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|
/* nn : Neural Networks for PD
by Davide Morelli - info@davidemorelli.it - http://www.davidemorelli.it
this software is simply an interface for FANN classes
http://fann.sourceforge.net/
FANN is obviously needed for compilation
this software is licensed under the GNU General Public License
*/
/*
hacked by Georg Holzmann for some additional methods, bug fixes, ...
2005, grh@mur.at
*/
#include <stdio.h>
#include <string.h>
#include "m_pd.h"
#include "fann.h"
#ifndef VERSION
#define VERSION "0.2"
#endif
#ifndef __DATE__
#define __DATE__ ""
#endif
#define TRAIN 0
#define RUN 1
static t_class *ann_mlp_class;
typedef struct _ann_mlp {
t_object x_obj;
struct fann *ann;
int mode; // 0 = training, 1 = running
t_symbol *filename; // name of the file where this ann is saved
t_symbol *filenametrain; // name of the file with training data
float desired_error;
unsigned int max_iterations;
unsigned int iterations_between_reports;
fann_type *input; // grh: storage for input
t_atom *output; // grh: storage for output (t_atom)
fann_type *out_float; // grh: storage for output (fann_type)
t_canvas *x_canvas;
t_outlet *l_out, *f_out;
} t_ann_mlp;
// allocation
static void ann_mlp_allocate_storage(t_ann_mlp *x)
{
int i;
if(!x->ann)
return;
x->input = (fann_type *)getbytes(x->ann->num_input*sizeof(fann_type));
x->output = (t_atom *)getbytes(x->ann->num_output*sizeof(t_atom));
x->out_float = (fann_type *)getbytes(x->ann->num_output*sizeof(fann_type));
// init storage with zeros
for (i=0; i<x->ann->num_input; i++)
x->input[i]=0;
for (i=0; i<x->ann->num_output; i++)
{
SETFLOAT(x->output+i, 0);
x->out_float[i]=0;
}
}
// deallocation
static void ann_mlp_free(t_ann_mlp *x)
{
if(!x->ann)
return;
freebytes(x->input, x->ann->num_input * sizeof(fann_type));
freebytes(x->output, x->ann->num_output * sizeof(t_atom));
freebytes(x->out_float, x->ann->num_output * sizeof(fann_type));
fann_destroy(x->ann);
}
static void ann_mlp_help(t_ann_mlp *x)
{
post("");
post("ann_mlp: neural nets for PD");
post("ann_mlp:Davide Morelli - info@davidemorelli.it - (c)2005");
post("ann_mlp:create or load an ann, train it and run it passing a list with inputs to the inlet, nn will give a list of float as output");
post("ann_mlp:main commands: create, filename, load, save, train-on-file, run");
post("ann_mlp:see help-nn.pd for details on commands and usage");
post("ann_mlp:this is an interface to FANN");
}
static void ann_mlp_createFann(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
unsigned int num_input = 2;
unsigned int num_output = 1;
unsigned int num_layers = 3;
unsigned int *neurons_per_layer = NULL;
int activated=0;
int i, count_args = 0;
float connection_rate = 1;
float learning_rate = (float)0.7;
// okay, start parsing init args ...
if (argc > count_args++)
num_input = atom_getint(argv++);
if (argc > count_args++)
num_output = atom_getint(argv++);
if (argc > count_args++)
{
int hidden=0;
num_layers = atom_getint(argv++);
hidden = num_layers-2;
neurons_per_layer = (unsigned int *)getbytes(num_layers*sizeof(unsigned int));
neurons_per_layer[0] = num_input;
// make standard initialization (if there are too few init args)
for (i=1; i<hidden+1; i++)
neurons_per_layer[i] = 3;
// now check init args
for (i=1; i<hidden+1; i++)
{
if (argc > count_args++)
neurons_per_layer[i] = atom_getint(argv++);
}
neurons_per_layer[num_layers-1] = num_output;
activated=1;
}
if (argc > count_args++)
connection_rate = atom_getfloat(argv++);
if (argc > count_args++)
learning_rate = atom_getfloat(argv++);
// make one hidden layer as standard, if there were too few init args
if(!activated)
{
neurons_per_layer = (unsigned int *)getbytes(3*sizeof(unsigned int));
neurons_per_layer[0] = num_input;
neurons_per_layer[1] = 3;
neurons_per_layer[2] = num_output;
}
// ... end of parsing init args
if(x->ann)
ann_mlp_free(x);
x->ann = fann_create_array(connection_rate, learning_rate, num_layers, neurons_per_layer);
// deallocate helper array
freebytes(neurons_per_layer, num_layers * sizeof(unsigned int));
if(!x->ann)
{
error("error creating the ann");
return;
}
ann_mlp_allocate_storage(x);
fann_set_activation_function_hidden(x->ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(x->ann, FANN_SIGMOID_SYMMETRIC);
// set error log to stdout, so that you see it in the pd console
//fann_set_error_log((struct fann_error*)x->ann, stdout);
// unfortunately this doesn't work ... but it should do in a similar way !!
