The UESMANN network, which it itself based on the BPNet code as it has the same architecture as the plain MLP. More...

#include <uesnet.hpp>

Inheritance diagram for UESNet:

Collaboration diagram for UESNet:

Public Member Functions
	UESNet (int nlayers, const int *layerCounts)
	The constructor is mostly identical to the BPNet constructor. More...

virtual void	setH (double h)
	Set the modulator level for subsequent runs and training of this network. More...

virtual double	getH () const
	get the modulator level More...

Public Member Functions inherited from BPNet
	BPNet (int nlayers, const int *layerCounts)
	Constructor - does not initialise the weights to random values so that we can reinitialise networks. More...

virtual	~BPNet ()
	destructor More...

virtual void	setInputs (double *d)
	Set the inputs to the network before running or training. More...

void	setInput (int n, double d)
	Used to set inputs manually, typically in HInputNet. More...

virtual double *	getOutputs () const
	Get the outputs after running. More...

virtual int	getLayerSize (int n) const
	Get the number of nodes in a given layer. More...

virtual int	getLayerCount () const
	Get the number of layers. More...

virtual int	getDataSize () const
	Get the length of the serialised data block for this network. More...

virtual void	save (double *buf) const
	Serialize the data (not including any network type magic number or layer/node counts) to the given memory (which must be of sufficient size). More...

virtual void	load (double *buf)
	Given that the pointer points to a data block of the correct size for the current network, copy the parameters from that data block into the current network overwriting the current parameters. More...

Public Member Functions inherited from Net
virtual	~Net ()
	virtual destructor which does nothing More...

void	setSeed (long seed)
	Set this network's random number generator, which is used for weight initialisation done at the start of training. More...

int	getInputCount () const
	get the number of inputs More...

int	getOutputCount () const
	get the number of outputs More...

double *	run (double *in)
	Run the network on some data. More...

double	test (ExampleSet &examples, int start=0, int num=-1)
	Test a network. Runs the network over a set of examples and returns the mean MSE for all outputs $\frac{1}{N\cdot N_{outs}}\sum^N_{e \in Examples} \sum_{i=0}^{N_{outs}} (e_o(i) - e_y(i))^2$ where is the number of examples, $N_{outs}$ is the number of outputs, is network's output for example , and is the desired output for the same example. More...

double	trainSGD (ExampleSet &examples, SGDParams &params)
	Train using stochastic gradient descent. Note that cross-validation parameters are slightly different from those given in the thesis. Here we give the number of slices and number of examples per slice; in the thesis we give the total number of examples to be held out and the number of slices. More...

Protected Member Functions
void	calcError (double in, double out)

virtual void	update ()
	Run a single update of the network. More...

virtual double	trainBatch (ExampleSet &ex, int start, int num, double eta)
	Train a network for batch (or mini-batch) (or single example). More...

Protected Member Functions inherited from BPNet
	BPNet ()
	Special constructor for subclasses which need to manipulate layer count before initialisation (e.g. HInputNet). More...

void	init (int nlayers, const int *layerCounts)
	Initialiser for use by the main constructor and the ctors of those subclasses mentioned in BPNet() More...

virtual void	initWeights (double initr)
	initialise weights to random values More...

double &	getw (int tolayer, int toneuron, int fromneuron) const
	get the value of a weight. More...

double &	getb (int layer, int neuron) const
	get the value of a bias More...

double &	getavggradw (int tolayer, int toneuron, int fromneuron) const
	get the value of the gradient for a given weight More...

double	getavggradb (int l, int n) const
	get the value of a bias gradient More...

void	calcError (double in, double out)
	run a single example and calculate the errors; used in training. More...

Protected Member Functions inherited from Net
	Net (NetType tp)
	Constructor - protected because others inherit it and it's not used directly. More...

double	drand (double mn, double mx)
	get a random number using this net's PRNG data More...

Additional Inherited Members
Public Attributes inherited from Net
NetType	type
	type of the network, used for load/save More...

drand48_data	rd
	PRNG data (thread safe) More...

Protected Attributes inherited from BPNet
int	numLayers
	number of layers, including input and output More...

int *	layerSizes
	array of layer sizes More...

int	largestLayerSize
	number of nodes in largest layer More...

double **	weights
	Array of weights as [tolayer][tonode+largestLayerSize*fromnode]. More...

double **	biases
	array of biases, stored as a rectangular array of [layer][node] More...

double **	outputs
	outputs of each layer: one array of doubles for each More...

double **	errors
	the error for each node, calculated by calcError() More...

double **	gradAvgsWeights
	average gradient for each weight (built during training) More...

double **	gradAvgsBiases
	average gradient for each bias (built during training) More...

Detailed Description

The UESMANN network, which it itself based on the BPNet code as it has the same architecture as the plain MLP.

Definition at line 17 of file uesnet.hpp.

Constructor & Destructor Documentation

UESNet::UESNet	(	int	nlayers,
		const int *	layerCounts
	)

inline

The constructor is mostly identical to the BPNet constructor.

Definition at line 28 of file uesnet.hpp.

Member Function Documentation

void UESNet::calcError	(	double *	in,
		double *	out
	)

inlineprotected

Definition at line 46 of file uesnet.hpp.

virtual double UESNet::getH ( ) const

inlinevirtual

get the modulator level

Reimplemented from BPNet.

Definition at line 40 of file uesnet.hpp.

virtual void UESNet::setH ( double h )

inlinevirtual

Set the modulator level for subsequent runs and training of this network.

Reimplemented from BPNet.

Definition at line 36 of file uesnet.hpp.

virtual double UESNet::trainBatch	(	ExampleSet &	ex,
		int	start,
		int	num,
		double	eta
	)

inlineprotectedvirtual

Train a network for batch (or mini-batch) (or single example).

This will

zero the average gradient variables for all weights and biases
zero the total error
for each example
- calculate the error with calcError() which itself calls update()
- add to the total mean squared error (see NOTE below)
for each weight and bias
- calculate the means across all provided examples
- apply the mean to the weight or bias
return the mean squared error (NOTE: different from original, which returned mean absolute error) for all outputs and examples:
$\frac{1}{N\cdot N_{outs}}\sum^N_{e \in Examples} \sum_{i=0}^{N_{outs}} (e_o(i) - e_y(i))^2$
where is the number of examples, $N_{outs}$ is the number of outputs, is network's output for example , and is the desired output for the same example.
Parameters

ex example set

start index of first example to use

num number of examples. For a single example, you'd just use 1.

eta learning rate

Returns
the sum of mean squared errors in the output layer (see formula in method documentation)

Reimplemented from BPNet.

Definition at line 90 of file uesnet.hpp.

virtual void UESNet::update ( )

inlineprotectedvirtual

Run a single update of the network.

Precondition: input layer must be filled with values

Postcondition: output layer contains result

Reimplemented from BPNet.

Definition at line 76 of file uesnet.hpp.

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

/home/travis/build/jimfinnis/uesmanncpp/uesnet.hpp

ex	example set
start	index of first example to use
num	number of examples. For a single example, you'd just use 1.
eta	learning rate

Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation