MLP with one hidden layer - HTML documentation under construction.

Inheritance:

Public Methods

	TwoLayer ()
	TwoLayer (unsigned nout, unsigned nhidden, unsigned nin, act_func fout=sigmoid, act_func fhid1=sigmoid)
	~TwoLayer ()
inline unsigned	Nhidden1 () const
void	init_weights_norm (int seed = 0)
void	init_weights_const (double x)
void	print_weights (void)
vect&	compute (vect& vinput)
void	backprop (vect& t, vect& x)
void	print_info_grad (void)
void	save_weights (char* namefile)

Protected Fields

unsigned

n_hidden1

Protected Methods

virtual void	save_weights_heading (char* namefile) It saves onto a file the general paramenters of the net It saves the number of layers, size of the layers and type of the activation function
virtual void	save_weights_heading (ofstream& f, char* namebuf) It saves in a stream the general paramenters of the net It saves the number of layers, size of the layers and type of the activation function
virtual void	save_weights_body (char* namefile) It appends onto a file the weights of the net.
virtual void	save_weights_body (ofstream& f) It appends to a stream the weights of the net.

Inherited from OneLayer:

Public Fields

static const double SIG_OFF

static const double SIG_TRESHOLD

static const double TANH_ON

static const double TANH_OFF

static const double TANH_TRESHOLD

static const double ID_ON

static const double ID_OFF

static const double ID_TRESHOLD

Public Methods

virtual void read_weights(char* namefile)

virtual unsigned predictclassWTA(vect& vinput)

virtual unsigned predictclass(vect& vinput)

void print_C(void)

inline queue<float> & read_qerror()

void save_errors(char* file)

virtual stop_cond Learn_by_pattern(TrainingSet& t_set, Learning& learn, unsigned& iter, double& error, double errtol = ERR_TOL, unsigned maxits = MAXITS, bool printout = true)

virtual stop_cond Learn_by_epoch(TrainingSet& t_set, Learning& learn, unsigned& iter, double& error, double errtol = ERR_TOL, unsigned maxits = MAXITS, bool printout = true)

matrix test(TrainingSet& tset)

matrix test(TrainingSet& tset, deque<unsigned>& outputs)

virtual void save_out(TrainingSet& tset, char* file)

void save_out(TrainingSet& tset, ofstream& f)

void save_predicted(deque<unsigned>& outputs, char* file)

void save_decomp_matrix(ofstream& f)

int CrossValidate(char* name, unsigned nfold, unsigned num_attr, char* filerr, LearnType& learninfo, char* savecvfile = "", char* readcvfile = "", char* outfile = "", int saveout = 0, int seed=0, bool printout=true)

virtual int CrossValidateTrialWeights(char* name, unsigned nfold, unsigned num_attr, char* filerr, LearnType& learninfo, char* outfile = "", int saveout = 0, int seed=0, int times=3, bool printout=true)

void SaveCV(char* name, vector<unsigned>& iter, vect& abserr, vect& percerr, unsigned ntrain, matrix& conf, clock_t start, clock_t end)

void print_resultCV(vector<unsigned>& iter, vect& abserr, vect& percerr, unsigned ntrain, clock_t start, clock_t end)

void print_errors()

unsigned Code_act_fun(act_func g)

act_func Decode_act_fun(unsigned fun)

int Read_seed(void)

double get_perc_errors(void)

unsigned Read_minHam(void)

Public Members

Activation, deactivation and threshold values with respectively sigmoid, hyperbolic tangent

Protected Fields

static const unsigned ERR_PRECISION

static const unsigned OUTPUT_PRECISION

static const unsigned MAXITS

static const double ERR_TOL

static const unsigned INTEST_SIZE

unsigned n_class

matrix C

LayerNetTrain layer[MAXLAYERS]

queue<float> Qerror

unsigned num_errors

double perc_errors

int seed

unsigned minHam

Protected Methods

virtual void Init_C(void)

void analyze(act_func g, double& on, double& off)

double Calc_square_error(vect& t, vect& out)

double calc_denom_RMS(vect& t, unsigned n_train)

double RMS_error(double err, double denom)

void read_weights(istream& f)

void save_weights_cv_heading(char* savecvfile, int nfold)

void save_weights_cv_body(char* namefile, int fold_n)

void do_Sum_deltas(unsigned n_layers, matrix* Sum_Delta_W[], vect* Sum_delta_bias[])

void Update_Sum_Delta(unsigned n_layers, matrix* Sum_Delta_W[], vect* Sum_delta_bias[])

void Zero(unsigned n_layers)

void Zero_Sum_Delta(unsigned n_layers, matrix* Sum_Delta_W[], vect* Sum_delta_bias[])

void Build_Sum_Delta(matrix* Sum_Delta_W[], vect* Sum_delta_bias[])

void Delete_Sum_Delta(matrix* Sum_Delta_W[], vect* Sum_delta_bias[])

void Copy_Sum_Delta_To_Delta(unsigned n_layers, LayerNetTrain layer[], matrix* Sum_Delta_W[], vect* Sum_delta_bias[])

virtual unsigned computeclassWTA(vect& out)

virtual unsigned computeclass(vect& out)

Inherited from Network:

Public Fields

static const char* const NET_EXT

static const char* const NETCV_EXT

static const char* const ERR_EXT

static const char* const OUT_EXT

static const unsigned OUTPUT

static const unsigned HIDDEN1

static const unsigned HIDDEN2

static const char* const CV_SUFFIX

Public Methods

inline unsigned Nlayers() const

inline unsigned Ninput() const

inline unsigned Noutput() const

Protected Fields

unsigned n_layers

unsigned n_inputs

unsigned n_outputs

Documentation

Two-Layer Perceptron
It Implemets a Two-Layer Perceptron for classification with learning capabilities

TwoLayer()

TwoLayer(unsigned nout, unsigned nhidden, unsigned nin, act_func fout=sigmoid, act_func fhid1=sigmoid)

~TwoLayer()

inline unsigned Nhidden1() const

void init_weights_norm(int seed = 0)

void init_weights_const(double x)

void print_weights(void)

vect& compute(vect& vinput)

void backprop(vect& t, vect& x)

void print_info_grad(void)

void save_weights(char* namefile)

unsigned n_hidden1

virtual void save_weights_heading(char* namefile)

It saves onto a file the general paramenters of the net
It saves the number of layers, size of the layers and type of the activation function

Parameters:

- namefile name of the file for saving the net. The suffix net. is appended to namefile

See Also:

save_weights

virtual void save_weights_heading(ofstream& f, char* namebuf)

It saves in a stream the general paramenters of the net
It saves the number of layers, size of the layers and type of the activation function

Parameters:

f - streamfile for saving the net. Used by save_weights_heading(char* namefile).
namebuf - name of the file for saving the net.

See Also:

save_weights

virtual void save_weights_body(char* namefile)

It appends onto a file the weights of the net.

Parameters:

- namefile name of the file for saving the net. The suffix net. is appended to namefile

See Also:

save_weights

virtual void save_weights_body(ofstream& f)

It appends to a stream the weights of the net.

Parameters:

f - name stream file for saving the net. Used by save_weights_body(char* namefile).

See Also:

save_weights

Direct child classes:

ThreeLayer

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