Interface for neural network layers. More...

#include "neural.h"
#include "neural_layer_args.h"

Include dependency graph for neural_layer.h:

This graph shows which files directly or indirectly include this file:

Data Structures
struct	Layer
	Neural network layer data structure. More...

struct	LayerVtbl
	Neural network layer interface data structure. More...

Macros
#define	CONNECTED (0)
	Layer type connected. More...

#define	DROPOUT (1)
	Layer type dropout. More...

#define	NOISE (2)
	Layer type noise. More...

#define	SOFTMAX (3)
	Layer type softmax. More...

#define	RECURRENT (4)
	Layer type recurrent. More...

#define	LSTM (5)
	Layer type LSTM. More...

#define	MAXPOOL (6)
	Layer type maxpooling. More...

#define	CONVOLUTIONAL (7)
	Layer type convolutional. More...

#define	AVGPOOL (8)
	Layer type average pooling. More...

#define	UPSAMPLE (9)
	Layer type upsample. More...

#define	STRING_CONNECTED ("connected\0")
	Connected. More...

#define	STRING_DROPOUT ("dropout\0")
	Dropout. More...

#define	STRING_NOISE ("noise\0")
	Noise. More...

#define	STRING_SOFTMAX ("softmax\0")
	Softmax. More...

#define	STRING_RECURRENT ("recurrent\0")
	Recurrent. More...

#define	STRING_LSTM ("lstm\0")
	LSTM. More...

#define	STRING_MAXPOOL ("maxpool\0")
	Maxpool. More...

#define	STRING_CONVOLUTIONAL ("convolutional\0")
	Convolutional. More...

#define	STRING_AVGPOOL ("avgpool\0")
	Avgpool. More...

#define	STRING_UPSAMPLE ("upsample\0")
	Upsample. More...

#define	LAYER_EVOLVE_WEIGHTS (1 << 0)
	Layer may evolve weights. More...

#define	LAYER_EVOLVE_NEURONS (1 << 1)
	Layer may evolve neurons. More...

#define	LAYER_EVOLVE_FUNCTIONS (1 << 2)
	Layer may evolve functions. More...

#define	LAYER_SGD_WEIGHTS (1 << 3)
	Layer may perform gradient descent. More...

#define	LAYER_EVOLVE_ETA (1 << 4)
	Layer may evolve rate of gradient descent. More...

#define	LAYER_EVOLVE_CONNECT (1 << 5)
	Layer may evolve connectivity. More...

#define	NEURON_MIN (-100)
	Minimum neuron state. More...

#define	NEURON_MAX (100)
	Maximum neuron state. More...

#define	WEIGHT_MIN (-10)
	Minimum value of a weight or bias. More...

#define	WEIGHT_MAX (10)
	Maximum value of a weight or bias. More...

#define	N_WEIGHTS_MAX (20000000)
	Maximum number of weights per layer. More...

#define	N_INPUTS_MAX (2000000)
	Maximum number of inputs per layer. More...

#define	N_OUTPUTS_MAX (2000000)
	Maximum number of outputs per layer. More...

#define	WEIGHT_SD_INIT (0.1)
	Std dev of Gaussian for weight initialisation. More...

#define	WEIGHT_SD (0.1)
	Std dev of Gaussian for weight resizing. More...

#define	WEIGHT_SD_RAND (1.0)
	Std dev of Gaussian for weight randomising. More...

Functions
static double *	layer_output (const struct Layer *l)
	Returns the outputs of a layer. More...

static void	layer_forward (const struct Layer l, const struct Net net, const double *input)
	Forward propagates an input through the layer. More...

static void	layer_backward (const struct Layer l, const struct Net net, const double input, double delta)
	Backward propagates the error through a layer. More...

static void	layer_update (const struct Layer *l)
	Updates the weights and biases of a layer. More...

static bool	layer_mutate (struct Layer *l)
	Performs layer mutation. More...

static void	layer_resize (struct Layer l, const struct Layer prev)
	Resizes a layer using the previous layer's inputs. More...

static struct Layer *	layer_copy (const struct Layer *src)
	Creates and returns a copy of a specified layer. More...

static void	layer_free (const struct Layer *l)
	Frees the memory used by the layer. More...

static void	layer_rand (struct Layer *l)
	Randomises a layer. More...

static void	layer_print (const struct Layer *l, const bool print_weights)
	Prints the layer. More...

static char *	layer_json_export (const struct Layer *l, const bool return_weights)
	Returns a json formatted string representation of a layer. More...

