{"id":64175,"date":"2018-07-17T11:42:17","date_gmt":"2018-07-17T02:42:17","guid":{"rendered":"https:\/\/www.sejuku.net\/blog\/?p=64175"},"modified":"2024-05-06T11:49:48","modified_gmt":"2024-05-06T02:49:48","slug":"ph1","status":"publish","type":"post","link":"https:\/\/www.sejuku.net\/blog\/64175","title":{"rendered":"PyTorch\u3068\u306f\uff1f\u6982\u8981\u3068\u5c0e\u5165\u65b9\u6cd5\u3092\u30c1\u30a7\u30c3\u30af\u3057\u3088\u3046\uff01"},"content":{"rendered":"\n<p>Deep Learning\u306e\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u306b\u3064\u3044\u3066\u3001\u4ee5\u524d\u7d39\u4ecb\u8a18\u4e8b\u3092\u66f8\u304d\u307e\u3057\u305f\u3002<\/p>\n\n\n            <a href=\"https:\/\/www.sejuku.net\/blog\/62012\" class=\"blog-card-anchor\">\n                <div class=\"blog-card\">\n                <div class=\"blog-card-content\">\n                    <div class=\"blog-card-thumbnail\">\n                    <img decoding=\"async\" class=\"blog-card-thumbnail-img\" src=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/dlfw-150x86.png\">\n                    <\/div>\n                    <div class=\"blog-card-title-wrap\">\n                    <div class=\"blog-card-title\">Tensorflow\u3060\u3051\u3058\u3083\u306a\u3044\uff1f\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af8\u9078\uff01<\/div>\n                    <div class=\"blog-card-update-date\">\u66f4\u65b0\u65e5\uff1a2024\u5e745\u67086\u65e5<\/div>\n                    <\/div>\n                <\/div>\n                <\/div>\n            <\/a>\n            \n\n\n\n<p>\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001\u305d\u306e\u8a18\u4e8b\u3067\u3082\u7d39\u4ecb\u3057\u305f\u6df1\u5c64\u5b66\u7fd2\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u306e\u4e00\u3064\u3001PyTorch\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3059\uff01<\/p>\n\n\n\n<p>Deep Learning\u7814\u7a76\u306e\u5206\u91ce\u3067\u5927\u6d3b\u8e8d\u306ePyTorch\u3001\u66f8\u304d\u3084\u3059\u3055\u3068\u5b9f\u52b9\u901f\u5ea6\u306e\u30d0\u30e9\u30f3\u30b9\u304c\u53d6\u308c\u305f\u3059\u3054\u3044\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002<\/p>\n\n\n\n<p>\u203b \u3053\u306e\u8a18\u4e8b\u306e\u30b3\u30fc\u30c9\u306fPython 3.6, PyTorch 1.0\u3067\u52d5\u4f5c\u78ba\u8a8d\u3057\u307e\u3057\u305f\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">PyTorch\u3068\u306f<\/h2>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" width=\"1025\" height=\"205\" src=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/05\/pytorch.png\" alt=\"\" class=\"wp-image-51820\" srcset=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/05\/pytorch.png 1025w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/05\/pytorch-150x30.png 150w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/05\/pytorch-300x60.png 300w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/05\/pytorch-640x128.png 640w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/05\/pytorch-810x162.png 810w\" sizes=\"(max-width: 1025px) 100vw, 1025px\" \/><\/figure><\/div>\n\n\n\n<p><div class=\"img-caption\">\u5f15\u7528\u5143\uff1aPyTorch\u3000<a href=\"https:\/\/pytorch.org\/tutorials\/\">https:\/\/pytorch.org\/tutorials\/<\/div><\/a><\/p>\n\n\n\n<p><b>PyTorch\u306e\u7279\u5fb4<\/b><\/p>\n\n\n\n<p>PyTorch\u306f\u3001Python\u5411\u3051\u306eDeep Learning\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u3002<\/p>\n\n\n\n<p>Facebook\u304c\u958b\u767a\u3092\u4e3b\u5c0e\u3057\u3001\u305d\u306e\u66f8\u304d\u3084\u3059\u3055\u3068\u4f7f\u3044\u3084\u3059\u3055\u304b\u3089\u4eba\u6c17\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n<p>\u3053\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u306f\u975e\u5e38\u306b\u67d4\u8edf\u306a\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u8a18\u8ff0\u304c\u3067\u304d\u3001\u4eca\u4e3b\u6d41\u3067\u3042\u308bDeep