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externals/ann/examples/ann_mlp_example4/trainfile.dat deleted 100644 → 0
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4 2 4
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1 0 0 0
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0 1 0 0
-1 1
0 0 1 0
1 1
0 0 0 1
externals/ann/examples/ann_mlp_example4/trainfile2.dat deleted 100644 → 0
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4 2 24
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externals/ann/examples/ann_som/ann_som.pd deleted 100644 → 0
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#N canvas 258 163 640 725 10;
#X msg 131 535 print;
#X msg 132 568 new 5 8 8;
#X msg 127 139 init;
#X msg 128 314 train;
#X msg 129 336 test;
#X msg 128 427 write;
#X obj 70 599 ann_som 4 9 10;
#X msg 70 49 1 0 0 1;
#X msg 70 68 0 1 0 1;
#X msg 70 87 2 1 0 0;
#X msg 128 158 init 0.5;
#X msg 128 178 init 1 0.5 0 0.5;
#X text 234 141 init all weights with "0";
#X text 235 160 init all weights with "0.5";
#X text 235 177 init weights for each sensor;
#X msg 128 203 learn 1;
#X msg 128 237 learn 1 0.9 0.1;
#X text 226 203 set learning rate to 1;
#X msg 128 220 learn 0.5 0.999;
#X text 227 219 set learning rate to 0.5 and factor to 0.999;
#X text 227 237 set learning rate to 1 \, factor to 0.9 and offset
to 0.1;
#X msg 128 254 neighbour 1;
#X msg 128 271 neighbour 0.5 0.999;
#X msg 128 288 neighbour 1 0.9 0.1;
#X text 248 255 set neighbourhood to 1;
#X text 249 271 set neighbourhoodto 0.5 and factor to 0.999;
#X text 249 289 set neighbourhood to 1 \, factor to 0.9 and offset
to 0.1;
#X text 180 309 set som to "train" mode (learn from sensor-input and
output winning neuron);
#X text 179 331 set som to "test" mode (output winning neuron for sensor-input
\, but do not learn !);
#X msg 129 368 rule INSTAR;
#X msg 129 385 rule OUTSTAR;
#X msg 129 402 rule KOHONEN;
#X text 218 367 learn with IN-STAR rule;
#X text 219 385 learn with OUT-STAR rule;
#X text 219 402 learn with KOHONENrule;
#X msg 128 445 write mysom.som;
#X msg 129 469 read;
#X msg 129 487 read mysom.som;
#X text 156 68 present various data to the SOM;
#X text 203 535 for debugging;
#X text 207 570 create a new SOM with 8x8 neurons \, each having 5
sensors;
#X text 204 601 create a new SOM with 9x10 neurons \, each having 4
sensors;
#X floatatom 70 654 4 0 0 0 - - -;
#X text 113 658 winning neuron;
#N canvas 13 0 889 630 SOMs 0;
#X text 76 27 SOM :: Self-Organized Maps;
#X text 55 53 SOMs are "Artificial Neural Networks" \, that are trying
to learn something about the data presented to them without a supervisor/teacher.
;
#X text 59 118 in short:;
#X text 120 119 the neuron \, whose weight-configuration matches the
presented data best is the winner (its number (counting from the lower-left
corner) is sent to the output);
#X text 121 163 to match the data better the next time it is presented
\, the weights of the winning neuron are adjusted.;
#X text 121 188 the weights of the neurons neighbouring the winner
are adjusted to match the data too \, but not so strong as the winner's.
;
#X text 121 276 lr(n+1)=lr(n)*factor;
#X text 275 277 learning_rate=lr+offset;
#X text 121 289 nb(n+1)=nb(n)*factor;
#X text 275 290 neighbourhood=nb+offset;
#X text 121 230 both neighbourhood and learning-rate (==amount of how
much the weights of the winner (and \, proportional \, the weights
of the neighbours) are adjusted) are decreasing recursively with time.
;
#X text 119 319 thus you will sooner or (most of the time) later get
a "brain map" \, where similar inputs will activate neurons in specifique
regions (like there are regions for seeing and regions for hearing
in our brains);
#X text 97 381 there are various rules \, how to re-adjust the weights
of the neurons : in-star \, out-star and kohonen (maybe there are others
\, but these i found in literature);
#X obj 607 220 +;
#X text 640 182 ...;
#X obj 579 185 * \$1;
#X obj 607 185 * \$2;
#X obj 670 185 * \$0;
#X obj 579 128 unpack 0 0 0 0 0;
#X text 602 111 n sensors;
#X text 705 186 weights 1 to n;
#X obj 579 90 inlet;
#X obj 607 288 outlet;
#X text 594 62 a neuron;
#X text 566 307 the neuron with the highest weighted sum;
#X text 567 318 matches best and is therefore the winner;
#X text 53 452 notes:;
#X text 101 453 each neuron of the SOM has n sensors. you have to present
a list of n floats to the SOM to make it work;
#X text 102 482 you should init the weights for each sensor with the
expected mean of the sensor values before you start training to get
best and fastest results;
#X text 55 87 they were first proposed by the Finnish scientist T.Kohonen
in the 80ies (i think).;
#X text 98 543 if you have no clue \, what's this all about \, maybe
you do not need SOMs (which i doubt) or you should have a look at;
#X text 118 577 http://www.eas.asu.edu/~eee511;
#X text 118 591 http://www.cis.hut.fi/projects/ica;
#X connect 13 0 22 0;
#X connect 15 0 13 0;
#X connect 16 0 13 0;
#X connect 17 0 13 0;
#X connect 18 0 15 0;
#X connect 18 1 16 0;
#X connect 18 4 17 0;
#X connect 21 0 18 0;
#X restore 535 44 pd SOMs;
#X text 81 13 ann_som :: train and test Self-Organized Maps;
#X obj 73 700 ann_som test.som;
#X text 211 704 load a SOM-file;
#X msg 128 119 rinit 10;
