[PD-cvs] externals/grh/pix_recNN Makefile, NONE, 1.1 NNActivation.h, NONE, 1.1 NNException.h, NONE, 1.1 NNet.h, NONE, 1.1 Neuron.cpp, NONE, 1.1 Neuron.h, NONE, 1.1 RecurrentNeuron.cpp, NONE, 1.1 RecurrentNeuron.h, NONE, 1.1 gpl.txt, NONE, 1.1 help-pix_recNN.pd, NONE, 1.1 pix_recNN.cpp, NONE, 1.1 pix_recNN.h, NONE, 1.1 readme.txt, NONE, 1.1
Georg Holzmann
grholzi at users.sourceforge.net
Tue Jul 12 16:40:24 CEST 2005
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Update of /cvsroot/pure-data/externals/grh/pix_recNN
In directory sc8-pr-cvs1.sourceforge.net:/tmp/cvs-serv14254/pix_recNN
Added Files:
Makefile NNActivation.h NNException.h NNet.h Neuron.cpp
Neuron.h RecurrentNeuron.cpp RecurrentNeuron.h gpl.txt
help-pix_recNN.pd pix_recNN.cpp pix_recNN.h readme.txt
Log Message:
initial commit of pix_recNN
--- NEW FILE: RecurrentNeuron.cpp ---
/////////////////////////////////////////////////////////////////////////////
//
// class RecurrentNeuron
//
// source file
//
// Copyright (c) 2005 Georg Holzmann <grh at gmx.at>
//
// This program is free software; you can redistribute it and/or
// modify it under the terms of the GNU General Public License
// as published by the Free Software Foundation; either version 2
// of the License, or (at your option) any later version.
//
/////////////////////////////////////////////////////////////////////////////
#include "RecurrentNeuron.h"
namespace TheBrain
{
//--------------------------------------------------
/* Constructor
*/
RecurrentNeuron::RecurrentNeuron(int inputs, int memory)
: Neuron(inputs), LW_(NULL), mem_data_(NULL)
{
memory_ = (memory<0) ? 1 : memory+1;
}
//--------------------------------------------------
/* Destructor
*/
RecurrentNeuron::~RecurrentNeuron()
{
if(LW_)
delete[] LW_;
if(mem_data_)
delete[] mem_data_;
}
//--------------------------------------------------
/* creates a new IW-matrix (size: inputs_) and
* b1-vector
* ATTENTION: if they exist they'll be deleted
*/
void RecurrentNeuron::create()
throw(NNExcept)
{
// delete if they exist
if(IW_)
delete[] IW_;
if(LW_)
delete[] LW_;
if(mem_data_)
delete[] mem_data_;
IW_ = new float[inputs_];
LW_ = new float[memory_];
mem_data_ = new float[memory_];
if(!IW_ || !LW_ || !mem_data_)
throw NNExcept("No memory for Neurons!");
index_=0;
}
//--------------------------------------------------
/* inits the weight matrix and the bias vector of
* the network with random values between [min|max]
*/
void RecurrentNeuron::initRand(const int &min, const int &max)
throw(NNExcept)
{
if(!IW_ || !LW_)
throw NNExcept("You must first create the Net!");
// make randomvalue between 0 and 1
// then map it to the bounds
b1_ = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
for(int i=0; i<inputs_; i++)
{
IW_[i] = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
}
for(int i=0; i<memory_; i++)
{
//LW_[i] = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
LW_[i] = ((float)rand()/(float)RAND_MAX)*(min);
}
}
//--------------------------------------------------
/* inits the net with given weight matrix and bias
* (makes a deep copy)
* ATTENTION: the dimension of IW-pointer must be the same
* as the inputs (also for LW) !!!
*/
void RecurrentNeuron::init(const float *IW, const float *LW, float b1)
throw(NNExcept)
{
if(!IW_ || !LW_)
throw NNExcept("You must first create the Net!");
b1_ = b1;
for(int i=0; i<inputs_; i++)
IW_[i] = IW[i];
for(int i=0; i<memory_; i++)
LW_[i] = LW[i];
}
//--------------------------------------------------
/* calculates the output with the current IW, b1 values
* ATTENTION: the array input_data must be in the same
* size as inputs_
*/
float RecurrentNeuron::calculate(float *input_data)
{
float output = 0;
// multiply the inputs with the weight matrix IW
for(int i=0; i<inputs_; i++)
{
output += input_data[i] * IW_[i];
}
// map input values to the range
output /= range_;
// multiply memory with weight matrix LW
// the index is used to make something
// like a simple list or ringbuffer
for(int i=0; i<memory_; i++)
{
output += mem_data_[index_] * LW_[i];
index_ = (index_+1) % memory_;
}
// now add bias
output += b1_;
// finally save the new output in memory
mem_data_[index_] = output;
index_ = (index_+1) % memory_;
//post("input: %f %f %f, IW: %f %f %f, b: %f",
// input_data[0], input_data[1], input_data[2],
// IW_[0], IW_[1], IW_[2], b1_);
//post("output: %f",output);
return (output);
}
//--------------------------------------------------
/* this method trains the network:
* input_data is, as above, the input data, output_data is the
* output of the current net with input_data (output_data is not
* calculated in that method !), target_output is the desired
* output data
* (this is the LMS-algorithm to train linear neural networks)
* ATTENTION: the array input_data must be in the same
* size as inputs_
* returns the calculated output
*/
// float RecurrentNeuron::trainLMS(const float *input_data,
// const float &target_output)
// {
// // calculate output value:
// float output = 0;
// // multiply the inputs with the weight matrix IW
// for(int i=0; i<inputs_; i++)
// {
// output += input_data[i] * IW_[i];
// }
// // map input values to the range
// output /= range_;
// // multiply memory with weight matrix LW
// // the index is used to make something
// // like a simple list or ringbuffer
// for(int i=0; i<memory_; i++)
// {
// output += mem_data_[index_] * LW_[i];
// index_ = (index_+1) % memory_;
// }
// // now add bias
// output += b1_;
// //----------------
// // this is the LMS-algorithm to train linear
// // neural networks
// // calculate the error signal:
// float error = (target_output - output);
// // now change IW
// for(int i=0; i<inputs_; i++)
// IW_[i] += 2 * learn_rate_ * error * (input_data[i]/range_);
// // change LW
// for(int i=0; i<memory_; i++)
// {
// LW_[i] += 2 * learn_rate_ * error * mem_data_[index_];
// index_ = (index_+1) % memory_;
// }
// // and the bias
// b1_ += 2 * learn_rate_ * error;
// //-----------------
// // finally save the new output in memory
// mem_data_[index_] = output;
// index_ = (index_+1) % memory_;
// return (output);
// }
} // end of namespace
--- NEW FILE: gpl.txt ---
GNU GENERAL PUBLIC LICENSE
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--- NEW FILE: NNet.h ---
/////////////////////////////////////////////////////////////////////////////
//
// class NNet
//
// this is a template for all the nets
// (see NeuralNet documentations for more information)
//
// header file
//
// Copyright (c) 2005 Georg Holzmann <grh at gmx.at>
//
//
// This program is free software; you can redistribute it and/or
// modify it under the terms of the GNU General Public License
// as published by the Free Software Foundation; either version 2
// of the License, or (at your option) any later version.
//
/////////////////////////////////////////////////////////////////////////////
#ifndef _INCLUDE_NEURAL_TEMPLATE_NET__
#define _INCLUDE_NEURAL_TEMPLATE_NET__
#include "NNActivation.h"
#include "NNException.h"
namespace TheBrain
{
template <class HiddNeuronType,class OutNeuronType>
class NNet
{
protected:
/* the number of output values
* this is automatically also the
* number of output neurons !
