From 3fa36292c85af3eda0a221e5f86c13a6d85c8d1b Mon Sep 17 00:00:00 2001 From: Georg Holzmann Date: Fri, 5 Jan 2007 16:44:32 +0000 Subject: new help file standard svn path=/trunk/externals/grh/; revision=7216 --- pix_linNN/help-pix_linNN.pd | 135 -------------------------------------------- pix_linNN/pix_linNN-help.pd | 135 ++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 135 insertions(+), 135 deletions(-) delete mode 100755 pix_linNN/help-pix_linNN.pd create mode 100755 pix_linNN/pix_linNN-help.pd diff --git a/pix_linNN/help-pix_linNN.pd b/pix_linNN/help-pix_linNN.pd deleted file mode 100755 index 932116f..0000000 --- a/pix_linNN/help-pix_linNN.pd +++ /dev/null @@ -1,135 +0,0 @@ -#N canvas 871 74 498 738 10; -#X obj 28 237 gemwin; -#X msg 28 211 create \, 1; -#N canvas 463 0 765 790 pix2sig_stuff~ 0; -#X obj 120 35 gemhead; -#X obj 120 132 pix_texture; -#X obj 119 274 outlet~; -#X obj 139 185 square 4; -#X obj 139 163 separator; -#X obj 61 165 separator; -#X obj 120 101 pix_video; -#X msg 186 64 dimen 640 480; -#X obj 26 36 block~ 2048; -#X msg 186 38 dimen 320 240; -#X msg 76 535 getprecision; -#X msg 93 696 getlearnrate; -#X msg 65 671 learnrate 0.2; -#X msg 424 459 getneurons; -#X msg 404 206 train; -#X obj 31 227 inlet~; -#X msg 65 647 learnrate 0.05; -#X text 296 49 <- input dimension; -#X msg 76 498 precision \$1; -#X floatatom 76 481 5 0 0 0 - - -; -#X text 42 335 precision:; -#X text 53 358 1: means every pixel is used in calculation; -#X text 53 372 2: only every second pixel; -#X text 53 386 ...; -#X obj 62 411 loadbang; -#X msg 407 401 neurons 2048; -#X msg 407 422 neurons 64; -#X text 403 336 neurons:; -#X text 416 357 nr. of neurons used in the calculation; -#X text 415 370 (_MUST_ be the same as the buffersize !!!); -#X text 43 615 learnrate:; -#X msg 62 456 precision 1; -#X msg 62 436 precision 4; -#X text 397 126 train:; -#X text 417 152 trains the neural net; -#X text 418 166 (the current video frame to; -#X text 425 178 the current audio block); -#X obj 61 252 pix_linNN; -#X text 346 592 save/load; -#X text 359 614 saves/load the actual trained net to/from a file; -#X msg 440 684 load net.dat; -#X msg 440 664 save net.dat; -#X obj 78 226 r \$0-linNN; -#X obj 404 233 s \$0-linNN; -#X obj 62 564 s \$0-linNN; -#X obj 407 492 s \$0-linNN; -#X obj 65 725 s \$0-linNN; -#X obj 440 723 s \$0-linNN; -#X connect 0 0 6 0; -#X connect 1 0 4 0; -#X connect 1 0 5 0; -#X connect 4 0 3 0; -#X connect 5 0 37 0; -#X connect 6 0 1 0; -#X connect 7 0 6 0; -#X connect 9 0 6 0; -#X connect 10 0 44 0; -#X connect 11 0 46 0; -#X connect 12 0 46 0; -#X connect 13 0 45 0; -#X connect 14 0 43 0; -#X connect 15 0 37 0; -#X connect 16 0 46 0; -#X connect 18 0 44 0; -#X connect 19 0 18 0; -#X connect 24 0 32 0; -#X connect 25 0 45 0; -#X connect 26 0 45 0; -#X connect 31 0 44 0; -#X connect 32 0 44 0; -#X connect 37 1 2 0; -#X connect 40 0 47 0; -#X connect 41 0 47 0; -#X connect 42 0 37 0; -#X restore 87 492 pd pix2sig_stuff~; -#X msg 102 212 0 \, destroy; -#X obj 114 537 unsig~; -#X obj 204 382 osc~ 440; -#X obj 203 406 *~; -#X obj 235 406 tgl 15 0 empty empty empty 0 -6 0 8 -262144 -1 -1 0 -1; -#X obj 205 446 sig~ 0; -#X floatatom 115 558 8 0 0 0 - - -; -#X text 199 230 <- create gemwin; -#X obj 39 392 readsf~; -#X obj 39 351 openpanel; -#X msg 39 371 open \$1; -#X obj 39 330 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1 --1; -#X text 65 329 <- load sample for training; -#X obj 120 367 tgl 25 0 empty empty empty 0 -6 0 8 -195568 -1 -1 0 -1; -#X floatatom 204 364 5 0 0 0 - - -; -#X text 270 381 <- simple osc for training; -#X text 260 447 <- to train silence; -#X obj 83 413 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1 --1; -#X text 214 491 <- audio/video work; -#X obj 88 634 dac~; -#X obj 88 609 *~; -#X obj 116 609 dbtorms; -#X floatatom 116 591 5 0 0 0 - - -; -#X text 166 588 <- outvol in dB; -#X text 110 703 Georg Holzmann \, 2004; -#X text 24 23 pix_linNN:; -#X text 22 58 (see also pix_recNN !!!); -#X text 24 90 pix_linNN~ calculates an audio