{"id":3700,"date":"2022-07-22T09:00:00","date_gmt":"2022-07-22T00:00:00","guid":{"rendered":"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/?p=3700"},"modified":"2022-07-22T09:00:00","modified_gmt":"2022-07-22T00:00:00","slug":"%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d","status":"publish","type":"post","link":"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/","title":{"rendered":"\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u306e\u8a55\u4fa1\u65b9\u6cd5\u306e\u691c\u8a0e"},"content":{"rendered":"\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-white ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title ez-toc-toggle\" style=\"cursor:pointer\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E6%A6%82%E8%A6%81\" >\u6982\u8981<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E6%96%87%E7%8C%AE%E6%83%85%E5%A0%B1\" >\u6587\u732e\u60c5\u5831<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E5%95%8F%E9%A1%8C\" >\u554f\u984c<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E5%AE%9F%E9%A8%931\" >\u5b9f\u9a131<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E3%83%87%E3%83%BC%E3%82%BF%E6%BA%96%E5%82%99\" >\u30c7\u30fc\u30bf\u6e96\u5099<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E3%83%9C%E3%83%AB%E3%83%84%E3%83%9E%E3%83%B3%E3%83%9E%E3%82%B7%E3%83%B3%E3%82%92%E5%AE%9A%E7%BE%A9\" >\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u3092\u5b9a\u7fa9<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E3%83%9C%E3%83%AB%E3%83%84%E3%83%9E%E3%83%B3%E3%83%9E%E3%82%B7%E3%83%B3%E3%81%AE%E5%AD%A6%E7%BF%92\" >\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u306e\u5b66\u7fd2<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E5%88%86%E9%A1%9E%E3%83%A2%E3%83%87%E3%83%AB%E3%81%AE%E5%AD%A6%E7%BF%92\" >\u5206\u985e\u30e2\u30c7\u30eb\u306e\u5b66\u7fd2<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E5%88%86%E9%A1%9E%E3%83%A2%E3%83%87%E3%83%AB%E3%81%AB%E3%82%88%E3%82%8B%E8%A9%95%E4%BE%A1\" >\u5206\u985e\u30e2\u30c7\u30eb\u306b\u3088\u308b\u8a55\u4fa1<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E5%AF%BE%E6%95%B0%E5%B0%A4%E5%BA%A6%E8%A8%88%E7%AE%97\" >\u5bfe\u6570\u5c24\u5ea6\u8a08\u7b97<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E5%AE%9F%E9%A8%932\" >\u5b9f\u9a132<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E3%83%87%E3%83%BC%E3%82%BF%E6%BA%96%E5%82%99-2\" >\u30c7\u30fc\u30bf\u6e96\u5099<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E3%83%9C%E3%83%AB%E3%83%84%E3%83%9E%E3%83%B3%E3%83%9E%E3%82%B7%E3%83%B3%E3%81%AE%E5%AD%A6%E7%BF%92-2\" >\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u306e\u5b66\u7fd2<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E5%88%86%E9%A1%9E%E3%83%A2%E3%83%87%E3%83%AB%E3%81%AE%E5%AD%A6%E7%BF%92-2\" >\u5206\u985e\u30e2\u30c7\u30eb\u306e\u5b66\u7fd2<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