bc::nn Namespace Reference

Namespaces
	detail
	functions

Classes
struct	Adam
struct	Cache
	A Dictionary designed to store any type using the 'store' and 'load' functions. More...
struct	cache_key
struct	Convolution
struct	FeedForward
struct	Flatten
struct	Function
struct	Layer_Base
class	Layer_Input_Base
struct	Layer_Loader
struct	Layer_Manager
struct	Layer_Output_Base
struct	layer_traits
struct	LayerChain
	Layer_Chain is an iterator-like object that connects different types of neural-network layers and defines convenient iterator-like methods. More...
struct	LayerChain< Index, Derived, CurrentLayer, Layers... >
struct	Logging_Output_Layer
struct	LSTM
struct	Max_Pooling
struct	Mean_Absolute_Error
struct	Mean_Absolute_Percent_Error
struct	Mean_Squared_Error
struct	Momentum
struct	network_runtime_traits
struct	NeuralNetwork
	the Neural_Network More...
struct	Optimizer_Base
struct	Output_Layer
struct	Polymorphic_Layer_Base
struct	Recurrent
struct	Root_Mean_Squared_Error
struct	SoftMax
struct	Stochastic_Gradient_Descent
struct	tensor_descriptor
struct	Tensor_Descriptor

Typedefs
template<class ValueType , class SystemTag , class... AltAllocator>
using	nn_default_allocator_type = bc::allocators::Recycle_Allocator< ValueType, SystemTag, AltAllocator... >
using	nn_default_system_tag = bc::host_tag
using	nn_default_optimizer_type = Momentum
template<class SystemTag , class ValueType >
using	layer_default_allocator = bc::allocators::Polymorphic_Allocator< SystemTag, ValueType >

Enumerations
enum	cache_key_type { inherit, always_recurrent, always_forward }
	A type designed to act as a key to the Cache object. More...