*/
int output_val_;
/* the number of hidden neurons
* per one output neuron
* (this net has one hidden layer,
* so this is the number of hidden
* neurons is hidden_val_*output_val_)
*/
int hidden_val_;
/* nr of input values per one output neuron
* (so the number of input values are
* input_val_*output_val_)
*/
int input_val_;
/* the memory of the output layer
* if you use a recurrent neuron, this
* determines how much output values the
* recurrent neurons can remeber
* these values are fed back as new input
*/
int memory_out_;
/* the memory of the hidden layer
* if you use a recurrent neuron, this
* determines how much output values the
* recurrent neurons can remeber
* these values are fed back as new input
*/
int memory_hidden_;
/* these are the output neurons
*/
OutNeuronType *out_neurons_;
/* these are the hidden neurons
*/
HiddNeuronType *hidden_neurons_;
/* function pointer to the activation
* function of the output neurons
*/
float (*output_act_f)(float value);
/* function pointer to the activation
* function of the hidden neurons
*/
float (*hidden_act_f)(float value);
/* function pointer to the derivation of the
* activation function of the hidden neurons
*/
float (*hidden_act_f_d)(float value);
public:
/* Constructor
*/
NNet(int input_val=1, int hidden_val=1, int output_val=1, int memory_out=0,
int memory_hidden=1, int HIDDEN_ACT_FUNC=0, int OUT_ACT_FUNC=0);
/* Destructor
*/
virtual ~NNet();
//-----------------------------------------------------
/* Set/Get learning rate
*/
virtual void setLearningRate(float learn_rate);
virtual float getLearningRate() const;
/* Set/Get range
* (see Neuron.h)
*/
virtual void setRange(float range);
virtual float getRange() const;
/* some more get/set methods
*/
virtual void setOutputVal(int output_val)
throw();
virtual int getOutputVal() const;
virtual void setHiddenVal(int hidden_val)
throw();
virtual int getHiddenVal() const;
virtual void setInputVal(int input_val)
throw();
virtual int getInputVal() const;
virtual void setMemoryOut(int memory)
throw();
virtual int getMemoryOut() const;
virtual void setMemoryHidden(int memory)
throw();
virtual int getMemoryHidden() const;
//-----------------------------------------------------
/* creates the network
*/
virtual void create()
throw(NNExcept);
/* inits the weight matrix and the bias vector of
* the network with random values between [min|max]
*/
virtual void initRand(const int &min, const int &max)
throw(NNExcept);
/* calculates the output with the current Net and writes
* it in the array output_data
* ATTENTION: array input_data must be a matrix in the form:
* float[output_val_][input_val_]
* array output_data must be in size output_val_
* (there is no checking !!!)
*/
virtual void calculate(float **input_data, float *output_data);
/* this method trains the network:
* input_data is, as above, the input data, output_data is the
* output of the current net with input_data, target_output is
* the desired output data
* (this is the a truncated backpropagation through time
* algorithm to train the network)
* ATTENTION: array input_data must be a matrix in the form:
* float[output_val_][input_val_]
* array output_data must be in size output_val_
* array target_output must be in size output_val_
* (there is no checking !!!)
*/
virtual void trainBTT(float **input_data, float *output_data,
float *target_output);
//-----------------------------------------------------
/* saves the contents of the current net to file
*/
virtual void save(string filename)
throw(NNExcept);
/* loads the parameters of the net from file
*/
virtual void load(string filename)
throw(NNExcept);
//-----------------------------------------------------
private:
/* output of the hidden layer with activation function
*/
float *hidden_a_;
/* output of the hidden layer without activation function
*/
float *hidden_s_;
/* error signal of the neurons in the hidden layer
*/
float *hidden_error_;
/* out signal without activation function
*/
float out_s_;
/* error signal of the output layer
*/
float out_error_;
/* Copy Construction is not allowed
*/
NNet(const NNet<HiddNeuronType,OutNeuronType> &src)
{ }
/* assignement operator is not allowed
*/
const NNet<HiddNeuronType,OutNeuronType>& operator=
(const NNet<HiddNeuronType,OutNeuronType>& src)
{ return *this; }
};
//--------------------------------------------------
/* Constructor
*/
template <class HiddNeuronType, class OutNeuronType>
NNet<HiddNeuronType,OutNeuronType>
::NNet(int input_val, int hidden_val, int output_val, int memory_out,
int memory_hidden, int HIDDEN_ACT_FUNC, int OUT_ACT_FUNC)
: out_neurons_(NULL), hidden_neurons_(NULL), hidden_a_(NULL),
hidden_s_(NULL), hidden_error_(NULL)
{
output_val_ = (output_val<1) ? 1 : output_val;
hidden_val_ = (hidden_val<0) ? 0 : hidden_val;
input_val_ = (input_val<1) ? 1 : input_val;
memory_out_ = (memory_out<0) ? 0 : memory_out;
memory_hidden_ = (memory_hidden<0) ? 0 : memory_hidden;
// choose hidden activation function:
switch(HIDDEN_ACT_FUNC)
{
case SIGMOID:
hidden_act_f = act_sigmoid;
hidden_act_f_d = act_sigmoid_derive;
break;
case TANH:
hidden_act_f = act_tanh;
hidden_act_f_d = act_tanh_derive;
break;
default:
case LINEAR:
hidden_act_f = act_linear;
hidden_act_f_d = act_linear_derive;
break;
}
// choose out function:
switch(OUT_ACT_FUNC)
{
case SIGMOID:
output_act_f = act_sigmoid;
break;
case TANH:
output_act_f = act_tanh;
break;
default:
case LINEAR:
output_act_f = act_linear;
break;
}
}
//--------------------------------------------------
/* Destructor
*/
template <class HiddNeuronType, class OutNeuronType>
NNet<HiddNeuronType, OutNeuronType>::~NNet()
{
if(hidden_neurons_)
delete[] hidden_neurons_;
if(out_neurons_)
delete[] out_neurons_;
if(hidden_a_)
delete[] hidden_a_;
if(hidden_s_)
delete[] hidden_s_;
if(hidden_error_)
delete[] hidden_error_;
}
//--------------------------------------------------
/* creates the network
*/
template <class HiddNeuronType, class OutNeuronType>
void NNet<HiddNeuronType,OutNeuronType>::create()
throw(NNExcept)
{
// delete if they exist
if(out_neurons_)
delete[] out_neurons_;
if(hidden_neurons_)
delete[] hidden_neurons_;
if(hidden_a_)
delete[] hidden_a_;
if(hidden_s_)
delete[] hidden_s_;
if(hidden_error_)
delete[] hidden_error_;
out_neurons_ = new OutNeuronType[output_val_](input_val_,memory_out_);
hidden_neurons_ = new HiddNeuronType[hidden_val_*output_val_](input_val_,memory_hidden_);
if(!out_neurons_ || !hidden_neurons_)
throw NNExcept("No memory for Neurons!");
// create the temporary storage
hidden_a_ = new float[hidden_val_];
hidden_s_ = new float[hidden_val_];
hidden_error_ = new float[hidden_val_];
if(!hidden_a_ || !hidden_s_ || !hidden_error_)
throw NNExcept("No memory for Neurons!");
// create all the neurons
for(int i=0; i<output_val_; i++)
out_neurons_[i].create();
for(int i=0; i<hidden_val_*output_val_; i++)
hidden_neurons_[i].create();
}
//--------------------------------------------------
/* inits the weight matrix and the bias vector of
* the network with random values between [min|max]
*/
template <class HiddNeuronType, class OutNeuronType>
void NNet<HiddNeuronType,OutNeuronType>::initRand(const int &min, const int &max)
throw(NNExcept)
{
if(!out_neurons_)
throw NNExcept("You must first create the Net!");
// init all the neurons
for(int i=0; i<output_val_; i++)
out_neurons_[i].initRand(min,max);
for(int i=0; i<hidden_val_*output_val_; i++)
hidden_neurons_[i].initRand(min,max);
}
//--------------------------------------------------
/* calculates the output with the current Net and writes
* it in the array output_data
* ATTENTION: array input_data must be a matrix in the form:
* float[output_val_][input_val_]
* array output_data must be in size output_val_
* (there is no checking !!!)
*/
template <class HiddNeuronType, class OutNeuronType>
void NNet<HiddNeuronType,OutNeuronType>::calculate(float **input_data, float *output_data)
{
for(int i=0; i<output_val_; i++)
{
// 1.: calculation of the hidden layer
for(int j=0; j<hidden_val_; j++)
{
hidden_a_[j] = hidden_act_f(
hidden_neurons_[i*hidden_val_+j].calculate(input_data[i]) );
}
// 2.: calculation of the output layer
*output_data++ = output_act_f( out_neurons_[i].calculate(hidden_a_) );
}
}
//--------------------------------------------------
/* this method trains the network:
* input_data is, as above, the input data, output_data is the
* output of the current net with input_data, target_output is
* the desired output data
* (this is the a truncated backpropagation through time
* algorithm to train the network)
* ATTENTION: array input_data must be a matrix in the form:
* float[output_val_][input_val_]
* array output_data must be in size output_val_
* array target_output must be in size output_val_
* (there is no checking !!!)
*/
template <class HiddNeuronType, class OutNeuronType>
void NNet<HiddNeuronType,OutNeuronType>::trainBTT(float **input_data, float *output_data,
float *target_output)
{
post("train");
for(int i=0; i<output_val_; i++)
{
//---------------------------------------------------------
// 1. Forward - Pass:
//
// the output of the hidden and the output-layer
// are calculated and saved (before and after
// the activation function)
// calculation of the hidden layer
for(int j=0; j<hidden_val_; j++)
{
hidden_s_[j] = hidden_neurons_[i*hidden_val_+j].calculate(input_data[i]);
hidden_a_[j] = hidden_act_f(hidden_s_[j]);
}
// calculation of the output layer
out_s_ = out_neurons_[i].calculate(hidden_a_);
output_data[i] = output_act_f(out_s_);
//---------------------------------------------------------
// 2. Backward - Pass:
//
// calculation of the error signals
// (they are also stored)
// output layer
out_error_ = output_data[i] - target_output[i];
// hidden layer:
for(int j=0; j<hidden_val_; j++)
{
hidden_error_[j] = hidden_act_f_d( hidden_s_[j]+0.1 ) *
( out_error_ * out_neurons_[i].getIW(j) );
}
//---------------------------------------------------------
// 3. Modification of the weights:
for(int j=0; j<hidden_val_; j++)
{
// output layer:
out_neurons_[i].setIW(j,
out_neurons_[i].getIW(j) -
getLearningRate() * out_error_
* hidden_a_[j] );
// hidden layer:
for(int k=0; k<input_val_; k++)
{
hidden_neurons_[i*hidden_val_+j].setIW(k,
hidden_neurons_[i*hidden_val_+j].getIW(k) -
getLearningRate() * hidden_error_[j]
* input_data[i][k]/hidden_neurons_[0].getRange() );
}
// recurrent part of the hidden layer:
float delta = getLearningRate() * hidden_error_[j] * hidden_a_[j];
for(int k=0; k<memory_hidden_; k++)
{
hidden_neurons_[i*hidden_val_+j].setLW(k,
hidden_neurons_[i*hidden_val_+j].getLW(k) - delta);
}
}
// recurrent part of the output layer:
float delta = getLearningRate() * out_error_ * output_data[i];
for(int j=0; j<memory_out_; j++)
{
out_neurons_[i].setLW(j,
out_neurons_[i].getLW(j) - delta);
}
}
}
//--------------------------------------------------
/* saves the contents of the current net to file
*/
template <class HiddNeuronType, class OutNeuronType>
void NNet<HiddNeuronType,OutNeuronType>::save(string filename)
throw(NNExcept)
{
}
//--------------------------------------------------
/* loads the parameters of the net from file
*/
template <class HiddNeuronType, class OutNeuronType>
void NNet<HiddNeuronType,OutNeuronType>::load(string filename)
throw(NNExcept)
{
}
//-----------------------------------------------------
/* Set/Get learning rate
* (see Neuron.h)
*/
template <class HiddNeuronType, class OutNeuronType>
void NNet<HiddNeuronType,OutNeuronType>::setLearningRate(float learn_rate)
{
learn_rate = (learn_rate<0) ? 0 : learn_rate;
for(int i=0; i<output_val_; i++)
out_neurons_[i].setLearningRate(learn_rate);
for(int i=0; i<hidden_val_*output_val_; i++)
hidden_neurons_[i].setLearningRate(learn_rate);
}
template <class HiddNeuronType, class OutNeuronType>
float NNet<HiddNeuronType, OutNeuronType>::getLearningRate() const
{
return out_neurons_[0].getLearningRate();
}
//-----------------------------------------------------
/* Set/Get range
* (see Neuron.h)
*/
template <class HiddNeuronType, class OutNeuronType>
void NNet<HiddNeuronType,OutNeuronType>::setRange(float range)
{
for(int i=0; i<output_val_; i++)
out_neurons_[i].setRange(1);
for(int i=0; i<hidden_val_*output_val_; i++)
hidden_neurons_[i].setRange(range);
}
template <class HiddNeuronType, class OutNeuronType>
float NNet<HiddNeuronType, OutNeuronType>::getRange() const
{
return hidden_neurons_[0].getRange();
}
//-----------------------------------------------------
/* get/set output_val_
*/
template <class HiddNeuronType, class OutNeuronType>
void NNet<HiddNeuronType,OutNeuronType>::setOutputVal(int output_val)
throw()
{
output_val_ = (output_val<1) ? 1 : output_val;
create();
}
template <class HiddNeuronType, class OutNeuronType>
int NNet<HiddNeuronType,OutNeuronType>::getOutputVal() const
{
return output_val_;
}
//-----------------------------------------------------
/* get/set hidden_val_
*/
template <class HiddNeuronType, class OutNeuronType>
void NNet<HiddNeuronType,OutNeuronType>::setHiddenVal(int hidden_val)
throw()
{
hidden_val_ = (hidden_val<1) ? 1 : hidden_val;
create();
}
template <class HiddNeuronType, class OutNeuronType>
int NNet<HiddNeuronType,OutNeuronType>::getHiddenVal() const
{
return hidden_val_;
}
//-----------------------------------------------------
/* get/set input_val_
*/
template <class HiddNeuronType, class OutNeuronType>
void NNet<HiddNeuronType,OutNeuronType>::setInputVal(int input_val)
throw()
{
input_val_ = (input_val<1) ? 1 : input_val;
create();
}
template <class HiddNeuronType, class OutNeuronType>
int NNet<HiddNeuronType,OutNeuronType>::getInputVal() const
{
return input_val_;
}
//-----------------------------------------------------
/* get/set memory of the output layer
*/
template <class HiddNeuronType, class OutNeuronType>
void NNet<HiddNeuronType,OutNeuronType>::setMemoryOut(int memory)
throw()
{
memory_out_ = (memory<0) ? 0 : memory;
create();
}
template <class HiddNeuronType, class OutNeuronType>
int NNet<HiddNeuronType,OutNeuronType>::getMemoryOut() const
{
return memory_out_;
}
//-----------------------------------------------------
/* get/set memory of the hidden layer
*/
template <class HiddNeuronType, class OutNeuronType>
void NNet<HiddNeuronType,OutNeuronType>::setMemoryHidden(int memory)
throw()
{
memory_hidden_ = (memory<0) ? 0 : memory;
create();
}
template <class HiddNeuronType, class OutNeuronType>
int NNet<HiddNeuronType,OutNeuronType>::getMemoryHidden() const
{
return memory_hidden_;
}
} // end of namespace
#endif //_INCLUDE_LIN_NEURAL_NET__
--- NEW FILE: NNActivation.h ---
/////////////////////////////////////////////////////////////////////////////
//
// NNActivation.h
//
// all the activation functions of the neurons
//
// header file
//
// Copyright (c) 2005 Georg Holzmann <grh at gmx.at>
//
// This program is free software; you can redistribute it and/or
// modify it under the terms of the GNU General Public License
// as published by the Free Software Foundation; either version 2
// of the License, or (at your option) any later version.
//
/////////////////////////////////////////////////////////////////////////////
#ifndef _INCLUDE_ACTIVATION_NET__
#define _INCLUDE_ACTIVATION_NET__
#include <math.h>
namespace TheBrain
{
//------------------------------------------------------
/* implementation of the different activation functions
* and it's derivations
*/
/* Linear activation function.
* span: -inf < y < inf
* y = x
*/
#define LINEAR 0
/* Sigmoid activation function.
* span: 0 < y < 1
* y = 1/(1 + exp(-x)), y' = y*(1 - y)
*/
#define SIGMOID 1
/* Symmetric sigmoid activation function, aka. tanh.
* span: -1 < y < 1
* y = tanh(x) = 2/(1 + exp(-2*x)) - 1, d = 1-(y*y)
*/
#define TANH 2
// linear function
float act_linear(float value)
{ return value; }
// derivation of the linear function
float act_linear_derive(float value)
{ return 1; }
// sigmoid function
float act_sigmoid(float value)
{ return (1.0f/(1.0f + exp(-value))); }
// derivation of the sigmoid function
float act_sigmoid_derive(float value)
{ return (value * (1.0f - value)); }
// tanh function
float act_tanh(float value)
{ return (2.0f/(1.0f + exp(-2.0f * value)) - 1.0f); }
// derivation of the tanh function
float act_tanh_derive(float value)
{ return (1.0f - (value*value)); }
} // end of namespace
#endif // _INCLUDE_ACTIVATION_NET__
--- NEW FILE: help-pix_recNN.pd ---
#N canvas 871 74 498 783 10;
#X obj 36 327 gemwin;
#X msg 36 301 create \, 1;
#N canvas 75 72 765 790 pix2sig_stuff~ 0;
#X obj 120 35 gemhead;
#X obj 120 132 pix_texture;
#X obj 119 274 outlet~;
#X obj 139 185 square 4;
#X obj 139 163 separator;
#X obj 61 165 separator;
#X obj 120 101 pix_video;
#X msg 186 64 dimen 640 480;
#X obj 26 36 block~ 2048;
#X msg 186 38 dimen 320 240;
#X msg 76 535 getprecision;
#X msg 93 696 getlearnrate;
#X msg 65 671 learnrate 0.2;
#X msg 424 459 getneurons;
#X msg 404 206 train;
#X obj 31 227 inlet~;
#X msg 65 647 learnrate 0.05;
#X msg 381 708 getmemory;
#X msg 361 639 memory 0;
#X msg 361 660 memory 1;
#X obj 61 252 pix_recNN;
#X text 296 49 <- input dimension;
#X obj 78 226 r \$0-recNN;
#X obj 62 564 s \$0-recNN;
#X msg 76 498 precision \$1;
#X floatatom 76 481 5 0 0 0 - - -;
#X text 42 335 precision:;
#X text 53 358 1: means every pixel is used in calculation;
#X text 53 372 2: only every second pixel;
#X text 53 386 ...;
#X obj 62 411 loadbang;
#X msg 407 401 neurons 2048;
#X msg 407 422 neurons 64;
#X obj 407 492 s \$0-recNN;
#X text 403 336 neurons:;
#X text 416 357 nr. of neurons used in the calculation;
#X text 415 370 (_MUST_ be the same as the buffersize !!!);
#X text 43 615 learnrate:;
#X obj 65 725 s \$0-recNN;
#X msg 361 681 memory 3;
#X obj 361 741 s \$0-recNN;
#X text 343 543 memory:;
#X text 356 565 this determines \, how much values from the past the
recurrent net considers in the calculation;
#X text 357 604 (be carefull with large values !!!);
#X msg 62 456 precision 1;
#X msg 62 436 precision 4;
#X obj 404 233 s \$0-recNN;
#X text 397 126 train:;
#X text 417 152 trains the neural net;
#X text 418 166 (the current video frame to;
#X text 425 178 the current audio block);
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#X connect 18 0 40 0;
#X connect 19 0 40 0;
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#X msg 110 302 0 \, destroy;
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1;
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#X text 25 23 pix_recNN:;
#X text 24 57 pix_recNN is an instument/interface. This instrument
should be useful as a general experimental video interface to generate
audio. You can train the neural net with playing audio samples to specific
video frames in real-time. The main interest for me was not to train
the net exactly to reproduce these samples \, but to make experimental
sounds \, which are "between" all the trained samples.;
#X text 22 214 (but this version is unfinished - e.g. the training
algorithm must be tuned etc. - so it's only a very basic prototype...)
;
#X text 207 320 <- create gemwin;
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-1;
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1;
#X floatatom 206 414 5 0 0 0 - - -;
#X text 272 431 <- simple osc for training;
#X text 262 497 <- to train silence;
#X obj 85 463 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1
-1;
#X text 216 541 <- audio/video work;
#X obj 90 684 dac~;
#X obj 90 659 *~;
#X obj 118 659 dbtorms;
#X floatatom 118 641 5 0 0 0 - - -;
#X text 168 638 <- outvol in dB;
#X text 22 170 pix_recNN uses a 2 layer recurrent neural net (for more
detailed info look at the source code.);
#X text 119 737 Georg Holzmann <grh at mur.at> \, 2004;
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--- NEW FILE: Makefile ---
PD-PATH=/usr/lib/pd
PD-SCR=/usr/include
# location of the GEM sources and Gem.pd_linux:
GEM-SCR=/home/Georg/pd-cvs/gem/Gem/src
GEM-LIB=$(PD-PATH)/extra/Gem.pd_linux
CC = g++
LD = g++
INCLUDE=-I$(PD-SCR) -I$(GEM-SCR) -I./src
LIB=-lc -lm -L$(GEM-LIB)
CC_FLAGS = -c -Wall -g -g -O2 -mmmx -fno-builtin -O3 -funroll-loops -ffast-math
LD_FLAGS = --export-dynamic -shared -o
TARGET=pix_recNN.pd_linux
OBJ=RecurrentNeuron.o Neuron.o pix_recNN.o
#--------------------------------------------------------
all: pd_linux
pd_linux: $(TARGET)
$(TARGET): $(OBJ)
$(LD) $(LD_FLAGS) $(TARGET) $(OBJ) $(LIB)
strip --strip-unneeded $(TARGET)
chmod 755 $(TARGET)
pix_recNN.o: RecurrentNeuron.o pix_recNN.h pix_recNN.cpp NNet.h NNException.h
$(CC) $(CC_FLAGS) $(INCLUDE) pix_recNN.cpp
RecurrentNeuron.o: RecurrentNeuron.cpp RecurrentNeuron.h Neuron.o NNActivation.h
Neuron.o: Neuron.cpp Neuron.h NNActivation.h
#--------------------------------------------------------
clean:
rm -f $(OBJ) $(TARGET)
install:
cp -f $(TARGET) $(PD-PATH)/externs
cp -f *.pd $(PD-PATH)/doc/5.reference
--- NEW FILE: NNException.h ---
/////////////////////////////////////////////////////////////////////////////
//
// NNDefines.h
//
// global stuff for all the nets
//
// header file
//
// Copyright (c) 2005 Georg Holzmann <grh at gmx.at>
//
// This program is free software; you can redistribute it and/or
// modify it under the terms of the GNU General Public License
// as published by the Free Software Foundation; either version 2
// of the License, or (at your option) any later version.