E5%88%86%E9%A1%9E%E3%83%A2%E3%83%87%E3%83%AB%E3%81%AB%E3%82%88%E3%82%8B%E8%A9%95%E4%BE%A1-2\" >\u5206\u985e\u30e2\u30c7\u30eb\u306b\u3088\u308b\u8a55\u4fa1<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E5%B0%A4%E5%BA%A6%E8%A8%88%E7%AE%97\" >\u5c24\u5ea6\u8a08\u7b97<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E7%B5%90%E8%AB%96\" >\u7d50\u8ad6<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E3%81%82%E3%81%A8%E3%81%8C%E3%81%8D\" >\u3042\u3068\u304c\u304d<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/qard.is.tohoku.ac.jp\/T-Wave\/2022\/07\/22\/%e8%87%aa%e5%8b%95%e4%b8%8b%e6%9b%b8%e3%81%8d\/#%E6%9C%AC%E8%A8%98%E4%BA%8B%E3%81%AE%E6%8B%85%E5%BD%93%E8%80%85\" >\u672c\u8a18\u4e8b\u306e\u62c5\u5f53\u8005<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"%E6%A6%82%E8%A6%81\"><\/span>\u6982\u8981<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u300c<a href=\"\/T-Wave\/?p=2621\">\u91cf\u5b50\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u306b\u3088\u308b\u753b\u50cf\u751f\u6210\u306e\u8a55\u4fa1\u30e2\u30c7\u30eb<\/a>\u300d\u3067\u306f\uff0c\u624b\u66f8\u304d\u6587\u5b57\u753b\u50cf\u306e\u753b\u50cf\u751f\u6210\u3092D-Wave\u30de\u30b7\u30f3\uff0cMCMC(\u30de\u30eb\u30b3\u30d5\u9023\u9396\u30e2\u30f3\u30c6\u30ab\u30eb\u30ed\u6cd5)\u3092\u7528\u3044\u3066\u884c\u3044\uff0c\u305d\u306e\u753b\u50cf\u3092\u5b66\u7fd2\u6e08\u307f\u306e\u5206\u985e\u5668\u3067\u8a55\u4fa1\u3057\u3066\u3044\u307e\u3057\u305f\uff0e<\/p>\n<p>\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u306e\u8a55\u4fa1\u306f\u4e00\u822c\u7684\u306b\u30ab\u30eb\u30d0\u30c3\u30af\u30fb\u30e9\u30a4\u30d6\u30e9\u30fc(KL)\u60c5\u5831\u91cf\u3084\u5bfe\u6570\u5c24\u5ea6\u3092\u7528\u3044\u308b\u306e\u304c\u4e00\u822c\u7684\u3067\u3059\uff0e<br>\u78ba\u304b\u306b\uff0c\u5bfe\u6570\u5c24\u5ea6\u306a\u3069\u3067\u306f\u751f\u6210\u753b\u50cf\u305d\u306e\u3082\u306e\u3092\u8a55\u4fa1\u3059\u308b\u3053\u3068\u306f\u96e3\u3057\u3044\u3067\u3059\u304c\uff0c\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u306e\u8a55\u4fa1\u306f\u3084\u306f\u308a\u5c24\u5ea6\u3067\u8a55\u4fa1\u3059\u308b\u306e\u304c\u81ea\u7136\u3060\u3068\u8003\u3048\u3089\u308c\u307e\u3059\uff0e<\/p>\n<p>\u305d\u3053\u3067\uff0c\u672c\u8a18\u4e8b\u3067\u306f\u8ad6\u6587\u306e\u8a55\u4fa1\u6307\u6a19\u3068\u5bfe\u6570\u5c24\u5ea6\u306b\u3088\u308b\u8a55\u4fa1\u3068\u3092\u6bd4\u8f03\u3057\u3066\u3044\u304d\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2><span class=\"ez-toc-section\" id=\"%E6%96%87%E7%8C%AE%E6%83%85%E5%A0%B1\"><\/span>\u6587\u732e\u60c5\u5831<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li>\u30bf\u30a4\u30c8\u30eb: Assessment of image generation by quantum annealer<\/li>\n<li>\u8457\u8005: Takehito Sato, Masayuki Ohzeki, and Kazuyuki Tanaka<\/li>\n<li>\u66f8\u8a8c\u60c5\u5831: Sci Rep 11, 13523 (2021). <a href=\"https:\/\/doi.org\/10.1038\/s41598-021-92295-9\">https:\/\/doi.org\/10.1038\/s41598-021-92295-9<\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2><span class=\"ez-toc-section\" id=\"%E5%95%8F%E9%A1%8C\"><\/span>\u554f\u984c<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u3092\u7528\u3044\u3066\u624b\u66f8\u304d\u6570\u5b57\u753b\u50cf\u3092\u751f\u6210\u3057\uff0c\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u3092\u8a55\u4fa1\u3057\u307e\u3059\uff0e<br>\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u306e\u8a55\u4fa1\u3068\u3057\u3066\uff0c\u4e0a\u306e\u8ad6\u6587\u3067\u306f\u5b66\u7fd2\u6e08\u307f\u306e\u5206\u985e\u30e2\u30c7\u30eb\u3092\u7528\u3044\u3066\u884c\u3063\u3066\u3044\u307e\u3057\u305f\u304c\uff0c\u672c\u8a18\u4e8b\u3067\u306f\u305d\u308c\u306b\u52a0\u3048\uff0c\u5bfe\u6570\u5c24\u5ea6\u306b\u3088\u308b\u8a55\u4fa1\u3082\u884c\u3063\u3066\u3044\u304d\u307e\u3059\uff0e<\/p>\n<p>\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u306e\u8aac\u660e\u306f\u300c<a href=\"\/T-Wave\/?p=2621\">\u91cf\u5b50\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u306b\u3088\u308b\u753b\u50cf\u751f\u6210\u306e\u8a55\u4fa1\u30e2\u30c7\u30eb<\/a>\u300d\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2><span class=\"ez-toc-section\" id=\"%E5%AE%9F%E9%A8%931\"><\/span>\u5b9f\u9a131<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u307e\u305a\u306f\uff0c\u8ad6\u6587\u3067\u4f7f\u7528\u3055\u308c\u3066\u3044\u308b\u30c7\u30fc\u30bf\uff0c\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u7528\u3044\u3066\u518d\u73fe\u5b9f\u9a13\u3092\u884c\u3063\u3066\u3044\u304d\u307e\u3059\uff0e<\/p>\n<p>\u5b9f\u9a13\u306e\u6d41\u308c\u3068\u3057\u3066\u306f\u4ee5\u4e0b\u306e\u3068\u304a\u308a\u3067\u3059\uff0e<\/p>\n<ol>\n<li>\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u3092\u5b66\u7fd2\u3059\u308b<\/li>\n<li>\u5206\u985e\u30e2\u30c7\u30eb\u3092\u5b66\u7fd2\u3059\u308b<\/li>\n<li>\u5b66\u7fd2\u6e08\u307f\u306e\u4e21\u8005\u3092\u7528\u3044\u3066\uff0c\u8ad6\u6587\u306e\u8a55\u4fa1\u6307\u6a19\u306e\u5024\u3092\u8a08\u7b97\u3059\u308b\uff0e<\/li>\n<li>\u5bfe\u6570\u5c24\u5ea6\u3092\u8a08\u7b97\u3059\u308b\uff0e<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u306f\u3058\u3081\u306b\uff0c\u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066\u304a\u304d\u307e\u3057\u3087\u3046\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code># \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30dd\u30fc\u30c8\nimport os\nimport math\nimport itertools\nimport more_itertools\nimport random\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom pathlib import Path\n# \u30d7\u30ed\u30b0\u30ec\u30b9\u30d0\u30fc\u7528\nfrom tqdm import tqdm\n# PyTorch GPU\u4f7f\u7528\u306e\u305f\u3081(CPU\u3067\u3082\u5b9f\u884c\u53ef\u80fd)\nimport torch\nimport torch.nn.functional