Learning\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u4e2d\u3067\u3082\u304b\u306a\u308a\u306e\u4eba\u6c17\u3092\u8a87\u308a\u307e\u3059\u3002<br><div class=\"box01\">\u516c\u5f0f\u30da\u30fc\u30b8\uff1a <a href=\"https:\/\/pytorch.org\/\">https:\/\/pytorch.org\/<\/a><br>Github\u30ea\u30dd\u30b8\u30c8\u30ea\uff1a<a href=\"https:\/\/github.com\/pytorch\/pytorch\">https:\/\/github.com\/pytorch\/pytorch<\/a><\/div><br>\u516c\u5f0f\u30da\u30fc\u30b8\u306e<a href=\"https:\/\/pytorch.org\/about\/\">about<\/a>\u306b\u3088\u308b\u3068<br><div class=\"box01\"><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><b>\u5f37\u529b\u306aGPU\u30a2\u30af\u30bb\u30e9\u30ec\u30fc\u30b7\u30e7\u30f3\u306b\u3088\u308b\u30c6\u30f3\u30bd\u30eb\u8a08\u7b97<\/b><\/li><li><b>\uff08\u30ea\u30d0\u30fc\u30b9\u30e2\u30fc\u30c9\u306e\uff09\u81ea\u52d5\u5fae\u5206\u3092\u4f7f\u3044\u30b7\u30f3\u30d7\u30eb\u306a\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u8a18\u8ff0\u304c\u53ef\u80fd<\/b><\/li><\/ul>\n\n\n\n<p><\/div><br>\u306a\u3069\u306e\u7279\u5fb4\u3092\u6301\u3061\u307e\u3059\u3002<\/p>\n\n\n\n<p>Deep Learning\u306e\u4e16\u754c\u3067\u306fGPU\u3092\u4f7f\u3063\u305f\u30a2\u30af\u30bb\u30e9\u30ec\u30fc\u30b7\u30e7\u30f3\uff08\u9ad8\u901f\u5316\uff09\u306f\u5fc5\u9808\u3067\u3059\u3002\u3053\u308c\u306f\u6709\u540d\u306aDeep Learning\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u306a\u3089\u3070\u6b86\u3069\u304c\u5099\u3048\u308b\u6a5f\u80fd\u3067\u3059\u3002<\/p>\n\n\n\n<p>\u307e\u305f\u3001\u81ea\u52d5\u5fae\u5206\u306b\u3064\u3044\u3066\u3082\u540c\u69d8\u3067\u3059\u3002<\/p>\n\n\n\n<p>\u3053\u306ePyTorch\u306fNumpy\u3001Scipy\u3001Cython\u306a\u3069\u306e\u3001Python\u3067\u306e\u79d1\u5b66\u8a08\u7b97\u3067\u3088\u304f\u4f7f\u3046\u30e9\u30a4\u30d6\u30e9\u30ea\u306b\u3088\u3063\u3066\u62e1\u5f35\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n<p>\u305d\u306e\u305f\u3081\u3001\u7d30\u304b\u3044\u3068\u3053\u308d\u307e\u3067Python\u3067\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u3059\u308b\u4e8b\u304c\u3067\u304d\u307e\u3059\u3002\u6700\u65b0\u306eDeep Learning\u30e2\u30c7\u30eb\u3092\u5b9f\u88c5\u3059\u308b\u306b\u306f\u3046\u3063\u3066\u3064\u3051\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u3059\u306d\u3002<\/p>\n\n\n\n<p>\u307e\u305f\u3001Chainer\u3068\u3044\u3046\u56fd\u7523\u30e9\u30a4\u30d6\u30e9\u30ea\u306b\u975e\u5e38\u306b\u4f3c\u305f\u4f7f\u3044\u5fc3\u5730\u306a\u306e\u3067\u3001Chainer\u3092\u4f7f\u3063\u305f\u3053\u3068\u304c\u3042\u308b\u30e6\u30fc\u30b6\u30fc\u306a\u3089\u3070\u7c21\u5358\u306b\u7fd2\u5f97\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3067\u3057\u3087\u3046\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">PyTorch\u306f\u4eba\u6c17\u306e\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af<\/h3>\n\n\n\n<p>Github\u3067\u306e\u6ce8\u76ee\u5177\u5408\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" width=\"907\" height=\"510\" src=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/deeplearningstar.png\" alt=\"\" class=\"wp-image-64184\" srcset=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/deeplearningstar.png 907w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/deeplearningstar-150x84.png 150w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/deeplearningstar-300x169.png 300w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/deeplearningstar-640x360.png 