bool	layer_mutate_connectivity (struct Layer *l, const double mu_enable, const double mu_disable)
	Mutates a layer's connectivity by zeroing weights. More...

bool	layer_mutate_eta (struct Layer *l, const double mu)
	Mutates the gradient descent rate of a neural layer. More...

bool	layer_mutate_functions (struct Layer *l, const double mu)
	Mutates a layer's activation function by random selection. More...

bool	layer_mutate_weights (struct Layer *l, const double mu)
	Mutates a layer's weights and biases by adding random numbers from a Gaussian normal distribution with zero mean and standard deviation equal to the mutation rate. More...

int	layer_mutate_neurons (const struct Layer *l, const double mu)
	Returns the number of neurons to add or remove from a layer. More...

void	layer_add_neurons (struct Layer *l, const int n)
	Adds N neurons to a layer. Negative N removes neurons. More...

void	layer_calc_n_active (struct Layer *l)
	Recalculates the number of active connections within a layer. More...

void	layer_defaults (struct Layer *l)
	Initialises a layer to default values. More...

void	layer_init_eta (struct Layer *l)
	Initialises a layer's gradient descent rate. More...

void	layer_set_vptr (struct Layer *l)
	Sets a neural network layer's functions to the implementations. More...

void	layer_weight_clamp (const struct Layer *l)
	Clamps a layer's weights and biases in range [WEIGHT_MIN, WEIGHT_MAX]. More...

void	layer_weight_print (const struct Layer *l, const bool print_weights)
	Prints a layer's weights and biases. More...

char *	layer_weight_json (const struct Layer *l, const bool return_weights)
	Returns a json formatted string representation of a layer's weights. More...

void	layer_weight_rand (struct Layer *l)
	Randomises a layer's weights and biases. More...

void	layer_ensure_input_represention (struct Layer *l)
	Ensures that each neuron is connected to at least one input and each input is connected to at least one neuron. More...

const char *	layer_type_as_string (const int type)
	Returns a string representation of a layer type from an integer. More...

int	layer_type_as_int (const char *type)
	Returns the integer representation of a layer type given a name. More...

bool	layer_receives_images (const int type)
	Returns a whether a layer type expects images as input. More...

void	layer_guard_biases (const struct Layer *l)
	Check number of biases is within bounds. More...

void	layer_guard_outputs (const struct Layer *l)
	Check number of outputs is within bounds. More...

void	layer_guard_weights (const struct Layer *l)
	Check number of weights is within bounds. More...

static struct Layer *	layer_init (const struct ArgsLayer *args)
	Creates and initialises a new layer. More...

static size_t	layer_save (const struct Layer l, FILE fp)
	Writes the layer to a file. More...

static size_t	layer_load (struct Layer l, FILE fp)
	Reads the layer from a file. More...

Detailed Description

Interface for neural network layers.

Author: Richard Preen rpree.nosp@m.n@gm.nosp@m.ail.c.nosp@m.om

Copyright: The Authors.

Date: 2016–2024.

Definition in file neural_layer.h.

Macro Definition Documentation

◆ AVGPOOL

#define AVGPOOL (8)

Layer type average pooling.

Definition at line 37 of file neural_layer.h.

◆ CONNECTED

#define CONNECTED (0)

Layer type connected.

Definition at line 29 of file neural_layer.h.

◆ CONVOLUTIONAL

#define CONVOLUTIONAL (7)

Layer type convolutional.

Definition at line 36 of file neural_layer.h.

◆ DROPOUT

#define DROPOUT (1)

Layer type dropout.

Definition at line 30 of file neural_layer.h.

◆ LAYER_EVOLVE_CONNECT

#define LAYER_EVOLVE_CONNECT (1 << 5)

Layer may evolve connectivity.

Definition at line 56 of file neural_layer.h.

◆ LAYER_EVOLVE_ETA

#define LAYER_EVOLVE_ETA (1 << 4)

Layer may evolve rate of gradient descent.

Definition at line 55 of file neural_layer.h.

◆ LAYER_EVOLVE_FUNCTIONS

#define LAYER_EVOLVE_FUNCTIONS (1 << 2)

Layer may evolve functions.

Definition at line 53 of file neural_layer.h.

◆ LAYER_EVOLVE_NEURONS

#define LAYER_EVOLVE_NEURONS (1 << 1)

Layer may evolve neurons.

Definition at line 52 of file neural_layer.h.

◆ LAYER_EVOLVE_WEIGHTS

#define LAYER_EVOLVE_WEIGHTS (1 << 0)

Layer may evolve weights.

Definition at line 51 of file neural_layer.h.

◆ LAYER_SGD_WEIGHTS

#define LAYER_SGD_WEIGHTS (1 << 3)

Layer may perform gradient descent.

Definition at line 54 of file neural_layer.h.

◆ LSTM

#define LSTM (5)

Layer type LSTM.

Definition at line 34 of file neural_layer.h.

◆ MAXPOOL

#define MAXPOOL (6)

Layer type maxpooling.

Definition at line 35 of file neural_layer.h.

◆ N_INPUTS_MAX

#define N_INPUTS_MAX (2000000)

Maximum number of inputs per layer.

Definition at line 63 of file neural_layer.h.

◆ N_OUTPUTS_MAX

#define N_OUTPUTS_MAX (2000000)

Maximum number of outputs per layer.

Definition at line 64 of file neural_layer.h.

◆ N_WEIGHTS_MAX

#define N_WEIGHTS_MAX (20000000)

Maximum number of weights per layer.

Definition at line 62 of file neural_layer.h.

◆ NEURON_MAX

#define NEURON_MAX (100)

Maximum neuron state.

Definition at line 59 of file neural_layer.h.

◆ NEURON_MIN

#define NEURON_MIN (-100)

Minimum neuron state.

Definition at line 58 of file neural_layer.h.

◆ NOISE

#define NOISE (2)

Layer type noise.

Definition at line 31 of file neural_layer.h.

◆ RECURRENT

#define RECURRENT (4)

Layer type recurrent.

Definition at line 33 of file neural_layer.h.

◆ SOFTMAX

#define SOFTMAX (3)

Layer type softmax.

Definition at line 32 of file neural_layer.h.

◆ STRING_AVGPOOL

#define STRING_AVGPOOL ("avgpool\0")

Avgpool.

Definition at line 48 of file neural_layer.h.

◆ STRING_CONNECTED

#define STRING_CONNECTED ("connected\0")

Connected.

Definition at line 40 of file neural_layer.h.

◆ STRING_CONVOLUTIONAL

#define STRING_CONVOLUTIONAL ("convolutional\0")

Convolutional.

Definition at line 47 of file neural_layer.h.

◆ STRING_DROPOUT

#define STRING_DROPOUT ("dropout\0")

Dropout.

Definition at line 41 of file neural_layer.h.

◆ STRING_LSTM

#define STRING_LSTM ("lstm\0")

LSTM.

Definition at line 45 of file neural_layer.h.

◆ STRING_MAXPOOL

#define STRING_MAXPOOL ("maxpool\0")

Maxpool.

Definition at line 46 of file neural_layer.h.

◆ STRING_NOISE

#define STRING_NOISE ("noise\0")

Noise.

Definition at line 42 of file neural_layer.h.

◆ STRING_RECURRENT

#define STRING_RECURRENT ("recurrent\0")

Recurrent.

Definition at line 44 of file neural_layer.h.

◆ STRING_SOFTMAX

#define STRING_SOFTMAX ("softmax\0")

Softmax.

Definition at line 43 of file neural_layer.h.

◆ STRING_UPSAMPLE

#define STRING_UPSAMPLE ("upsample\0")

Upsample.

Definition at line 49 of file neural_layer.h.

◆ UPSAMPLE

#define UPSAMPLE (9)

Layer type upsample.

Definition at line 38 of file neural_layer.h.

◆ WEIGHT_MAX

#define WEIGHT_MAX (10)

Maximum value of a weight or bias.

Definition at line 61 of file neural_layer.h.

◆ WEIGHT_MIN

#define WEIGHT_MIN (-10)

Minimum value of a weight or bias.

Definition at line 60 of file neural_layer.h.

◆ WEIGHT_SD

#define WEIGHT_SD (0.1)

Std dev of Gaussian for weight resizing.

Definition at line 67 of file neural_layer.h.

◆ WEIGHT_SD_INIT

#define WEIGHT_SD_INIT (0.1)

Std dev of Gaussian for weight initialisation.

Definition at line 66 of file neural_layer.h.

◆ WEIGHT_SD_RAND

#define WEIGHT_SD_RAND (1.0)

Std dev of Gaussian for weight randomising.

Definition at line 68 of file neural_layer.h.

Function Documentation

◆ layer_add_neurons()

void layer_add_neurons	(	struct Layer *	l,
		const int	N
	)

Adds N neurons to a layer. Negative N removes neurons.

Precondition: N must be appropriately bounds checked for the layer.