#X text 234 121 init all weights with time-seeded random values from
0 to 10;
#X connect 0 0 6 0;
#X connect 1 0 6 0;
#X connect 2 0 6 0;
#X connect 3 0 6 0;
#X connect 4 0 6 0;
#X connect 5 0 6 0;
#X connect 6 0 42 0;
#X connect 7 0 6 0;
#X connect 8 0 6 0;
#X connect 9 0 6 0;
#X connect 11 0 6 0;
#X connect 15 0 6 0;
#X connect 16 0 6 0;
#X connect 18 0 6 0;
#X connect 21 0 6 0;
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#X connect 23 0 6 0;
#X connect 29 0 6 0;
#X connect 30 0 6 0;
#X connect 31 0 6 0;
#X connect 35 0 6 0;
#X connect 36 0 6 0;
#X connect 37 0 6 0;
#X connect 48 0 6 0;
externals/ann/examples/ann_som/test.som deleted 100644 → 0
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SOM:
10 10 10 INSTAR
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0.999999500000777 0.999999999666666 1.000000000000000
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externals/ann/examples/ann_td/example.pd deleted 100644 → 0
View file @ 0dd92c6e
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#X text 128 14 1- change to training mode;
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#X msg 170 282 run;
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#X text 224 182 3- run the nn switching this metro ON;
#X text 277 213 (note \, you must be in run mode);
#X msg 281 231 run;
#X text 364 275 4- when you are happy with you nn save it;
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#X text 300 422 mse is usefull while training on-the-fly: tells you
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output.;
#X text 325 115 2- train on the fly in realtime;
#X obj 64 348 unpack f f f f;
#X msg 249 279 save tdnn.net;
#X msg 249 298 load tdnn.net;
#X text 24 412 still;
#X text 72 412 left;
#X text 111 413 right;
#X text 156 415 na;
#X obj 66 325 ann_tdnn 2 5 tdnn.net;
#N canvas 111 7 822 554 inputs 0;
#X obj 107 65 r posx;
#X obj 166 65 r posy;
#X obj 68 103 f;
#X obj 41 46 inlet;
#X obj 55 300 outlet;
#X obj 72 138 pack f f;
#X connect 0 0 2 1;
#X connect 1 0 5 1;
#X connect 2 0 5 0;
#X connect 3 0 2 0;
#X connect 5 0 4 0;
#X restore 156 245 pd inputs;
#X msg 66 15 create 2 4 5;
#X text 363 301 remember that you can load it also passing filename
as the 3rd argument;
#X connect 0 0 6 0;
#X connect 1 0 7 0;
#X connect 2 0 8 0;
#X connect 3 0 9 0;
#X connect 4 0 27 0;
#X connect 5 0 4 0;
#X connect 11 0 26 0;
#X connect 14 0 26 0;
#X connect 19 0 0 0;
#X connect 19 1 1 0;
#X connect 19 2 2 0;
#X connect 19 3 3 0;
#X connect 20 0 26 0;
#X connect 21 0 26 0;
#X connect 26 0 19 0;
#X connect 26 1 16 0;
#X connect 26 1 11 0;
#X connect 27 0 26 0;
#X connect 28 0 26 0;
#X restore 9 224 pd train and run nn;
#X text 94 94 1- start rendering;
#X text 72 203 2- open the subpatch and follow instructions;
#X text 15 5 TDNN implementation (Time Delay Neural Network) useful
for gesture recognition;
#X connect 1 0 0 0;
#X connect 2 0 0 1;
externals/ann/examples/ann_td/tdnn.net deleted 100644 → 0
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FANN_FLO_1.1
3 0.700000 1.000000 0 5 5 5.00000000000000000000e-001 5.00000000000000000000e-001
11 4 5
0 0 0 0 0 0 0 0 0 0 0
11 11 11 0
4 4 4 4 0
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externals/ann/examples/tutorial-ann_mlp.pd deleted 100644 → 0
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#X obj 65 379 ann_mlp;
#X text 39 59 input: mouse coord;
#X text 37 76 output: 1 if mouse is in the upper left part of the screen
;
#N canvas 106 171 572 442 step1-creation 0;
#X text 141 13 let's create the ANN;
#X obj 130 354 outlet;
#X msg 132 185 create 2 1;
#X text 59 102 let's assume you have 2 inputs value X and Y and you
want 1 output value that tells 1 when the mouse in in the upper left
part of the screen;
#X text 242 185 send this message;
#X connect 2 0 1 0;
#X restore 128 158 pd step1-creation;
#N canvas 46 38 652 612 step2-train 0;
#X obj 97 530 outlet;
#X msg 44 130 train;
#X text 102 129 first switch to train mode;
#X obj 139 367 pack s f f f;
#N canvas 0 0 466 316 normalized-inputs 0;
#X obj 105 64 r X;
#X obj 173 67 r Y;
#X obj 173 92 / 240;
#X obj 105 91 / 320;
#X obj 104 116 - 1;
#X obj 173 118 - 1;
#X text 221 108 normalize inputs to [-1 \, 1];
#X obj 104 211 outlet;
#X obj 177 215 outlet;
#X text 144 35 receive X and Y;
#X connect 0 0 3 0;
#X connect 1 0 2 0;
#X connect 2 0 5 0;
#X connect 3 0 4 0;
#X connect 4 0 7 0;
#X connect 5 0 8 0;
#X restore 127 271 pd normalized-inputs;
#N canvas 0 0 675 324 desired-output 0;
#X obj 383 63 key;
#X obj 383 92 select 97;
#X msg 382 118 1;
#X obj 528 97 select 97;
#X obj 528 68 keyup;
#X msg 527 123 0;
#X obj 183 195 outlet;
#X text 90 269 i send 1 when "a" is pressed \, 0 when released;
#X obj 180 108 r out;
#X text 81 231 optionally \, you can use the "a" key instead of the
mouse button;
#X connect 0 0 1 0;
#X connect 1 0 2 0;
#X connect 2 0 6 0;
#X connect 3 0 5 0;
#X connect 4 0 3 0;
#X connect 5 0 6 0;
#X connect 8 0 6 0;
#X restore 239 332 pd desired-output;
#X obj 91 192 tgl 15 0 empty empty train 0 -6 0 8 -258699 -1 -1 0 1
;
#X text 117 190 <-- toggle to train;
#X text 119 404 everytime the metro bangs you send a training pattern
to ann_mlp;
#X floatatom 247 357 5 0 0 0 - - -;
#X floatatom 287 295 5 0 0 0 - - -;
#X floatatom 148 295 5 0 0 0 - - -;
#X obj 110 328 metro 50;
#X text 53 45 (for this to work you need to give focus to this window
but the gem window must be visible);
#X text 335 293 <-- check mouse position;
#X text 112 213 then move the mouse and press left button when you
are in the upper left part of the screen \, release button when you
are not in the upper left part of the screen;
#X text 293 357 <-- check button pressed/not pressed;
#X obj 124 475 unpack s f f f;
#X obj 145 501 pack f f f;
#X floatatom 296 518 5 0 0 0 mse mse -;
#X text 116 447 go on until mse is low enough (< 0.01);
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#X msg 134 71 0 \, destroy;
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#X restore 287 443 pd mouseInput;
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#X obj 105 91 / 320;
#X obj 104 116 - 1;
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#X text 221 108 normalize inputs to [-1 \, 1];
#X obj 104 211 outlet;
#X obj 177 215 outlet;
#X text 144 35 receive X and Y;
#X connect 0 0 3 0;
#X connect 1 0 2 0;
#X connect 2 0 5 0;
#X connect 3 0 4 0;
#X connect 4 0 7 0;
#X connect 5 0 8 0;
#X restore 178 167 pd normalized-inputs;
#X obj 97 92 tgl 15 0 empty empty run 0 -6 0 8 -258699 -1 -1 0 1;
#X obj 145 267 pack s f f;
#X msg 50 30 run;
#X text 108 29 first switch to run mode;
#X text 123 90 <-- toggle to run;
#X obj 116 228 metro 50;
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#X floatatom 191 193 5 0 0 0 - - -;
#X obj 145 293 unpack s f f;
#X obj 176 321 pack f f;
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#X connect 4 0 0 0;
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#X connect 10 1 11 0;
#X connect 10 2 11 1;
#X connect 11 0 0 0;
#X restore 139 284 pd step3-run;
#X floatatom 64 450 5 0 0 0 - - -;
#X connect 0 0 11 0;
#X connect 0 1 9 0;
#X connect 3 0 0 0;
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#X connect 6 0 5 0;
#X connect 7 0 5 1;
#X connect 8 0 6 0;
#X connect 10 0 0 0;
externals/ann/helps/ann_mlp-help.pd deleted 100644 → 0
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#N canvas 1 53 717 456 12;
#N canvas 160 189 627 328 creation 0;
#X obj 52 235 outlet;
#X msg 49 10 create;
#X msg 72 68 create 2 1;
#X msg 81 97 create 3 1;
#X msg 93 128 create 3 2;
#X msg 59 38 create 3 2 3 3 1 0.7;
#X text 121 7 create with default values;
#X text 236 38 specifying all;
#X text 166 68 2 inputs 1 output;
#X text 176 99 3 inputs 1 output;
#X text 189 128 3 inputs 2 output;
#X text 159 222 TIP:don't set the layers param too high;
#X text 158 179 params: num_input \, num_output \, num_layers \, num_neurons_hidden
\, connection_rate \, learning_rate;
#X connect 1 0 0 0;
#X connect 2 0 0 0;
#X connect 3 0 0 0;
#X connect 4 0 0 0;
#X connect 5 0 0 0;
#X restore 52 68 pd creation examples;
#N canvas 83 141 728 356 run 0;
#X obj 90 219 outlet;
#X msg 123 69 0 1;
#X msg 124 92 1 0;
#X msg 125 115 1 1;
#X msg 126 140 0 0;
#X text 40 17 now you can run your nn passing it a list with inputs
;
#X text 169 70 send a list of data and watch the console for output
;
#X text 39 35 the output is sent as a list of float;
#X text 184 134 these inputs are good for a nn like the one in example1
directory;
#X connect 1 0 0 0;
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#X restore 86 180 pd run the net;
#N canvas 1 53 619 610 other 0;
#X obj 43 401 outlet;
#X msg 102 37 train;
#X msg 103 63 run;
#X msg 152 37 setmode 0;
#X msg 153 63 setmode 1;
#X text 249 40 set training/running mode;
#X text 247 63 training mode currently not implemented;
#N canvas 113 201 690 335 training 0;
#X obj 71 288 outlet;
#X msg 82 195 FANN_TRAIN_INCREMENTAL;
#X msg 82 216 FANN_TRAIN_BATCH;
#X msg 81 238 FANN_TRAIN_RPROP;
#X msg 81 258 FANN_TRAIN_QUICKPROP;
#X text 40 28 you can set the training algorithm simply sending a message
with the name of the algorithm chosen. possible values are: FANN_TRAIN_INCREMENTAL
FANN_TRAIN_BATCH FANN_TRAIN_RPROP FANN_TRAIN_QUICKPROP the default
is: FANN_TRAIN_RPROP see the FANN manual for details on each algorithm:
http://fann.sourceforge.net/html/r1996.html;
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#X restore 150 153 pd training algorithm;
#X text 360 175 some advanced param;
#N canvas 34 162 698 395 training 0;
#X obj 52 230 outlet;
#X msg 69 118 desired_error 0.01;
#X msg 79 146 max_iterations 500000;
#X msg 90 178 iterations_between_reports 1000;
#X text 58 28 you can change training parameters. see FANN manual for
details (http://fann.sourceforge.net);
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#X connect 3 0 0 0;
#X restore 151 179 pd training params;
#N canvas 329 121 694 391 activation 0;
#X obj 49 335 outlet;
#X text 40 28 you can set ti output activation algorithm passing a
message to nn. see the FANN manual for description of the algorithms
;
#X msg 69 118 set_activation_function_output FANN_THRESHOLD;
#X msg 83 139 set_activation_function_output FANN_THRESHOLD_SYMMETRIC
;
#X msg 95 163 set_activation_function_output FANN_LINEAR;
#X msg 98 184 set_activation_function_output FANN_SIGMOID;
#X msg 106 206 set_activation_function_output FANN_SIGMOID_STEPWISE
;
#X msg 108 233 set_activation_function_output FANN_SIGMOID_SYMMETRIC
;
#X msg 115 256 set_activation_function_output FANN_SIGMOID_SYMMETRIC_STEPWISE
;
#X connect 2 0 0 0;
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#X connect 4 0 0 0;
#X connect 5 0 0 0;
#X connect 6 0 0 0;
#X connect 7 0 0 0;
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#X restore 150 203 pd activation algorithm;
#X msg 151 287 details;
#X text 229 285 details on the current nn;
#X msg 145 333 help;
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#X connect 13 0 0 0;
#X restore 107 258 pd other commands;
#N canvas 2 82 653 513 save 0;
#X obj 39 264 outlet;
#X msg 64 20 filename test.net;
#X msg 66 46 save;
#X msg 82 103 load;
#X text 221 19 set the filename;
#X text 214 42 save the net to the file;
#X text 138 104 you can reload it too;
#X text 144 182 nn can be loaded from a file at creation time simply
passing the filename as argument;
#X msg 68 71 save test.net;
#X msg 93 130 load test.net;
#X text 144 217 like [ann_mlp test.net];
#X connect 1 0 0 0;
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#X connect 3 0 0 0;
#X connect 8 0 0 0;
#X connect 9 0 0 0;
#X restore 97 218 pd save the net;
#X text 229 66 create a nn;
#X text 223 179 run your net;
#X text 237 215 save your net;
#N canvas 2 82 712 542 tips 0;
#X text 51 84 for better performances inputs value should be normalized
\, all input should have the same range (if one input has a larger
range it will be more "important"). the range of each input should
be 0 centered. so [-1 \, 1] is good [-2 \, 2] is good \, [0 \, 1] not
so good [1 \, 2] is bad. the range sould not be too small ([-0.1 \,
0.1] is bad).;
#X text 41 19 TIPS;
#X text 41 56 inputs;
#X text 39 211 outputs;
#X text 50 235 each class of outputs should have its own output value:
don't use the same output for 2 meanings \, use 2 outputs intead \,
1 for each.;
#X restore 156 303 pd tips;
#X text 270 333 an interface to fann classes (http://fann.sourceforge.net)
;
#X text 272 351 by Davide Morelli - info@davidemorelli.it;
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#N canvas 8 48 990 509 build 0;
#X obj 65 417 textfile;
#X msg 190 337 clear;
#N canvas 0 0 462 312 alternate 0;
#X obj 103 117 + 1;
#X obj 70 119 f 0;
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#X msg 95 90 0;
#X obj 68 31 inlet;
#X obj 140 40 inlet;
#X obj 140 63 bang;
#X obj 68 55 bang;
#X obj 65 205 outlet;
#X obj 125 206 outlet;
#X text 59 6 bang;
#X text 139 18 reset to 0 without bang;
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#X connect 3 0 2 0;
#X connect 4 0 1 1;
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#X connect 7 0 4 0;
#X connect 8 0 1 0;
#X restore 58 227 pd alternate;
#X obj 24 81 bng 15 250 50 0 empty empty write-once 0 -6 0 8 -262144
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#X obj 341 183 bng 15 250 50 0 empty empty reset 0 -6 0 8 -262144 -1
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#X obj 61 153 pack s f f;
#X obj 63 200 pack f f;
#X obj 61 176 unpack s f f;
#X msg 66 223 add \$1 \$2;
#X obj 66 257 outlet;
#X text 120 258 to textfile;
#X obj 24 42 inlet;
#X text 23 22 bang;
#X text 66 77 here go the inputs;
#X obj 94 52 r input1;
#X obj 163 52 r input2;
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#X restore 58 306 pd inputs;
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#X obj 61 176 unpack s f f;
#X msg 66 223 add \$1 \$2;
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#X text 120 258 to textfile;
#X obj 24 42 inlet;
#X text 23 22 bang;
#X text 66 77 here go the outputs;
#X obj 91 51 r output1;
#X obj 166 51 r output2;
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#X restore 149 284 pd outputs;
#X obj 230 223 f 0;
#X obj 260 223 + 1;
#X obj 239 257 nbx 5 14 -1e+37 1e+37 0 0 empty empty how_many_patterns
0 -6 0 10 -262144 -1 -1 0 256;
#X text 156 406 todo: write header (a line at the beginning of file
with 3 int: how many tests \, num_input \, num_output);
#X obj 122 190 delay 50;
#X obj 115 159 metro 100;
#X floatatom 259 72 5 100 5000 2 msec_between_snapshots - -;
#X obj 127 80 tgl 15 0 empty empty toggle_on-off 0 -6 0 8 -262144 -1
-1 0 1;
#X obj 219 189 / 2;
#X obj 260 16 loadbang;
#X msg 260 36 100;
#X msg 326 342 write test.txt cr;
#X text 293 224 comment;
#N canvas 262 68 647 603 README 0;
#X text 67 432 please help me getting this patch more usable: - how
to add a line at the very beginning of a text file after i have filled
it? - how to manage inputs and outputs of different sized without forcing
the user to edit the patch?;
#X text 9 63 how to use: 1) modify [pd inputs] and [ps outputs] inserting
[r] objects to receive input data \, and modify [pack]s to handle the
right number of inputs 2) do the same with [pd outputs] 3) click on
reset 4) toggle ON and start collecting data 5) when you are ready
toggle OFF 6) edit [write filename cr( with the actual filename you
want for your training data (always keep the cr after the filename)
7) open the file with training data 8) add a line at the beginning
and write 3 integers: the 1st is the number of training patterns written
(see "how many patterns" number box) \, the 2nd is how many inputs
your ann has \, the 3th is how many outputs e.g. i collected 100 training
snapshots \, for a ann with 10 ins and 2 outs I write: 100 10 2 at
the very beginning of the file now the training file is ready and can
be read from nn via train-on-file command;
#X text 9 7 this tricky sub-patch is usefull to write a file to train
ann and is intended to be used with the nn external;
#X restore 25 16 pd README;
#X text 479 210 by davide morelli info@davidemorelli.it;
#X text 106 14 <--readme!;
#X text 242 283 <--edit here!;
#X text 142 308 <--edit here!;
#X text 429 86 usage: read [pd README] \, edit [pd inputs] and [pd
outputs] \, toggle on and record inputs and outputs \, toggle off when
ready \, write to a file \, edit the file adding a line at the beginning
(see REAMDE);
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#X connect 18 0 0 0;
#X restore 86 42 pd build training file;
#X msg 88 74 train-on-file test.txt;
#X text 285 45 build a training file;
#X text 287 74 train the nn with the training file;
#X obj 56 139 outlet;
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#X restore 79 103 pd train you net using a train file;
#N canvas 120 72 892 558 train 0;
#X obj 55 487 outlet;
#X msg 60 31 train;
#X text 126 33 1- set the train mode;
#X text 192 120 be shure you provide the correct numbers of inputs
and outputs;
#X obj 168 202 pack s f f f;
#X obj 197 248 pack f f f;
#X obj 168 225 unpack s f f f;
#X msg 190 464 run;
#X obj 198 170 tgl 15 0 empty empty in1 0 -6 0 8 -262144 -1 -1 0 1
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#X obj 228 170 tgl 15 0 empty empty in2 0 -6 0 8 -262144 -1 -1 0 1
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#X obj 259 170 tgl 15 0 empty empty output 0 -6 0 8 -262144 -1 -1 0
1;
#X obj 148 169 bng 15 250 50 0 empty empty train! 0 -6 0 8 -262144
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#X text 312 160 set inputs and output value \, then send the list clicking
on the "train!" bang;
#X msg 316 261 create 2 1;
#X text 227 464 3- when you are ready switch again to run mode before
exiting;
#X text 315 226 NOTE1: before training with this example you should
have created a nn with 2 ins and 1 out with a command like:;
#N canvas 255 158 517 436 autotrain 0;
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#X msg 126 215 0 1 1;
#X msg 82 168 1 0 1;
#X msg 150 244 1 1 1;
#X obj 82 112 random 4;
#X obj 83 138 sel 0 1 2 3;
#X obj 226 125 f 0;
#X obj 256 124 + 1;
#X floatatom 226 149 8 0 0 0 - - -;
#X text 113 36 <--train OR untile mse is low enough;
#X text 143 51 (you must be in train mode);
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#X connect 3 0 0 0;
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#X restore 224 363 pd autotrain OR;
#X text 172 101 2a)- build a list with inputs and desired output;
#X text 336 291 NOTE2: while training the right outlet gives you the
mean square error after each training pattern. continue training until
mse is low enough.;
#X text 221 383 2b) use autotrain for the OR function;
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#X connect 11 0 4 0;
#X connect 13 0 0 0;
#X connect 16 0 0 0;
#X restore 68 50 pd train it on the fly;
#X text 62 5 there are 2 ways to train your net;
#X text 253 47 on the fly is simpler;
#X text 88 128 with a trainfile the net could be more accurate;
#X connect 1 0 0 0;
#X connect 2 0 0 0;
#X restore 74 119 pd train;
#X text 149 118 train a nn;
#X obj 103 345 print mse;
#X obj 52 373 print out;
#X obj 52 313 ann_mlp;
#X text 9 2 ann_mlp: multi layer perceptrons neural networks in PD
;
#N canvas 405 166 494 332 META 0;
#X text 12 190 HELP_PATCH_AUTHORS "pd meta" information added by Jonathan
Wilkes for Pd version 0.42.;
#X text 12 25 LICENSE GPL v2;
#X text 12 5 KEYWORDS control;
#X text 12 45 DESCRIPTION multi layer perceptrons neural networks in
PD;
#X text 12 130 OUTLET_0;
#X text 12 150 OUTLET_1;
#X text 12 170 AUTHOR Davide Morelli - info@davidemorelli.it;
#X text 12 65 INLET_0 list create train filename save load setmode
FANN_TRAIN_INCREMENTAL FANN_TRAIN_BATCH FANN_TRAIN_RPROP FANN_TRAIN_QUICKPROP
desired_error max_iterations iterations_between_reports set_activation_function_output
;
#X restore 646 402 pd META;
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-----------------ann_mlp manual
by davide morelli - www.davidemorelli.it
-----------What is a neural network?
To be shure you fully understand what is and why to use a ANN, read
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html
-----------Why use a ANN?
Because they are useful in Pattern recognition, gesure recognition (patterns
over time), associative recall of data (images, sounds, etc), predictions
(e.g. time-series forecasting), complex data handling, etc..
--ANNs can handle noisy inputs:
if you trained your ANN that [1,1] -> 1
Then if you pass [1.1, 0.9] -> 1 probably..
--ANNs can be trained without writing code:
see ann/examples/ann_mlp_example2 (in CVS), you can teach the ANN to tell
you when all these balls are close together or still.. How could you do this
coding? You'd have to compute the distance of every ball from every other
ball, then sum all the distances and ... Very complex and difficult! With
ANN you simply teach when the balls are close and when they are not, you
don't have to write code at all, you just have to use pd.
-----------How to build a ANN?
INPUTS:
You must code your input data as a list of float.