post("created ann with:");
post("num_input = %i", num_input);
post("num_output = %i", num_output);
post("num_layers = %i", num_layers);
post("connection_rate = %f", connection_rate);
post("learning_rate = %f", learning_rate);
}
static void ann_mlp_print_status(t_ann_mlp *x)
{
if (x->mode == TRAIN)
post("nn:training");
else
post("nn:running");
}
static void ann_mlp_train(t_ann_mlp *x)
{
x->mode=TRAIN;
if (x->ann == 0)
{
error("ann not initialized");
return;
}
fann_reset_MSE(x->ann);
ann_mlp_print_status(x);
}
static void ann_mlp_run(t_ann_mlp *x)
{
x->mode=RUN;
ann_mlp_print_status(x);
}
static void ann_mlp_set_mode(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
if (argc<1)
{
error("usage: setmode 0/1: 0 for training, 1 for running");
}
else
{
x->mode = atom_getint(argv++);
ann_mlp_print_status(x);
}
}
static void ann_mlp_train_on_file(t_ann_mlp *x, t_symbol *s)
{
// make correct path
char patcher_path[MAXPDSTRING];
char filename[MAXPDSTRING];
if (x->ann == 0)
{
error("ann not initialized");
return;
}
// make correct path
canvas_makefilename(x->x_canvas, s->s_name, patcher_path, MAXPDSTRING);
sys_bashfilename(patcher_path, filename);
x->filenametrain = gensym(filename);
if(!x->filenametrain)
return;
post("nn: starting training on file %s, please be patient and wait ... (it could take severeal minutes to complete training)", x->filenametrain->s_name);
fann_train_on_file(x->ann, x->filenametrain->s_name, x->max_iterations,
x->iterations_between_reports, x->desired_error);
post("ann_mlp: finished training on file %s", x->filenametrain->s_name);
}
static void ann_mlp_set_desired_error(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
float desired_error = (float)0.001;
if (0<argc)
{
desired_error = atom_getfloat(argv);
x->desired_error = desired_error;
post("nn:desired_error set to %f", x->desired_error);
} else
{
error("you must pass me a float");
}
}
static void ann_mlp_set_max_iterations(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
unsigned int max_iterations = 500000;
if (argc>0)
{
max_iterations = atom_getint(argv);
x->max_iterations = max_iterations;
post("nn:max_iterations set to %i", x->max_iterations);
} else
{
error("you must pass me an int");
}
}
static void ann_mlp_set_iterations_between_reports(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
unsigned int iterations_between_reports = 1000;
if (argc>0)
{
iterations_between_reports = atom_getint(argv);
x->iterations_between_reports = iterations_between_reports;
post("nn:iterations_between_reports set to %i", x->iterations_between_reports);
} else
{
error("you must pass me an int");
}
}
// run the ann using floats in list passed to the inlet as input values
// and send result to outlet as list of float
static void ann_mlp_run_the_net(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
int i=0;
fann_type *calc_out;
if (x->ann == 0)
{
error("ann not initialized");
return;
}
if(argc < x->ann->num_input)
{
error("ann_mlp: too few input values!!");
return;
}
// fill input array with actual data sent to inlet
for (i=0;i<x->ann->num_input;i++)
{
x->input[i] = atom_getfloat(argv++);
}
// run the ann
calc_out = fann_run(x->ann, x->input);
// fill the output array with result from ann
for (i=0;i<x->ann->num_output;i++)
SETFLOAT(x->output+i, calc_out[i]);
// send output array to outlet
outlet_anything(x->l_out, gensym("list"),
x->ann->num_output, x->output);
}
static void ann_mlp_train_on_the_fly(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
int i=0;
int quantiINs, quantiOUTs;
float mse;
if (x->ann == 0)
{
error("ann not initialized");
return;
}
quantiINs = x->ann->num_input;
quantiOUTs = x->ann->num_output;
if ((quantiINs + quantiOUTs)>argc)
{
error("insufficient number of arguments passed, in training mode you must prive me a list with (num_input + num_output) floats");
return;
}
// fill input array with actual data sent to inlet
for (i=0;i<quantiINs;i++)
x->input[i] = atom_getfloat(argv++);
for (i=0;i<quantiOUTs;i++)
x->out_float[i] = atom_getfloat(argv++);
//fann_reset_MSE(x->ann);
fann_train(x->ann, x->input, x->out_float);
mse = fann_get_MSE(x->ann);
outlet_float(x->f_out, mse);
}
static void ann_mlp_manage_list(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
if (x->mode)
ann_mlp_run_the_net(x, sl, argc, argv);
else
{
ann_mlp_train_on_the_fly(x, sl, argc, argv);
}
}
static void ann_mlp_set_filename(t_ann_mlp *x, t_symbol *s)
{
// make correct path
char patcher_path[MAXPDSTRING];
char filename[MAXPDSTRING];
if(!s)
return;
// make correct path
canvas_makefilename(x->x_canvas, s->s_name, patcher_path, MAXPDSTRING);
sys_bashfilename(patcher_path, filename);
x->filename = gensym(filename);
}
static void ann_mlp_load_ann_from_file(t_ann_mlp *x, t_symbol *s)
{
ann_mlp_set_filename(x,s);
if(!x->filename)
{
error("ann: no filename !!!");
return;
}
// deallocate storage
if(x->ann)
ann_mlp_free(x);
x->ann = fann_create_from_file(x->filename->s_name);
if (x->ann == 0)
error("error opening %s", x->filename->s_name);
else
post("nn:ann loaded fom file %s", x->filename->s_name);
// allocate storage
ann_mlp_allocate_storage(x);
}
static void ann_mlp_save_ann_to_file(t_ann_mlp *x, t_symbol *s)
{
ann_mlp_set_filename(x,s);
if(!x->filename)
{
error("ann: no filename !!!");
return;
}
if (x->ann == 0)
{
error("ann is not initialized");
} else
{
fann_save(x->ann, x->filename->s_name);
post("nn:ann saved in file %s", x->filename->s_name);
}
}
// functions for training algo:
static void ann_mlp_set_FANN_TRAIN_INCREMENTAL(t_ann_mlp *x)
{
if (x->ann == 0)
{
error("ann is not initialized");
} else
{
fann_set_training_algorithm(x->ann, FANN_TRAIN_INCREMENTAL);
post("nn:training algorithm set to FANN_TRAIN_INCREMENTAL");
}
}
static void ann_mlp_set_FANN_TRAIN_BATCH(t_ann_mlp *x)
{
if (x->ann == 0)
{
error("ann is not initialized");
} else
{
fann_set_training_algorithm(x->ann, FANN_TRAIN_BATCH);
post("nn:training algorithm set to FANN_TRAIN_BATCH");
}
}
static void ann_mlp_set_FANN_TRAIN_RPROP(t_ann_mlp *x)
{
if (x->ann == 0)
{
error("ann is not initialized");
} else
{
fann_set_training_algorithm(x->ann, FANN_TRAIN_RPROP);
post("nn:training algorithm set to FANN_TRAIN_RPROP");
}
}
static void ann_mlp_set_FANN_TRAIN_QUICKPROP(t_ann_mlp *x)
{
if (x->ann == 0)
{
error("ann is not initialized");
} else
{
fann_set_training_algorithm(x->ann, FANN_TRAIN_QUICKPROP);
post("nn:training algorithm set to FANN_TRAIN_QUICKPROP");
}
}
static void ann_mlp_set_activation_function_output(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
t_symbol *parametro = 0;
int funzione = 0;
if (x->ann == 0)
{
error("ann not initialized");
return;
}
if (argc>0) {
parametro = atom_gensym(argv);
if (strcmp(parametro->s_name, "FANN_THRESHOLD")==0)
funzione = FANN_THRESHOLD;
if (strcmp(parametro->s_name, "FANN_THRESHOLD_SYMMETRIC")==0)
funzione = FANN_THRESHOLD_SYMMETRIC;
if (strcmp(parametro->s_name, "FANN_LINEAR")==0)
funzione = FANN_LINEAR;
if (strcmp(parametro->s_name, "FANN_SIGMOID")==0)
funzione = FANN_SIGMOID;
if (strcmp(parametro->s_name, "FANN_SIGMOID_STEPWISE")==0)
funzione = FANN_SIGMOID_STEPWISE;
if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC")==0)
funzione = FANN_SIGMOID_SYMMETRIC;
if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC_STEPWISE")==0)
funzione = FANN_SIGMOID_SYMMETRIC_STEPWISE;
if (strcmp(parametro->s_name, "FANN_GAUSSIAN")==0)
funzione = FANN_GAUSSIAN;
