Classes
struct	CONVOLUTION_LAYER

struct	POOLING_LAYER

Typedefs
typedef std::pair< std::vector< std::vector< Real > >, std::vector< uint16_t > >	MNIST_DATA

typedef std::pair< std::vector< std::vector< std::vector< Real > > >, std::vector< uint16_t > >	SPIKING_MNIST

typedef std::vector< std::vector< std::vector< std::vector< Real > > > >	CONVOLUTION_FILTER

typedef struct CONVOLUTION_LAYER	CONVOLUTION_LAYER

typedef struct POOLING_LAYER	POOLING_LAYER

Enumerations

Functions
MNIST_DATA	loadMnistData (const size_t num_data, const std::string path)
	Read in MNIST data from files. More...

void	print_image (const std::vector< Real > &img, size_t wrap)
	Prints image to std::cout. More...

std::vector< std::vector< std::vector< Real > > >	image_to_rate (const std::vector< std::vector< Real >> &images, const Real duration, const Real max_freq, size_t num_images, bool poisson=true)
	Converts a vector of images to a rate based representation. More...

SPIKING_MNIST	mnist_to_spike (const MNIST_DATA &mnist_data, const Real duration, const Real max_freq, size_t num_images, bool poisson=true)
	Converts the full MNIST dataset to a spiking MNIST dataset. More...

std::vector< MNIST_DATA >	create_batches (const SPIKING_MNIST &mnist_data, const size_t batch_size, Real duration, Real pause, const bool shuffle=false, unsigned seed=0)
	Creates batches of spikes representing the MNIST data. More...

cypress::Population< SpikeSourceArray >	create_spike_source (Network &netw, const MNIST_DATA &spikes)
	Creates Spike sources in network from spikes. More...

cypress::Population< SpikeSourceArray > &	update_spike_source (cypress::Population< SpikeSourceArray > &source, const MNIST_DATA &spikes)
	Update Spike sources in network from spikes. More...

Json	read_network (std::string path, bool msgpack=true)
	Read in the network file from json of msgpack. The Repo provides a script which creates compatible files. More...

template<typename T >
Real	max_weight (const T &json)
	Calculate the max weight, ignore negative values. More...

template<typename T >
Real	max_weight (const Matrix< T > &mat)
	Calculate the max weight, ignore negative values. More...

template<typename T >
Real	min_weight (const T &json)
	Calculate the min weight. More...

template<typename T >
Real	min_weight (const Matrix< T > &json)
	Calculate the min weight. More...

template<typename T >
Real	max_weight_abs (const T &json)
	Calculate the max absolute weight. More...

template<typename T >
Real	max_weight_abs (const Matrix< T > &json)
	Calculate the max absolute weight. More...

std::vector< LocalConnection >	dense_weights_to_conn (const Matrix< Real > &mat, Real scale, Real delay)
	Convert a dense layer to list of Local Connections. More...

std::vector< LocalConnection >	conv_weights_to_conn (const mnist_helper::CONVOLUTION_LAYER &layer, Real scale, Real delay)
	Converts a conv layer to list of Local Connections. More...

std::vector< std::vector< LocalConnection > >	pool_to_conn (const mnist_helper::POOLING_LAYER &layer, Real max_pool_weight, Real pool_inhib_weight, Real delay, Real pool_delay)

std::vector< uint16_t >	spikes_to_labels (const PopulationBase &pop, Real duration, Real pause, size_t batch_size)
	Converts the simulation results into label data. More...

void	conv_spikes_per_kernel (const std::string &filename, const PopulationBase &pop, Real duration, Real pause, size_t batch_size, Real norm=0.0)
	Saves the spikes of a convolution layer in a file. More...

std::vector< std::vector< Real > >	spikes_to_rates (const PopulationBase pop, Real duration, Real pause, size_t batch_size, Real norm=0.0)
	Converts the simulation results into values between 0 and 1. More...

size_t	compare_labels (std::vector< uint16_t > &label, std::vector< uint16_t > &res)
	Compare original labels with simulation labels, return number of correct labels. More...

std::vector< Real >	av_pooling_image (std::vector< Real > &image, size_t height, size_t width, size_t pooling_size)
	Downscale an image by average pooling. More...

MNIST_DATA	scale_mnist (MNIST_DATA &data, size_t pooling_size=3)
	downscale the complete MNIST dataset More...