640w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/deeplearningstar-810x455.png 810w\" sizes=\"(max-width: 907px) 100vw, 907px\" \/><\/figure><\/div>\n\n\n\n<p>\u3053\u306e\u30b0\u30e9\u30d5\u306f\u3001\u300cDeep Learning\u306e\u4e3b\u8981\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u300d\u306eGithub Star\u6570\u3067\u3059\u3002<\/p>\n\n\n\n<p>Tensorflow\u3001Keras\u306b\u7d9a\u304d\u7b2c\u4e09\u4f4d\u306e\u4f4d\u7f6e\u306b\u3042\u308a\u307e\u3059\u3002PyTorch\u306fTensorflow\u306b\u6bd4\u3079\u3066\u3084\u3084\u5f8c\u767a\u306a\u306e\u3067\u3059\u304c\u3001\u5065\u95d8\u3057\u3066\u3044\u307e\u3059\u306d\u3002<\/p>\n\n\n\n<p>Tensorflow\u304cGoogle\u306e\u5f37\u529b\u306a\u30d0\u30c3\u30af\u30a2\u30c3\u30d7\u3067\u958b\u767a\u3068\u5ba3\u4f1d\u304c\u9032\u3081\u3089\u308c\u3066\u3044\u308b\u70b9\u3092\u8003\u616e\u3059\u308b\u3068\u3001PyTorch\u306e\u4eba\u6c17\u5177\u5408\u304c\u308f\u304b\u308b\u304b\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\u6559\u6750\u306e\u8c4a\u5bcc\u3055<\/h3>\n\n\n\n<p>PyTorch\u306e\u89e3\u8aac\u52d5\u753b\u3082\u5927\u91cf\u306b\u3042\u308b\u306e\u3067\u3001\u52c9\u5f37\u3082\u6bd4\u8f03\u7684\u5bb9\u6613\u3067\u3059\u3002<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe title=\"PyTorch in 5 Minutes\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/nbJ-2G2GXL0?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>\u307e\u305f\u3001\u66f8\u7c4d\u3082\u6570\u591a\u304f\u767a\u8868\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n<p><a href=\"https:\/\/www.amazon.co.jp\/dp\/B07DZVRXFK\/ref=dp-kindle-redirect?_encoding=UTF8&amp;btkr=1\">[\u3064\u304f\u308a\u306a\u304c\u3089\u5b66\u3076! \u6df1\u5c64\u5f37\u5316\u5b66\u7fd2 ~PyTorch\u306b\u3088\u308b\u5b9f\u8df5\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0~ \u5358\u884c\u672c\uff08\u30bd\u30d5\u30c8\u30ab\u30d0\u30fc\uff09 \u2013 2018\/6\/28 \u682a\u5f0f\u4f1a\u793e\u96fb\u901a\u56fd\u969b\u60c5\u5831\u30b5\u30fc\u30d3\u30b9 \u5c0f\u5ddd\u96c4\u592a\u90ce (\u8457)]<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.amazon.co.jp\/PyTorch\u3067\u59cb\u3081\u308b\u6df1\u5c64\u5b66\u7fd2-\u2015\u2015\u6570\u5f0f\u306a\u3057\u3067\u57fa\u790e\u304b\u3089\u5b9f\u88c5\u307e\u3067-\u5c0f\u6cc9-\u8a13\/dp\/4865941304\/ref=sr_1_1?s=digital-text&amp;ie=UTF8&amp;qid=1531863474&amp;sr=8-1&amp;keywords=PyTorch\u3067\u59cb\u3081\u308b\u6df1\u5c64\u5b66\u7fd2+\u2015\u2015\u6570\u5f0f\u306a\u3057\u3067\u57fa\u790e\u304b\u3089\u5b9f\u88c5\u307e\u3067\">[PyTorch\u3067\u59cb\u3081\u308b\u6df1\u5c64\u5b66\u7fd2 \u2015\u2015\u6570\u5f0f\u306a\u3057\u3067\u57fa\u790e\u304b\u3089\u5b9f\u88c5\u307e\u3067 \u5358\u884c\u672c\uff08\u30bd\u30d5\u30c8\u30ab\u30d0\u30fc\uff09 \u2013 2018\/5\/21 \u5c0f\u6cc9 \u8a13 (\u8457)]<\/a><\/p>\n\n\n\n<p>PyTorch\u306f\u305d\u306e\u7fd2\u5f97\u306e\u3057\u3084\u3059\u3055\u3084\u3001\u7814\u7a76\u958b\u767a\u3068\u306e\u89aa\u548c\u6027\u306e\u9ad8\u3055\u304b\u3089\u3001\u767a\u8868\u3055\u308c\u3066\u3059\u3050\u306b\u4e16\u754c\u4e2d\u3067\u4eba\u6c17\u306b\u306a\u308a\u307e\u3057\u305f\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">PyTorch\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/h2>\n\n\n\n<p>PyTorch\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u65b9\u6cd5\u306f\u3001\u516c\u5f0f\u30da\u30fc\u30b8\u306b\u304b\u304b\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" width=\"1636\" height=\"744\" src=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/8bef3116d4a648c4af0d23cd7231ac32.png\" alt=\"\" class=\"wp-image-83080\" srcset=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/8bef3116d4a648c4af0d23cd7231ac32.png 1636w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/8bef3116d4a648c4af0d23cd7231ac32-150x68.png 150w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/8bef3116d4a648c4af0d23cd7231ac32-300x136.png 300w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/8bef3116d4a648c4af0d23cd7231ac32-640x291.png 640w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/8bef3116d4a648c4af0d23cd7231ac32-810x368.png 810w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/8bef3116d4a648c4af0d23cd7231ac32-1140x518.png 1140w\" sizes=\"(max-width: 1636px) 100vw, 1636px\" \/><\/figure><\/div>\n\n\n\n<p><div class=\"img-caption\">\u5f15\u7528\u5143\uff1aPyTorch\u3000<a href=\"https:\/\/pytorch.org\/\">https:\/\/pytorch.org\/<\/div><\/a><\/p>\n\n\n\n<p>Get Started.\u306e\u53f3\u5074\u306b\u3042\u308b\u30dc\u30bf\u30f3\u306f\u4ee5\u4e0b\u306e\u624b\u9806\u3067\u9078\u629e\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n<ol class=\"wp-block-list\"><li>PyTorch Build\n<ol>\n<li>\u5b89\u5b9a\u7248\u304b\u30d7\u30ec\u30d3\u30e5\u30fc\u7248\u304b\u3092\u9078\u3073\u307e\u3059\u3002<\/li>\n<li>\u666e\u901a\u306f\u5b89\u5b9a\u7248\u3067OK\u3067\u3059\u3002<\/li>\n<\/ol>\n<\/li><li>Your&nbsp; OS\n<ol>\n<li>PyTorch\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305f\u3044PC\u306eOS\u3092\u6307\u5b9a\u3057\u307e\u3057\u3087\u3046\u3002<\/li>\n<\/ol>\n<\/li><li>Package\n<ol>\n<li>\u666e\u901a\u306fpip\u3001Anaconda\u3092\u4f7f\u3063\u3066\u3044\u308b\u5834\u5408\u306fconda\u3092\u9078\u629e\u3057\u307e\u3057\u3087\u3046\u3002<\/li>\n<\/ol>\n<\/li><li>Language\n<ol>\n<li>PyTorch\u3092\u4f7f\u3046\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u8a00\u8a9e\u3092\u9078\u3073\u307e\u3059\u3002\u4ee5\u524d\u306fPython\u3060\u3051\u3067\u3057\u305f\u304c\u3001C++\u304c\u8ffd\u52a0\u3055\u308c\u307e\u3057\u305f\u3002<\/li>\n<li>Python\u3092\u4f7f\u3046\u5834\u5408\u306f\u7aef\u672b\u30a8\u30df\u30e5\u30ec\u30fc\u30bf\u3067\u300cPython -V\u300d\u3068\u6253\u3064\u3068\u30d0\u30fc\u30b8\u30e7\u30f3\u304c\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/li>\n<\/ol>\n<\/li><li>CUDA\n<ol>\n<li>NVIDIA\u306eGPU\u3092\u4f7f\u3063\u3066\u3044\u308b\u4eba\u306fCUDA\u306e\u30d0\u30fc\u30b8\u30e7\u30f3\u3092\u6307\u5b9a\u3057\u307e\u3059\u3002<\/li>\n<li>GPU\u304c\u306a\u3044\u5834\u5408\u306fNone\u3092\u9078\u629e\u3057\u307e\u3059\u3002<\/li>\n<\/ol>\n<\/li><\/ol>\n\n\n\n<p>\u81ea\u5206\u306e\u958b\u767a\u74b0\u5883\u306b\u3042\u3063\u305f\u30dc\u30bf\u30f3\u3092\u62bc\u3059\u3068\u3001\u305d\u306e\u6761\u4ef6\u306b\u3042\u3063\u305f\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u30b3\u30de\u30f3\u30c9\u304c\u300cRun this command:\u300d\u306e\u6a2a\u306b\u8868\u793a\u3055\u308c\u307e\u3059\u3002\u30b3\u30d4\u30fc\u3057\u3066\u7aef\u672b\u30a8\u30df\u30e5\u30ec\u30fc\u30bf\u3067\u5b9f\u884c\u3059\u308b\u3068\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u4f5c\u696d\u304c\u59cb\u307e\u308a\u307e\u3059\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">PyTorch\u306e\u30b3\u30fc\u30c9<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Define by Run<\/h3>\n\n\n\n<p>PyTorch\u306f<b>Define by Run\u5f0f\u306e\u30e9\u30a4\u30d6\u30e9\u30ea<\/b>\u3067\u3059\u3002<\/p>\n\n\n\n<p>\u7c21\u5358\u306b\u8a00\u3046\u3068\u3001\u300cPython\u30b3\u30fc\u30c9\u3092\u305d\u306e\u307e\u307e\u66f8\u304f\u3060\u3051\u3067Deep Learning\u30e2\u30c7\u30eb\u304c\u5b9f\u88c5\u3067\u304d\u308b\u300d\u30bf\u30a4\u30d7\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u306b\u306a\u308a\u307e\u3059\u3002\u307e\u305f\u3001Deep