if (strcmp(parametro->s_name, "FANN_GAUSSIAN_STEPWISE")==0)
funzione = FANN_GAUSSIAN_STEPWISE;
if (strcmp(parametro->s_name, "FANN_ELLIOT")==0)
funzione = FANN_ELLIOT;
if (strcmp(parametro->s_name, "FANN_ELLIOT_SYMMETRIC")==0)
funzione = FANN_ELLIOT_SYMMETRIC;
fann_set_activation_function_output(x->ann, funzione);
} else
{
error("you must specify the activation function");
}
post("nn:activation function set to %s (%i)", parametro->s_name, funzione);
}
static void ann_mlp_set_activation_function_hidden(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
t_symbol *parametro = 0;
int funzione = 0;
if (x->ann == 0)
{
error("ann not initialized");
return;
}
if (argc>0) {
parametro = atom_gensym(argv);
if (strcmp(parametro->s_name, "FANN_THRESHOLD")==0)
funzione = FANN_THRESHOLD;
if (strcmp(parametro->s_name, "FANN_THRESHOLD_SYMMETRIC")==0)
funzione = FANN_THRESHOLD_SYMMETRIC;
if (strcmp(parametro->s_name, "FANN_LINEAR")==0)
funzione = FANN_LINEAR;
if (strcmp(parametro->s_name, "FANN_SIGMOID")==0)
funzione = FANN_SIGMOID;
if (strcmp(parametro->s_name, "FANN_SIGMOID_STEPWISE")==0)
funzione = FANN_SIGMOID_STEPWISE;
if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC")==0)
funzione = FANN_SIGMOID_SYMMETRIC;
if (strcmp(parametro->s_name, "FANN_SIGMOID_SYMMETRIC_STEPWISE")==0)
funzione = FANN_SIGMOID_SYMMETRIC_STEPWISE;
if (strcmp(parametro->s_name, "FANN_GAUSSIAN")==0)
funzione = FANN_GAUSSIAN;
if (strcmp(parametro->s_name, "FANN_GAUSSIAN_STEPWISE")==0)
funzione = FANN_GAUSSIAN_STEPWISE;
if (strcmp(parametro->s_name, "FANN_ELLIOT")==0)
funzione = FANN_ELLIOT;
if (strcmp(parametro->s_name, "FANN_ELLIOT_SYMMETRIC")==0)
funzione = FANN_ELLIOT_SYMMETRIC;
fann_set_activation_function_hidden(x->ann, funzione);
} else
{
error("you must specify the activation function");
}
post("nn:activation function set to %s (%i)", parametro->s_name, funzione);
}
static void ann_mlp_randomize_weights(t_ann_mlp *x, t_symbol *sl, int argc, t_atom *argv)
{
t_float min = -1;
t_float max = 1;
if(!x->ann)
{
post("ann_mlp: ann is not initialized");
return;
}
if (argc>0)
min = atom_getfloat(argv++);
if (argc>1)
max = atom_getfloat(argv++);
fann_randomize_weights(x->ann, min, max);
}
static void ann_mlp_learnrate(t_ann_mlp *x, t_float f)
{
int learnrate = 0;
if(!x->ann)
{
post("ann_mlp: ann is not initialized");
return;
}
learnrate = (f<0) ? 0 : f;
fann_set_learning_rate(x->ann, learnrate);
}
static void ann_mlp_set_activation_steepness_hidden(t_ann_mlp *x, t_float f)
{
if(!x->ann)
{
post("ann_mlp: ann is not initialized");
return;
}
fann_set_activation_steepness_hidden(x->ann, f);
}
static void ann_mlp_set_activation_steepness_output(t_ann_mlp *x, t_float f)
{
if(!x->ann)
{
post("ann_mlp: ann is not initialized");
return;
}
fann_set_activation_steepness_output(x->ann, f);
}
void fann_set_activation_steepness_hidden(struct fann * ann, fann_type steepness);
static void ann_mlp_print_ann_details(t_ann_mlp *x)
{
if (x->ann == 0)
{
post("ann_mlp:ann is not initialized");
} else
{
post("follows a description of the current ann:");
post("num_input=%i", x->ann->num_input);
post("num_output=%i", x->ann->num_output);
post("learning_rate=%f", x->ann->learning_rate);
post("connection_rate=%f", x->ann->connection_rate);
post("total_neurons=%i", x->ann->total_neurons);
post("total_connections=%i", x->ann->total_connections);
post("last error=%i", x->ann->errstr);
if (x->filename == 0)
{
post("ann_mlp:filename not set");
} else
{
post("filename=%s", x->filename->s_name);
}
}
}
static void ann_mlp_print_ann_print(t_ann_mlp *x)
{
if(!x->ann)
{
post("ann_mlp: ann is not initialized");
return;
}
fann_print_connections(x->ann);
fann_print_parameters(x->ann);
}
static void *ann_mlp_new(t_symbol *s, int argc, t_atom *argv)
