diff options
Diffstat (limited to 'helps/ann_td-help.pd')
-rw-r--r-- | helps/ann_td-help.pd | 488 |
1 files changed, 251 insertions, 237 deletions
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; |