Functions
template<class SystemTag = BLACKCAT_DEFAULT_SYSTEM_T, class Optimizer = nn_default_optimizer_type>
auto	convolution (SystemTag system_tag, Dim< 3 > img_dims, Dim< 3 > krnl_dims, Dim< 2 > padding=Dim< 2 >().fill(0), Dim< 2 > strides=Dim< 2 >().fill(1), Dim< 2 > dilation=Dim< 2 >().fill(1), Optimizer=Optimizer())
template<class SystemTag = BLACKCAT_DEFAULT_SYSTEM_T, class Optimizer = nn_default_optimizer_type>
auto	recurrent_convolution (SystemTag system_tag, Dim< 3 > img_dims, Dim< 3 > krnl_dims, Dim< 2 > padding=Dim< 2 >().fill(0), Dim< 2 > strides=Dim< 2 >().fill(1), Dim< 2 > dilation=Dim< 2 >().fill(1), Optimizer=Optimizer())
template<class SystemTag , class Optimizer >
auto	convolution (SystemTag system_tag, Dim< 3 > img_dims, Dim< 3 > krnl_dims, Optimizer, Dim< 2 > padding=Dim< 2 >().fill(0), Dim< 2 > strides=Dim< 2 >().fill(1), Dim< 2 > dilation=Dim< 2 >().fill(1))
template<class SystemTag , class Optimizer >
auto	recurrent_convolution (SystemTag system_tag, Dim< 3 > img_dims, Dim< 3 > krnl_dims, Optimizer, Dim< 2 > padding=Dim< 2 >().fill(0), Dim< 2 > strides=Dim< 2 >().fill(1), Dim< 2 > dilation=Dim< 2 >().fill(1))
template<class SystemTag , class Optimizer = nn_default_optimizer_type>
auto	feedforward (SystemTag system_tag, int inputs, int outputs, Optimizer=Optimizer())
template<class Optimizer = nn_default_optimizer_type>
auto	feedforward (int inputs, int outputs, Optimizer=Optimizer())
template<class ValueType , class SystemTag , int X>
auto	flatten (SystemTag system_tag, Dim< X > shape)
template<class SystemTag , int X>
auto	flatten (SystemTag system_tag, Dim< X > shape)
template<int X>
auto	flatten (Dim< X > shape)
template<class ValueType , class SystemTag , class ErrorFunction = Mean_Absolute_Error>
Logging_Output_Layer< SystemTag, ValueType >	logging_output_layer (SystemTag system_tag, bc::size_t inputs, ErrorFunction error_function=ErrorFunction(), std::ostream &os=std::cout)
template<class SystemTag , class ErrorFunction = Mean_Absolute_Error>
auto	logging_output_layer (SystemTag system_tag, bc::size_t inputs, ErrorFunction error_function=ErrorFunction(), std::ostream &os=std::cout)
template<class ErrorFunction = Mean_Absolute_Error>
auto	logging_output_layer (int inputs, ErrorFunction error_function=ErrorFunction(), std::ostream &os=std::cout)
template<class SystemTag , class Optimizer = nn_default_optimizer_type>
auto	lstm (SystemTag system_tag, int inputs, int outputs, Optimizer=Optimizer())
template<class Optimizer = nn_default_optimizer_type>
auto	lstm (int inputs, int outputs, Optimizer=Optimizer())
template<class ValueType , class SystemTag >
Max_Pooling< SystemTag, ValueType >	max_pooling (SystemTag system_tag, Dim< 3 > img_dims, Dim< 2 > krnl_dims={3, 3}, Dim< 2 > padding={0, 0}, Dim< 2 > strides={-1,-1})
template<class SystemTag >
auto	max_pooling (SystemTag system_tag, Dim< 3 > img_dims, Dim< 2 > krnl_dims={3, 3}, Dim< 2 > padding={0, 0}, Dim< 2 > strides={-1,-1})
auto	max_pooling (Dim< 3 > img_dims, Dim< 2 > krnl_dims={3, 3}, Dim< 2 > padding={0, 0}, Dim< 2 > strides={-1,-1})
auto	tanh (bc::size_t inputs)
template<class ValueType , class SystemTag >
auto	tanh (SystemTag system, bc::size_t inputs)
template<class SystemTag >
auto	tanh (SystemTag system, bc::size_t inputs)
template<class ValueType , class SystemTag , int X>
auto	tanh (SystemTag system, bc::Dim< X > inputs)
template<class SystemTag , int X>
auto	tanh (SystemTag system, bc::Dim< X > inputs)
auto	logistic (bc::size_t inputs)
template<class ValueType , class SystemTag >
auto	logistic (SystemTag system, bc::size_t inputs)
template<class SystemTag >
auto	logistic (SystemTag system, bc::size_t inputs)
template<class ValueType , class SystemTag , int X>
auto	logistic (SystemTag system, bc::Dim< X > inputs)
template<class SystemTag , int X>
auto	logistic (SystemTag system, bc::Dim< X > inputs)
auto	relu (bc::size_t inputs)
template<class ValueType , class SystemTag >
auto	relu (SystemTag system, bc::size_t inputs)
template<class SystemTag >
auto	relu (SystemTag system, bc::size_t inputs)
template<class ValueType , class SystemTag , int X>
auto	relu (SystemTag system, bc::Dim< X > inputs)
template<class SystemTag , int X>
auto	relu (SystemTag system, bc::Dim< X > inputs)
auto	softplus (bc::size_t inputs)
template<class ValueType , class SystemTag >
auto	softplus (SystemTag system, bc::size_t inputs)
template<class SystemTag >
auto	softplus (SystemTag system, bc::size_t inputs)
template<class ValueType , class SystemTag , int X>
auto	softplus (SystemTag system, bc::Dim< X > inputs)
template<class SystemTag , int X>
auto	softplus (SystemTag system, bc::Dim< X > inputs)
auto	mish (bc::size_t inputs)
template<class ValueType , class SystemTag >
auto	mish (SystemTag system, bc::size_t inputs)
template<class SystemTag >
auto	mish (SystemTag system, bc::size_t inputs)
template<class ValueType , class SystemTag , int X>
auto	mish (SystemTag system, bc::Dim< X > inputs)
template<class SystemTag , int X>
auto	mish (SystemTag system, bc::Dim< X > inputs)
template<class ValueType , class SystemTag >
Output_Layer< SystemTag, ValueType >	output_layer (SystemTag system_tag, int inputs)
template<class SystemTag >
auto	output_layer (SystemTag system_tag, int inputs)
auto	output_layer (int inputs)
template<class... ArgsA, class... ArgsB>
void	link (std::shared_ptr< Layer_Base< ArgsA... >> prev, std::shared_ptr< Layer_Base< ArgsB... >> next)
template<class ValueType , class SystemTag >
Recurrent< SystemTag, ValueType >	recurrent (SystemTag system_tag, int inputs, int outputs)
template<class SystemTag >
auto	recurrent (SystemTag system_tag, int inputs, int outputs)
auto	recurrent (int inputs, int outputs)
template<class ValueType , class SystemTag >
SoftMax< SystemTag, ValueType >	softmax (SystemTag system_tag, int inputs)
template<class SystemTag >
auto	softmax (SystemTag system_tag, int inputs)
auto	softmax (int inputs)
template<class ValueType , class SystemTag , class Functor >
Function< SystemTag, ValueType, Functor >	function (SystemTag system_tag, int inputs, Functor function=Functor())
template<class SystemTag , class Functor >
auto	function (SystemTag system_tag, int inputs, Functor function=Functor())
template<class ValueType , class SystemTag , class Functor , int X>
Function< SystemTag, ValueType, Functor >	function (SystemTag system_tag, Dim< X > shape, Functor function=Functor())
template<class SystemTag , class Functor , int X>
auto	function (SystemTag system_tag, bc::Dim< X > shape, Functor function=Functor())
template<class ... Layers>
auto	neuralnetwork (Layers ... layers)
	Factory method for creating neural_networks. More...

