Update of /cvsroot/pure-data/externals/ann/helps In directory sc8-pr-cvs1.sourceforge.net:/tmp/cvs-serv22469/helps

Added Files: ann_mlp-help.pd ann_td-help.pd Removed Files: help-ann_mlp.pd help-ann_td.pd Log Message: changed to follow help file standard

--- NEW FILE: ann_mlp-help.pd --- #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;

--- NEW FILE: ann_td-help.pd --- #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;

--- help-ann_mlp.pd DELETED ---

--- help-ann_td.pd DELETED ---