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\uc0c8\ub85c\uc6b4 \uac00\ub2a5\uc131\uc744 \uc5f4\uc5b4\uc8fc\uc5c8\uc2b5\ub2c8\ub2e4.\u2026","rel":"","context":""\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac"\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac","link":"https:\/\/atmokpo.com\/w\/category\/%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%9e%90%ec%97%b0%ec%96%b4%ec%b2%98%eb%a6%ac\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":25269,"url":"https:\/\/atmokpo.com\/w\/25269\/","url_meta":{"origin":29873,"position":3},"title":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ud53c\ub4dc \ud3ec\uc6cc\ub4dc \uc2e0\uacbd\ub9dd \uc5b8\uc5b4 \ubaa8\ub378 (Neural Network Language Model, NNLM)","author":"root","date":"2024\ub144 10\uc6d4 26\uc77c","format":false,"excerpt":"\uc791\uc131\uc790: \uc870\uad11\ud615 \uc791\uc131\uc77c: 2024\ub144 11\uc6d4 26\uc77c 1. \uc11c\ub860 \ucd5c\uadfc \uba87 \ub144\uac04 \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \ub525 \ub7ec\ub2dd(deep learning)\uc758 \ubc1c\uc804\uc740 \ub9e4\uc6b0 \ub208\ubd80\uc2e0 \ubcc0\ud654\uc640 \ud601\uc2e0\uc744 \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \ub525 \ub7ec\ub2dd\uc740 \ud2b9\ud788 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(Natural Language Processing, NLP) \ubd84\uc57c\uc5d0\uc11c \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc73c\uba70, \uadf8\uc5d0 \ub530\ub77c \ub2e4\uc591\ud55c \ubaa8\ub378\ub4e4\uc774 \uac1c\ubc1c\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud53c\ub4dc \ud3ec\uc6cc\ub4dc \uc2e0\uacbd\ub9dd \uc5b8\uc5b4 \ubaa8\ub378(Neural Network Language\u2026","rel":"","context":""\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac"\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac","link":"https:\/\/atmokpo.com\/w\/category\/%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%9e%90%ec%97%b0%ec%96%b4%ec%b2%98%eb%a6%ac\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":29291,"url":"https:\/\/atmokpo.com\/w\/29291\/","url_meta":{"origin":29873,"position":4},"title":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \uc804\uc774\ud559\uc2b5 \uc801\uc740 \ub370\uc774\ud130\ub85c \ub354 \ube60\ub978 \ud6c8\ub828","author":"root","date":"2024\ub144 10\uc6d4 27\uc77c","format":false,"excerpt":"2023\ub144, \ucd5c\ucca8\ub2e8 \uae30\uc220\uc744 \ud1b5\ud574 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\ub294 \uc810\uc810 \ub354 \ub9ce\uc740 \uac70\ub798\uc790\ub4e4\uc774 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc744 \ucc44\ud0dd\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\ub3d9\ub9e4\ub9e4 \uc2dc\uc2a4\ud15c\uc740 \uae30\uc874\uc758 \uaddc\uce59 \uae30\ubc18 \uc2dc\uc2a4\ud15c\uc5d0 \ube44\ud574 \ub354 \ub192\uc740 \uc131\ub2a5\uacfc \uc720\uc5f0\uc131\uc744 \uc790\ub791\ud569\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5, \uadf8\ub9ac\uace0 \uc804\uc774 \ud559\uc2b5\uc744 \ud1b5\ud574 \uc801\uc740 \ub370\uc774\ud130\ub85c\ub3c4 \ud6a8\uacfc\uc801\uc778 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\u2026","rel":"","context":""\ub525\ub7ec\ub2dd \uba38\uc2e0\ub7ec\ub2dd \uc790\ub3d9 \ud2b8\ub808\uc774\ub529"\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd \uba38\uc2e0\ub7ec\ub2dd \uc790\ub3d9 \ud2b8\ub808\uc774\ub529","link":"https:\/\/atmokpo.com\/w\/category\/%eb%94%a5%eb%9f%ac%eb%8b%9d-%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d-%ec%9e%90%eb%8f%99-%ed%8a%b8%eb%a0%88%ec%9d%b4%eb%94%a9\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":30000,"url":"https:\/\/atmokpo.com\/w\/30000\/","url_meta":{"origin":29873,"position":5},"title":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uacf5\uac04 \ud53c\ub77c\ubbf8\ub4dc \ud480\ub9c1","author":"root","date":"2024\ub144 10\uc6d4 28\uc77c","format":false,"excerpt":"\uc791\uc131\uc790: \uc870\uad11\ud615 \uc791\uc131\uc77c: [\ub0a0\uc9dc] 1. \uacf5\uac04 \ud53c\ub77c\ubbf8\ub4dc \ud480\ub9c1(SPP)\uc774\ub780? \uacf5\uac04 \ud53c\ub77c\ubbf8\ub4dc \ud480\ub9c1(SPP, Spatial Pyramid Pooling)\uc740 \uc774\ubbf8\uc9c0 \ubd84\ub958\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \ube44\uc804 \ud0dc\uc2a4\ud06c\uc758 \ubaa8\ub378\uc5d0\uc11c \uc0ac\uc6a9\ub418\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(CNN)\uc740 \uace0\uc815\ub41c \ud06c\uae30\uc758 \uc785\ub825\uc744 \ud544\uc694\ub85c \ud558\uc9c0\ub9cc, SPP\ub294 \ubcc0\ub3d9\ud558\ub294 \ud06c\uae30\uc758 \uc774\ubbf8\uc9c0\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc744 \uc218 \uc788\ub294 \ud2b9\uc131\uc744 \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 SPP\uac00 \uc785\ub825 \uc774\ubbf8\uc9c0\uc758 \ud06c\uae30\ub97c \uc5ec\ub7ec \uacb9\uc73c\ub85c \ub098\ub204\ub294\u2026","rel":"","context":""\ud30c\uc774\ud1a0\uce58 \uac15\uc88c"\uc5d0\uc11c","block_context":{"text":"\ud30c\uc774\ud1a0\uce58 \uac15\uc88c","link":"https:\/\/atmokpo.com\/w\/category\/%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/posts\/29873","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/comments?post=29873"}],"version-history":[{"count":1,"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/posts\/29873\/revisions"}],"predecessor-version":[{"id":29874,"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/posts\/29873\/revisions\/29874"}],"wp:attachment":[{"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/media?parent=29873"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/categories?post=29873"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/atmokpo.com\/w\/wp-json\/wp\/v2\/tags?post=29873"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