Variables
struct bc::nn::Mean_Absolute_Error	MAE
struct bc::nn::Root_Mean_Squared_Error	RMSE
struct bc::nn::Mean_Squared_Error	MSE
struct bc::nn::Mean_Absolute_Percent_Error	MAPE
struct bc::nn::Adam	adam
struct bc::nn::Momentum	momentum
struct bc::nn::Stochastic_Gradient_Descent	sgd

Typedef Documentation

layer_default_allocator

template<class SystemTag , class ValueType >

using bc::nn::layer_default_allocator = typedef bc::allocators::Polymorphic_Allocator<SystemTag, ValueType>

nn_default_allocator_type

template<class ValueType , class SystemTag , class... AltAllocator>

using bc::nn::nn_default_allocator_type = typedef bc::allocators::Recycle_Allocator<ValueType, SystemTag, AltAllocator...>

nn_default_optimizer_type

using bc::nn::nn_default_optimizer_type = typedef Momentum

nn_default_system_tag

using bc::nn::nn_default_system_tag = typedef bc::host_tag

Enumeration Type Documentation

cache_key_type

enum bc::nn::cache_key_type

A type designed to act as a key to the Cache object.

Arguments: K - any class to use as a key, generally "Name<char...>" class is used to create a constexpr name to the class. V - the type to return from the given key CacheKeyOverrider - Determines if the storing should override the most recent member store or if it should be stored in a separate location for back-propagation through time.

