	hyperbolic_tangent (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	logistic (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	threshold (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	symmetric_threshold (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	rectified_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	scaled_exponential_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	soft_plus (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	soft_sign (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	exponential_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	softmax (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	binary (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	competitive (const Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	hard_sigmoid_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	hyperbolic_tangent_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	logistic_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	threshold_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	symmetric_threshold_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	rectified_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	scaled_exponential_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	soft_plus_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	soft_sign_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	exponential_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const

void	logistic_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 3 > &) const

void	softmax_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 3 > &) const

void	linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	logistic (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	hyperbolic_tangent (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	threshold (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	symmetric_threshold (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	rectified_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	scaled_exponential_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	soft_plus (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	soft_sign (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	hard_sigmoid (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	exponential_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	logistic_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	hyperbolic_tangent_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	threshold_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	symmetric_threshold_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	rectified_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	scaled_exponential_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	soft_plus_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	soft_sign_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	hard_sigmoid_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

void	exponential_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const

Detailed Description

Definition at line 38 of file convolutional_layer.h.

Member Enumeration Documentation

◆ ActivationFunction

enum class ActivationFunction

strong

Enumeration of available activation functions for the convolutional layer.

Definition at line 45 of file convolutional_layer.h.

◆ ConvolutionType

enum class ConvolutionType

strong

Definition at line 47 of file convolutional_layer.h.

Constructor & Destructor Documentation

◆ ConvolutionalLayer() [1/2]

ConvolutionalLayer ( )

explicit

Default constructor. It creates an empty ConvolutionalLayer object.

Definition at line 22 of file convolutional_layer.cpp.

◆ ConvolutionalLayer() [2/2]

ConvolutionalLayer	(	const Tensor< Index, 1 > &	new_inputs_dimensions,
		const Tensor< Index, 1 > &	new_kernels_dimensions
	)

explicit

Inputs' dimensions modifier constructor. After setting new dimensions for the inputs, it creates and initializes a ConvolutionalLayer object with a number of kernels of a given size. The initialization values are random values from a normal distribution.

Parameters

new_inputs_dimensions	A vector containing the new inputs' dimensions.
kernels_dimensions	A vector containing the number of kernels, their rows and columns.

Definition at line 35 of file convolutional_layer.cpp.

Member Function Documentation

◆ calculate_activations()

void calculate_activations	(	const Tensor< type, 4 > &	inputs,
		Tensor< type, 4 > &	activations
	)		const

Calculates activations.

Definition at line 134 of file convolutional_layer.cpp.

◆ calculate_activations_derivatives()

void calculate_activations_derivatives	(	const Tensor< type, 4 > &	combinations_4d,
		Tensor< type, 4 > &	activations,
		Tensor< type, 4 > &	activations_derivatives
	)		const

Definition at line 163 of file convolutional_layer.cpp.

◆ calculate_convolutions() [1/2]

void calculate_convolutions	(	const Tensor< type, 4 > &	inputs,
		const Tensor< type, 2 > &	potential_biases,
		const Tensor< type, 4 > &	potential_synaptic_weights,
		Tensor< type, 4 > &	convolutions
	)		const

Definition at line 104 of file convolutional_layer.cpp.

◆ calculate_convolutions() [2/2]

void calculate_convolutions	(	const Tensor< type, 4 > &	inputs,
		Tensor< type, 4 > &	combinations
	)		const

Calculate convolutions.

Definition at line 82 of file convolutional_layer.cpp.

◆ calculate_error_gradient() [1/2]

void calculate_error_gradient	(	const Tensor< type, 2 > &	inputs,
		LayerForwardPropagation *	forward_propagation,
		LayerBackPropagation &	back_propagation
	)		const

Definition at line 838 of file convolutional_layer.cpp.

◆ calculate_error_gradient() [2/2]

void calculate_error_gradient	(	const Tensor< type, 4 > &	inputs,
		LayerForwardPropagation *	forward_propagation,
		LayerBackPropagation &	back_propagation
	)		const

Definition at line 776 of file convolutional_layer.cpp.