Parameters

[in]	l	The neural network layer to mutate.
[in]	N	The number of neurons to add.

Definition at line 130 of file neural_layer.c.

References Layer::bias_updates, Layer::biases, Layer::delta, layer_calc_n_active(), LAYER_EVOLVE_CONNECT, layer_guard_outputs(), layer_guard_weights(), Layer::n_biases, Layer::n_inputs, Layer::n_outputs, Layer::n_weights, Layer::options, Layer::output, rand_normal(), rand_uniform(), Layer::state, Layer::weight_active, WEIGHT_SD, Layer::weight_updates, and Layer::weights.

Referenced by mutate_neurons(), and neural_layer_connected_mutate().

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◆ layer_backward()

static void layer_backward	(	const struct Layer *	l,
		const struct Net *	net,
		const double *	input,
		double *	delta
	)

static

Backward propagates the error through a layer.

Parameters

[in]	l	The layer to be backward propagated.
[in]	net	Network containing the layer.
[in]	input	The input to the layer.
[out]	delta	The previous layer's delta.

Definition at line 194 of file neural_layer.h.

References Layer::delta, LayerVtbl::layer_impl_backward, and Layer::layer_vptr.

Referenced by neural_layer_lstm_backward(), neural_layer_recurrent_backward(), and neural_learn().

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◆ layer_calc_n_active()

void layer_calc_n_active ( struct Layer * l )

Recalculates the number of active connections within a layer.

Parameters

[in] l The layer to recalculate the number of active connections.

Definition at line 384 of file neural_layer.c.

References Layer::n_active, Layer::n_weights, and Layer::weight_active.

Referenced by layer_add_neurons(), neural_layer_connected_resize(), neural_layer_convolutional_add_filters(), and neural_layer_convolutional_resize().

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◆ layer_copy()

static struct Layer* layer_copy ( const struct Layer * src )

static

Creates and returns a copy of a specified layer.

Parameters

[in] src The source layer.

Returns: A new copied layer.

Definition at line 239 of file neural_layer.h.

References LayerVtbl::layer_impl_copy, and Layer::layer_vptr.

Referenced by neural_copy(), neural_layer_lstm_copy(), and neural_layer_recurrent_copy().

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◆ layer_defaults()

void layer_defaults ( struct Layer * l )

Initialises a layer to default values.

Parameters

[in] l The layer to initialise.

Definition at line 413 of file neural_layer.c.

References Layer::bias_updates, Layer::biases, Layer::c, Layer::cell, Layer::channels, Layer::dc, Layer::decay, Layer::delta, Layer::eta, Layer::eta_max, Layer::eta_min, Layer::f, Layer::function, Layer::g, Layer::h, Layer::height, Layer::i, Layer::indexes, Layer::input_layer, Layer::layer_vptr, Layer::max_neuron_grow, Layer::max_outputs, Layer::momentum, Layer::mu, Layer::n_active, Layer::n_biases, Layer::n_filters, Layer::n_inputs, Layer::n_outputs, Layer::n_weights, Layer::o, Layer::options, Layer::out_c, Layer::out_h, Layer::out_w, Layer::output, Layer::output_layer, Layer::pad, Layer::prev_cell, Layer::prev_state, Layer::probability, Layer::recurrent_function, Layer::scale, Layer::self_layer, Layer::size, Layer::state, Layer::stride, Layer::temp, Layer::temp2, Layer::temp3, Layer::type, Layer::uf, Layer::ug, Layer::ui, Layer::uo, Layer::weight_active, Layer::weight_updates, Layer::weights, Layer::wf, Layer::wg, Layer::wi, Layer::width, and Layer::wo.

Referenced by layer_init(), layer_load(), neural_layer_avgpool_copy(), neural_layer_connected_copy(), neural_layer_convolutional_copy(), neural_layer_dropout_copy(), neural_layer_lstm_copy(), neural_layer_maxpool_copy(), neural_layer_noise_copy(), neural_layer_recurrent_copy(), neural_layer_softmax_copy(), and neural_layer_upsample_copy().

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◆ layer_ensure_input_represention()

void layer_ensure_input_represention ( struct Layer * l )

Ensures that each neuron is connected to at least one input and each input is connected to at least one neuron.

Parameters

[in] l A neural network layer.

Definition at line 204 of file neural_layer.c.

References Layer::n_active, Layer::n_inputs, Layer::n_outputs, rand_normal(), rand_uniform_int(), Layer::weight_active, WEIGHT_SD, and Layer::weights.