SPIKING_MNIST	read_data_to_spike (const size_t num_images, bool train_data, const Real duration, const Real max_freq, bool poisson=true, bool scale_down=false)
	Reads in MNIST test or train data. More...

std::vector< LocalConnection >	conns_from_mat (const cypress::Matrix< Real > &weights, Real delay, Real scale_factor=0.0)
	Generate connection from given weight matrix. More...

void	update_conns_from_mat (const std::vector< cypress::Matrix< Real >> &weights, Network &netw, Real delay=1.0, Real scale_factor=0.0)
	Updates the connector is a given network with the weights provided. More...

Class Documentation

struct mnist_helper::CONVOLUTION_LAYER

Definition at line 38 of file helper_functions.hpp.

Class Members
CONVOLUTION_FILTER	filter
vector< size_t >	input_sizes
vector< size_t >	output_sizes
size_t	padding
size_t	stride

struct mnist_helper::POOLING_LAYER

Definition at line 46 of file helper_functions.hpp.

Class Members
vector< size_t >	input_sizes
vector< size_t >	output_sizes
vector< size_t >	size
size_t	stride

Typedef Documentation

typedef std::vector<std::vector<std::vector<std::vector<Real> > > > mnist_helper::CONVOLUTION_FILTER

Definition at line 37 of file helper_functions.hpp.

typedef struct CONVOLUTION_LAYER mnist_helper::CONVOLUTION_LAYER

Definition at line 45 of file helper_functions.hpp.

typedef std::pair<std::vector<std::vector<Real> >, std::vector<uint16_t> > mnist_helper::MNIST_DATA

Definition at line 32 of file helper_functions.hpp.

typedef struct POOLING_LAYER mnist_helper::POOLING_LAYER

Definition at line 52 of file helper_functions.hpp.

typedef std::pair<std::vector<std::vector<std::vector<Real> > >, std::vector<uint16_t> > mnist_helper::SPIKING_MNIST

Definition at line 35 of file helper_functions.hpp.

Enumeration Type Documentation

enum mnist_helper::LAYER_TYPE

Enumerator
Dense
Conv
Pooling

Definition at line 53 of file helper_functions.hpp.

53 {Dense, Conv, Pooling};

mnist_helper::Pooling

Definition: helper_functions.hpp:53

mnist_helper::Conv

Definition: helper_functions.hpp:53

mnist_helper::Dense

Definition: helper_functions.hpp:53

Function Documentation

std::vector<Real> mnist_helper::av_pooling_image	(	std::vector< Real > &	image,
		size_t	height,
		size_t	width,
		size_t	pooling_size
	)

Downscale an image by average pooling.

Parameters

image	the image itself
height	height of the image
width	width of the image
pooling_size	size of the pooling window (e.g. 2)

Returns: the downscaled image

size_t mnist_helper::compare_labels	(	std::vector< uint16_t > &	label,
		std::vector< uint16_t > &	res
	)

Compare original labels with simulation labels, return number of correct labels.

Parameters

label	data label
res	label from simulation

Returns: number of correct labels

std::vector<LocalConnection> mnist_helper::conns_from_mat	(	const cypress::Matrix< Real > &	weights,
		Real	delay,
		Real	scale_factor = `0.0`
	)

Generate connection from given weight matrix.

Parameters

weights	the weight matrix, type cypress::Matrix<double>
delay	synaptic delay, use 1.0 is unsure
scale_factor	scale all weights, do not scale if set to zero

Returns: a list of connections

void mnist_helper::conv_spikes_per_kernel	(	const std::string &	filename,
		const PopulationBase &	pop,
		Real	duration,
		Real	pause,
		size_t	batch_size,
		Real	norm = `0.0`
	)

Saves the spikes of a convolution layer in a file.

Parameters

filename	the file to save to
pop	the neuron population to count spikes from
duration	presentation time of a sample
pause	pause time in between samples
batch_size	number of samples interpreted by these neurons (batch size)
norm	divide the number of spikes in bin by this value, ignore if it is set to zero

std::vector<LocalConnection> mnist_helper::conv_weights_to_conn	(	const mnist_helper::CONVOLUTION_LAYER &	layer,
		Real	scale,
		Real	delay
	)

Converts a conv layer to list of Local Connections.

Parameters

layer	struct of convolution layer information
scale	scale factor for weights
delay	synaptic delay

Returns: vector of connections

std::vector<MNIST_DATA> mnist_helper::create_batches	(	const SPIKING_MNIST &	mnist_data,
		const size_t	batch_size,
		Real	duration,
		Real	pause,
		const bool	shuffle = `false`,
		unsigned	seed = `0`
	)

Creates batches of spikes representing the MNIST data.

Parameters

mnist_data	Spiking MNIST data
batch_size	number of images per batch
duration	duration of every image
pause	time in between images
shuffle	True for shuffling images. Defaults to false.
seed	Seed for shuffling images Defaults to 0.