{"id":29873,"date":"2024-10-28T03:00:40","date_gmt":"2024-10-28T03:00:40","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29873"},"modified":"2024-11-26T06:51:01","modified_gmt":"2024-11-26T06:51:01","slug":"%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98%eb%a5%bc-%ed%99%9c%ec%9a%a9%ed%95%9c-gan-%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%b2%ab-%eb%b2%88%ec%a7%b8-%ec%9d%8c%ec%95%85-%ec%83%9d%ec%84%b1-rnn","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29873\/","title":{"rendered":"\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \uccab \ubc88\uc9f8 \uc74c\uc545 \uc0dd\uc131 RNN"},"content":{"rendered":"

<\/p>\n

1. \uc11c\ub860<\/h2>\n
\uc778\uacf5\uc9c0\ub2a5(AI) \uae30\uc220\uc774 \ubc1c\uc804\ud568\uc5d0 \ub530\ub77c \uc74c\uc545 \uc0dd\uc131 \ubd84\uc57c\uc5d0\uc11c\ub3c4 \ub2e4\uc591\ud55c \uc2dc\ub3c4\uac00 \uc774\ub8e8\uc5b4\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub525\ub7ec\ub2dd \ubaa8\ub378 \uc911 \uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd(GAN)\uc740 \uae30\uc874 \ub370\uc774\ud130\uc758 \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc785\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc774\uc6a9\ud558\uc5ec RNN(Recurrent Neural Network) \uae30\ubc18\uc758 \uc74c\uc545 \uc0dd\uc131 \ubaa8\ub378\uc744 \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 GAN\uc758 \uc6d0\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \uc790\uc5f0\uc2a4\ub7ec\uc6b4 \uc74c\uc545\uc744 \uc0dd\uc131\ud558\ub294 \ub370 \uc911\uc810\uc744 \ub461\ub2c8\ub2e4.<\/p>\n

