path: root/helps/help-ann_mlp.pd

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#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;
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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;
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#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;
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#X msg 82 195 FANN_TRAIN_INCREMENTAL;
#X msg 82 216 FANN_TRAIN_BATCH;
#X msg 81 238 FANN_TRAIN_RPROP;
#X msg 81 258 FANN_TRAIN_QUICKPROP;
#X text 40 28 you can set the training algorithm simply sending a message
with the name of the algorithm chosen. possible values are: FANN_TRAIN_INCREMENTAL
FANN_TRAIN_BATCH FANN_TRAIN_RPROP FANN_TRAIN_QUICKPROP the default
is: FANN_TRAIN_RPROP see the FANN manual for details on each algorithm:
http://fann.sourceforge.net/html/r1996.html;
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#X restore 150 153 pd training algorithm;
#X text 360 175 some advanced param;
#N canvas 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);
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#X restore 151 179 pd training params;
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#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 0 653 513 save 0;
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#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];
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#X restore 118 218 pd save the net;
#X text 270 66 create a nn;
#X text 244 179 run your net;
#X text 258 215 save your net;
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#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;
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#X obj 140 63 bang;
#X obj 68 55 bang;
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#X text 59 6 bang;
#X text 139 18 reset to 0 without bang;
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#X restore 58 227 pd alternate;
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#X obj 341 183 bng 15 250 50 0 empty empty reset 0 -6 0 8 -262144 -1
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#X msg 66 223 add \$1 \$2;
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#X text 120 258 to textfile;
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#X text 23 22 bang;
#X text 66 77 here go the inputs;
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#X restore 58 306 pd inputs;
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#X text 120 258 to textfile;
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#X text 23 22 bang;
#X text 66 77 here go the outputs;
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#X obj 166 51 r output2;
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#X restore 149 284 pd outputs;
#X obj 230 223 f 0;
#X obj 260 223 + 1;
#X obj 239 257 nbx 5 14 -1e+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
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#X obj 219 189 / 2;
#X obj 260 16 loadbang;
#X msg 260 36 100;
#X msg 326 342 write test.txt cr;
#X text 293 224 comment;
#N canvas 262 68 647 603 README 0;
#X text 67 432 please help me getting this patch more usable: - how
to add a line at the very beginning of a text file after i have filled
it? - how to manage inputs and outputs of different sized without forcing
the user to edit the patch?;
#X text 9 63 how to use: 1) modify [pd inputs] and [ps outputs] inserting
[r] objects to receive input data \, and modify [pack]s to handle the
right number of inputs 2) do the same with [pd outputs] 3) click on
reset 4) toggle ON and start collecting data 5) when you are ready
toggle OFF 6) edit [write filename cr( with the actual filename you
want for your training data (always keep the cr after the filename)
7) open the file with training data 8) add a line at the beginning
and write 3 integers: the 1st is the number of training patterns written
(see "how many patterns" number box) \, the 2nd is how many inputs
your ann has \, the 3th is how many outputs e.g. i collected 100 training
snapshots \, for a ann with 10 ins and 2 outs I write: 100 10 2 at
the very beginning of the file now the training file is ready and can
be read from nn via train-on-file command;
#X text 9 7 this tricky sub-patch is usefull to write a file to train
ann and is intended to be used with the nn external;
#X restore 25 16 pd README;
#X text 479 210 by davide morelli info@davidemorelli.it;
#X text 106 14 <--readme!;
#X text 242 283 <--edit here!;
#X text 142 308 <--edit here!;
#X text 429 86 usage: read [pd README] \, edit [pd inputs] and [pd
outputs] \, toggle on and record inputs and outputs \, toggle off when
ready \, write to a file \, edit the file adding a line at the beginning
(see REAMDE);
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#X 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;
#N canvas 120 72 892 558 train 0;
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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
and outputs;
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#X msg 190 464 run;
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#X text 312 160 set inputs and output value \, then send the list clicking
on the "train!" bang;
#X msg 316 261 create 2 1;
#X text 227 464 3- when you are ready switch again to run mode before
exiting;
#X text 315 226 NOTE1: before training with this example you should
have created a nn with 2 ins and 1 out with a command like:;
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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;
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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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