E.g. If you want timbre recognition you must fft a signal then build a list
with fft's partial and feed ann_mlp with it
E.g. if you want midi chord recognition and you played A4 C5 E6 then use the
midi values of the notes of the chord to build a list with 3 integers (57 60
64)
Tip: inputs should be 0 centered
the example of chord recognition should not work well (hard to train)
because possible input values go from 30 to 90, you should remap them so
they go from -30 to 30
Notice how the inputs in ann/examples/ann_mlp_example2 go from -1 to 1
If you can't make inputs 0 centered they should at least start from 0
Tip: inputs should be normalized
If you have one input that goes from -10 to 10 and another input that goes
from -1 to 1 the first input will be more important than the second input
OUTPUTS:
Each "meaning" you want your ANN to detect should have its own output.
Notice ann/examples/ann_mlp_example2:
"Calm" and "chaos" have their outputs even if they are related.
I could have set only 1 output 0 for calm and 1 for chaos.
But having separated outputs I can see if my ANN has been trained well or
not, but also could be that a situation is neither calm nor chaotic, or
somehow calm AND chaotic..
-----------TRAINING ON THE FLY:
It is much easier to train the ANN on the fly rather than using a train
file.
To train on the fly you simply must pass a list with
[inputs + expected outputs(
If you have 3 inputs and 2 outputs then you must pass a list with 5 floats
E.g. You want to train a ANN for a simple logical function: OR
You build a ann_mlp with 2 inputs and 1 output
You set |train(
You pass lists like
|0 0 0( inputs are 0 0 output is 0
|1 0 1( inputs are 1 0 output is 1
|0 1 1( inputs are 0 1 output is 1
And so on.. Repeating until MSE is low enough
MSE tells you the general error the ANN currently has with the inputs and
outputs you are giving
When you are ready set |run(
And start passing lists with only inputs values
|0 0(
|1 0(
Etc..
The left outlet of ann_mlp will start sending lists of float, in this case a
list with only 1 float
-----------PUTTING IT TOGETHER:
1) Create a ANN passing ann_mlp a message with num_inputs and num_outputs
E.g. (the simple ann for logical function OR)
|create 2 1(
|
[ann_mlp]
2) set train mode
|train(
|
[ann_mlp]
3) train the ANN passing lists num_inputs+num_outputs long
|0 0 0(
|
[ann_mlp]
(repeat at will using different inputs until mse is low, see right outlet)
4) set run mode
|run(
|
[ann_mlp]
5) run the net passing lists num_inputs long, the left outlet will send a
list with the results
|0 0(
|
[ann_mlp]
When everything is fine you can save it to a file
|save filename(
|
[ann_mlp]
Can be loaded in 2 ways
|load filename(
|
[ann_mlp]
Or as argument
[ann_mlp filename]
-----------
For a more in-depth sight over technical issues:
http://fann.sourceforge.net/report/report.html
See fann manual for details on advanced params
(activation functions,training params, etc..)
http://fann.sourceforge.net/
-----------
questions and suggestions to info(a)davidemorelli.it
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#X obj 68 31 inlet;
#X obj 140 40 inlet;
#X obj 140 63 bang;
#X obj 68 55 bang;
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#X obj 125 206 outlet;
#X text 59 6 bang;
#X text 139 18 reset to 0 without bang;
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#X restore 58 227 pd alternate;
#X obj 24 81 bng 15 250 50 0 empty empty write-once 0 -6 0 8 -262144
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#X obj 506 106 bng 15 250 50 0 empty empty reset 0 -6 0 8 -262144 -1
-1;
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#X text 120 258 to textfile;
#X obj 24 42 inlet;
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#X text 66 77 here go the inputs;
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#X text 66 77 here go the outputs;
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#X restore 149 284 pd outputs;
#X obj 230 223 f 0;
#X obj 260 223 + 1;
#X obj 239 257 nbx 5 14 -1e+037 1e+037 0 0 empty empty how_many_patterns
0 -6 0 10 -262144 -1 -1 0 256;
#X text 156 406 todo: write header (a line at the beginning of file
with 3 int: how many tests \, num_input \, num_output);
#X obj 122 190 delay 50;
#X obj 115 159 metro 100;
#X floatatom 346 70 5 100 5000 2 msec_between_snapshots - -;
#X obj 127 80 tgl 15 0 empty empty toggle_on-off 0 -6 0 8 -262144 -1
-1 0 1;
#X obj 219 189 / 2;
#X obj 347 14 loadbang;
#X msg 347 34 100;
#X msg 385 314 write test.txt cr;
#X text 293 224 comment;
#N canvas 262 68 639 595 README 0;
#X text 67 432 please help me getting this patch more usable: - how
to add a line at the very beginning of a text file after i have filled
it? - how to manage inputs and outputs of different sized without forcing
the user to edit the patch?;
#X text 9 7 this tricky patch is usefull to write a file to train ann
and is intended to be used with the nn external;
#X text 9 63 how to use: 1) modify [pd inputs] and [ps outputs] inserting
[r] objects to receive input data \, and modify [pack]s to handle the
right number of inputs 2) do the same with [pd outputs] 3) click on
reset 4) toggle ON and start collecting data 5) when you are ready
toggle OFF 6) edit [write filename cr( with the actual filename you
want for your training data (always keep the cr after the filename)
7) open the file with training data 8) add a line at the beginning
and write 3 integers: the 1st is the number of training patterns written
(see "how many patterns" number box) \, the 2nd is how many inputs