Returns: A vector of spike batches, every vector entry gives a container. std::get<0> give spikes for every pixel representing all images in one batch, std::get<1> returns labels

cypress::Population<SpikeSourceArray> mnist_helper::create_spike_source	(	Network &	netw,
		const MNIST_DATA &	spikes
	)

Creates Spike sources in network from spikes.

Parameters

netw	a cypress network
spikes	One batch from the return value of "create_batch"

Returns: SpikeSourceArray Population

std::vector<LocalConnection> mnist_helper::dense_weights_to_conn	(	const Matrix< Real > &	mat,
		Real	scale,
		Real	delay
	)

Convert a dense layer to list of Local Connections.

Parameters

mat	cypress matrix of weights
scale	scale factor for weights
delay	synaptic delay

Returns: vector of connections

std::vector<std::vector<std::vector<Real> > > mnist_helper::image_to_rate	(	const std::vector< std::vector< Real >> &	images,
		const Real	duration,
		const Real	max_freq,
		size_t	num_images,
		bool	poisson = `true`
	)

Converts a vector of images to a rate based representation.

Parameters

images	vector of images
duration	duration of the rate
max_freq	maximal rate/frequency
num_images	number of images to read in
poisson	False: regular spiking. True: poisson rates. Defaults to true.

Returns: vector (images) of vector (pixel) of spikes

MNIST_DATA mnist_helper::loadMnistData	(	const size_t	num_data,
		const std::string	path
	)

Read in MNIST data from files.

Parameters

num_data	Number of images
path	path to file, without end, e.g. /path/to/data/train for training data, /path/to/data/t10k for test data

Returns: pair, std::get<0> is a vector of images, std::get<1> a vector of labels

template<typename T >

Real mnist_helper::max_weight ( const T & json )

Calculate the max weight, ignore negative values.

Parameters

json	json array containing the weights

Returns: max weight

Definition at line 157 of file helper_functions.hpp.

 {
     Real max = 0.0;
     for (size_t i = 0; i < json.size(); i++) {
         for (size_t j = 0; j < json[i].size(); j++) {
             Real tmp = Real(json[i][j]);
             if (tmp > max) {
                 max = tmp;
             }
         }
     }
     return max;
 }

template<typename T >

Real mnist_helper::max_weight ( const Matrix< T > & mat )

Calculate the max weight, ignore negative values.

Parameters

mat	cypress Matrix containing the weights

Returns: max weight

Definition at line 178 of file helper_functions.hpp.

 {
     Real max = 0.0;
     for (size_t i = 0; i < mat.size(); i++) {
         Real tmp = Real(mat[i]);
         if (tmp > max) {
             max = tmp;
         }
     }
     return max;
 }

template<typename T >

Real mnist_helper::max_weight_abs ( const T & json )

Calculate the max absolute weight.

Parameters

json	json array containing the weights

Returns: max weight

Definition at line 237 of file helper_functions.hpp.

 {
     Real max = 0.0;
     for (size_t i = 0; i < json.size(); i++) {
         for (size_t j = 0; j < json[i].size(); j++) {
             Real tmp = fabs(Real(json[i][j]));
             if (tmp > max) {
                 max = tmp;
             }
         }
     }
     return max;
 }

template<typename T >

Real mnist_helper::max_weight_abs ( const Matrix< T > & json )

Calculate the max absolute weight.

Parameters

mat	cypress Matrix containing the weights

Returns: max weight

Definition at line 258 of file helper_functions.hpp.

 {
     Real max = 0.0;
     for (size_t i = 0; i < json.size(); i++) {
         Real tmp = fabs(Real(json[i]));
         if (tmp > max) {
             max = tmp;
         }
     }
     return max;
 }

template<typename T >

Real mnist_helper::min_weight ( const T & json )

Calculate the min weight.

Parameters

json	json array containing the weights

Returns: min weight

Definition at line 197 of file helper_functions.hpp.

 {
     Real min = 0.0;
     for (size_t i = 0; i < json.size(); i++) {
         for (size_t j = 0; j < json[i].size(); j++) {
             Real tmp = Real(json[i][j]);
             if (tmp < min) {
                 min = tmp;
             }
         }
     }
     return min;
 }

template<typename T >

Real mnist_helper::min_weight ( const Matrix< T > & json )

Calculate the min weight.

Parameters

mat	cypress Matrix containing the weights

Returns: min weight

Definition at line 218 of file helper_functions.hpp.