//
/////////////////////////////////////////////////////////////////////////////
#ifndef _INCLUDE_NNDEFINES_NET__
#define _INCLUDE_NNDEFINES_NET__
#include <string>
using std::string;
namespace TheBrain
{
//------------------------------------------------------
/* the exception class for all the neural network stuff
*/
class NNExcept
{
protected:
string message_;
public:
NNExcept(string message="")
{ message_ = message; }
virtual ~NNExcept() { }
virtual string what()
{ return message_; }
};
} // end of namespace NNet
#endif //_INCLUDE_NNDEFINES_NET__
--- NEW FILE: pix_recNN.cpp ---
/////////////////////////////////////////////////////////////////////////////
//
// GEM - Graphics Environment for Multimedia
//
// pix_recNN
//
// Implementation file
//
// Copyright (c) 2005 Georg Holzmann <grh at gmx.at>
// (and of course lot's of other developers for PD and GEM)
//
// For information on usage and redistribution, and for a DISCLAIMER OF ALL
// WARRANTIES, see the file, "GEM.LICENSE.TERMS" in this distribution.
//
/////////////////////////////////////////////////////////////////////////////
#include "pix_recNN.h"
CPPEXTERN_NEW_WITH_THREE_ARGS(pix_recNN, t_floatarg, A_DEFFLOAT,
t_floatarg, A_DEFFLOAT, t_floatarg, A_DEFFLOAT)
//----------------------------------------------------------
/* Constructor
*/
pix_recNN::pix_recNN(t_floatarg arg0=64, t_floatarg arg1=1, t_floatarg arg2=1) :
m_data_(NULL), m_xsize_(0), m_ysize_(0), m_csize_(0),
train_on_(false), net_(NULL), temp_pix_(NULL)
{
// init args ?????????????????????????????????
neuron_nr_=2048; //static_cast<int>((arg0<0)?2:arg0);
memory_=0;
precision_=2; //static_cast<int>((arg2<1)?1:arg2);
//post("arg0: %d, arg1: %d",arg0,arg1);
// generate the in- and outlet:
out0_ = outlet_new(this->x_obj, &s_signal);
inlet_new(this->x_obj, &this->x_obj->ob_pd, &s_signal, &s_signal);
// set random seed:
srand( (unsigned)time(NULL) );
// build the net
buildNewNet();
}
//----------------------------------------------------------
/* Destructor
*/
pix_recNN::~pix_recNN()
{
outlet_free(out0_);
m_data_ = NULL;
m_xsize_ = 0;
m_ysize_ = 0;
// delete net
delete net_;
// delete temp_pix_
for(int i=0; i<neuron_nr_; i++)
delete[] temp_pix_[i];
delete[] temp_pix_;
}
//----------------------------------------------------------
/* a helper to build a new net
*/
void pix_recNN::buildNewNet()
{
try
{
if(net_)
delete net_;
if(temp_pix_)
{
for(int i=0; i<neuron_nr_; i++)
delete[] temp_pix_[i];
delete[] temp_pix_;
}
// create the net
net_ = new NNet<RecurrentNeuron,RecurrentNeuron>(3,3,neuron_nr_,memory_,
0,TANH,LINEAR);
if(!net_)
{
post("pix_recNN~: no memory for neural nets!");
net_=NULL;
return;
}
// create the temp_pix
temp_pix_ = new float*[neuron_nr_];
if(!temp_pix_)
{
post("pix_recNN~: no memory for temp_pix_!");
temp_pix_=NULL;
return;
}
for(int i=0; i<neuron_nr_; i++)
{
temp_pix_[i] = new float[3];
if(!temp_pix_[i])
{
post("pix_recNN~: no memory for temp_pix_!");
temp_pix_=NULL;
return;
}
}
// initialize temp_pix_ with 0
for(int i=0; i<neuron_nr_; i++)
{
for(int j=0; j<3; j++)
{
temp_pix_[i][j] = 0;
}
}
// init the net
net_->create();
net_->initRand(-1,1);
net_->setRange(255);
net_->setLearningRate(0.01);
}
catch(NNExcept &exc)
{
post("pix_recNN: %s", exc.what().c_str());
}
}
//----------------------------------------------------------
/* processImage
*/
void pix_recNN::processImage(imageStruct &image)
{
m_data_ = image.data;
m_xsize_ = image.xsize;
m_ysize_ = image.ysize;
m_csize_ = image.csize;
m_format_ = image.format;
}
//----------------------------------------------------------
/* DSP perform
*/
t_int* pix_recNN::perform(t_int* w)
{
pix_recNN *x = GetMyClass((void*)w[1]);
t_float* in_signal = (t_float*)(w[2]);
t_float* out_signal = (t_float*)(w[3]);
int blocksize = (t_int)(w[4]);
if(blocksize != x->neuron_nr_)
{
post("pix_recNN~: neurons and buffersize are different! You MUST have the same neuron nr as the buffersize !!!");
post("neurons: %d, buffersize: %d", x->neuron_nr_, blocksize);
return (w+5);
}
// some needed data
long int pix_size = x->m_xsize_ * x->m_ysize_;
int pix_blocksize = (blocksize<pix_size)?blocksize:pix_size;
// splits the frame into slices, so that the average
// of one slice can be used for the network input
// there are as much slices as the buffsize is
float nr = sqrt(blocksize); // the number of slices at the
// x- and y-axis
float x_slice = x->m_xsize_ / nr; // x size of a slice in pixels
float y_slice = x->m_ysize_ / nr; // x size of a slice in pixels
int x_slice_int = static_cast<int>( x_slice );
int y_slice_int = static_cast<int>( y_slice );
// the number of slices on one axis (is the float nr
// from above rounded up)
int slice_nr = static_cast<int>(nr) + 1;
if (x->m_data_)
{
switch(x->m_format_)
{
case GL_RGBA:
{
for(int n=0; n<pix_blocksize; n++)
{
//post("Block %d:",n);
// calulate the pixel in left upper edge of every slice
int lu_pix_x = static_cast<int>( (n % slice_nr) * x_slice );
int lu_pix_y = static_cast<int>( static_cast<int>(n / slice_nr) * y_slice );
//post("lu_pix: %d, %d", lu_pix_x, lu_pix_y);
// now sum up all the pixels of one slice and then divide through the
// number of pixels
// the storage to sum the pixels:
unsigned long int temp_data[3] = { 0, 0, 0 };
// only for optimization:
int helper1 = x->m_xsize_ * x->m_csize_;
int add_count = 0;
for(int i=0; i<x_slice_int; i+=x->precision_)
{
for(int j=0; j<y_slice_int; j+=x->precision_)
{
// the way to access the pixels: (C=chRed, chBlue, ...)