◆ calculate_hidden_delta()

void calculate_hidden_delta	(	Layer *	next_layer_pointer,
		LayerForwardPropagation *	forward_propagation,
		const Tensor< type, 2 > &	next_layer_delta,
		Tensor< type, 2 > &	hidden_delta
	)		const

Definition at line 355 of file convolutional_layer.cpp.

◆ calculate_hidden_delta_convolutional()

void calculate_hidden_delta_convolutional	(	ConvolutionalLayer *	next_layer_pointer,
		const Tensor< type, 4 > &	activations,
		const Tensor< type, 4 > &	activations_derivatives,
		const Tensor< type, 4 > &	next_layer_delta,
		Tensor< type, 2 > &	hidden_delta
	)		const

Definition at line 408 of file convolutional_layer.cpp.

◆ calculate_hidden_delta_perceptron()

void calculate_hidden_delta_perceptron	(	const PerceptronLayer *	next_layer_pointer,
		const Tensor< type, 4 > &	,
		const Tensor< type, 2 > &	activations_derivatives,
		const Tensor< type, 2 > &	next_layer_delta,
		Tensor< type, 2 > &	hidden_delta
	)		const

Todo:

Definition at line 649 of file convolutional_layer.cpp.

◆ calculate_hidden_delta_pooling()

void calculate_hidden_delta_pooling	(	PoolingLayer *	next_layer_pointer,
		const Tensor< type, 4 > &	activations,
		const Tensor< type, 4 > &	activations_derivatives,
		const Tensor< type, 2 > &	next_layer_delta,
		Tensor< type, 2 > &	hidden_delta
	)		const

Definition at line 485 of file convolutional_layer.cpp.

◆ calculate_hidden_delta_probabilistic()

void calculate_hidden_delta_probabilistic	(	ProbabilisticLayer *	next_layer_pointer,
		const Tensor< type, 4 > &	activations,
		const Tensor< type, 4 > &	activations_derivatives,
		const Tensor< type, 2 > &	next_layer_delta,
		Tensor< type, 2 > &	hidden_delta
	)		const

Definition at line 709 of file convolutional_layer.cpp.

◆ calculate_outputs()

void calculate_outputs	(	const Tensor< type, 4 > &	inputs,
		Tensor< type, 4 > &	outputs
	)

Returns the output of the convolutional layer applied to a batch of images.

Parameters

inputs	The batch of images.
outputs	Tensor where the outputs will be stored.

Definition at line 198 of file convolutional_layer.cpp.

◆ forward_propagate() [1/4]

void forward_propagate	(	const Tensor< type, 2 > &	inputs,
		LayerForwardPropagation *	forward_propagation
	)

virtual

Reimplemented from Layer.

Definition at line 247 of file convolutional_layer.cpp.

◆ forward_propagate() [2/4]

void forward_propagate	(	const Tensor< type, 2 > &	inputs,
		Tensor< type, 1 >	potential_parameters,
		LayerForwardPropagation *	forward_propagation
	)

virtual

Reimplemented from Layer.

Definition at line 335 of file convolutional_layer.cpp.

◆ forward_propagate() [3/4]

void forward_propagate	(	const Tensor< type, 4 > &	inputs,
		LayerForwardPropagation *	forward_propagation
	)

virtual

Reimplemented from Layer.

Definition at line 212 of file convolutional_layer.cpp.

◆ forward_propagate() [4/4]

void forward_propagate	(	const Tensor< type, 4 > &	inputs,
		Tensor< type, 1 >	potential_parameters,
		LayerForwardPropagation *	forward_propagation
	)

virtual

Reimplemented from Layer.

Definition at line 262 of file convolutional_layer.cpp.

◆ get_activation_function()

ConvolutionalLayer::ActivationFunction get_activation_function ( ) const

Returns the convolutional layer's activation function.

Definition at line 876 of file convolutional_layer.cpp.

◆ get_biases()

const Tensor< type, 1 > & get_biases ( ) const

Returns the layer's biases.

Definition at line 1331 of file convolutional_layer.cpp.

◆ get_column_stride()

Index get_column_stride ( ) const

Returns the column stride.

Definition at line 939 of file convolutional_layer.cpp.