Referenced by neural_layer_connected_mutate(), and neural_layer_connected_resize().

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◆ layer_forward()

static void layer_forward	(	const struct Layer *	l,
		const struct Net *	net,
		const double *	input
	)

static

Forward propagates an input through the layer.

Parameters

[in]	l	Layer to be forward propagated.
[in]	net	Network containing the layer.
[in]	input	Input to the layer.

Definition at line 181 of file neural_layer.h.

References LayerVtbl::layer_impl_forward, and Layer::layer_vptr.

Referenced by neural_layer_lstm_forward(), neural_layer_recurrent_forward(), and neural_propagate().

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◆ layer_free()

static void layer_free ( const struct Layer * l )

static

Frees the memory used by the layer.

Parameters

[in] l The layer to be freed.

Definition at line 249 of file neural_layer.h.

References LayerVtbl::layer_impl_free, and Layer::layer_vptr.

Referenced by neural_free(), neural_layer_lstm_free(), neural_layer_recurrent_free(), and neural_remove().

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◆ layer_guard_biases()

void layer_guard_biases ( const struct Layer * l )

Check number of biases is within bounds.

Parameters

[in] l Layer to check.

Definition at line 581 of file neural_layer.c.

References layer_print(), Layer::n_biases, and N_OUTPUTS_MAX.

Referenced by guard_malloc().

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◆ layer_guard_outputs()

void layer_guard_outputs ( const struct Layer * l )

Check number of outputs is within bounds.

Parameters

[in] l Layer to check.

Definition at line 595 of file neural_layer.c.

References layer_print(), Layer::n_outputs, and N_OUTPUTS_MAX.

Referenced by guard_malloc(), layer_add_neurons(), malloc_layer_arrays(), and realloc_layer_arrays().

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◆ layer_guard_weights()

void layer_guard_weights ( const struct Layer * l )

Check number of weights is within bounds.

Parameters

[in] l Layer to check.

Definition at line 609 of file neural_layer.c.

References layer_print(), Layer::n_weights, and N_WEIGHTS_MAX.

Referenced by guard_malloc(), layer_add_neurons(), and malloc_layer_arrays().

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◆ layer_init()

static struct Layer* layer_init ( const struct ArgsLayer * args )

static

Creates and initialises a new layer.

Parameters

[in] args Layer parameters used to initialise the layer.

Returns: A pointer to the new layer.

Definition at line 356 of file neural_layer.h.

References layer_defaults(), LayerVtbl::layer_impl_init, layer_set_vptr(), Layer::layer_vptr, Layer::type, and ArgsLayer::type.

Referenced by neural_create(), neural_layer_lstm_init(), neural_layer_recurrent_init(), pred_neural_ae_to_classifier(), and pred_neural_expand().

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◆ layer_init_eta()

void layer_init_eta ( struct Layer * l )

Initialises a layer's gradient descent rate.

Parameters

[in] l The layer to initialise.

Definition at line 399 of file neural_layer.c.

References Layer::eta, Layer::eta_max, Layer::eta_min, LAYER_EVOLVE_ETA, Layer::options, and rand_uniform().

Referenced by neural_layer_connected_init(), and neural_layer_convolutional_init().

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◆ layer_json_export()

static char* layer_json_export	(	const struct Layer *	l,
		const bool	return_weights
	)

static

Returns a json formatted string representation of a layer.

Parameters

[in]	l	The layer to be returned.
[in]	return_weights	Whether to return the weights.

Definition at line 281 of file neural_layer.h.

References LayerVtbl::layer_impl_json_export, and Layer::layer_vptr.

Referenced by neural_json_export().

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◆ layer_load()

static size_t layer_load	(	struct Layer *	l,
		FILE *	fp
	)

static

Reads the layer from a file.

Parameters

[in]	l	The layer to be read.
[in]	fp	Pointer to the file to be read.

Returns: The number of elements read.

Definition at line 387 of file neural_layer.h.

References layer_defaults(), LayerVtbl::layer_impl_load, layer_set_vptr(), Layer::layer_vptr, and Layer::type.

Referenced by neural_layer_lstm_load(), neural_layer_recurrent_load(), and neural_load().

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◆ layer_mutate()

static bool layer_mutate ( struct Layer * l )

static

Performs layer mutation.

Parameters

[in] l The layer to mutate.

Returns: Whether any alterations were made.

Definition at line 216 of file neural_layer.h.

References LayerVtbl::layer_impl_mutate, and Layer::layer_vptr.

Referenced by neural_mutate().

