diff options
Diffstat (limited to 'helps')
-rw-r--r-- | helps/ann_mlp-help.pd | 815 | ||||
-rw-r--r-- | helps/ann_td-help.pd | 488 |
2 files changed, 666 insertions, 637 deletions
diff --git a/helps/ann_mlp-help.pd b/helps/ann_mlp-help.pd index 6f59c27..37213ed 100644 --- a/helps/ann_mlp-help.pd +++ b/helps/ann_mlp-help.pd @@ -1,400 +1,415 @@ -#N canvas 99 68 846 456 12;
-#N canvas 181 295 627 328 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 93 68 pd creation examples;
-#N canvas 136 60 728 356 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;
-#X connect 2 0 0 0;
-#X connect 3 0 0 0;
-#X connect 4 0 0 0;
-#X restore 107 180 pd run the net;
-#N canvas 0 0 619 610 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 265 255 690 335 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;
-#X connect 2 0 0 0;
-#X connect 3 0 0 0;
-#X connect 4 0 0 0;
-#X restore 150 153 pd training algorithm;
-#X text 360 175 some advanced param;
-#N canvas 371 92 698 395 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;
-#X connect 2 0 0 0;
-#X connect 3 0 0 0;
-#X restore 151 179 pd training params;
-#N canvas 371 92 694 391 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;
-#X connect 3 0 0 0;
-#X connect 4 0 0 0;
-#X connect 5 0 0 0;
-#X connect 6 0 0 0;
-#X connect 7 0 0 0;
-#X connect 8 0 0 0;
-#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;
-#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 7 0 0 0;
-#X connect 9 0 0 0;
-#X connect 10 0 0 0;
-#X connect 11 0 0 0;
-#X connect 13 0 0 0;
-#X restore 128 258 pd other commands;
-#N canvas 0 0 653 513 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;
-#X connect 2 0 0 0;
-#X connect 3 0 0 0;
-#X connect 8 0 0 0;
-#X connect 9 0 0 0;
-#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 156 303 pd tips;
-#X text 270 353 an interface to fann classes (http://fann.sourceforge.net)
-;
-#X text 272 371 by Davide Morelli - info@davidemorelli.it;
-#N canvas 468 204 580 411 train 0;
-#X obj 32 241 outlet;
-#N canvas 0 0 458 308 train 0;
-#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;
-#X obj 70 171 sel 0 1;
-#X obj 70 146 mod 2;
-#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;
-#X connect 0 0 1 1;
-#X connect 1 0 0 0;
-#X connect 1 0 3 0;
-#X connect 2 0 9 0;
-#X connect 2 1 10 0;
-#X connect 3 0 2 0;
-#X connect 4 0 1 1;
-#X connect 5 0 8 0;
-#X connect 6 0 7 0;
-#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
--1 -1;
-#X obj 341 183 bng 15 250 50 0 empty empty reset 0 -6 0 8 -262144 -1
--1;
-#N canvas 0 0 466 316 inputs 0;
-#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;
-#X connect 0 0 2 0;
-#X connect 1 0 3 0;
-#X connect 2 1 1 0;
-#X connect 2 2 1 1;
-#X connect 3 0 4 0;
-#X connect 6 0 0 0;
-#X connect 9 0 0 1;
-#X connect 10 0 0 2;
-#X restore 58 306 pd inputs;
-#N canvas 0 0 466 316 outputs 0;
-#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 outputs;
-#X obj 91 51 r output1;
-#X obj 166 51 r output2;
-#X connect 0 0 2 0;
-#X connect 1 0 3 0;
-#X connect 2 1 1 0;
-#X connect 2 2 1 1;
-#X connect 3 0 4 0;
-#X connect 6 0 0 0;
-#X connect 9 0 0 1;
-#X connect 10 0 0 2;
-#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 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);
-#X connect 1 0 0 0;
-#X connect 2 0 5 0;
-#X connect 2 1 6 0;
-#X connect 2 1 7 0;
-#X connect 3 0 11 0;
-#X connect 3 0 2 0;
-#X connect 4 0 2 1;
-#X connect 4 0 1 0;
-#X connect 5 0 0 0;
-#X connect 6 0 0 0;
-#X connect 7 0 8 0;
-#X connect 7 0 9 0;
-#X connect 8 0 7 1;
-#X connect 11 0 2 0;
-#X connect 12 0 11 0;
-#X connect 12 0 2 0;
-#X connect 13 0 12 1;
-#X connect 13 0 15 0;
-#X connect 14 0 12 0;
-#X connect 15 0 11 1;
-#X connect 16 0 17 0;
-#X connect 17 0 13 0;
-#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;
-#X connect 1 0 4 0;
-#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
-;
-#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 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;
-#X obj 89 286 outlet;
-#X obj 85 87 metro 10;
-#X obj 85 38 tgl 15 0 empty empty toggle_training 0 -6 0 8 -262144
--1 -1 0 1;
-#X msg 101 192 0 0 0;
-#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);