Enumerator
inherit
always_recurrent
always_forward

Function Documentation

convolution() [1/2]

template<class SystemTag = BLACKCAT_DEFAULT_SYSTEM_T, class Optimizer = nn_default_optimizer_type>

auto bc::nn::convolution	(	SystemTag	system_tag,
		Dim< 3 >	img_dims,
		Dim< 3 >	krnl_dims,
		Dim< 2 >	padding = Dim<2>().fill(0),
		Dim< 2 >	strides = Dim<2>().fill(1),
		Dim< 2 >	dilation = Dim<2>().fill(1),
		Optimizer	= Optimizer()
	)

convolution() [2/2]

template<class SystemTag , class Optimizer >

auto bc::nn::convolution	(	SystemTag	system_tag,
		Dim< 3 >	img_dims,
		Dim< 3 >	krnl_dims,
		Optimizer	,
		Dim< 2 >	padding = Dim<2>().fill(0),
		Dim< 2 >	strides = Dim<2>().fill(1),
		Dim< 2 >	dilation = Dim<2>().fill(1)
	)

feedforward() [1/2]

template<class SystemTag , class Optimizer = nn_default_optimizer_type>

auto bc::nn::feedforward	(	SystemTag	system_tag,
		int	inputs,
		int	outputs,
		Optimizer	= Optimizer()
	)

feedforward() [2/2]

template<class Optimizer = nn_default_optimizer_type>

auto bc::nn::feedforward	(	int	inputs,
		int	outputs,
		Optimizer	= Optimizer()
	)

flatten() [1/3]

template<class ValueType , class SystemTag , int X>

auto bc::nn::flatten	(	SystemTag	system_tag,
		Dim< X >	shape
	)

flatten() [2/3]

template<class SystemTag , int X>

auto bc::nn::flatten	(	SystemTag	system_tag,
		Dim< X >	shape
	)

flatten() [3/3]

template<int X>

auto bc::nn::flatten

(

Dim< X >

shape

)

function() [1/4]

template<class ValueType , class SystemTag , class Functor >

Function<SystemTag, ValueType, Functor> bc::nn::function	(	SystemTag	system_tag,
		int	inputs,
		Functor	function = Functor()
	)

function() [2/4]

template<class SystemTag , class Functor >

auto bc::nn::function	(	SystemTag	system_tag,
		int	inputs,
		Functor	function = Functor()
	)

function() [3/4]

template<class ValueType , class SystemTag , class Functor , int X>

Function<SystemTag, ValueType, Functor> bc::nn::function	(	SystemTag	system_tag,
		Dim< X >	shape,
		Functor	function = Functor()
	)

function() [4/4]

template<class SystemTag , class Functor , int X>

auto bc::nn::function	(	SystemTag	system_tag,
		bc::Dim< X >	shape,
		Functor	function = Functor()
	)

link()

template<class... ArgsA, class... ArgsB>

void bc::nn::link	(	std::shared_ptr< Layer_Base< ArgsA... >>	prev,
		std::shared_ptr< Layer_Base< ArgsB... >>	next
	)

logging_output_layer() [1/3]

template<class ValueType , class SystemTag , class ErrorFunction = Mean_Absolute_Error>

Logging_Output_Layer<SystemTag, ValueType> bc::nn::logging_output_layer	(	SystemTag	system_tag,
		bc::size_t	inputs,
		ErrorFunction	error_function = ErrorFunction(),
		std::ostream &	os = std::cout
	)

logging_output_layer() [2/3]

template<class SystemTag , class ErrorFunction = Mean_Absolute_Error>

auto bc::nn::logging_output_layer	(	SystemTag	system_tag,
		bc::size_t	inputs,
		ErrorFunction	error_function = ErrorFunction(),
		std::ostream &	os = std::cout
	)

logging_output_layer() [3/3]

template<class ErrorFunction = Mean_Absolute_Error>

auto bc::nn::logging_output_layer	(	int	inputs,
		ErrorFunction	error_function = ErrorFunction(),
		std::ostream &	os = std::cout
	)

logistic() [1/5]

auto bc::nn::logistic

(

bc::size_t

inputs

)

logistic() [2/5]

template<class ValueType , class SystemTag >

auto bc::nn::logistic	(	SystemTag	system,
		bc::size_t	inputs
	)

logistic() [3/5]

template<class SystemTag >

auto bc::nn::logistic	(	SystemTag	system,
		bc::size_t	inputs
	)

logistic() [4/5]