as F\nfrom torch.utils.data import TensorDataset, DataLoader<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code># \u30b7\u30fc\u30c9\u5024\u306e\u56fa\u5b9a\ndef seed_everything(seed: int):\n    tf.random.set_seed(seed)\n    random.seed(seed)\n    os.environ[&#39;PYTHONHASHSEED&#39;] = str(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n    torch.use_deterministic_algorithms = True\n    \nSEED = 42\nseed_everything(SEED)\n\ndef seed_worker(worker_id):\n    worker_seed = torch.initial_seed() % 2**32\n    np.random.seed(worker_seed)\n    random.seed(worker_seed)\n\ng = torch.Generator()\ng.manual_seed(SEED)<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-plain\" data-file=\"Output\"><code>&lt;torch._C.Generator at 0x7f055cc33930&gt;<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h3><span class=\"ez-toc-section\" id=\"%E3%83%87%E3%83%BC%E3%82%BF%E6%BA%96%E5%82%99\"><\/span>\u30c7\u30fc\u30bf\u6e96\u5099<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u3067\u306f\uff0c\u30c7\u30fc\u30bf\u306e\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u304b\u3089\u59cb\u3081\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code># \u30c7\u30fc\u30bf\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\n! gdown https:\/\/drive.google.com\/uc?id=18wXPH_lsKcDRohSI57iU4FA8Sf6aB5wk<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-plain\" data-file=\"Output\"><code>Downloading...\nFrom: https:\/\/drive.google.com\/uc?id=18wXPH_lsKcDRohSI57iU4FA8Sf6aB5wk\nTo: \/content\/datasetdigit2.csv\n\n  0% 0.00\/1.19M [00:00&lt;?, ?B\/s]\n100% 1.19M\/1.19M [00:00&lt;00:00, 99.0MB\/s]<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u30c7\u30fc\u30bf\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u5f62\u3092\u3057\u3066\u3044\u307e\u3059\uff0e<\/p>\n<p>\u30c7\u30fc\u30bf\u306e\u5f62 \uff1a (\\(N\\), \\(D\\))<\/p>\n<p>\\(N\\)\uff1a\u30c7\u30fc\u30bf\u6570<br>\\(D\\)\uff1a\u7279\u5fb4\u91cf\u6b21\u5143\u6570 53\u6b21\u5143 (\u624b\u66f8\u304d\u6570\u5b57\u753b\u50cf\u90e8\u5206 48\u6b21\u5143(8&#215;6) + \u6570\u5b57\u306e\u7a2e\u985e\u3092\u8868\u3059One-Hot\u8868\u73fe 5\u6b21\u5143)<\/p>\n<p>\u624b\u66f8\u304d\u6570\u5b57 5-9 \u3092\u4f7f\u7528<br>\u4f8b\u3048\u3070\uff0c8\u3092\u8868\u3059One-Hot\u8868\u73fe\u306f\\((0, 0, 0, 1, 0)\\)<\/p>\n<p>\u3067\u306f\uff0c\u30c7\u30fc\u30bf\u3092\u30ed\u30fc\u30c9\u3057\u3066\u6700\u521d\u306e5\u3064\u306e\u753b\u50cf\u3092\u8868\u793a\u3057\u3066\u307f\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-plain\"><code># \u30c7\u30fc\u30bf\u306e\u30ed\u30fc\u30c9\ndata = np.loadtxt(&#39;datasetdigit2.csv&#39;)\nN, D = data.shape\n# One-Hot\u306e\u6b21\u5143\nN_SELECT = 5\n# \u624b\u66f8\u304d\u6570\u5b57\u90e8\u5206\u306e\u6b21\u5143\nIMG_DIM = D - N_SELECT\n# \u624b\u66f8\u304d\u6570\u5b57\u306e\u753b\u50cf\u30b5\u30a4\u30ba\nHEIGHT, WIDTH = 8, 