-#X connect 1 0 7 0;
-#X connect 1 0 9 0;
-#X connect 2 0 1 0;
-#X connect 3 0 0 0;
-#X connect 4 0 0 0;
-#X connect 5 0 0 0;
-#X connect 6 0 0 0;
-#X connect 7 0 8 0;
-#X connect 8 0 5 0;
-#X connect 8 1 3 0;
-#X connect 8 2 4 0;
-#X connect 8 3 6 0;
-#X connect 9 0 10 0;
-#X connect 9 0 11 0;
-#X connect 10 0 9 1;
-#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;
-#X connect 1 0 0 0;
-#X connect 4 0 6 0;
-#X connect 5 0 0 0;
-#X connect 6 1 5 0;
-#X connect 6 2 5 1;
-#X connect 6 3 5 2;
-#X connect 7 0 0 0;
-#X connect 8 0 4 1;
-#X connect 9 0 4 2;
-#X connect 10 0 4 3;
-#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 115 119 pd train;
-#X text 190 118 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
-;
-#X connect 0 0 14 0;
-#X connect 1 0 14 0;
-#X connect 2 0 14 0;
-#X connect 3 0 14 0;
-#X connect 10 0 14 0;
-#X connect 14 0 13 0;
-#X connect 14 1 12 0;
+#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; +#X connect 2 0 0 0; +#X connect 3 0 0 0; +#X connect 4 0 0 0; +#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; +#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 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); +#X connect 1 0 0 0; +#X connect 2 0 0 0; +#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; +#X connect 3 0 0 0; +#X connect 4 0 0 0; +#X connect 5 0 0 0; +#X connect 6 0 0 0; +#X connect 7 0 0 0; +#X connect 8 0 0 0; +#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; +#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 7 0 0 0; +#X connect 9 0 0 0; +#X connect 10 0 0 0; +#X connect 11 0 0 0; +#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; +#X connect 2 0 0 0; +#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; +#N canvas 146 200 580 411 train 0; +#X obj 32 241 outlet; +#N canvas 0 0 458 308 train 0; +#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; +#X obj 70 171 sel 0 1; +#X obj 70 146 mod 2; +#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; +#X connect 0 0 1 1; +#X connect 1 0 0 0; +#X connect 1 0 3 0; +#X connect 2 0 9 0; +#X connect 2 1 10 0; +#X connect 3 0 2 0; +#X connect 4 0 1 1; +#X connect 5 0 8 0; +#X connect 6 0 7 0; +#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 +-1 -1; +#X obj 341 183 bng 15 250 50 0 empty empty reset 0 -6 0 8 -262144 -1 +-1; +#N canvas 0 0 466 316 inputs 0; +#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; +#X connect 0 0 2 0; +#X connect 1 0 3 0; +#X connect 2 1 1 0; +#X connect 2 2 1 1; +#X connect 3 0 4 0; +#X connect 6 0 0 0; +#X connect 9 0 0 1; +#X connect 10 0 0 2; +#X restore 58 306 pd inputs; +#N canvas 0 0 466 316 outputs 0; +#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 outputs; +#X obj 91 51 r output1; +#X obj 166 51 r output2; +#X connect 0 0 2 0; +#X connect 1 0 3 0; +#X connect 2 1 1 0; +#X connect 2 2 1 1; +#X connect 3 0 4 0; +#X connect 6 0 0 0; +#X connect 9 0 0 1; +#X connect 10 0 0 2; +#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); +#X connect 1 0 0 0; +#X connect 2 0 5 0; +#X connect 2 1 6 0; +#X connect 2 1 7 0; +#X connect 3 0 11 0; +#X connect 3 0 2 0; +#X connect 4 0 2 1; +#X connect 4 0 1 0; +#X connect 5 0 0 0; +#X connect 6 0 0 0; +#X connect 7 0 8 0; +#X connect 7 0 9 0; +#X connect 8 0 7 1; +#X connect 11 0 2 0; +#X connect 12 0 11 0; +#X connect 12 0 2 0; +#X connect 13 0 12 1; +#X connect 13 0 15 0; +#X connect 14 0 12 0; +#X connect 15 0 11 1; +#X connect 16 0 17 0; +#X connect 17 0 13 0; +#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; +#X connect 1 0 4 0; +#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 +; +#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 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; +#X obj 89 286 outlet; +#X obj 85 87 metro 10; +#X obj 85 38 tgl 15 0 empty empty toggle_training 0 -6 0 8 -262144 +-1 -1 0 1; +#X msg 101 192 0 0 0; +#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); +#X connect 1 0 7 0; +#X connect 1 0 9 0; +#X connect 2 0 1 0; +#X connect 3 0 0 0; +#X connect 4 0 0 0; +#X connect 5 0 0 0; +#X connect 6 0 0 0; +#X connect 7 0 8 0; +#X connect 8 0 5 0; +#X connect 8 1 3 0; +#X connect 8 2 4 0; +#X