your ann has \, the 3th is how many outputs e.g. i collected 100 training
snapshots \, for a ann with 10 ins and 2 outs I write: 100 10 2 at
the very beginning of the file now the training file is ready and can
be read from nn via train-on-file command;
#X restore 25 16 pd README;
#X text 479 210 by davide morelli info@davidemorelli.it;
#X text 106 14 <--readme!;
#X text 242 283 <--edit here!;
#X text 142 308 <--edit here!;
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#N canvas 1 53 858 468 12;
#N canvas 376 163 647 348 creation 0;
#X obj 52 235 outlet;
#X text 246 38 specifying all;
#X text 159 216 TIP:don't set the layers param too high;
#X msg 49 10 create 2 1 5;
#X text 175 6 create with 2 inputs \, 1 output and 5 frames;
#X msg 59 38 create 2 1 5 3 3 1 0.7;
#X text 159 179 params: num_input \, num_output \, frames \, num_layers
\, num_neurons_hidden \, connection_rate \, learning_rate;
#N canvas 219 181 650 413 what 0;
#X text 37 134 you pass [0 0.1] to ann_tdnn;
#X text 34 152 internally now there is this array: [0 0.1 0 0 0 0]
;
#X text 38 196 next input is [0.2 1];
#X text 36 211 internally now there is this array: [0.2 1 0 0.1 0 0]
;
#X text 37 255 next input is [0.3 0.4];
#X text 35 270 internally now there is this array: [0.3 0.4 0.2 1 0
0.1];
#X text 36 317 next input is [0.7 0];
#X text 34 332 internally now there is this array: [0.7 0 0.3 0.4 0.2
1];
#X text 35 168 a normal ann_mlp is run with this inputs;
#X text 38 225 a normal ann_mlp is run with this inputs;
#X text 33 284 a normal ann_mlp is run with this inputs;
#X text 33 347 a normal ann_mlp is run with this inputs;
#X text 12 139 1);
#X text 14 197 2);
#X text 15 258 3);
#X text 13 319 4);
#X text 33 4 this implementation od tdnn is simply a normal ann_mlp
with num_input*frame inputs and num_output outputs. ann_tdnn simply
helps managing the delay \, frames and buffers.;
#X text 65 385 ...and so on...;
#X text 34 64 frames can be seen as the delay feedback: how many times
an input is internally held in the input array;
#X text 35 104 eg: 2 inputs 3 frames = internally 6 inputs;
#X restore 155 109 pd what frames are?;
#X connect 3 0 0 0;
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#X restore 93 68 pd creation examples;
#N canvas 137 89 728 356 run 0;
#X obj 90 219 outlet;
#X msg 123 69 0 1;
#X msg 124 92 1 0;
#X msg 125 115 1 1;
#X msg 126 140 0 0;
#X text 40 17 now you can run your nn passing it a list with inputs
;
#X text 169 70 send a list of data and watch the console for output
;
#X text 39 35 the output is sent as a list of float;
#X text 184 134 these inputs are good for a nn like the one in example1
directory;
#X connect 1 0 0 0;
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#X restore 107 180 pd run the net;
#N canvas 1 53 619 610 other 0;
#X obj 43 401 outlet;
#X msg 102 37 train;
#X msg 103 63 run;
#X msg 152 37 setmode 0;
#X msg 153 63 setmode 1;
#X text 249 40 set training/running mode;
#X text 247 63 training mode currently not implemented;
#N canvas 266 284 690 335 training 0;
#X obj 71 288 outlet;
#X msg 82 195 FANN_TRAIN_INCREMENTAL;
#X msg 82 216 FANN_TRAIN_BATCH;
#X msg 81 238 FANN_TRAIN_RPROP;
#X msg 81 258 FANN_TRAIN_QUICKPROP;
#X text 40 28 you can set the training algorithm simply sending a message
with the name of the algorithm chosen. possible values are: FANN_TRAIN_INCREMENTAL
FANN_TRAIN_BATCH FANN_TRAIN_RPROP FANN_TRAIN_QUICKPROP the default
is: FANN_TRAIN_RPROP see the FANN manual for details on each algorithm:
http://fann.sourceforge.net/html/r1996.html;
#X connect 1 0 0 0;
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#X restore 150 153 pd training algorithm;
#X text 360 175 some advanced param;
#N canvas 325 121 698 395 training 0;
#X obj 52 230 outlet;
#X msg 69 118 desired_error 0.01;
#X msg 79 146 max_iterations 500000;
#X msg 90 178 iterations_between_reports 1000;
#X text 58 28 you can change training parameters. see FANN manual for
details (http://fann.sourceforge.net);
#X connect 1 0 0 0;
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#X restore 151 179 pd training params;
#N canvas 329 121 694 391 activation 0;
#X obj 49 335 outlet;
#X text 40 28 you can set ti output activation algorithm passing a
message to nn. see the FANN manual for description of the algorithms
;
#X msg 69 118 set_activation_function_output FANN_THRESHOLD;
#X msg 83 139 set_activation_function_output FANN_THRESHOLD_SYMMETRIC
;
#X msg 95 163 set_activation_function_output FANN_LINEAR;
#X msg 98 184 set_activation_function_output FANN_SIGMOID;
#X msg 106 206 set_activation_function_output FANN_SIGMOID_STEPWISE
;
#X msg 108 233 set_activation_function_output FANN_SIGMOID_SYMMETRIC
;
#X msg 115 256 set_activation_function_output FANN_SIGMOID_SYMMETRIC_STEPWISE
;
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#X restore 150 203 pd activation algorithm;
#X msg 151 287 details;
#X text 229 285 details on the current nn;
#X msg 145 333 help;
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#X restore 128 258 pd other commands;
#N canvas 1 53 665 525 save 0;
#X obj 39 264 outlet;
#X msg 64 20 filename test.net;
#X msg 66 46 save;
#X msg 82 103 load;
#X text 221 19 set the filename;
#X text 214 42 save the net to the file;
#X text 138 104 you can reload it too;
#X text 144 182 nn can be loaded from a file at creation time simply
passing the filename as argument;
#X msg 68 71 save test.net;
#X msg 93 130 load test.net;
#X text 144 217 like [ann_td num_inputs frames filename];
#X connect 1 0 0 0;