template<class ValueType , class SystemTag , int X>

auto bc::nn::logistic	(	SystemTag	system,
		bc::Dim< X >	inputs
	)

logistic() [5/5]

template<class SystemTag , int X>

auto bc::nn::logistic	(	SystemTag	system,
		bc::Dim< X >	inputs
	)

lstm() [1/2]

template<class SystemTag , class Optimizer = nn_default_optimizer_type>

auto bc::nn::lstm	(	SystemTag	system_tag,
		int	inputs,
		int	outputs,
		Optimizer	= Optimizer()
	)

lstm() [2/2]

template<class Optimizer = nn_default_optimizer_type>

auto bc::nn::lstm	(	int	inputs,
		int	outputs,
		Optimizer	= Optimizer()
	)

max_pooling() [1/3]

template<class ValueType , class SystemTag >

Max_Pooling<SystemTag, ValueType> bc::nn::max_pooling	(	SystemTag	system_tag,
		Dim< 3 >	img_dims,
		Dim< 2 >	krnl_dims = {3,3},
		Dim< 2 >	padding = {0,0},
		Dim< 2 >	strides = {-1,-1}
	)

max_pooling() [2/3]

template<class SystemTag >

auto bc::nn::max_pooling	(	SystemTag	system_tag,
		Dim< 3 >	img_dims,
		Dim< 2 >	krnl_dims = {3,3},
		Dim< 2 >	padding = {0,0},
		Dim< 2 >	strides = {-1,-1}
	)

max_pooling() [3/3]

auto bc::nn::max_pooling	(	Dim< 3 >	img_dims,
		Dim< 2 >	krnl_dims = {3,3},
		Dim< 2 >	padding = {0,0},
		Dim< 2 >	strides = {-1,-1}
	)

mish() [1/5]

auto bc::nn::mish

(

bc::size_t

inputs

)

mish() [2/5]

template<class ValueType , class SystemTag >

auto bc::nn::mish	(	SystemTag	system,
		bc::size_t	inputs
	)

mish() [3/5]

template<class SystemTag >

auto bc::nn::mish	(	SystemTag	system,
		bc::size_t	inputs
	)

mish() [4/5]

template<class ValueType , class SystemTag , int X>

auto bc::nn::mish	(	SystemTag	system,
		bc::Dim< X >	inputs
	)

mish() [5/5]

template<class SystemTag , int X>

auto bc::nn::mish	(	SystemTag	system,
		bc::Dim< X >	inputs
	)

neuralnetwork()

template<class ... Layers>

auto bc::nn::neuralnetwork

(

Layers ...

layers

)

Factory method for creating neural_networks.

Each layer defines its own respective factory_methods. It is generally recommended to use these factory methods opposed to instantiating a layer object manually.

output_layer() [1/3]

template<class ValueType , class SystemTag >

Output_Layer<SystemTag, ValueType> bc::nn::output_layer	(	SystemTag	system_tag,
		int	inputs
	)

output_layer() [2/3]

template<class SystemTag >

auto bc::nn::output_layer	(	SystemTag	system_tag,
		int	inputs
	)

output_layer() [3/3]

auto bc::nn::output_layer

(

int

inputs

)

recurrent() [1/3]

template<class ValueType , class SystemTag >

Recurrent<SystemTag, ValueType> bc::nn::recurrent	(	SystemTag	system_tag,
		int	inputs,
		int	outputs
	)

recurrent() [2/3]

template<class SystemTag >

auto bc::nn::recurrent	(	SystemTag	system_tag,
		int	inputs,
		int	outputs
	)

recurrent() [3/3]

auto bc::nn::recurrent	(	int	inputs,
		int	outputs
	)

recurrent_convolution() [1/2]

template<class SystemTag = BLACKCAT_DEFAULT_SYSTEM_T, class Optimizer = nn_default_optimizer_type>