Learning\u306e\u5b9f\u88c5\u3067\u306f\u3001\u591a\u304f\u306e\u5834\u5408<b>\u30e2\u30c7\u30eb\u306e\u8a13\u7df4\u306b\u504f\u5fae\u5206<\/b>\u304c\u5fc5\u8981\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n<p>\u5fae\u5206\u306e\u8a08\u7b97\u3082\u3001\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30e2\u30c7\u30eb\u304c\u8907\u96d1\u306b\u306a\u308b\u3068\u5927\u5909\u306b\u306a\u308a\u307e\u3059\u3002\u305d\u308c\u3053\u305d\u4eba\u306e\u624b\u3067\u3044\u3061\u3044\u3061\u5fae\u5206\u3092\u3057\u3066\u3044\u3066\u306f\u8ffd\u3044\u3064\u304b\u306a\u3044\u7a0b\u306b\u3002<\/p>\n\n\n\n<p>PyTorch\u3067\u306f\u3001\u4ed6\u306e\u591a\u304f\u306eDeep Learning\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u3068\u540c\u69d8\u306b\u3001\u81ea\u52d5\u5fae\u5206\u6a5f\u80fd\u304c\u3064\u3044\u3066\u3044\u307e\u3059\u3002PyTorch\u306e\u95a2\u6570\u3092\u4f7f\u3063\u3066\u5b9f\u88c5\u3057\u3066\u3044\u308b\u9650\u308a\u3001\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u30d0\u30c3\u30af\u30d7\u30ed\u30d1\u30b2\u30fc\u30b7\u30e7\u30f3\u306b\u3064\u3044\u3066\u5b9f\u88c5\u3059\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/p>\n\n\n\n<p>\u30e2\u30c7\u30eb\u306e\u5b9f\u88c5\u3068\u3001\u640d\u5931\u95a2\u6570\u306e\u5b9a\u7fa9\u307e\u3067\u3092\u884c\u3063\u3066\u3057\u307e\u3048\u3070\u3001\u30d0\u30c3\u30af\u30d7\u30ed\u30d1\u30b2\u30fc\u30b7\u30e7\u30f3\u306e\u8a08\u7b97\u3092\u81ea\u52d5\u3067\u884c\u3063\u3066\u304f\u308c\u307e\u3059\u3002<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" width=\"718\" height=\"404\" src=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/dynamic_graph.gif\" alt=\"\" class=\"wp-image-64197\"\/><\/figure><\/div>\n\n\n\n<p><div class=\"img-caption\">\u5f15\u7528\u5143\uff1aPyTorch\u3000<a href=\"https:\/\/pytorch.org\/about\/\">https:\/\/pytorch.org\/about\/<\/div><\/a><\/p>\n\n\n\n<p>\u4e0a\u306egif\u3092\u898b\u308b\u3068\u3001\u5909\u6570loss\u306b\u640d\u5931\u95a2\u6570\u304c\u5165\u3063\u3066\u3044\u3066\u3001\u305d\u306e\u640d\u5931\u95a2\u6570\u3092loss.backward()\u3059\u308b\u3060\u3051\u3067\u52fe\u914d\u304c\u8a08\u7b97\u3067\u304d\u308b\u3068\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\u5b9f\u969b\u306e\u30b3\u30fc\u30c9<\/h3>\n\n\n\n<p>PyTorch\u3067\u5b9f\u969b\u306b\u3001\u7c21\u5358\u306a\u4e09\u5c64\u306e\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u5b9f\u88c5\u3057\u3066\u307f\u307e\u3057\u305f\u3002<\/p>\n\n\n\n<p>Jupyter\u4e0a\u3067\u5b9f\u884c\u3057\u3066\u898b\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">\u30e9\u30a4\u30d6\u30e9\u30ea\u306eimport<\/h4>\n\n\n\n<pre class=\"wp-block-preformatted\">import torch\nimport torch.nn as nn\nfrom torchvision import datasets\nimport torchvision.transforms as transforms\nfrom torch.autograd import Variable\n\nimport matplotlib.pyplot as plt\n% matplotlib inline<\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u8a2d\u5b9a<\/h4>\n\n\n\n<pre class=\"wp-block-preformatted\"># Hyper Parameters \ninput_size = 784\nhidden_size = 500\nnum_classes = 10\nnum_epochs = 5\nbatch_size = 100\nlearning_rate = 0.001<\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u7528\u610f<\/h4>\n\n\n\n<p>torchvision.datasets\u304b\u3089MNIST\u3068\u3044\u3046\u624b\u66f8\u304d\u6587\u5b57\u8a8d\u8b58\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u5229\u7528\u3057\u307e\u3059\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\"># MNIST Dataset \ntrain_dataset = datasets.MNIST(root='..\/data', \n                            train=True, \n                            transform=transforms.ToTensor(),  \n                            download=True)\n\ntest_dataset = datasets.MNIST(root='..