connect 8 3 6 0; +#X connect 9 0 10 0; +#X connect 9 0 11 0; +#X connect 10 0 9 1; +#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; +#X connect 1 0 0 0; +#X connect 4 0 6 0; +#X connect 5 0 0 0; +#X connect 6 1 5 0; +#X connect 6 2 5 1; +#X connect 6 3 5 2; +#X connect 7 0 0 0; +#X connect 8 0 4 1; +#X connect 9 0 4 2; +#X connect 10 0 4 3; +#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; +#X connect 0 0 14 0; +#X connect 1 0 14 0; +#X connect 2 0 14 0; +#X connect 3 0 14 0; +#X connect 10 0 14 0; +#X connect 14 0 13 0; +#X connect 14 1 12 0; diff --git a/helps/ann_td-help.pd b/helps/ann_td-help.pd index 997e19e..50d7b9a 100644 --- a/helps/ann_td-help.pd +++ b/helps/ann_td-help.pd @@ -1,237 +1,251 @@ -#N canvas 117 9 858 468 12;
-#N canvas 445 134 647 348 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 218 152 650 413 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;
-#X connect 5 0 0 0;
-#X restore 93 68 pd creation examples;
-#N canvas 136 60 728 356 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;
-#X connect 2 0 0 0;
-#X connect 3 0 0 0;
-#X connect 4 0 0 0;
-#X restore 107 180 pd run the net;
-#N canvas 0 0 619 610 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 265 255 690 335 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;
-#X connect 2 0 0 0;
-#X connect 3 0 0 0;
-#X connect 4 0 0 0;
-#X restore 150 153 pd training algorithm;
-#X text 360 175 some advanced param;
-#N canvas 371 92 698 395 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;
-#X connect 2 0 0 0;
-#X connect 3 0 0 0;
-#X restore 151 179 pd training params;
-#N canvas 371 92 694 391 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;
-#X connect 3 0 0 0;
-#X connect 4 0 0 0;
-#X connect 5 0 0 0;
-#X connect 6 0 0 0;
-#X connect 7 0 0 0;
-#X connect 8 0 0 0;
-#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;
-#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 7 0 0 0;
-#X connect 9 0 0 0;
-#X connect 10 0 0 0;
-#X connect 11 0 0 0;
-#X connect 13 0 0 0;
-#X restore 128 258 pd other commands;
-#N canvas 0 0 665 525 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;
-#X connect 9 0 0 0;
-#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 227 183 580 411 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:;
-#X connect 1 0 0 0;
-#X connect 5 0 7 0;
-#X connect 6 0 0 0;
-#X connect 7 1 6 0;
-#X connect 7 2 6 1;
-#X connect 7 3 6 2;
-#X connect 8 0 0 0;
-#X connect 9 0 5 1;
-#X connect 10 0 5 2;
-#X connect 11 0 5 3;
-#X connect 12 0 5 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 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;
-#X connect 0 0 16 0;
-#X connect 1 0 16 0;
-#X connect 2 0 16 0;
-#X connect 3 0 16 0;
-#X connect 10 0 16 0;
-#X connect 16 0 13 0;
-#X connect 16 1 12 0;
+#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; +#X connect 5 0 0 0; +#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; +#X connect 2 0 0 0; +#X connect 3 0 0 0; +#X connect 4 0 0 0; +#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; +#X connect 2 0 0 0; +#X connect 3 0 0 0; +#X connect 4 0 0 0; +#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; +#X connect 2 0 0 0; +#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; +#X connect 3 0 0 0; +#X connect 4 0 0 0; +#X connect 5 0 0 0; +#X connect 6 0 0 0; +#X connect 7 0 0 0; +#X connect 8 0 0 0; +#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; +#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 7 0 0 0; +#X connect 9 0 0 0; +#X connect 10 0 0 0; +#X connect 11 0 0 0; +#X connect 13 0 0 0; +#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; +#X connect 9 0 0 0; +#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:; +#X connect 1 0 0 0; +#X connect 5 0 7 0; +#X connect 6 0 0 0; +#X connect 7 1 6 0; +#X connect 7 2 6 1; +#X connect 7 3 6 2; +#X connect 8 0 0 0; +#X connect 9 0 5 1; +#X connect 10 0 5 2; +#X connect 11 0 5 3; +#X connect 12 0 5 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 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; +#X connect 2 0 16 0; +#X connect 3 0 16 0; +#X connect 10 0 16 0; +#X connect 16 0 13 0; +#X connect 16 1 12 0; |