#X connect 2 0 0 0;
#X connect 3 0 0 0;
#X connect 8 0 0 0;
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#X restore 118 218 pd save the net;
#X text 270 66 create a nn;
#X text 244 179 run your net;
#X text 258 215 save your net;
#N canvas 0 0 712 542 tips 0;
#X text 51 84 for better performances inputs value should be normalized
\, all input should have the same range (if one input has a larger
range it will be more "important"). the range of each input should
be 0 centered. so [-1 \, 1] is good [-2 \, 2] is good \, [0 \, 1] not
so good [1 \, 2] is bad. the range sould not be too small ([-0.1 \,
0.1] is bad).;
#X text 41 19 TIPS;
#X text 41 56 inputs;
#X text 39 211 outputs;
#X text 50 235 each class of outputs should have its own output value:
don't use the same output for 2 meanings \, use 2 outputs intead \,
1 for each.;
#X restore 167 285 pd tips;
#X text 272 371 an interface to fann classes (http://fann.sourceforge.net)
;
#X text 274 389 by Davide Morelli - info@davidemorelli.it;
#N canvas 228 212 580 411 train 0;
#X obj 32 241 outlet;
#N canvas 100 44 892 558 train 0;
#X obj 57 397 outlet;
#X msg 60 31 train;
#X text 126 33 1- set the train mode;
#X text 116 81 2- build a list with inputs and desired output;
#X text 139 101 be shure you provide the correct numbers of inputs
and outputs;
#X obj 168 202 pack s f f f;
#X obj 197 248 pack f f f;
#X obj 168 225 unpack s f f f;
#X msg 192 374 run;
#X obj 198 170 tgl 15 0 empty empty in1 0 -6 0 8 -262144 -1 -1 0 1
;
#X obj 228 170 tgl 15 0 empty empty in2 0 -6 0 8 -262144 -1 -1 0 1
;
#X obj 259 170 tgl 15 0 empty empty output 0 -6 0 8 -262144 -1 -1 0
1;
#X obj 148 169 bng 15 250 50 0 empty empty train! 0 -6 0 8 -262144
-1 -1;
#X text 299 183 set inputs and output value \, then send the list clicking
on the "train!" bang;
#X text 229 374 3- when you are ready switch again to run mode before
exiting;
#X text 311 308 NOTE2: while training the right outlet gives you the
mean square error after each training pattern.;
#X msg 316 278 create 2 1 5;
#X text 315 226 NOTE1: before training with this example you should
have created a nn with 2 ins and 1 out and 5 frames with a command
like:;
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#X restore 68 50 pd train it on the fly;
#X text 62 5 there are 2 ways to train your net;
#X text 253 47 on the fly is simpler;
#X text 86 128 with a trainfile the net could be more accurate;
#X msg 89 149 train-on-file test.txt;
#X connect 1 0 0 0;
#X connect 5 0 0 0;
#X restore 115 119 pd train;
#X text 190 118 train a nn;
#X obj 113 360 print mse;
#X obj 54 391 print out;
#X text 150 315 2 args needed: num_inputs and frames;
#X text 148 331 see [pd creation examples] for details;
#X obj 33 319 ann_td 2 5;
#X text 9 2 ann_td: time delay neural networks in pd;
#N canvas 406 195 494 332 META 0;
#X text 12 210 HELP_PATCH_AUTHORS "pd meta" information added by Jonathan
Wilkes for Pd version 0.42.;
#X text 12 25 LICENSE GPL v2;
#X text 12 5 KEYWORDS control;
#X text 12 150 OUTLET_0;
#X text 12 170 OUTLET_1;
#X text 12 190 AUTHOR Davide Morelli - info@davidemorelli.it;
#X text 12 45 DESCRIPTION time delay neural networks in pd;
#X text 12 65 INLET_0 list create train train-on-file filename save
load setmode run FANN_TRAIN_INCREMENTAL FANN_TRAIN_BATCH FANN_TRAIN_RPROP
FANN_TRAIN_QUICKPROP desired_error max_iterations iterations_between_reports
set_activation_function_output;
#X restore 734 408 pd META;
#X connect 0 0 16 0;
#X connect 1 0 16 0;
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#X connect 3 0 16 0;
#X connect 10 0 16 0;
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/* ...this is an externals for comouting Aritficial Neural Networks...
thikn aboiut this
0201:forum::für::umläute:2001
*/
#include "ann.h"
//#include "ann_som.c"
//#include "ann_mlp.c"
//#include "ann_td.c"
typedef struct ann
{
t_object t_ob;
} t_ann;
t_class *ann_class;
/* do a little help thing */
static void ann_help(void)
{
post("\n\n...this is the ann external "VERSION"..\n");
post("self-organized maps"
"\n\tann_som"
"");
post("\n(l) forum::für::umläute 2001\n"
"this software is under the GnuGPL that is provided with these files");
}
void *ann_new(void)
{
t_ann *x = (t_ann *)pd_new(ann_class);
return (void *)x;
}
void ann_som_setup(void);
void ann_mlp_setup(void);
void ann_td_setup(void);
/*
waiting to be released in near future:
ANN_SOM : self organized maps
ANN_PERCEPTRON : perceptrons
ANN_MLP : multilayer perceptrons
waiting to be realeased sometimes
ANN_RBF : radial basis functions
*/
void ann_setup(void)
{
ann_som_setup();
ann_mlp_setup();
ann_td_setup();
/* ************************************** */
post("\n\t................................");
post("\t...artificial neural networks...");
post("\t..........version "VERSION"..........");
post("\t....forum::für::umläute 2001....");
post("\t....send me a 'help' message....");
post("\t................................\n");
ann_class = class_new(gensym("ann"), ann_new, 0, sizeof(t_ann), 0, 0);
class_addmethod(ann_class, ann_help, gensym("help"), 0);
}
externals/ann/src/ann.h deleted 100644 → 0
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/* ********************************************** */
/* the ANN external */
/* ********************************************** */
/* forum::für::umläute */
/* ********************************************** */
#ifndef INCLUDE_ANN_H__
#define INCLUDE_ANN_H__
#include "m_pd.h"
/* to beautify the logo make sure to make the VERSION-info 4 chars long */
#define VERSION "0.1."
#endif