signal out of a video -frame with a linear neural network \, which can be trained.; -#X text 24 124 The network has one neuron per audio sample: this neuron -has three inputs (a RGB-signal) \, a weight vector for each of the -inputs \, a bias value and a linear output function.; -#X connect 1 0 0 0; -#X connect 2 0 4 0; -#X connect 2 0 23 0; -#X connect 3 0 0 0; -#X connect 4 0 9 0; -#X connect 5 0 6 0; -#X connect 6 0 2 0; -#X connect 7 0 6 1; -#X connect 8 0 2 0; -#X connect 11 0 2 0; -#X connect 11 1 20 0; -#X connect 12 0 13 0; -#X connect 13 0 11 0; -#X connect 14 0 12 0; -#X connect 16 0 11 0; -#X connect 17 0 5 0; -#X connect 23 0 22 0; -#X connect 23 0 22 1; -#X connect 24 0 23 1; -#X connect 25 0 24 0; diff --git a/pix_linNN/pix_linNN-help.pd b/pix_linNN/pix_linNN-help.pd new file mode 100755 index 0000000..932116f --- /dev/null +++ b/pix_linNN/pix_linNN-help.pd @@ -0,0 +1,135 @@ +#N canvas 871 74 498 738 10; +#X obj 28 237 gemwin; +#X msg 28 211 create \, 1; +#N canvas 463 0 765 790 pix2sig_stuff~ 0; +#X obj 120 35 gemhead; +#X obj 120 132 pix_texture; +#X obj 119 274 outlet~; +#X obj 139 185 square 4; +#X obj 139 163 separator; +#X obj 61 165 separator; +#X obj 120 101 pix_video; +#X msg 186 64 dimen 640 480; +#X obj 26 36 block~ 2048; +#X msg 186 38 dimen 320 240; +#X msg 76 535 getprecision; +#X msg 93 696 getlearnrate; +#X msg 65 671 learnrate 0.2; +#X msg 424 459 getneurons; +#X msg 404 206 train; +#X obj 31 227 inlet~; +#X msg 65 647 learnrate 0.05; +#X text 296 49 <- input dimension; +#X msg 76 498 precision \$1; +#X floatatom 76 481 5 0 0 0 - - -; +#X text 42 335 precision:; +#X text 53 358 1: means every pixel is used in calculation; +#X text 53 372 2: only every second pixel; +#X text 53 386 ...; +#X obj 62 411 loadbang; +#X msg 407 401 neurons 2048; +#X msg 407 422 neurons 64; +#X text 403 336 neurons:; +#X text 416 357 nr. of neurons used in the calculation; +#X text 415 370 (_MUST_ be the same as the buffersize !!!); +#X text 43 615 learnrate:; +#X msg 62 456 precision 1; +#X msg 62 436 precision 4; +#X text 397 126 train:; +#X text 417 152 trains the neural net; 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+#X connect 42 0 37 0; +#X restore 87 492 pd pix2sig_stuff~; +#X msg 102 212 0 \, destroy; +#X obj 114 537 unsig~; +#X obj 204 382 osc~ 440; +#X obj 203 406 *~; +#X obj 235 406 tgl 15 0 empty empty empty 0 -6 0 8 -262144 -1 -1 0 +1; +#X obj 205 446 sig~ 0; +#X floatatom 115 558 8 0 0 0 - - -; +#X text 199 230 <- create gemwin; +#X obj 39 392 readsf~; +#X obj 39 351 openpanel; +#X msg 39 371 open \$1; +#X obj 39 330 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1 +-1; +#X text 65 329 <- load sample for training; +#X obj 120 367 tgl 25 0 empty empty empty 0 -6 0 8 -195568 -1 -1 0 +1; +#X floatatom 204 364 5 0 0 0 - - -; +#X text 270 381 <- simple osc for training; +#X text 260 447 <- to train silence; +#X obj 83 413 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1 +-1; +#X text 214 491 <- audio/video work; +#X obj 88 634 dac~; +#X obj 88 609 *~; +#X obj 116 609 dbtorms; +#X floatatom 116 591 5 0 0 0 - - -; +#X text 166 588 <- outvol in dB; +#X text 110 703 Georg Holzmann \, 2004; +#X text 24 23 pix_linNN:; +#X text 22 58 (see also pix_recNN !!!); +#X text 24 90 pix_linNN~ calculates an audio signal out of a video +frame with a linear neural network \, which can be trained.; +#X text 24 124 The network has one neuron per audio sample: this neuron +has three inputs (a RGB-signal) \, a weight vector for each of the +inputs \, a bias value and a linear output function.; +#X connect 1 0 0 0; +#X connect 2 0 4 0; +#X connect 2 0 23 0; +#X connect 3 0 0 0; +#X connect 4 0 9 0; +#X connect 5 0 6 0; +#X connect 6 0 2 0; +#X connect 7 0 6 1; +#X connect 8 0 2 0; +#X connect 11 0 2 0; +#X connect 11 1 20 0; +#X connect 12 0 13 0; +#X connect 13 0 11 0; +#X connect 14 0 12 0; +#X connect 16 0 11 0; +#X connect 17 0 5 0; +#X connect 23 0 22 0; +#X connect 23 0 22 1; +#X connect 24 0 23 1; +#X connect 25 0 24 0; -- cgit v1.2.1