◆ get_convolution_type()

ConvolutionalLayer::ConvolutionType get_convolution_type ( ) const

Returns the padding option.

Definition at line 931 of file convolutional_layer.cpp.

◆ get_input_variables_dimensions()

Tensor< Index, 1 > get_input_variables_dimensions ( ) const

Returns the dimension of the input variables.

Definition at line 923 of file convolutional_layer.cpp.

◆ get_inputs_channels_number()

Index get_inputs_channels_number ( ) const

Returns the number of channels of the input.

Definition at line 1355 of file convolutional_layer.cpp.

◆ get_inputs_columns_number()

Index get_inputs_columns_number ( ) const

Returns the number of columns of the input.

Definition at line 1371 of file convolutional_layer.cpp.

◆ get_inputs_number()

Index get_inputs_number ( ) const

virtual

Returns the number of inputs.

Reimplemented from Layer.

Definition at line 1029 of file convolutional_layer.cpp.

◆ get_inputs_rows_number()

Index get_inputs_rows_number ( ) const

Returns the number of rows of the input.

Definition at line 1363 of file convolutional_layer.cpp.

◆ get_kernels_channels_number()

Index get_kernels_channels_number ( ) const

Returns the number of channels of the layer's kernels.

Definition at line 963 of file convolutional_layer.cpp.

◆ get_kernels_columns_number()

Index get_kernels_columns_number ( ) const

Returns the number of columns of the layer's kernels.

Definition at line 979 of file convolutional_layer.cpp.

◆ get_kernels_number()

Index get_kernels_number ( ) const

Returns the number of kernels of the layer.

Definition at line 955 of file convolutional_layer.cpp.

◆ get_kernels_rows_number()

Index get_kernels_rows_number ( ) const

Returns the number of rows of the layer's kernels.

Definition at line 971 of file convolutional_layer.cpp.

◆ get_neurons_number()

Index get_neurons_number ( ) const

virtual

Returns the number of neurons.

Reimplemented from Layer.

Definition at line 1037 of file convolutional_layer.cpp.

◆ get_outputs_columns_number()

Index get_outputs_columns_number ( ) const

Returns the number of columns the result of applying the layer's kernels to an image will have.

Definition at line 896 of file convolutional_layer.cpp.

◆ get_outputs_dimensions()

Tensor< Index, 1 > get_outputs_dimensions ( ) const

Returns a vector containing the number of channels, rows and columns of the result of applying the layer's kernels to an image.

Definition at line 908 of file convolutional_layer.cpp.

◆ get_outputs_rows_number()

Index get_outputs_rows_number ( ) const

Returns the number of rows the result of applying the layer's kernels to an image will have.

Definition at line 884 of file convolutional_layer.cpp.

◆ get_padding_height()

Index get_padding_height ( ) const

Returns the total number of rows of zeroes to be added to an image before applying a kernel, which depends on the padding option set.

Definition at line 1008 of file convolutional_layer.cpp.

◆ get_padding_width()

Index get_padding_width ( ) const

Returns the total number of columns of zeroes to be added to an image before applying a kernel, which depends on the padding option set.

Definition at line 987 of file convolutional_layer.cpp.

◆ get_parameters()

Tensor< type, 1 > get_parameters ( ) const

virtual

Returns the layer's parameters in the form of a vector.

Reimplemented from Layer.

Definition at line 1049 of file convolutional_layer.cpp.

◆ get_parameters_number()

Index get_parameters_number ( ) const

virtual

Returns the number of parameters of the layer.

Reimplemented from Layer.

Definition at line 1089 of file convolutional_layer.cpp.

◆ get_row_stride()

Index get_row_stride ( ) const

Returns the row stride.

Definition at line 947 of file convolutional_layer.cpp.

◆ get_synaptic_weights()

const Tensor< type, 4 > & get_synaptic_weights ( ) const

Returns the layer's synaptic weights.

Definition at line 1339 of file convolutional_layer.cpp.

◆ get_synaptic_weights_number()

Index get_synaptic_weights_number ( ) const

virtual

Returns the number of layer's synaptic weights.

Reimplemented from Layer.

