[PD-cvs] externals/ann/helps ann_mlp-help.pd, NONE, 1.1 ann_td-help.pd, NONE, 1.1 help-ann_mlp.pd, 1.2, NONE help-ann_td.pd, 1.1, NONE

29 Dec 2006


      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 ---
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#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;
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#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;
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#X restore 107 180 pd run the net;
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#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 text 360 175 some advanced param;
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#X msg 79 146 max_iterations 500000;
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#X text 58 28 you can change training parameters. see FANN manual for
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#X text 40 28 you can set ti output activation algorithm passing a
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;
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#X msg 83 139 set_activation_function_output FANN_THRESHOLD_SYMMETRIC
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#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
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#X msg 108 233 set_activation_function_output FANN_SIGMOID_SYMMETRIC
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#X msg 115 256 set_activation_function_output FANN_SIGMOID_SYMMETRIC_STEPWISE
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#X text 229 285 details on the current nn;
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#X msg 66 46 save;
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#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
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#X msg 68 71 save test.net;
#X msg 93 130 load test.net;
#X text 144 217 like [ann_mlp test.net];
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#X text 270 66 create a nn;
#X text 244 179 run your net;
#X text 258 215 save your net;
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#X text 51 84 for better performances inputs value should be normalized
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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;
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#X text 139 18 reset to 0 without bang;
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#X text 66 77 here go the inputs;
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#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 - -;
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#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 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;
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#X restore 79 103 pd train you net using a train file;
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#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
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#X text 312 160 set inputs and output value , then send the list clicking
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#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:;
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#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 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 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;
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#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
;
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--- NEW FILE: ann_td-help.pd ---
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#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?;
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#X restore 93 68 pd creation examples;
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#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 connect 3 0 0 0;
#X connect 4 0 0 0;
#X restore 107 180 pd run the net;
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#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
;
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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 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;
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#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
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1;
#X obj 148 169 bng 15 250 50 0 empty empty train! 0 -6 0 8 -262144
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#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 ---

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[PD-cvs] externals/ann/helps ann_mlp-help.pd, NONE, 1.1 ann_td-help.pd, NONE, 1.1 help-ann_mlp.pd, 1.2, NONE help-ann_td.pd, 1.1, NONE