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◆ layer_mutate_connectivity()

bool layer_mutate_connectivity	(	struct Layer *	l,
		const double	mu_enable,
		const double	mu_disable
	)

Mutates a layer's connectivity by zeroing weights.

Parameters

[in]	l	The neural network layer to mutate.
[in]	mu_enable	Probability of enabling a currently disabled weight.
[in]	mu_disable	Probability of disabling a currently enabled weight.

Returns: Whether any alterations were made.

Definition at line 176 of file neural_layer.c.

References Layer::n_active, Layer::n_inputs, Layer::n_outputs, Layer::n_weights, rand_normal(), rand_uniform(), Layer::weight_active, WEIGHT_SD, and Layer::weights.

Referenced by mutate_connectivity(), neural_layer_connected_mutate(), and neural_layer_convolutional_mutate().

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◆ layer_mutate_eta()

bool layer_mutate_eta	(	struct Layer *	l,
		const double	mu
	)

Mutates the gradient descent rate of a neural layer.

Parameters

[in]	l	The neural network layer to mutate.
[in]	mu	The rate of mutation.

Returns: Whether any alterations were made.

Definition at line 88 of file neural_layer.c.

References clamp(), Layer::eta, Layer::eta_max, Layer::eta_min, and rand_normal().

Referenced by mutate_eta(), neural_layer_connected_mutate(), and neural_layer_convolutional_mutate().

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◆ layer_mutate_functions()

bool layer_mutate_functions	(	struct Layer *	l,
		const double	mu
	)

Mutates a layer's activation function by random selection.

Parameters

[in]	l	The neural network layer to mutate.
[in]	mu	The rate of mutation.

Returns: Whether any alterations were made.

Definition at line 283 of file neural_layer.c.

References Layer::function, LSTM, NUM_ACTIVATIONS, rand_uniform(), rand_uniform_int(), Layer::recurrent_function, and Layer::type.

Referenced by mutate_functions(), neural_layer_connected_mutate(), neural_layer_convolutional_mutate(), and neural_layer_lstm_mutate().

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◆ layer_mutate_neurons()

int layer_mutate_neurons	(	const struct Layer *	l,
		const double	mu
	)

Returns the number of neurons to add or remove from a layer.

Parameters

[in]	l	The neural network layer to mutate.
[in]	mu	The rate of mutation.

Returns: The number of neurons to be added or removed.

Definition at line 106 of file neural_layer.c.

References clamp(), Layer::max_neuron_grow, Layer::max_outputs, Layer::n_outputs, rand_normal(), and rand_uniform().

Referenced by mutate_neurons(), and neural_layer_connected_mutate().

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◆ layer_mutate_weights()

bool layer_mutate_weights	(	struct Layer *	l,
		const double	mu
	)

Mutates a layer's weights and biases by adding random numbers from a Gaussian normal distribution with zero mean and standard deviation equal to the mutation rate.

Parameters

[in]	l	The neural network layer to mutate.
[in]	mu	The rate of mutation.

Returns: Whether any alterations were made.

Definition at line 252 of file neural_layer.c.

References Layer::biases, clamp(), Layer::n_biases, Layer::n_weights, rand_normal(), Layer::weight_active, WEIGHT_MAX, WEIGHT_MIN, and Layer::weights.

Referenced by mutate_weights(), neural_layer_connected_mutate(), and neural_layer_convolutional_mutate().

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◆ layer_output()

static double* layer_output ( const struct Layer * l )

static

Returns the outputs of a layer.

Parameters

[in] l The layer whose outputs are to be returned.

Returns: The layer outputs.

Definition at line 169 of file neural_layer.h.

References LayerVtbl::layer_impl_output, and Layer::layer_vptr.

Referenced by neural_output(), neural_outputs(), and neural_propagate().

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◆ layer_print()

static void layer_print	(	const struct Layer *	l,
		const bool	print_weights
	)

static

Prints the layer.

Parameters

[in]	l	The layer to be printed.
[in]	print_weights	Whether to print the weights.

Definition at line 270 of file neural_layer.h.

References LayerVtbl::layer_impl_print, and Layer::layer_vptr.

Referenced by get_workspace_size(), layer_guard_biases(), layer_guard_outputs(), layer_guard_weights(), max_pool(), and neural_layer_connected_resize().

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◆ layer_rand()

static void layer_rand ( struct Layer * l )

static

Randomises a layer.

Parameters

[in] l The layer to be randomised.

Definition at line 259 of file neural_layer.h.