Definition at line 1347 of file convolutional_layer.cpp.

◆ insert_gradient()

void insert_gradient	(	LayerBackPropagation *	back_propagation,
		const Index &	index,
		Tensor< type, 1 > &	gradient
	)		const

virtual

Reimplemented from Layer.

Definition at line 856 of file convolutional_layer.cpp.

◆ insert_padding()

void insert_padding	(	const Tensor< type, 4 > &	inputs,
		Tensor< type, 4 > &	padded_output
	)

Todo:: Add stride.

Definition at line 59 of file convolutional_layer.cpp.

◆ is_empty()

bool is_empty ( ) const

Returns a boolean, true if convolutional layer is empty and false otherwise.

Definition at line 46 of file convolutional_layer.cpp.

◆ set() [1/2]

void set	(	const Tensor< Index, 1 > &	new_inputs_dimensions,
		const Tensor< Index, 1 > &	new_kernels_dimensions
	)

Sets and initializes the layer's parameters in accordance with the dimensions taken as input. The initialization values are random values from a normal distribution.

Parameters

new_inputs_dimensions	A vector containing the desired inputs' dimensions (number of images, number of channels, rows number, columns number).
new_kernels_dimensions	A vector containing the desired kernels' dimensions (number of kernels, number of channels, rows number, columns number).

Definition at line 1100 of file convolutional_layer.cpp.

◆ set() [2/2]

void set	(	const Tensor< type, 4 > &	new_inputs,
		const Tensor< type, 4 > &	new_kernels,
		const Tensor< type, 1 > &	new_biases
	)

Sets and initializes the layer's parameters in accordance with the dimensions taken as input. The initialization values are random values from a normal distribution.

Parameters

new_inputs	Layer inputs.
new_kernels	Layer synaptic weights.
new_biases	Layer biases.

Definition at line 1156 of file convolutional_layer.cpp.

◆ set_activation_function()

void set_activation_function ( const ActivationFunction & new_activation_function )

Sets the layer's activation function.

Parameters

new_activation_function The desired activation function.

Definition at line 1231 of file convolutional_layer.cpp.

◆ set_biases()

void set_biases ( const Tensor< type, 1 > & new_biases )

Sets the layer's biases.

Parameters

new_biases The desired biases.

Definition at line 1240 of file convolutional_layer.cpp.

◆ set_biases_constant()

void set_biases_constant ( const type & value )

Initializes the layer's biases to a given value.

Parameters

value The desired value.

Definition at line 1192 of file convolutional_layer.cpp.

◆ set_column_stride()

void set_column_stride ( const Index & new_stride_column )

Sets the kernels' column stride.

Parameters

new_stride_row The desired column stride.

Definition at line 1281 of file convolutional_layer.cpp.

◆ set_convolution_type()

void set_convolution_type ( const ConvolutionType & new_convolution_type )

Sets the padding option.

Parameters

new_convolution_type The desired convolution type.

Definition at line 1258 of file convolutional_layer.cpp.

◆ set_parameters()

void set_parameters	(	const Tensor< type, 1 > &	new_parameters,
		const Index &	index
	)

virtual

Sets the synaptic weights and biases to the given values.

Parameters

new_parameters A vector containing the synaptic weights and biases, in this order.

Reimplemented from Layer.

Definition at line 1295 of file convolutional_layer.cpp.

◆ set_parameters_constant()

void set_parameters_constant ( const type & value )

virtual

Initializes the layer's parameters to a given value.

Parameters

value The desired value.

Reimplemented from Layer.

Definition at line 1210 of file convolutional_layer.cpp.

◆ set_parameters_random()

void set_parameters_random ( )

virtual

Reimplemented from Layer.

Definition at line 1220 of file convolutional_layer.cpp.

◆ set_row_stride()

void set_row_stride ( const Index & new_stride_row )

Sets the kernels' row stride.

Parameters

new_stride_row The desired row stride.

Definition at line 1267 of file convolutional_layer.cpp.

◆ set_synaptic_weights()

void set_synaptic_weights ( const Tensor< type, 4 > & new_synaptic_weights )

Sets the layer's synaptic weights.