 {
     Real min = 0.0;
     for (size_t i = 0; i < json.size(); i++) {
         Real tmp = Real(json[i]);
         if (tmp < min) {
             min = tmp;
         }
     }
     return min;
 }

SPIKING_MNIST mnist_helper::mnist_to_spike	(	const MNIST_DATA &	mnist_data,
		const Real	duration,
		const Real	max_freq,
		size_t	num_images,
		bool	poisson = `true`
	)

Converts the full MNIST dataset to a spiking MNIST dataset.

Parameters

mnist_data	data container from loadMnistData
duration	duration of spikes per image
max_freq	Maximal rate (e.g. for px = 1)
num_images	number of images to read in
poisson	False: regular spiking. True: poisson rates. Defaults to true.

Returns: pair, std::get<0> is a vector of spiking images, std::get<1> a vector of labels

std::vector<std::vector<LocalConnection> > mnist_helper::pool_to_conn	(	const mnist_helper::POOLING_LAYER &	layer,
		Real	max_pool_weight,
		Real	pool_inhib_weight,
		Real	delay,
		Real	pool_delay
	)

Parameters

layer	struct of pooling layer information
max_pool_weight	scale factor for pooling weights
pool_inhib_weight	scale factor for cross inhibitory weights
delay	synaptic delay

Returns: vector of connections. First the inhibitory connections in the previous layer, then the connections to the pooling layer

void mnist_helper::print_image	(	const std::vector< Real > &	img,
		size_t	wrap
	)

Prints image to std::cout.

Parameters

img	the image
wrap	line wrap -> width of the image

SPIKING_MNIST mnist_helper::read_data_to_spike	(	const size_t	num_images,
		bool	train_data,
		const Real	duration,
		const Real	max_freq,
		bool	poisson = `true`,
		bool	scale_down = `false`
	)

Reads in MNIST test or train data.

Parameters

num_images	number of images to read in
train_data	true for training data, false for test
duration	duration of spikes per image
max_freq	Maximal rate (e.g. px = 1)
poisson	False: regular spiking. True: poisson rates. Defaults to true.
scale_down	Scales down the image by a factor of 3 in each dim

Returns: pair, std::get<0> is a vector of spiking images, std::get<1> a vector of labels

Json mnist_helper::read_network	(	std::string	path,
		bool	msgpack = `true`
	)

Read in the network file from json of msgpack. The Repo provides a script which creates compatible files.

Parameters

path	full path to file
msgpack	True: Compressed msgpack. False: plain Json. Defaults to true.

Returns: The json containing the json from file

MNIST_DATA mnist_helper::scale_mnist	(	MNIST_DATA &	data,
		size_t	pooling_size = `3`
	)

downscale the complete MNIST dataset

Parameters

data	The MNIST dataset in a container

Returns: downscaled MNIST dataset in a container

std::vector<uint16_t> mnist_helper::spikes_to_labels	(	const PopulationBase &	pop,
		Real	duration,
		Real	pause,
		size_t	batch_size
	)

Converts the simulation results into label data.

Parameters

pop	the label population
duration	presentation time of a sample
pause	pause time in between samples
batch_size	number of samples interpreted by these neurons (batch size)

Returns: a vector of labels, for one hot coded neurons, if two neurons had the same activation or there was no activation at all, std::numeric_limits<uint16_t>::max() is returned

std::vector<std::vector<Real> > mnist_helper::spikes_to_rates	(	const PopulationBase	pop,
		Real	duration,
		Real	pause,
		size_t	batch_size,
		Real	norm = `0.0`
	)

Converts the simulation results into values between 0 and 1.

Parameters

pop	the population to be converted to rates
duration	presentation time of a sample
pause	pause time in between samples
batch_size	number of samples interpreted by these neurons (batch size) norm divide the number of spikes in bin by this value, ignore if it is set to zero

Returns: for every sample a vector of rates for every neuron (vec[sample][neuron])

void mnist_helper::update_conns_from_mat	(	const std::vector< cypress::Matrix< Real >> &	weights,
		Network &	netw,
		Real	delay = `1.0`,
		Real	scale_factor = `0.0`
	)

Updates the connector is a given network with the weights provided.

Parameters

weights	the new weight in weights[layer](input, output)
netw	the network to alter
delay	synaptic delay Defaults to 1.0.
scale_factor	Scales the weights during conversion, no scale if set to zero

cypress::Population<SpikeSourceArray>& mnist_helper::update_spike_source	(	cypress::Population< SpikeSourceArray > &	source,
		const MNIST_DATA &	spikes
	)

Update Spike sources in network from spikes.

Parameters

source	a cypress source array
spikes	One batch from the return value of "create_batch"

Returns: SpikeSourceArray Population

Classes

Typedefs

Enumerations

Functions

Class Documentation

Typedef Documentation

Enumeration Type Documentation

Function Documentation