//data[Y * xsize * csize + X * csize + C]
//post("current pixel: %d %d",
// ((lu_pix_x+i)%x->m_xsize), ((lu_pix_y+j)%x->m_ysize) );
temp_data[0] += x->m_data_[
(lu_pix_y+j) * helper1
+ (lu_pix_x+i) * x->m_csize_ + chRed ];
temp_data[1] += x->m_data_[
((lu_pix_y+j)) * helper1
+ ((lu_pix_x+i)) * x->m_csize_ + chGreen ];
temp_data[2] += x->m_data_[
((lu_pix_y+j)%x->m_ysize_) * helper1
+ ((lu_pix_x+i)%x->m_xsize_) * x->m_csize_ + chBlue ];
add_count++;
}
}
x->temp_pix_[n][0] = temp_data[0] / add_count;
x->temp_pix_[n][1] = temp_data[1] / add_count;
x->temp_pix_[n][2] = temp_data[2] / add_count;
}
// learning, or calculation:
if(!x->train_on_)
x->net_->calculate(x->temp_pix_, out_signal);
else
x->net_->trainBTT(x->temp_pix_, out_signal, in_signal);
}
break;
default:
post("RGB only for now");
}
}
else
{
pix_blocksize=blocksize;
while (pix_blocksize--) *out_signal++=0;
}
x->train_on_=false;
return (w+5);
}
//----------------------------------------------------------
/* DSP-Message
*/
void pix_recNN::dspMess(void *data, t_signal** sp)
{
dsp_add(perform, 4, data, sp[0]->s_vec, sp[1]->s_vec, sp[0]->s_n);
}
//----------------------------------------------------------
/* saves the contents of the current net to file
*/
void pix_recNN::saveNet(string filename)
{
try
{
net_->save(filename);
post("pix_recNN~: saved to output-file %s", filename.c_str());
}
catch(NNExcept &exc)
{
post("pix_recNN: %s", exc.what().c_str());
}
}
//----------------------------------------------------------
/* loads the parameters of the net from file
*/
void pix_recNN::loadNet(string filename)
{
try
{
net_->load(filename);
post("pix_recNN~: loaded file %s", filename.c_str());
}
catch(NNExcept &exc)
{
post("pix_recNN: %s", exc.what().c_str());
}
}
//----------------------------------------------------------
/* setup callback
*/
void pix_recNN::obj_setupCallback(t_class *classPtr)
{
class_addcreator((t_newmethod)_classpix_recNN, gensym("pix_recNN~"), A_NULL);
class_addmethod(classPtr, (t_method)pix_recNN::setNeurons,
gensym("neurons"), A_FLOAT, A_NULL);
class_addmethod(classPtr, (t_method)pix_recNN::getNeurons,
gensym("getneurons"), A_NULL);
class_addmethod(classPtr, (t_method)pix_recNN::setMemory,
gensym("memory"), A_FLOAT, A_NULL);
class_addmethod(classPtr, (t_method)pix_recNN::getMemory,
gensym("getmemory"), A_NULL);
class_addmethod(classPtr, (t_method)pix_recNN::setPrecision,
gensym("precision"), A_FLOAT, A_NULL);
class_addmethod(classPtr, (t_method)pix_recNN::getPrecision,
gensym("getprecision"), A_NULL);
class_addmethod(classPtr, (t_method)pix_recNN::setTrainOn,
gensym("train"), A_NULL);
class_addmethod(classPtr, (t_method)pix_recNN::setLearnrate,
gensym("learnrate"), A_FLOAT, A_NULL);
class_addmethod(classPtr, (t_method)pix_recNN::getLearnrate,
gensym("getlearnrate"), A_NULL);
class_addmethod(classPtr, (t_method)pix_recNN::saveToFile,
gensym("save"), A_SYMBOL, A_NULL);
class_addmethod(classPtr, (t_method)pix_recNN::loadFromFile,
gensym("load"), A_SYMBOL, A_NULL);
class_addmethod(classPtr, (t_method)pix_recNN::dspMessCallback,
gensym("dsp"), A_NULL);
class_addmethod(classPtr, nullfn, gensym("signal"), A_NULL);
}
//----------------------------------------------------------
/* DSP callback
*/
void pix_recNN::dspMessCallback(void *data, t_signal** sp)
{
GetMyClass(data)->dspMess(data, sp);
}
//----------------------------------------------------------
/* sets the precision
*/
void pix_recNN::setPrecision(void *data, t_floatarg precision)
{
GetMyClass(data)->precision_ =
(precision<1) ? 1 : static_cast<int>(precision);
}
void pix_recNN::getPrecision(void *data)
{
post("pix_recNN~: precision: %d",GetMyClass(data)->precision_);
}
//----------------------------------------------------------
/* method to train the network
*/
void pix_recNN::setTrainOn(void *data)
{
GetMyClass(data)->train_on_ = true;
}
//----------------------------------------------------------
/* changes the number of neurons
* (which should be the same as the audio buffer)
* ATTENTION: a new net will be initialized
*/
void pix_recNN::setNeurons(void *data, t_floatarg neurons)
{
GetMyClass(data)->neuron_nr_ =
(neurons<1) ? 1 : static_cast<int>(neurons);
GetMyClass(data)->buildNewNet();
}
void pix_recNN::getNeurons(void *data)
{
post("pix_recNN~: nr of neurons: %d (MUST be the same as buffersize!)",
GetMyClass(data)->neuron_nr_);
}
//----------------------------------------------------------
/* changes the nblock size
* ATTENTION: a new net will be initialized
*/
void pix_recNN::setMemory(void *data, t_floatarg memory)
{
GetMyClass(data)->memory_ =
(memory<0) ? 0 : static_cast<int>(memory);
GetMyClass(data)->buildNewNet();
}
void pix_recNN::getMemory(void *data)
{
post("pix_recNN~: memory: %d",
GetMyClass(data)->memory_);
}
//----------------------------------------------------------
/* sets the learnrate of the net
*/
void pix_recNN::setLearnrate(void *data, t_floatarg learn_rate)
{
GetMyClass(data)->net_->setLearningRate(learn_rate);
}
void pix_recNN::getLearnrate(void *data)
{
post("pix_recNN~: learning rate: %f",GetMyClass(data)->net_->getLearningRate());
}
//----------------------------------------------------------
/* FileIO-stuff
*/
void pix_recNN::saveToFile(void *data, t_symbol *filename)
{
GetMyClass(data)->saveNet(filename->s_name);
}
void pix_recNN::loadFromFile(void *data, t_symbol *filename)
{
GetMyClass(data)->loadNet(filename->s_name);
}
--- NEW FILE: Neuron.cpp ---
/////////////////////////////////////////////////////////////////////////////
//
// class Neuron
//
// source file
//
// Copyright (c) 2005 Georg Holzmann <grh at gmx.at>
//
// This program is free software; you can redistribute it and/or
// modify it under the terms of the GNU General Public License
// as published by the Free Software Foundation; either version 2
// of the License, or (at your option) any later version.