References LayerVtbl::layer_impl_rand, and Layer::layer_vptr.

Referenced by neural_layer_lstm_rand(), neural_layer_recurrent_rand(), and neural_rand().

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◆ layer_receives_images()

bool layer_receives_images ( const int type )

Returns a whether a layer type expects images as input.

Parameters

[in] type Integer representation of a layer type.

Returns: Whether images (height × width × channels) are expected as inputs.

Definition at line 521 of file neural_layer.c.

References AVGPOOL, CONVOLUTIONAL, MAXPOOL, and UPSAMPLE.

Referenced by layer_args_json_export_inputs(), and layer_args_validate_inputs().

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◆ layer_resize()

static void layer_resize	(	struct Layer *	l,
		const struct Layer *	prev
	)

static

Resizes a layer using the previous layer's inputs.

Parameters

[in]	l	The layer to mutate.
[in]	prev	The layer prior to the one being mutated.

Returns: Whether any alterations were made.

Definition at line 228 of file neural_layer.h.

References LayerVtbl::layer_impl_resize, and Layer::layer_vptr.

Referenced by mutate_neurons(), neural_layer_lstm_resize(), neural_layer_recurrent_resize(), neural_mutate(), and neural_resize().

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◆ layer_save()

static size_t layer_save	(	const struct Layer *	l,
		FILE *	fp
	)

static

Writes the layer to a file.

Parameters

[in]	l	The layer to be written.
[in]	fp	Pointer to the file to be written.

Returns: The number of elements written.

Definition at line 373 of file neural_layer.h.

References LayerVtbl::layer_impl_save, Layer::layer_vptr, and Layer::type.

Referenced by neural_layer_lstm_save(), neural_layer_recurrent_save(), and neural_save().

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◆ layer_set_vptr()

void layer_set_vptr ( struct Layer * l )

Sets a neural network layer's functions to the implementations.

Parameters

[in] l The neural network layer to set.

Definition at line 42 of file neural_layer.c.

References AVGPOOL, CONNECTED, CONVOLUTIONAL, DROPOUT, layer_avgpool_vtbl, layer_connected_vtbl, layer_convolutional_vtbl, layer_dropout_vtbl, layer_lstm_vtbl, layer_maxpool_vtbl, layer_noise_vtbl, layer_recurrent_vtbl, layer_softmax_vtbl, layer_upsample_vtbl, Layer::layer_vptr, LSTM, MAXPOOL, NOISE, RECURRENT, SOFTMAX, Layer::type, and UPSAMPLE.

Referenced by layer_init(), and layer_load().

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◆ layer_type_as_int()

int layer_type_as_int ( const char * type )

Returns the integer representation of a layer type given a name.

Parameters

[in] type String representation of a layer type.

Returns: Integer representing the layer type.

Definition at line 540 of file neural_layer.c.

References AVGPOOL, CONNECTED, CONVOLUTIONAL, DROPOUT, LSTM, MAXPOOL, NOISE, RECURRENT, SOFTMAX, STRING_AVGPOOL, STRING_CONNECTED, STRING_CONVOLUTIONAL, STRING_DROPOUT, STRING_LSTM, STRING_MAXPOOL, STRING_NOISE, STRING_RECURRENT, STRING_SOFTMAX, STRING_UPSAMPLE, and UPSAMPLE.

Referenced by layer_args_json_import().

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◆ layer_type_as_string()

const char* layer_type_as_string ( const int type )

Returns a string representation of a layer type from an integer.

Parameters

[in] type Integer representation of a layer type.

Returns: String representing the name of the layer type.

Definition at line 486 of file neural_layer.c.

References AVGPOOL, CONNECTED, CONVOLUTIONAL, DROPOUT, LSTM, MAXPOOL, NOISE, RECURRENT, SOFTMAX, STRING_AVGPOOL, STRING_CONNECTED, STRING_CONVOLUTIONAL, STRING_DROPOUT, STRING_LSTM, STRING_MAXPOOL, STRING_NOISE, STRING_RECURRENT, STRING_SOFTMAX, STRING_UPSAMPLE, and UPSAMPLE.

Referenced by layer_args_json_export().

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◆ layer_update()

static void layer_update ( const struct Layer * l )

static

Updates the weights and biases of a layer.

Parameters

[in] l The layer to be updated.

Definition at line 205 of file neural_layer.h.

References LayerVtbl::layer_impl_update, and Layer::layer_vptr.

Referenced by neural_layer_lstm_update(), neural_layer_recurrent_update(), and neural_learn().