\/data', \n                              train=False, \n                              transform=transforms.ToTensor())<\/pre>\n\n\n\n<p>Output:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Downloading http:\/\/yann.lecun.com\/exdb\/mnist\/train-images-idx3-ubyte.gz\nDownloading http:\/\/yann.lecun.com\/exdb\/mnist\/train-labels-idx1-ubyte.gz\nDownloading http:\/\/yann.lecun.com\/exdb\/mnist\/t10k-images-idx3-ubyte.gz\nDownloading http:\/\/yann.lecun.com\/exdb\/mnist\/t10k-labels-idx1-ubyte.gz\nProcessing...\nDone!<\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">minibatch\u5b66\u7fd2\u306e\u6e96\u5099<\/h4>\n\n\n\n<p>\u5b66\u7fd2\u306f\u901a\u5e38\u3001minibatch\u5358\u4f4d\u3067\u884c\u3044\u307e\u3059\u3002<\/p>\n\n\n\n<p>\u5b66\u7fd2\u306e\u969b\u306bminibatch\u3092\u30c7\u30fc\u30bf\u5168\u4f53\u304b\u3089\u5207\u308a\u51fa\u3057\u3066\u304f\u308c\u308b\u30af\u30e9\u30b9\u3092\u5b9a\u7fa9\u3057\u3066\u304a\u304d\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\"># Data Loader (Input Pipeline)\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset, \n                                           batch_size=batch_size, \n                                           shuffle=True)\n\ntest_loader = torch.utils.data.DataLoader(dataset=test_dataset, \n                                          batch_size=batch_size, \n                                          shuffle=False)\n<\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u5b9a\u7fa9<\/h4>\n\n\n\n<p>PyTorch\u3092\u4f7f\u3063\u305f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u5b9a\u7fa9\u306f\u975e\u5e38\u306b\u898b\u3084\u3059\u3044\u3082\u306e\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n<p>Chainer\u3092\u4f7f\u3063\u305f\u3053\u3068\u304c\u3042\u308b\u65b9\u306b\u306f\u3001\u308f\u304b\u308a\u3084\u3059\u3044\u306e\u3067\u306f\u306a\u3044\u3067\u3057\u3087\u3046\u304b\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\"># Neural Network Model (1 hidden layer)\nclass Net(nn.Module):\n        def __init__(self, input_size, hidden_size, num_classes):\n                super().__init__()\n                self.fc1 = nn.Linear(input_size, hidden_size) \n                self.relu = nn.ReLU()\n                self.fc2 = nn.Linear(hidden_size, num_classes)  \n\n        def forward(self, x):\n                out = self.fc1(x)\n                out = self.relu(out)\n                out = self.fc2(out)\n                return out<\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">\u30e2\u30c7\u30eb\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u306e\u4f5c\u6210<\/h4>\n\n\n\n<p>\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30af\u30e9\u30b9\u304b\u3089\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u4f5c\u308a\u307e\u3059\u3002<\/p>\n\n\n\n<p>\u3053\u306e\u969b\u306bGPU\u3092\u4f7f\u3046\u306e\u3067\u3042\u308c\u3070\u3001\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9.cuda()\u3068\u3057\u3066GPU\u3067\u5b9f\u884c\u3067\u304d\u308b\u5f62\u306b\u3057\u3066\u304a\u304d\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">net = Net(input_size, hidden_size, num_classes)\nnet.cuda()<\/pre>\n\n\n\n<p>Output:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Net(\n  (fc1): Linear(in_features=784, out_features=500, bias=True)\n  (relu): ReLU()\n  (fc2): Linear(in_features=500, out_features=10, bias=True)\n)<\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">\u640d\u5931\u95a2\u6570\u3068\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u306e\u8a2d\u5b9a<\/h4>\n\n\n\n<p>\u3053\u3053\u3067\u306f\u4f8b\u3068\u3057\u3066MNIST\u306e\u5206\u985e\u3092\u884c\u3046\u306e\u3067\u3001\u640d\u5931\u95a2\u6570\u3068\u3057\u3066\u30af\u30ed\u30b9\u30a8\u30f3\u30c8\u30ed\u30d4\u30fc\u3092\u4f7f\u3044\u307e\u3059\u3002<\/p>\n\n\n\n<p>\u30aa\u30d7\u30c6\u30a3\u30de\u30a4\u30b6\u306f\u69d8\u3005\u3042\u308a\u307e\u3059\u304c\u3001Adam\u3092\u4f7f\u3063\u3066\u307f\u307e\u3059\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\"># Loss