//
/////////////////////////////////////////////////////////////////////////////
#include "Neuron.h"
namespace TheBrain
{
//--------------------------------------------------
/* Constructor
*/
Neuron::Neuron(int inputs, int dummy)
: learn_rate_(0), range_(1), IW_(NULL), b1_(0)
{
inputs_ = (inputs<1) ? 1 : inputs;
}
//--------------------------------------------------
/* Destructor
*/
Neuron::~Neuron()
{
if(IW_)
delete[] IW_;
}
//--------------------------------------------------
/* creates a new IW-matrix (size: inputs_) and
* b1-vector
* ATTENTION: if they exist they'll be deleted
*/
void Neuron::create()
throw(NNExcept)
{
// delete if they exist
if(IW_)
delete[] IW_;
IW_ = new float[inputs_];
if(!IW_)
throw NNExcept("No memory for Neurons!");
}
//--------------------------------------------------
/* inits the weight matrix and the bias vector of
* the network with random values between [min|max]
*/
void Neuron::initRand(const int &min, const int &max)
throw(NNExcept)
{
if(!IW_)
throw NNExcept("You must first create the Net!");
// make randomvalue between 0 and 1
// then map it to the bounds
b1_ = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
for(int i=0; i<inputs_; i++)
{
IW_[i] = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
}
//post("b1: %f, IW: %f %f %f", b1_, IW_[0], IW_[1], IW_[2]);
}
//--------------------------------------------------
/* inits the net with a given weight matrix and bias
* (makes a deep copy)
* ATTENTION: the dimension of IW-pointer must be the same
* as the inputs !!!
*/
void Neuron::init(const float *IW, float b1)
throw(NNExcept)
{
if(!IW_)
throw NNExcept("You must first create the Net!");
b1_ = b1;
for(int i=0; i<inputs_; i++)
IW_[i] = IW[i];
}
//--------------------------------------------------
/* calculates the output with the current IW, b1 values
* ATTENTION: the array input_data must be in the same
* size as inputs_
*/
float Neuron::calculate(float *input_data)
{
float output = 0;
// multiply the inputs with the weight matrix IW
// and add the bias vector b1
for(int i=0; i<inputs_; i++)
{
output += input_data[i] * IW_[i];
}
// map input values to the range
output /= range_;
//post("b1: %f, IW: %f %f %f", b1_, IW_[0], IW_[1], IW_[2]);
//post("range: %f, in: %f %f %f, out: %f",range_,input_data[0],
// input_data[1], input_data[2], output+b1_);
return (output+b1_);
}
//--------------------------------------------------
/* this method trains the network:
* input_data is, as above, the input data, output_data is the
* output of the current net with input_data (output_data is not
* calculated in that method !), target_output is the desired
* output data
* (this is the LMS-algorithm to train linear neural networks)
* ATTENTION: the array input_data must be in the same
* size as inputs_
* returns the calculated value
*/
// float Neuron::trainLMS(const float *input_data,
// const float &target_output)
// {
// float output = 0;
// // multiply the inputs with the weight matrix IW
// // and add the bias vector b1
// for(int i=0; i<inputs_; i++)
// {
// output += input_data[i] * IW_[i];
// }
// // map input values to the range
// output /= range_;
// output += b1_;
// //------------
// // this is the LMS-algorithm to train linear
// // neural networks
// // calculate the error signal:
// float error = (target_output - output);
// // now change the weights the bias
// for(int i=0; i<inputs_; i++)
// IW_[i] += 2 * learn_rate_ * error * (input_data[i]/range_);
// b1_ += 2 * learn_rate_ * error;
// //------------
// return (output);
// }
} // end of namespace
--- NEW FILE: RecurrentNeuron.h ---
/////////////////////////////////////////////////////////////////////////////
//
// class RecurrentNeuron
//
// this is an implementation of one neuron of a Recurrent Neural Network
// this neuron can have n input values, m values in it's memory and
// one output value
// (see NeuralNet documentations for more information)
//
// header file
//
// Copyright (c) 2005 Georg Holzmann <grh at gmx.at>
//
// This program is free software; you can redistribute it and/or
// modify it under the terms of the GNU General Public License
// as published by the Free Software Foundation; either version 2
// of the License, or (at your option) any later version.
//
/////////////////////////////////////////////////////////////////////////////
#ifndef _INCLUDE_RECURRENT_NEURON_NET__
#define _INCLUDE_RECURRENT_NEURON_NET__
#include <stdlib.h>
#include <stdexcept>
#include "Neuron.h"
namespace TheBrain
{
//------------------------------------------------------
/* class of one neuron
*/
class RecurrentNeuron : public Neuron
{
protected:
/* this determines how much output values the net
* can remeber
* these values are fed back as new input
*/
int memory_;
/* the weight matrix for the recurrent
* values (size: memory_)
*/
float *LW_;
public:
/* Constructor
*/
RecurrentNeuron(int inputs, int memory);
/* Destructor
*/
virtual ~RecurrentNeuron();
//-----------------------------------------------------
/* some more get/set methods
*/
virtual int getMemory() const
{ return memory_; }
virtual float *getLW() const
{ return LW_; }
virtual float getLW(int index) const
{ return LW_[index]; }
virtual void setLW(const float *LW)
{ for(int i=0; i<inputs_; i++) LW_[i] = LW[i]; }
virtual void setLW(int index, float value)
{ LW_[index] = value; }
//-----------------------------------------------------
/* creates a new IW-matrix (size: inputs_) and
* b1-vector
* ATTENTION: if they exist they'll be deleted
*/
virtual void create()
throw(NNExcept);
/* inits the weight matrix and the bias vector of
* the network with random values between [min|max]
*/
virtual void initRand(const int &min, const int &max)
throw(NNExcept);
/* inits the net with given weight matrix and bias
* (makes a deep copy)
* ATTENTION: the dimension of IW-pointer must be the same
* as the inputs (also for LW) !!!
*/
virtual void init(const float *IW, const float *LW, float b1)
throw(NNExcept);
/* calculates the output with the current IW, b1 values
* ATTENTION: the array input_data must be in the same
* size as inputs_
*/
virtual float calculate(float *input_data);
/* this method trains the network:
* input_data is, as above, the input data, output_data is the
* output of the current net with input_data (output_data is not
* calculated in that method !), target_output is the desired
* output data
* (this is the LMS-algorithm to train linear neural networks)
* ATTENTION: the array input_data must be in the same
* size as inputs_
* returns the calculated output
*/
/* virtual float trainLMS(const float *input_data, */
/* const float &target_output); */
//-----------------------------------------------------
private:
/* the storage for the memory data
*/
float *mem_data_;
/* this index is used to make something
* like a simple list or ringbuffer
*/
int index_;
/* Copy Construction is not allowed
*/
RecurrentNeuron(const RecurrentNeuron &src) : Neuron(1)
{ }
/* assignement operator is not allowed
*/
const RecurrentNeuron& operator= (const RecurrentNeuron& src)
{ return *this; }
};
} // end of namespace
#endif //_INCLUDE_RECURRENT_NEURON_NET__
--- NEW FILE: pix_recNN.h ---
/////////////////////////////////////////////////////////////////////////////
//
// GEM - Graphics Environment for Multimedia
//
// pix_recNN~
// Calculates an audio signal out of a video frame
// with a recurrent neural network
//
// (see RecurrentNeuralNet.h for more info)
//
// header file
//
// Copyright (c) 2005 Georg Holzmann <grh at gmx.at>
// (and of course lot's of other developers for PD and GEM)
//
// For information on usage and redistribution, and for a DISCLAIMER OF ALL
// WARRANTIES, see the file, "GEM.LICENSE.TERMS" in this distribution.