2. GAN\uc758 \uac1c\uc694\uc640 \uc6d0\ub9ac<\/h2>\n
GAN(Generative Adversarial Network)\uc740 \ub450 \uac1c\uc758 \uc2e0\uacbd\ub9dd, \uc989 \uc0dd\uc131\uc790(Generator)\uc640 \ud310\ubcc4\uc790(Discriminator)\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \uc2e4\uc81c\uc640 \uc720\uc0ac\ud55c \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub824\uace0 \ud558\uace0, \ud310\ubcc4\uc790\ub294 \uc0dd\uc131\ub41c \ub370\uc774\ud130\uac00 \uc2e4\uc81c \ub370\uc774\ud130\uc778\uc9c0 \uc0dd\uc131\ub41c \ub370\uc774\ud130\uc778\uc9c0\ub97c \uad6c\ubd84\ud558\ub824\uace0 \ud569\ub2c8\ub2e4. \uc774 \ub450 \ub124\ud2b8\uc6cc\ud06c\ub294 \uc11c\ub85c \uacbd\uc7c1\ud558\uba70 \ud559\uc2b5\ud558\uac8c \ub429\ub2c8\ub2e4.<\/p>\n

2.1 GAN\uc758 \uad6c\uc870<\/h3>\n

GAN \uad6c\uc870\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n

\uc0dd\uc131\uc790 (Generator): \ubb34\uc791\uc704 \ub178\uc774\uc988\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544\uc11c \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/li>\n

\ud310\ubcc4\uc790 (Discriminator): \uc0dd\uc131\ub41c \ub370\uc774\ud130\uc640 \uc2e4\uc81c \ub370\uc774\ud130\ub97c \uad6c\ubd84\ud558\ub294 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n

\uc774\ub7ec\ud55c \uad6c\uc870\ub85c \uc778\ud574 GAN\uc740 \ub9e4\uc6b0 \ucc3d\uc758\uc801\uc778 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

2.2 GAN \ud559\uc2b5 \uacfc\uc815<\/h3>\n

GAN\uc758 \ud559\uc2b5\uc740 \ub450 \uac1c\uc758 \ub124\ud2b8\uc6cc\ud06c\uac00 \ubc88\uac08\uc544 \uac00\uba70 \ud559\uc2b5\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4:<\/p>\n

\uba3c\uc800, \uc0dd\uc131\uc790\ub294 \ubb34\uc791\uc704 \ub178\uc774\uc988\ub97c \uc785\ub825\uc73c\ub85c \ubc1b\uc544 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/p>\n

\ub2e4\uc74c\uc73c\ub85c, \ud310\ubcc4\uc790\ub294 \uc774 \uac00\uc9dc \ub370\uc774\ud130\uc640 \uc2e4\uc81c \ub370\uc774\ud130\ub97c \ubaa8\ub450 \uc785\ub825\ubc1b\uc544 \uac01 \ub370\uc774\ud130\uc758 \uc9c4\uc704\ub97c \ud310\ub2e8\ud569\ub2c8\ub2e4.<\/li>\n

\uc0dd\uc131\uc790\ub294 \ud310\ubcc4\uc790\uac00 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc2e4\uc81c\ub77c\uace0 \uc798\ubabb \ud310\ub2e8\ud558\ub3c4\ub85d \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n

\ubc18\uba74, \ud310\ubcc4\uc790\ub294 \uac00\uc9dc \ub370\uc774\ud130\ub97c \uc815\ud655\ud558\uac8c \uad6c\ubd84\ud558\ub3c4\ub85d \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n<\/li<1><\/ul>\n

3. RNN\uc744 \ud65c\uc6a9\ud55c \uc74c\uc545 \uc0dd\uc131<\/h2>\n
\uc74c\uc545\uc740 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub85c, RNN\uc774 \uc774\ub7ec\ud55c \uc2dc\ud000\uc2a4\ub97c \ub2e4\ub8e8\ub294 \ub370 \uc801\ud569\ud569\ub2c8\ub2e4. RNN\uc740 \uc774\uc804 \uc2dc\uac04 \uc2a4\ud15d\uc758 \ucd9c\ub825\uc744 \ud604\uc7ac \uc785\ub825\uc5d0 \uc601\ud5a5\uc744 \uc904 \uc218 \uc788\ub3c4\ub85d \uc124\uacc4\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \ub530\ub77c\uc11c \uc74c\uc545 \uc0dd\uc131 \uc2dc\ud000\uc2a4\ub97c \uc0dd\uc131\ud558\uae30\uc5d0 \uc801\ud569\ud569\ub2c8\ub2e4.<\/p>\n

3.1 RNN\uc758 \uad6c\uc870<\/h3>\n

RNN\uc740 \uc8fc\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \uad6c\uc131 \uc694\uc18c\ub85c \uc774\ub8e8\uc5b4\uc838 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n

\uc785\ub825\uce35: \uac01 \uc2dc\uac04 \uc2a4\ud15d\uc5d0\uc11c \uc785\ub825\ub418\ub294 \ub370\uc774\ud130\uc785\ub2c8\ub2e4.<\/li>\n
\uc740\ub2c9\uce35: \uc774\uc804 \uc0c1\ud0dc\uc5d0 \ub300\ud55c \uc815\ubcf4\ub97c \ucc59\uae30\ub294 \uc5ed\ud560\uc744 \ub2f4\ub2f9\ud569\ub2c8\ub2e4.<\/li>\n

\ucd9c\ub825\uce35: \ubaa8\ub378\uc758 \ucd5c\uc885 \ucd9c\ub825\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n

3.2 RNN\uc758 \ud559\uc2b5<\/h3>\n
RNN\uc758 \ud559\uc2b5\uc740 \uc2dc\ud000\uc2a4 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \uc218\ud589\ub418\uba70, \uae30\uc900 \uc190\uc2e4 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd5c\uc801\ud654\ub97c \uc9c4\ud589\ud569\ub2c8\ub2e4. \uc190\uc2e4\uc744 \uacc4\uc0b0\ud558\uace0, \uc5ed\uc804\ud30c(backpropagation)\ub97c \ud1b5\ud574 \uac00\uc911\uce58\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4.<\/p>\n

4. \uc74c\uc545 \ub370\uc774\ud130 \uc900\ube44<\/h2>\n
\ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc74c\uc545 \ub370\uc774\ud130\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c MIDI \ud30c\uc77c \ud615\uc2dd\uc774 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\ub97c \ud14d\uc2a4\ud2b8 \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud558\uc5ec \ubaa8\ub378\uc5d0 \ub9de\uac8c \uc804\ucc98\ub9ac\ud569\ub2c8\ub2e4.<\/p>\n

4.1 MIDI \ud30c\uc77c \uc77d\uae30<\/h3>\n
\ud30c\uc774\uc36c\uc758 `mido<\/code>\uc640 \uac19\uc740 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec MIDI \ud30c\uc77c\uc744 \uc77d\uace0 \ud544\uc694\ud55c \uc815\ubcf4\ub97c \ucd94\ucd9c\ud569\ub2c8\ub2e4. \uc774\uc81c MIDI \ud30c\uc77c\uc5d0\uc11c \uc74c\ud45c \uc815\ubcf4\ub97c \ucd94\ucd9c\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n`