Parameters

new_synaptic_weights The desired synaptic weights.

Definition at line 1249 of file convolutional_layer.cpp.

◆ set_synaptic_weights_constant()

void set_synaptic_weights_constant ( const type & value )

Initializes the layer's synaptic weights to a given value.

Parameters

value The desired value.

Definition at line 1201 of file convolutional_layer.cpp.

◆ to_2d()

void to_2d	(	const Tensor< type, 4 > &	input_4d,
		Tensor< type, 2 > &	output_2d
	)		const

Definition at line 1377 of file convolutional_layer.cpp.

Member Data Documentation

◆ activation_function

ActivationFunction activation_function = ActivationFunction::RectifiedLinear

protected

Definition at line 227 of file convolutional_layer.h.

◆ biases

Tensor<type, 1> biases

protected

Bias is a neuron parameter that is summed with the neuron's weighted inputs and passed through the neuron's trabsfer function to generate the neuron's output.

Definition at line 217 of file convolutional_layer.h.

◆ column_stride

Index column_stride = 1

protected

Definition at line 221 of file convolutional_layer.h.

◆ convolution_type

ConvolutionType convolution_type = ConvolutionType::Valid

protected

Definition at line 225 of file convolutional_layer.h.

◆ input_variables_dimensions

Tensor<Index, 1> input_variables_dimensions

protected

Definition at line 223 of file convolutional_layer.h.

◆ row_stride

Index row_stride = 1

protected

Definition at line 219 of file convolutional_layer.h.

◆ synaptic_weights

Tensor<type, 4> synaptic_weights

protected

This tensor containing conection strengths from a layer's inputs to its neurons.

Definition at line 212 of file convolutional_layer.h.

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

Public Types

Public Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Member Enumeration Documentation

◆ ActivationFunction

◆ ConvolutionType

Constructor & Destructor Documentation

◆ ConvolutionalLayer() [1/2]

◆ ConvolutionalLayer() [2/2]

Member Function Documentation

◆ calculate_activations()

◆ calculate_activations_derivatives()

◆ calculate_convolutions() [1/2]

◆ calculate_convolutions() [2/2]

◆ calculate_error_gradient() [1/2]

◆ calculate_error_gradient() [2/2]

◆ calculate_hidden_delta()

◆ calculate_hidden_delta_convolutional()

◆ calculate_hidden_delta_perceptron()

◆ calculate_hidden_delta_pooling()

◆ calculate_hidden_delta_probabilistic()

◆ calculate_outputs()

◆ forward_propagate() [1/4]

◆ forward_propagate() [2/4]

◆ forward_propagate() [3/4]

◆ forward_propagate() [4/4]

◆ get_activation_function()

◆ get_biases()

◆ get_column_stride()

◆ get_convolution_type()

◆ get_input_variables_dimensions()

◆ get_inputs_channels_number()

◆ get_inputs_columns_number()

◆ get_inputs_number()

◆ get_inputs_rows_number()

◆ get_kernels_channels_number()

◆ get_kernels_columns_number()

◆ get_kernels_number()

◆ get_kernels_rows_number()

◆ get_neurons_number()

◆ get_outputs_columns_number()

◆ get_outputs_dimensions()

◆ get_outputs_rows_number()

◆ get_padding_height()

◆ get_padding_width()

◆ get_parameters()

◆ get_parameters_number()

◆ get_row_stride()

◆ get_synaptic_weights()

◆ get_synaptic_weights_number()

◆ insert_gradient()

◆ insert_padding()

◆ is_empty()

◆ set() [1/2]

◆ set() [2/2]

◆ set_activation_function()

◆ set_biases()

◆ set_biases_constant()

◆ set_column_stride()

◆ set_convolution_type()

◆ set_parameters()

◆ set_parameters_constant()

◆ set_parameters_random()

◆ set_row_stride()

◆ set_synaptic_weights()

◆ set_synaptic_weights_constant()

◆ to_2d()

Member Data Documentation

◆ activation_function

◆ biases

◆ column_stride

◆ convolution_type

◆ input_variables_dimensions

◆ row_stride

◆ synaptic_weights