From 924acb222e574ed0ed141e13ffbeb3f115fad001 Mon Sep 17 00:00:00 2001 From: Georg Holzmann Date: Fri, 5 Jan 2007 16:56:29 +0000 Subject: new helpfile standard svn path=/trunk/externals/grh/; revision=7218 --- pix_recNN/help-pix_recNN.pd | 146 -------------------------------------------- 1 file changed, 146 deletions(-) delete mode 100755 pix_recNN/help-pix_recNN.pd (limited to 'pix_recNN/help-pix_recNN.pd') diff --git a/pix_recNN/help-pix_recNN.pd b/pix_recNN/help-pix_recNN.pd deleted file mode 100755 index 4236941..0000000 --- a/pix_recNN/help-pix_recNN.pd +++ /dev/null @@ -1,146 +0,0 @@ -#N canvas 871 74 498 783 10; -#X obj 36 327 gemwin; -#X msg 36 301 create \, 1; -#N canvas 75 72 765 790 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 msg 381 708 getmemory; -#X msg 361 639 memory 0; -#X msg 361 660 memory 1; -#X obj 61 252 pix_recNN; -#X text 296 49 <- input dimension; -#X obj 78 226 r \$0-recNN; -#X obj 62 564 s \$0-recNN; -#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 obj 407 492 s \$0-recNN; -#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 obj 65 725 s \$0-recNN; -#X msg 361 681 memory 3; -#X obj 361 741 s \$0-recNN; -#X text 343 543 memory:; -#X text 356 565 this determines \, how much values from the past the -recurrent net considers in the calculation; -#X text 357 604 (be carefull with large values !!!); -#X msg 62 456 precision 1; -#X msg 62 436 precision 4; -#X obj 404 233 s \$0-recNN; -#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 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 20 0; -#X connect 6 0 1 0; -#X connect 7 0 6 0; -#X connect 9 0 6 0; -#X connect 10 0 23 0; -#X connect 11 0 38 0; -#X connect 12 0 38 0; -#X connect 13 0 33 0; -#X connect 14 0 46 0; -#X connect 15 0 20 0; -#X connect 16 0 38 0; -#X connect 17 0 40 0; -#X connect 18 0 40 0; -#X connect 19 0 40 0; -#X connect 20 1 2 0; -#X connect 22 0 20 0; -#X connect 24 0 23 0; -#X connect 25 0 24 0; -#X connect 30 0 45 0; -#X connect 31 0 33 0; -#X connect 32 0 33 0; -#X connect 39 0 40 0; -#X connect 44 0 23 0; -#X connect 45 0 23 0; -#X restore 89 542 pd pix2sig_stuff~; -#X msg 110 302 0 \, destroy; -#X obj 116 587 unsig~; -#X obj 206 432 osc~ 440; -#X obj 205 456 *~; -#X obj 237 456 tgl 15 0 empty empty empty 0 -6 0 8 -262144 -1 -1 0 -1; -#X obj 207 496 sig~ 0; -#X floatatom 117 608 8 0 0 0 - - -; -#X text 25 23 pix_recNN:; -#X text 24 57 pix_recNN is an instument/interface. This instrument -should be useful as a general experimental video interface to generate -audio. You can train the neural net with playing audio samples to specific -video frames in real-time. The main interest for me was not to train -the net exactly to reproduce these samples \, but to make experimental -sounds \, which are "between" all the trained samples.; -#X text 22 214 (but this version is unfinished - e.g. the training -algorithm must be tuned etc. - so it's only a very basic prototype...) -; -#X text 207 320 <- create gemwin; -#X obj 41 442 readsf~; -#X obj 41 401 openpanel; -#X msg 41 421 open \$1; -#X obj 41 380 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1 --1; -#X text 67 379 <- load sample for training; -#X obj 122 417 tgl 25 0 empty empty empty 0 -6 0 8 -195568 -1 -1 0 -1; -#X floatatom 206 414 5 0 0 0 - - -; -#X text 272 431 <- simple osc for training; -#X text 262 497 <- to train silence; -#X obj 85 463 bng 15 250 50 0 empty empty empty 0 -6 0 8 -262144 -1 --1; -#X text 216 541 <- audio/video work; -#X obj 90 684 dac~; -#X obj 90 659 *~; -#X obj 118 659 dbtorms; -#X floatatom 118 641 5 0 0 0 - - -; -#X text 168 638 <- outvol in dB; -#X text 22 170 pix_recNN uses a 2 layer recurrent neural net (for more -detailed info look at the source code.); -#X text 119 737 Georg Holzmann \, 2004; -#X connect 1 0 0 0; -#X connect 2 0 4 0; -#X connect 2 0 26 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 14 0 2 0; -#X connect 14 1 23 0; -#X connect 15 0 16 0; -#X connect 16 0 14 0; -#X connect 17 0 15 0; -#X connect 19 0 14 0; -#X connect 20 0 5 0; -#X connect 26 0 25 0; -#X connect 26 0 25 1; -#X connect 27 0 26 1; -#X connect 28 0 27 0; -- cgit v1.2.1