//
/////////////////////////////////////////////////////////////////////////////
#ifndef _INCLUDE_PIX_RECNN_H__
#define _INCLUDE_PIX_RECNN_H__
#include <string>
#include <sstream>
#include <fstream>
#include "Base/GemPixObj.h"
#include "NNet.h"
#include "RecurrentNeuron.h"
using std::string;
using std::endl;
using std::ifstream;
using std::ofstream;
using std::istringstream;
using namespace TheBrain;
/*-----------------------------------------------------------------
* CLASS
* pix_recNN~
*
* calculates an audio signal out of a video frame with
* a recurrent neural network
*
* KEYWORDS
* pix audio
*
* DESCRIPTION
* 1 signal-outlet
*/
class GEM_EXTERN pix_recNN : public GemPixObj
{
CPPEXTERN_HEADER(pix_recNN, GemPixObj)
public:
/* Constructor
*/
pix_recNN(t_floatarg arg0, t_floatarg arg1, t_floatarg arg2);
protected:
/* Destructor
*/
virtual ~pix_recNN();
//-----------------------------------
/* Image STUFF:
*/
/* The pixBlock with the current image
* pixBlock m_pixBlock;
*/
unsigned char *m_data_;
int m_xsize_;
int m_ysize_;
int m_csize_;
int m_format_;
/* precision of the image:
* 1 means every pixel is taken for the calculation,
* 2 every second pixel, 3 every third, ...
*/
int precision_;
/* temporary float for calculation
*/
float **temp_pix_;
/* processImage
*/
virtual void processImage(imageStruct &image);
//-----------------------------------
/* Neural Network STUFF:
*/
/* the neural net
* (size: buffsize)
*/
NNet<RecurrentNeuron,RecurrentNeuron> *net_;
/* training modus on
* (will only be on for one audio buffer)
*/
bool train_on_;
/* the number of neurons, which should be
* THE SAME as the audio buffer size
*/
int neuron_nr_;
/* memory determines, how much results from the past
* are used to calculate an output value
* (0 means only the result from the current frame,
* 2 also from the last frame, etc.)
*/
int memory_;
//-----------------------------------
/* Audio STUFF:
*/
/* the outlet
*/
t_outlet *out0_;
/* DSP perform
*/
static t_int* perform(t_int* w);
/* DSP-Message
*/
virtual void dspMess(void *data, t_signal** sp);
//-----------------------------------
/* File IO:
*/
/* saves the contents of the current net to file
*/
virtual void saveNet(string filename);
/* loads the parameters of the net from file
*/
virtual void loadNet(string filename);
private:
/* a helper to build a new net
*/
virtual void buildNewNet();
//-----------------------------------
/* static members
* (interface to the PD world)
*/
/* set/get the precision of the image calculation
*/
static void setPrecision(void *data, t_floatarg precision);
static void getPrecision(void *data);
/* method to train the network
*/
static void setTrainOn(void *data);
/* changes the number of neurons
* (which should be the same as the audio buffer)
* ATTENTION: a new net will be initialized
*/
static void setNeurons(void *data, t_floatarg neurons);
static void getNeurons(void *data);
/* changes the nblock size
* ATTENTION: a new net will be initialized
*/
static void setMemory(void *data, t_floatarg memory);
static void getMemory(void *data);
/* sets the learnrate of the net
*/
static void setLearnrate(void *data, t_floatarg learn_rate);
static void getLearnrate(void *data);
/* DSP callback
*/
static void dspMessCallback(void* data, t_signal** sp);
/* File IO:
*/
static void saveToFile(void *data, t_symbol *filename);
static void loadFromFile(void *data, t_symbol *filename);
};
#endif // for header file
--- NEW FILE: Neuron.h ---
/////////////////////////////////////////////////////////////////////////////
//
// class Neuron
//
// this is an implementation of one neuron of a Neural Network
// so this neuron has a Weight-Matrix IW and a bias vector b1
// this neuron can have n input values, but only one output value
// (see NeuralNet documentations for more information)
//
// header file
//
// Copyright (c) 2005 Georg Holzmann <grh at gmx.at>
//
// This program is free software; you can redistribute it and/or
// modify it under the terms of the GNU General Public License
// as published by the Free Software Foundation; either version 2
// of the License, or (at your option) any later version.
//
/////////////////////////////////////////////////////////////////////////////
#ifndef _INCLUDE_NEURON_NET__
#define _INCLUDE_NEURON_NET__
#include <stdlib.h>
#include <stdexcept>
#include "NNException.h"
#include "m_pd.h" //debug
namespace TheBrain
{
//------------------------------------------------------
/* class of one neuron
*/
class Neuron
{
protected:
/* this is the number of input values, which is
* automatically the input and the size of IW
*/
int inputs_;
/* the input weight matrix IW
* (size: inputs )
*/
float *IW_;
/* the bias vector b1
*/
float b1_;
/* the learning rate of the net
*/
float learn_rate_;
/* the range of the input values should be from 0
* to range_
* outputvalues are from -1 to 1
*/
float range_;
public:
/* Constructor
*/
Neuron(int inputs, int dummy=0);
/* Destructor
*/
virtual ~Neuron();
//-----------------------------------------------------
/* Set/Get learning rate
*/
virtual void setLearningRate(float learn_rate)
{ learn_rate_=learn_rate; }
virtual float getLearningRate() const
{ return learn_rate_; }
/* Set/Get range
*/
virtual void setRange(float range)
{ range_=range; }
virtual float getRange() const
{ return range_; }
/* some more get/set methods
*/
virtual int getInputs() const
{ return inputs_; }
virtual float *getIW() const
{ return IW_; }
virtual float getIW(int index) const
{ return IW_[index]; }
virtual void setIW(const float *IW)
{ for(int i=0; i<inputs_; i++) IW_[i] = IW[i]; }
virtual void setIW(int index, float value)
{ IW_[index] = value; }
virtual float getb1() const
{ return b1_; }
virtual void setb1(float b1)
{ b1_ = b1; }
/* dummies
*/
virtual int getMemory() const
{ return 0; }
virtual float *getLW() const
{ return NULL; }
virtual float getLW(int index) const
{ return 0; }
virtual void setLW(const float *LW)
{ }
virtual void setLW(int index, float value)
{ }
//-----------------------------------------------------
/* creates a new IW-matrix (size: inputs_) and
* b1-vector
* ATTENTION: if they exist they'll be deleted
*/
virtual void create()
throw(NNExcept);
/* inits the weight matrix and the bias vector of
* the network with random values between [min|max]
*/
virtual void initRand(const int &min, const int &max)
throw(NNExcept);
/* inits the net with a given weight matrix and bias
* (makes a deep copy)
* ATTENTION: the dimension of IW-pointer must be the same
* as the inputs !!!
*/
virtual void init(const float *IW, float b1)
throw(NNExcept);
/* calculates the output with the current IW, b1 values
* ATTENTION: the array input_data must be in the same
* size as inputs_
*/
virtual float calculate(float *input_data);
/* this method trains the network:
* input_data is, as above, the input data, output_data is the
* output of the current net with input_data (output_data is not
* calculated in that method !), target_output is the desired
* output data
* (this is the LMS-algorithm to train linear neural networks)
* ATTENTION: the array input_data must be in the same
* size as inputs_
* returns the calculated value
*/
/* virtual float trainLMS(const float *input_data, */
/* const float &target_output); */
//-----------------------------------------------------
private:
/* Copy Construction is not allowed
*/
Neuron(const Neuron &src)
{ }
/* assignement operator is not allowed
*/
const Neuron& operator= (const Neuron& src)
{ return *this; }
};
} // end of namespace
#endif //_INCLUDE_NEURON_NET__
--- NEW FILE: readme.txt ---
pix_recNN - by Georg Holzmann <grh at mur.at>, 2004
look at http://grh.mur.at/software/thebrain.html
--------------------------------license---------------------------------------
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
In the official pix_recNN distribution, the GNU General Public License is
in the file gpl.txt
-------------------------------information-----------------------------------
see the PD help patch
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