4.2 \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\npython\nimport mido\n\ndef extract_notes(midi_file):\n    midi = mido.MidiFile(midi_file)\n    notes = []\n    \n    for track in midi.tracks:\n        for message in track:\n            if message.type == 'note_on' and message.velocity > 0:\n                notes.append(message.note)\n    \n    return notes\n\nnotes = extract_notes('example.mid')\nprint(notes)\n<\/code><\/pre>\n\uc704 \ucf54\ub4dc\ub294 MIDI \ud30c\uc77c\uc5d0\uc11c \uc74c\ud45c \uc815\ubcf4\ub97c \ucd94\ucd9c\ud558\ub294 \ud568\uc218\uc785\ub2c8\ub2e4. \uac01 \uc74c\ud45c\ub294 MIDI \uc22b\uc790\ub85c \ud45c\ud604\ub429\ub2c8\ub2e4.<\/p>\n
5. \ubaa8\ub378 \uad6c\ud604<\/h2>\n\ubaa8\ub378 \uad6c\ud604 \ub2e8\uacc4\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec GAN \ubc0f RNN \ubaa8\ub378\uc744 \uad6c\uc131\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uc73c\ub85c RNN \uad6c\uc870\ub97c \uc124\uacc4\ud558\uace0 GAN \uad6c\uc870\uc640 \uacb0\ud569\ud558\uc5ec \ucd5c\uc885 \uc74c\uc545 \uc0dd\uc131 \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.<\/p>\n
5.1 RNN \ubaa8\ub378 \uc815\uc758<\/h3>\npython\nimport torch\nimport torch.nn as nn\n\nclass RNNModel(nn.Module):\n    def __init__(self, input_size, hidden_size, output_size):\n        super(RNNModel, self).__init__()\n        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)\n        self.fc = nn.Linear(hidden_size, output_size)\n\n    def forward(self, x):\n        out, _ = self.rnn(x)\n        out = self.fc(out[:, -1, :])\n        return out\n<\/code><\/pre>\n\uc774 \ucf54\ub4dc\ub294 RNN \ubaa8\ub378\uc744 \uc815\uc758\ud558\ub294 \ud074\ub798\uc2a4\uc785\ub2c8\ub2e4. \uc785\ub825 \ud06c\uae30, \uc740\ub2c9\uce35 \ud06c\uae30, \ucd9c\ub825 \ud06c\uae30\ub97c \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5.2 GAN \uad6c\uc870 \uc815\uc758<\/h3>\npython\nclass GAN(nn.Module):\n    def __init__(self, generator, discriminator):\n        super(GAN, self).__init__()\n        self.generator = generator\n        self.discriminator = discriminator\n\n    def forward(self, noise):\n        generated_data = self.generator(noise)\n        validity = self.discriminator(generated_data)\n        return validity\n<\/code><\/pre>\n\uc5ec\uae30\uc11c\ub294 \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790\ub97c \uac16\ub294 GAN \uad6c\uc870\ub97c \uc815\uc758\ud588\uc2b5\ub2c8\ub2e4. \uc0dd\uc131\uc790\ub294 \ub178\uc774\uc988\ub97c \uc785\ub825\ubc1b\uc544 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\uace0, \ud310\ubcc4\uc790\ub294 \uc774 \ub370\uc774\ud130\uc758 \uc9c4\uc704\ub97c \ud310\ubcc4\ud569\ub2c8\ub2e4.<\/p>\n
6. \ud6c8\ub828 \uacfc\uc815<\/h2>\n\ud6c8\ub828 \uacfc\uc815\uc5d0\uc11c\ub294 \uc0dd\uc131\uc790\uc640 \ud310\ubcc4\uc790 \ub124\ud2b8\uc6cc\ud06c\ub97c \ubc88\uac08\uc544 \uac00\uba70 \ud559\uc2b5\ud558\uc5ec \uac01\uac01\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \ud6c8\ub828 \ub8e8\ud504\uc758 \uc608\uc81c\uc785\ub2c8\ub2e4.<\/p>\n
6.1 \ud6c8\ub828 \ub8e8\ud504 \uad6c\ud604<\/h3>\npython\ndef train_gan(generator, discriminator, gan, dataloader, num_epochs, device):\n    criterion = nn.BCELoss()\n    optimizer_g = torch.optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n    optimizer_d = torch.optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))\n\n    for epoch in range(num_epochs):\n        for real_data in dataloader:\n            batch_size = real_data.size(0)\n            real_data = real_data.to(device)\n\n            # \ud310\ubcc4\uc790 \ud6c8\ub828\n            optimizer_d.zero_grad()\n            noise = torch.randn(batch_size, 100).to(device)\n            fake_data = generator(noise)\n            validity_real = discriminator(real_data)\n            validity_fake = discriminator(fake_data.detach())\n\n            loss_d = criterion(validity_real, torch.ones(batch_size, 1).to(device)) + \\\n                      criterion(validity_fake, torch.zeros(batch_size, 1).to(device))\n            loss_d.backward()\n            