and Optimizer\ncriterion = nn.CrossEntropyLoss()  \noptimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)  \n<\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0<\/h4>\n\n\n\n<p>\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u8a13\u7df4\u3092\u884c\u3046\u95a2\u6570\u3092\u5b9a\u7fa9\u3057\u3066\u3001\u3053\u308c\u3092\u8d70\u3089\u305b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n<p>\u3053\u3053\u306b\u3064\u3044\u3066\u306f\u3082\u3063\u3068\u30b9\u30de\u30fc\u30c8\u306a\u66f8\u304d\u65b9\u304c\u3042\u308a\u307e\u3059\u304c\u3001\u4eca\u306f\u30b7\u30f3\u30d7\u30eb\u306b\u4e00\u822c\u7684\u306a\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306efit\u95a2\u6570\u3092\u66f8\u3044\u3066\u307f\u307e\u3059\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">def fit(net, train_loader, num_epochs=num_epochs, t= 200):\n    # \u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\n    for epoch in range(num_epochs):\n        for i, (images, labels) in enumerate(train_loader):  \n            # pytorch\u306etensor\u3092Variable\u306b\u5909\u63db\n            images = Variable(images.view(-1, 28*28)).cuda()\n            labels = Variable(labels).cuda()\n\n            # \u30d5\u30a9\u30ef\u30fc\u30c9\u30d7\u30ed\u30d1\u30b2\u30fc\u30b7\u30e7\u30f3\/ \u30d0\u30c3\u30af\u30d7\u30ed\u30d1\u30b2\u30fc\u30b7\u30e7\u30f3\/ \u6700\u9069\u5316\n            optimizer.zero_grad()  # \u52fe\u914d\u30920\u521d\u671f\u5316\n            outputs = net(images)\n            loss = criterion(outputs, labels)\n            loss.backward()\n            optimizer.step()\n            \n            # t\u56de\u6bce\u306b\u30ed\u30b0\u3092\u8868\u793a\n            if (i+1) % t == 0:\n                print ('Epoch [%d\/%d], Step [%d\/%d], Loss: %.4f' \n                        %(epoch+1, num_epochs, i+1, len(train_dataset)\/\/batch_size, loss.data.item()))\n    return net\n\nnet = fit(net, train_loader)<\/pre>\n\n\n\n<p>Output:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Epoch [1\/5], Step [200\/600], Loss: 0.3556\nEpoch [1\/5], Step [400\/600], Loss: 0.1908\nEpoch [1\/5], Step [600\/600], Loss: 0.1428\nEpoch [2\/5], Step [200\/600], Loss: 0.1996\nEpoch [2\/5], Step [400\/600], Loss: 0.1316\nEpoch [2\/5], Step [600\/600], Loss: 0.0717\nEpoch [3\/5], Step [200\/600], Loss: 0.0298\nEpoch [3\/5], Step [400\/600], Loss: 0.0345\nEpoch [3\/5], Step [600\/600], Loss: 0.0895\nEpoch [4\/5], Step [200\/600], Loss: 0.0205\nEpoch [4\/5], Step [400\/600], Loss: 0.0703\nEpoch [4\/5], Step [600\/600], Loss: 0.0191\nEpoch [5\/5], Step [200\/600], Loss: 0.0310\nEpoch [5\/5], Step [400\/600], Loss: 0.0349\nEpoch [5\/5], Step [600\/600], Loss: 0.0399<\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">\u8a55\u4fa1<\/h4>\n\n\n\n<p>\u30e2\u30c7\u30eb\u3092\u8a55\u4fa1\u3057\u3066\u307f\u307e\u3059\u3002<\/p>\n\n\n\n<p>\u8a13\u7df4\u306b\u4f7f\u3063\u305f\u30c7\u30fc\u30bf\u3068\u306f\u5225\u306e\u30c7\u30fc\u30bf\uff08\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\uff09\u3092\u4f7f\u3063\u3066\u3001\u672a\u77e5\u306e\u30c7\u30fc\u30bf\u3092\u3069\u306e\u304f\u3089\u3044\u6b63\u78ba\u306b\u30af\u30e9\u30b9\u5206\u985e\u3067\u304d\u308b\u306e\u304b\u78ba\u304b\u3081\u307e\u3059\u3002<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">def evaluate(net, test_loader=test_loader):\n    # Test the Model\n    correct = 0\n    total = 0\n    for images, labels in test_loader:\n        images = Variable(images.view(-1, 28*28)).cuda()\n        outputs = net(images)\n        _, predicted = torch.max(outputs.data, 1)\n        total += labels.size(0)\n        correct += (predicted.cpu() == labels).sum()\n\n    print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct \/ total))\n\nevaluate(net)<\/pre>\n\n\n\n<p>Output:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">Accuracy of the network on the 10000 test images: 97 %<\/pre>\n\n\n\n<p>\u3082\u3063\u3068\u8907\u96d1\u306aDeep Learning\u30e2\u30c7\u30eb\u306e\u5b9f\u88c5\u3082\u3001PyTorch\u306a\u3089\u3070\u7f8e\u3057\u304f\u5b9f\u88c5\u3067\u304d\u308b\u3067\u3057\u3087\u3046\u3002<\/p>\n\n\n\n<p>\u672c\u8170\u3092\u5165\u308c\u3066PyTorch\u3068Deep Learning\u3092\u52c9\u5f37\u3057\u305f\u3044\u306a\u3089\u3070\u3001<a href=\"https:\/\/lp.sejuku.net\/lp1_blog_01\/?cid=ai_btn1_64175\">\u4f8d\u30a8\u30f3\u30b8\u30cb\u30a2\u306e\u30de\u30f3\u30c4\u30fc\u30de\u30f3\u30ec\u30c3\u30b9\u30f3<\/a>\u3092\u53d7\u8b1b\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n\n\n\n<p>\u6df1\u5c64\u5b66\u7fd2\u306b\u8a73\u3057\u3044\u30a8\u30f3\u30b8\u30cb\u30a2\u304c\u30a4\u30f3\u30b9\u30c8\u30e9\u30af\u30bf\u30fc\u306b\u306a\u3063\u3066\u3001\u3042\u306a\u305f\u306e\u52c9\u5f37\u3092\u30b5\u30dd\u30fc\u30c8\u3057\u307e\u3059\uff01<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u307e\u3068\u3081<\/h2>\n\n\n\n<p>\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001PyTorch\u306b\u3064\u3044\u3066\u307e\u3068\u3081\u307e\u3057\u305f\u3002<\/p>\n\n\n\n<p>PyTorch\u306f\u8fd1\u5e74\u306e\u8907\u96d1\u306aDeep Learning\u30e2\u30c7\u30eb\u306e\u5b9f\u88c5\u306b\u9069\u3057\u305f\u3001\u975e\u5e38\u306b\u67d4\u8edf\u306aDeep Learning\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u3067\u3059\u3002<\/p>\n\n\n\n<p>\u901f\u5ea6\u3082\u3042\u308a\u3001GPU\u3078\u306e\u5bfe\u5fdc\u3082\u7c21\u5358\u3067\u3059\u3002Deep Learning\u306e\u52c9\u5f37\u3092\u59cb\u3081\u308b\u306b\u306f\u6700\u9069\u306e\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u306a\u306e\u3067\u3001\u662f\u975e\u3068\u3082\u4f7f\u3063\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u306d\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Deep Learning\u306e\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u306b\u3064\u3044\u3066\u3001\u4ee5\u524d\u7d39\u4ecb\u8a18\u4e8b\u3092\u66f8\u304d\u307e\u3057\u305f\u3002 \u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001\u305d\u306e\u8a18\u4e8b\u3067\u3082\u7d39\u4ecb\u3057\u305f\u6df1\u5c64\u5b66\u7fd2\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u306e\u4e00\u3064\u3001PyTorch\u306b\u3064\u3044\u3066\u7d39\u4ecb\u3057\u307e\u3059\uff01 Deep Learning\u7814\u7a76\u306e\u5206\u91ce\u3067\u5927 [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":64418,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"swell_btn_cv_data":"","footnotes":""},"categories":[1],"tags":[1281,49],"class_list":["post-64175","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-others","tag-ai","tag-python"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>PyTorch\u3068\u306f\uff1f\u6982\u8981\u3068\u5c0e\u5165\u65b9\u6cd5\u3092\u30c1\u30a7\u30c3\u30af\u3057\u3088\u3046\uff01 | \u4f8d\u30a8\u30f3\u30b8\u30cb\u30a2\u30d6\u30ed\u30b0<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u8a18\u4e8b\u3067\u306f\u300c PyTorch\u3068\u306f\uff1f\u6982\u8981\u3068\u5c0e\u5165\u65b9\u6cd5\u3092\u30c1\u30a7\u30c3\u30af\u3057\u3088\u3046\uff01 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