{
t_ann_mlp *x = (t_ann_mlp *)pd_new(ann_mlp_class);
x->l_out = outlet_new(&x->x_obj, &s_list);
x->f_out = outlet_new(&x->x_obj, &s_float);
x->desired_error = (float)0.001;
x->max_iterations = 500000;
x->iterations_between_reports = 1000;
x->mode=RUN;
x->x_canvas = canvas_getcurrent();
x->filename = NULL;
x->filenametrain = NULL;
x->ann = NULL;
x->input = NULL;
x->output = NULL;
x->out_float = NULL;
if (argc>0) {
x->filename = atom_gensym(argv);
ann_mlp_load_ann_from_file(x, NULL);
}
return (void *)x;
}
void ann_mlp_setup(void) {
post("");
post("ann_mlp: multilayer perceptron for PD");
post("version: "VERSION"");
post("compiled: "__DATE__);
post("author: Davide Morelli");
post("contact: info@davidemorelli.it www.davidemorelli.it");
ann_mlp_class = class_new(gensym("ann_mlp"),
(t_newmethod)ann_mlp_new,
(t_method)ann_mlp_free, sizeof(t_ann_mlp),
CLASS_DEFAULT, A_GIMME, 0);
// general..
class_addmethod(ann_mlp_class, (t_method)ann_mlp_help, gensym("help"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_createFann, gensym("create"), A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_train, gensym("train"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_run, gensym("run"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_mode, gensym("setmode"), A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_train_on_file, gensym("train-on-file"), A_DEFSYMBOL, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_manage_list, gensym("data"), A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_filename, gensym("filename"), A_DEFSYMBOL, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_load_ann_from_file, gensym("load"),A_DEFSYMBOL, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_save_ann_to_file, gensym("save"),A_DEFSYMBOL, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_print_ann_details, gensym("details"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_print_ann_print, gensym("print"), 0);
// change training parameters
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_desired_error, gensym("desired_error"),A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_max_iterations, gensym("max_iterations"),A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_iterations_between_reports, gensym("iterations_between_reports"),A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_learnrate, gensym("learnrate"), A_FLOAT, 0);
// change training and activation algorithms
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_INCREMENTAL, gensym("FANN_TRAIN_INCREMENTAL"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_BATCH, gensym("FANN_TRAIN_BATCH"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_RPROP, gensym("FANN_TRAIN_RPROP"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_FANN_TRAIN_QUICKPROP, gensym("FANN_TRAIN_QUICKPROP"), 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_function_output, gensym("set_activation_function_output"),A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_function_hidden, gensym("set_activation_function_hidden"),A_GIMME, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_steepness_hidden, gensym("set_activation_steepness_hidden"), A_FLOAT, 0);
class_addmethod(ann_mlp_class, (t_method)ann_mlp_set_activation_steepness_output, gensym("set_activation_steepness_output"), A_FLOAT, 0);
// initialization:
class_addmethod(ann_mlp_class, (t_method)ann_mlp_randomize_weights, gensym("randomize_weights"),A_GIMME, 0);
// the most important one: running the ann
class_addlist(ann_mlp_class, (t_method)ann_mlp_manage_list);
// help patch
class_sethelpsymbol(ann_mlp_class, gensym("help-ann_mlp"));
}
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