optimizer_d.step()\n\n            # \uc0dd\uc131\uc790 \ud6c8\ub828\n            optimizer_g.zero_grad()\n            validity = discriminator(fake_data)\n            loss_g = criterion(validity, torch.ones(batch_size, 1).to(device))\n            loss_g.backward()\n            optimizer_g.step()\n        \n        print(f\"Epoch[{epoch}\/{num_epochs}] Loss D: {loss_d.item()}, Loss G: {loss_g.item()}\")\n<\/code><\/pre>\n\uc774 \ud568\uc218\ub294 GAN\uc758 \ud6c8\ub828 \uacfc\uc815\uc744 \uc815\uc758\ud569\ub2c8\ub2e4. \uac01 \uc5d0\ud3ec\ud06c\ub9c8\ub2e4 \ud310\ubcc4\uc790\uc640 \uc0dd\uc131\uc790\uc758 \uc190\uc2e4\uc744 \ucd9c\ub825\ud558\uc5ec \ud559\uc2b5 \uacfc\uc815\uc744 \ubaa8\ub2c8\ud130\ub9c1\ud569\ub2c8\ub2e4.<\/p>\n
7. \uacb0\uacfc \uc0dd\uc131<\/h2>\n\ud6c8\ub828\uc774 \uc644\ub8cc\ub41c \ud6c4 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \uc74c\uc545\uc744 \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc0dd\uc131\ub41c \uc74c\uc545\uc740 MIDI \ud30c\uc77c\ub85c \uc800\uc7a5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
7.1 \uc74c\uc545 \uc0dd\uc131 \ubc0f \uc800\uc7a5<\/h3>\npython\ndef generate_music(generator, num_samples, device):\n    noise = torch.randn(num_samples, 100).to(device)\n    generated_music = generator(noise)\n    \n    # MIDI \ud30c\uc77c\ub85c \uc800\uc7a5\ud558\ub294 \ucf54\ub4dc \ucd94\uac00\n    # ...\n    \n    return generated_music\n<\/code><\/pre>\n8. \uacb0\ub860<\/h2>\n\uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud558\uc5ec GAN \uae30\ubc18\uc758 \uc74c\uc545 \uc0dd\uc131 RNN \ubaa8\ub378\uc744 \uad6c\ud604\ud558\ub294 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. GAN\uc758 \uc6d0\ub9ac\uc640 RNN\uc758 \ud2b9\uc9d5\uc744 \ud65c\uc6a9\ud558\uc5ec \uc74c\uc545 \uc0dd\uc131\uc758 \uc0c8\ub85c\uc6b4 \uac00\ub2a5\uc131\uc744 \ud0d0\uc0c9\ud588\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa8\ub378\uc744 \ud1b5\ud574 \uc2e4\ud5d8\uc801\uc73c\ub85c \uc74c\uc545\uc744 \uc0dd\uc131\ud558\uace0, \ub354 \ub098\uc544\uac00 \uc74c\uc545 \uc0b0\uc5c5\uc5d0 \ucc3d\uc758\uc801\uc778 \ubcc0\ud654\ub97c \uac00\uc838\uc62c \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.<\/p>\n
9. \ucc38\uace0 \uc790\ub8cc<\/h2>\n\nGoodfellow et al., “Generative Adversarial Nets,” NeurIPS, 2014.<\/li>\n
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation.<\/li>\n
PyTorch Documentation: https:\/\/pytorch.org\/docs\/stable\/index.html<\/a><\/li>\n
Mido Documentation: https:\/\/mido.readthedocs.io\/en\/latest\/<\/a><\/li>\n<\/ul>\n<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"1. \uc11c\ub860 \uc778\uacf5\uc9c0\ub2a5(AI) \uae30\uc220\uc774 \ubc1c\uc804\ud568\uc5d0 \ub530\ub77c \uc74c\uc545 \uc0dd\uc131 \ubd84\uc57c\uc5d0\uc11c\ub3c4 \ub2e4\uc591\ud55c \uc2dc\ub3c4\uac00 \uc774\ub8e8\uc5b4\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub525\ub7ec\ub2dd \ubaa8\ub378 \uc911 \uc0dd\uc131\uc801 \uc801\ub300 \uc2e0\uacbd\ub9dd(GAN)\uc740 \uae30\uc874 \ub370\uc774\ud130\uc758 \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\ub97c \uc0dd\uc131\ud558\ub294 \ub370 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc785\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc774\uc6a9\ud558\uc5ec RNN(Recurrent Neural Network) \uae30\ubc18\uc758 \uc74c\uc545 \uc0dd\uc131 \ubaa8\ub378\uc744 \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 GAN\uc758 \uc6d0\ub9ac\ub97c \ud65c\uc6a9\ud558\uc5ec \uc790\uc5f0\uc2a4\ub7ec\uc6b4 \uc74c\uc545\uc744 \uc0dd\uc131\ud558\ub294 \ub370 \uc911\uc810\uc744 \ub461\ub2c8\ub2e4. … \ub354 \ubcf4\uae30 “\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \uccab \ubc88\uc9f8 \uc74c\uc545 \uc0dd\uc131 RNN”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[32],"tags":[],"class_list":["post-29873","post","type-post","status-publish","format-standard","hentry","category-gan--"],"yoast_head":"\n\ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c GAN \ub525\ub7ec\ub2dd, \uccab \ubc88\uc9f8 \uc74c\uc545 \uc0dd\uc131 RNN - 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