auto bc::nn::recurrent_convolution	(	SystemTag	system_tag,
		Dim< 3 >	img_dims,
		Dim< 3 >	krnl_dims,
		Dim< 2 >	padding = Dim<2>().fill(0),
		Dim< 2 >	strides = Dim<2>().fill(1),
		Dim< 2 >	dilation = Dim<2>().fill(1),
		Optimizer	= Optimizer()
	)

recurrent_convolution() [2/2]

template<class SystemTag , class Optimizer >

auto bc::nn::recurrent_convolution	(	SystemTag	system_tag,
		Dim< 3 >	img_dims,
		Dim< 3 >	krnl_dims,
		Optimizer	,
		Dim< 2 >	padding = Dim<2>().fill(0),
		Dim< 2 >	strides = Dim<2>().fill(1),
		Dim< 2 >	dilation = Dim<2>().fill(1)
	)

relu() [1/5]

template<class SystemTag , int X>

auto bc::nn::relu	(	SystemTag	system,
		bc::Dim< X >	inputs
	)

relu() [2/5]

template<class ValueType , class SystemTag , int X>

auto bc::nn::relu	(	SystemTag	system,
		bc::Dim< X >	inputs
	)

relu() [3/5]

template<class SystemTag >

auto bc::nn::relu	(	SystemTag	system,
		bc::size_t	inputs
	)

relu() [4/5]

template<class ValueType , class SystemTag >

auto bc::nn::relu	(	SystemTag	system,
		bc::size_t	inputs
	)

relu() [5/5]

auto bc::nn::relu

(

bc::size_t

inputs

)

softmax() [1/3]

template<class ValueType , class SystemTag >

SoftMax<SystemTag, ValueType> bc::nn::softmax	(	SystemTag	system_tag,
		int	inputs
	)

softmax() [2/3]

template<class SystemTag >

auto bc::nn::softmax	(	SystemTag	system_tag,
		int	inputs
	)

softmax() [3/3]

auto bc::nn::softmax

(

int

inputs

)

softplus() [1/5]

template<class SystemTag >

auto bc::nn::softplus	(	SystemTag	system,
		bc::size_t	inputs
	)

softplus() [2/5]

template<class ValueType , class SystemTag >

auto bc::nn::softplus	(	SystemTag	system,
		bc::size_t	inputs
	)

softplus() [3/5]

template<class ValueType , class SystemTag , int X>

auto bc::nn::softplus	(	SystemTag	system,
		bc::Dim< X >	inputs
	)

softplus() [4/5]

auto bc::nn::softplus

(

bc::size_t

inputs

)

softplus() [5/5]

template<class SystemTag , int X>

auto bc::nn::softplus	(	SystemTag	system,
		bc::Dim< X >	inputs
	)

tanh() [1/5]

template<class ValueType , class SystemTag , int X>

auto bc::nn::tanh	(	SystemTag	system,
		bc::Dim< X >	inputs
	)

tanh() [2/5]

template<class SystemTag , int X>

auto bc::nn::tanh	(	SystemTag	system,
		bc::Dim< X >	inputs
	)

tanh() [3/5]

template<class ValueType , class SystemTag >

auto bc::nn::tanh	(	SystemTag	system,
		bc::size_t	inputs
	)

tanh() [4/5]

auto bc::nn::tanh

(

bc::size_t

inputs

)

tanh() [5/5]

template<class SystemTag >

auto bc::nn::tanh	(	SystemTag	system,
		bc::size_t	inputs
	)

Variable Documentation

adam

struct bc::nn::Adam bc::nn::adam

MAE

struct bc::nn::Mean_Absolute_Error bc::nn::MAE

MAPE

struct bc::nn::Mean_Absolute_Percent_Error bc::nn::MAPE

momentum

struct bc::nn::Momentum bc::nn::momentum

MSE

struct bc::nn::Mean_Squared_Error bc::nn::MSE

RMSE

struct bc::nn::Root_Mean_Squared_Error bc::nn::RMSE

sgd

struct bc::nn::Stochastic_Gradient_Descent bc::nn::sgd