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◆ layer_weight_clamp()

void layer_weight_clamp ( const struct Layer * l )

Clamps a layer's weights and biases in range [WEIGHT_MIN, WEIGHT_MAX].

Parameters

[in] l The neural network layer to clamp.

Definition at line 365 of file neural_layer.c.

References Layer::biases, clamp(), Layer::n_biases, Layer::n_weights, Layer::weight_active, WEIGHT_MAX, WEIGHT_MIN, and Layer::weights.

Referenced by neural_layer_connected_update(), and neural_layer_convolutional_update().

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◆ layer_weight_json()

char* layer_weight_json	(	const struct Layer *	l,
		const bool	return_weights
	)

Returns a json formatted string representation of a layer's weights.

Parameters

[in]	l	The layer to return.
[in]	return_weights	Whether to return the values of weights and biases.

Returns: String encoded in json format.

Definition at line 324 of file neural_layer.c.

References Layer::biases, Layer::n_active, Layer::n_biases, Layer::n_weights, and Layer::weights.

Referenced by layer_weight_print(), neural_layer_connected_json_export(), neural_layer_convolutional_json_export(), neural_layer_lstm_json_export(), and neural_layer_recurrent_json_export().

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◆ layer_weight_print()

void layer_weight_print	(	const struct Layer *	l,
		const bool	print_weights
	)

Prints a layer's weights and biases.

Parameters

[in]	l	The neural network layer to print.
[in]	print_weights	Whether to print each individual weight and bias.

Definition at line 309 of file neural_layer.c.

References layer_weight_json().

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◆ layer_weight_rand()

void layer_weight_rand ( struct Layer * l )

Randomises a layer's weights and biases.

Parameters

[in] l The neural network layer to randomise.

Definition at line 348 of file neural_layer.c.

References Layer::biases, Layer::n_active, Layer::n_biases, Layer::n_weights, rand_normal(), Layer::weight_active, WEIGHT_SD_RAND, and Layer::weights.

Referenced by neural_layer_connected_rand(), and neural_layer_convolutional_rand().

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Data Structures

Macros

Functions

Detailed Description

Macro Definition Documentation

◆ AVGPOOL

◆ CONNECTED

◆ CONVOLUTIONAL

◆ DROPOUT

◆ LAYER_EVOLVE_CONNECT

◆ LAYER_EVOLVE_ETA

◆ LAYER_EVOLVE_FUNCTIONS

◆ LAYER_EVOLVE_NEURONS

◆ LAYER_EVOLVE_WEIGHTS

◆ LAYER_SGD_WEIGHTS

◆ LSTM

◆ MAXPOOL

◆ N_INPUTS_MAX

◆ N_OUTPUTS_MAX

◆ N_WEIGHTS_MAX

◆ NEURON_MAX

◆ NEURON_MIN

◆ NOISE

◆ RECURRENT

◆ SOFTMAX

◆ STRING_AVGPOOL

◆ STRING_CONNECTED

◆ STRING_CONVOLUTIONAL

◆ STRING_DROPOUT

◆ STRING_LSTM

◆ STRING_MAXPOOL

◆ STRING_NOISE

◆ STRING_RECURRENT

◆ STRING_SOFTMAX

◆ STRING_UPSAMPLE

◆ UPSAMPLE

◆ WEIGHT_MAX

◆ WEIGHT_MIN

◆ WEIGHT_SD

◆ WEIGHT_SD_INIT

◆ WEIGHT_SD_RAND

Function Documentation

◆ layer_add_neurons()

◆ layer_backward()

◆ layer_calc_n_active()

◆ layer_copy()

◆ layer_defaults()

◆ layer_ensure_input_represention()

◆ layer_forward()

◆ layer_free()

◆ layer_guard_biases()

◆ layer_guard_outputs()

◆ layer_guard_weights()

◆ layer_init()

◆ layer_init_eta()

◆ layer_json_export()

◆ layer_load()

◆ layer_mutate()

◆ layer_mutate_connectivity()

◆ layer_mutate_eta()

◆ layer_mutate_functions()

◆ layer_mutate_neurons()

◆ layer_mutate_weights()

◆ layer_output()

◆ layer_print()

◆ layer_rand()

◆ layer_receives_images()

◆ layer_resize()

◆ layer_save()

◆ layer_set_vptr()

◆ layer_type_as_int()

◆ layer_type_as_string()

◆ layer_update()

◆ layer_weight_clamp()

◆ layer_weight_json()

◆ layer_weight_print()

◆ layer_weight_rand()