6\n\n# \u6700\u521d\u306e5\u3064\u306e\u30c7\u30fc\u30bf\u3092\u8868\u793a\nplt.figure(figsize=(12, 5))\nfor i in range(5):\n    num = i + N_SELECT\n    plt.subplot(1, N_SELECT, i+1)\n    plt.imshow(data[i, :IMG_DIM].reshape((HEIGHT, WIDTH)))<\/code><\/pre><\/div>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-full\"><img decoding=\"async\" src=\"\/T-Wave\/wp-content\/uploads\/2022\/07\/image.png\" alt=\"\" class=\"wp-image-3732\"\/><\/figure><\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h3><span class=\"ez-toc-section\" id=\"%E3%83%9C%E3%83%AB%E3%83%84%E3%83%9E%E3%83%B3%E3%83%9E%E3%82%B7%E3%83%B3%E3%82%92%E5%AE%9A%E7%BE%A9\"><\/span>\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u3092\u5b9a\u7fa9<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u306e\u6982\u8981\u306b\u3064\u3044\u3066\u306f\u300c<a href=\"\/T-Wave\/?p=2621\">\u91cf\u5b50\u30a2\u30cb\u30fc\u30ea\u30f3\u30b0\u306b\u3088\u308b\u753b\u50cf\u751f\u6210\u306e\u8a55\u4fa1\u30e2\u30c7\u30eb<\/a>\u300d\u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u4e0a\u306e\u30ea\u30f3\u30af\u5148\u306e\u8a18\u4e8b\u306b\u306f\u5bfe\u6570\u5c24\u5ea6\u306e\u5f0f\u3092\u967d\u306b\u793a\u3057\u3066\u3044\u306a\u304b\u3063\u305f\u306e\u3067\uff0c\u4ee5\u4e0b\u306b\u793a\u3057\u307e\u3059\uff0e<\/p>\n<p>\u5bfe\u6570\u5c24\u5ea6\\(\\log L(\\theta)\\)\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u5909\u5f62\u3067\u304d\u307e\u3059\uff0e<\/p>\n<p>$$<br>\\begin{aligned}<br>\\log L(\\theta) &amp;= \\log \\prod_{n = 1}^N p(\\mathbf{x_n} | \\theta) \\\\<br>&amp;= \\sum_{n = 1}^N \\log p(\\mathbf{x_n} | \\theta) \\\\<br>&amp;= \\sum_{n = 1}^N \\log \\left( \\frac{1}{Z(\\theta)} \\exp(-\\Phi(\\mathbf{x_n}, \\theta)) \\right) \\\\<br>&amp;= \\sum_{n = 1}^N \\left( -\\Phi(\\mathbf{x_n}, \\theta) &#8211; \\log Z(\\theta) \\right) \\\\<br>&amp;= \\sum_{n = 1}^N -\\Phi(\\mathbf{x_n}, \\theta) &#8211; N \\log Z(\\theta)<br>\\end{aligned}<br>$$<\/p>\n<p>\u3053\u3053\u3067\uff0c<\/p>\n<p>$$<br>\\begin{aligned}<br>p(\\mathbf{x} | \\theta) &amp;= \\frac{1}{Z(\\theta)} \\exp (- \\Phi(\\mathbf{x}, \\theta)) \\\\<br>\\Phi(\\mathbf{x}, \\theta) &amp;= &#8211; \\sum_{i = 1}^{M} b_i x_i &#8211; \\sum_{\\{i, j\\} \\in E} w_{ij} x_i x_j \\\\<br>Z(\\theta) &amp;= \\sum_{\\mathbf{x}} \\exp (-\\Phi(\\mathbf{x}, \\theta))<br>\\end{aligned}<br>$$<\/p>\n<ul>\n<li>\\(M\\): \u30ce\u30fc\u30c9\u6570<\/li>\n<li>\\(E\\): \u30a8\u30c3\u30b8\u96c6\u5408<\/li>\n<\/ul>\n<p>\u3067\u3059\uff0e<\/p>\n<p>\u4ee5\u964d\uff0c\u5bfe\u6570\u5c24\u5ea6\u3068\u3044\u3046\u5834\u5408\uff0c\u4fbf\u5b9c\u4e0a\u4e21\u8fba\u3092\\(N\\)\u3067\u5272\u3063\u305f\\(\\frac{1}{N}\\log L(\\theta)\\)\u3092\u6307\u3059\u3053\u3068\u3068\u3057\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u3067\u306f\uff0c\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u306e\u30af\u30e9\u30b9\u3092\u5b9a\u7fa9\u3057\u307e\u3059\uff0e<\/p>\n<p>\u4e3b\u306a\u30e1\u30bd\u30c3\u30c9\u306f\u4ee5\u4e0b\u306e\u3068\u304a\u308a\u3067\u3059\uff0e<\/p>\n<ul>\n<li>train : \u5b66\u7fd2\u7528\u30e1\u30bd\u30c3\u30c9<\/li>\n<li>generate : \u6307\u5b9a\u3057\u305f\u6570\u5b57\u3092\u751f\u6210\u3059\u308b\u30e1\u30bd\u30c3\u30c9<\/li>\n<li>calc_likelihood : \u5bfe\u6570\u5c24\u5ea6\u3092\u8a08\u7b97\u3059\u308b\u30e1\u30bd\u30c3\u30c9<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>class BolzmannMachine(torch.nn.Module):\n    &quot;&quot;&quot;\n    n_feats: \u753b\u50cf\u90e8\u5206\u306e\u7279\u5fb4\u91cf\u6b21\u5143\n    n_select: \u4f7f\u7528\u3059\u308b\u6570\u5b57\u306e\u7a2e\u985e\n    momentum: \u30e2\u30fc\u30e1\u30f3\u30bf\u30e0\u6cd5\u306e\u4fc2\u6570\n    weight_decay: l2 \u6b63\u5247\u5316\u306e\u4fc2\u6570\n    burn_in: \u30d0\u30fc\u30f3\u30a4\u30f3\u30bf\u30a4\u30e0\n    sampling_interval: \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u9593\u9694\n    eta: \u5b66\u7fd2\u7387\n    &quot;&quot;&quot;\n    # \u8ad6\u6587\u306e\u5024\u304c\u30c7\u30d5\u30a9\u30eb\u30c8\u5024\n    def __init__(self, \n                 img_dim, \n                 n_select,\n                 output_dir,\n                 eta=2.5e-2,\n                 momentum=0.5, \n                 weight_decay=1e-5, \n                 burn_in=200,\n                 sampling_interval=10,\n                 batch_size=256):\n        super().__init__()\n        self.img_dim = img_dim\n        self.n_select = n_select\n        self.dim = img_dim + n_select\n        self.momentum = momentum\n        self.weight_decay = weight_decay\n        self.burn_in = burn_in\n        self.sampling_interval = sampling_interval\n        self.eta = eta\n        self.batch_size = batch_size\n\n        self.output_dir = output_dir\n        \n        # CPU\u304bGPU\u3092\u4f7f\u7528\u3059\u308b\u304b\u5224\u5b9a\n        # GPU\u304c\u4f7f\u3048\u308c\u3070GPU\n        self.device = torch.device(&#39;cuda&#39; if torch.cuda.is_available() else &#39;cpu&#39;)\n        # \u4e0b\u4e09\u89d2 + \u5bfe\u89d2\u6210\u5206\u304c0, \u305d\u308c\u4ee5\u5916\u306f1\u306e\u884c\u5217\u3092\u5b9a\u7fa9\n        self.mask = 1. - torch.tril(torch.ones(self.dim, self.dim, device=self.device))\n        # \u91cd\u307f\u884c\u5217\n        # \u91cd\u307f\u306f\u4e0b\u4e09\u89d2\u90e8\u5206\u3068\u5bfe\u89d2\u90e8\u5206\u306e\u6210\u5206\u306f\u6301\u305f\u306a\u3044\u3088\u3046\u306b\u3059\u308b\n        w = torch.randn(self.dim, self.dim, device=self.device) * self.mask\n        # \u30d0\u30a4\u30a2\u30b9\n        b = torch.randn(self.dim, device=self.device)\n\n        # \u3053\u306e\u51e6\u7406\u3067\u30af\u30e9\u30b9\u306e\u5c5e\u6027\u3068\u3057\u3066\u3044\u308b\uff0e\n        self.register_buffer(&#39;w&#39;, w)\n        self.register_buffer(&#39;b&#39;, b)\n\n        # \u524d\u30b9\u30c6\u30c3\u30d7\u306ev\u3092\u4fdd\u6301\u3059\u308b\u305f\u3081\u306e\u5909\u6570\n        self.v_w = 0\n        self.v_b = 0\n    \n    # \u8a13\u7df4\u30e1\u30bd\u30c3\u30c9\n    @torch.no_grad()\n    def train(self, num_epochs, data):\n        &quot;&quot;&quot;\n        num_epochs: \u30a8\u30dd\u30c3\u30af\u6570\n        x: \u624b\u66f8\u304d\u6587\u5b57\u90e8\u5206\u306e\u7279\u5fb4\u91cf (N, D) - N: \u30c7\u30fc\u30bf\u6570, D: \u30c7\u30fc\u30bf\u306e\u6b21\u5143\n        y: \u6570\u5b57\u306e\u7a2e\u985e\u3092\u793a\u3059One-Hot \u30d9\u30af\u30c8\u30eb\u306e\u96c6\u5408 (N, 5) - N: \u30c7\u30fc\u30bf\u6570\n           \u4eca\u56de\u306e\u5834\u5408\u306f5-9\u306e\u6570\u5b57\u3092\u4f7f\u7528\u3059\u308b\u305f\u30815\u6b21\u5143 \n        &quot;&quot;&quot;\n        x, y = data[:, :self.img_dim], data[:, self.img_dim:]\n        x = torch.tensor(x, dtype=torch.float)\n        y = torch.tensor(y, dtype=torch.float)\n        # \u30c7\u30fc\u30bf\u6e96\u5099\n        ds = TensorDataset(x, y)\n        dl = DataLoader(\n            ds,\n            batch_size=self.batch_size,\n            shuffle=True,\n            worker_init_fn=seed_worker,\n            generator=g,\n        )\n        # \u5b66\u7fd2\n        for e in tqdm(range(num_epochs), total=num_epochs, desc=&#39;Epoch : &#39;):\n            for x, y in dl:\n                x, y = x.to(self.device), y.to(self.device)\n                self.update(x, y)\n            # 10\u30a8\u30dd\u30c3\u30af\u6bce\u306b\u30d0\u30a4\u30a2\u30b9\u3068\u91cd\u307f\u3092\u4fdd\u5b58\n            if (e + 1) % 10 == 0:\n                torch.save(self.state_dict(), self.output_dir \/ f&#39;ckpt_{e+1:04d}.pth&#39;)\n    \n    # \u751f\u6210\u7528\u30e1\u30bd\u30c3\u30c9\n    @torch.no_grad()\n    def generate(self, num):\n        &quot;&quot;&quot;\n        num: \u751f\u6210\u3057\u305f\u3044\u6570\u5b57(5-9)\n        &quot;&quot;&quot;\n        # One-Hot\u8868\u73fe\u306b\u5909\u63db\n        y = F.one_hot(torch.LongTensor([num - self.n_select]).view(1, -1), num_classes=self.n_select)\n        # \u30ae\u30d6\u30b9\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3092\u884c\u3044, \u8fd4\u3059\n        return self.sampling(y)[-1, :-self.n_select].cpu().numpy().reshape(-1)\n\n    @torch.no_grad()\n    def generate_multi(self, num_list):\n        y = F.one_hot(torch.LongTensor(num_list) - 5, num_classes=5)\n        sample = self.sampling(y).cpu().numpy()[:, :-self.n_select]\n        return sample, y.cpu().numpy()\n\n    # \u30d1\u30e9\u30e1\u30fc\u30bf\u66f4\u65b0\u7528\u30e1\u30bd\u30c3\u30c9\n    def update(self, x, y):\n        &quot;&quot;&quot;\n        x: \u624b\u66f8\u304d\u6587\u5b57\u90e8\u5206\u306e\u7279\u5fb4\u91cf\n        y: \u6587\u5b57\u306e\u7a2e\u985e\u3092\u8868\u3059One-Hot\u7279\u5fb4\u91cf\n        &quot;&quot;&quot;\n        # \u30ae\u30d6\u30b9\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3092\u884c\u3046\n        samples = self.sampling(y)\n        # \u52fe\u914d\u3092\u8a08\u7b97\n        grad_b, grad_w = self.grad_b(x, y, samples), self.grad_w(x, y, samples)\n        self.v_b = self.momentum * self.v_b + (1 - self.momentum) * grad_b\n        self.v_w = self.momentum * self.v_w + (1 - self.momentum) * grad_w\n        # \u5909\u5316\u91cf\u3092\u8a08\u7b97\n        # \u7b2c1\u9805\u76ee: \u5b66\u7fd2\u7387 * \u30e2\u30fc\u30e1\u30f3\u30bf\u30e0\u6cd5\u306b\u304a\u3051\u308b\u901f\u5ea6\n        # \u7b2c3\u9805\u76ee: L2\u6b63\u5247\u5316\u9805\n        delta_b = self.eta * self.v_b - self.weight_decay * self.b\n        delta_w = self.eta * self.v_w - self.weight_decay * self.w\n        # \u30d1\u30e9\u30e1\u30fc\u30bf\u66f4\u65b0\n        self.b += delta_b\n        self.w += delta_w\n        self.w *= self.mask\n        return grad_b, grad_w\n\n    # \u30d0\u30a4\u30a2\u30b9 \uff42 \u306e\u52fe\u914d\u3092\u6c42\u3081\u308b\u30e1\u30bd\u30c3\u30c9\n    def grad_b(self, x, y, samples):\n        &quot;&quot;&quot;\n        x: \u624b\u66f8\u304d\u6587\u5b57\u90e8\u5206\u306e\u7279\u5fb4\u91cf\n        y: \u6587\u5b57\u306e\u7a2e\u985e\u3092\u8868\u3059One-Hot\u7279\u5fb4\u91cf\n        samples: \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3057\u305f\u3082\u306e\n        &quot;&quot;&quot;\n        N = x.size(0)\n        # \u624b\u66f8\u304d\u6587\u5b57\u306e\u90e8\u5206\u3068One-Hot\u3092\u7d50\u5408: cat((N, C&#39;), (N, 5)) =&gt; (N, C)\n        _x = torch.cat([x, y], dim=-1)\n        # \u30c7\u30fc\u30bf\u65b9\u5411\u306b\u5e73\u5747\u3092\u53d6\u308b: mean((N, C), dim=0) =&gt; (C,)\n        e_data = _x.mean(dim=0)\n        # \u30b5\u30f3\u30d7\u30eb\u306b\u3064\u3044\u3066\u3082\u540c\u69d8\u306b\u6c42\u3081\u308b: mean((N, C), dim=0) =&gt; (C,)\n        e_model = samples.mean(dim=0)\n        # \u30d0\u30a4\u30a2\u30b9\u306e\u52fe\u914d\u3092\u8fd4\u3059\n        return e_data - e_model\n    \n    # \u91cd\u307f w \u306e\u52fe\u914d\u3092\u6c42\u3081\u308b\u30e1\u30bd\u30c3\u30c9\n    def grad_w(self, x, y, samples):\n        &quot;&quot;&quot;\n        x: \u624b\u66f8\u304d\u6587\u5b57\u90e8\u5206\u306e\u7279\u5fb4\u91cf\n        y: \u6587\u5b57\u306e\u7a2e\u985e\u3092\u8868\u3059One-Hot\u7279\u5fb4\u91cf\n        samples: \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3057\u305f\u3082\u306e\n        &quot;&quot;&quot;\n        # \u30c7\u30fc\u30bf\u6570\n        N = x.size(0)\n        # \u624b\u66f8\u304d\u6587\u5b57\u306e\u90e8\u5206\u3068One-Hot\u3092\u7d50\u5408: cat((N, C&#39;), (N, 5)) =&gt; (N, C)\n        _x = torch.cat([x, y], dim=-1)\n        # \u884c\u5217\u7a4d\u3092\u6c42\u3081\u3066\uff0c\u30c7\u30fc\u30bf\u65b9\u5411\u306b\u5e73\u5747\u3092\u6c42\u3081\u308b: mean((N, C, 1) x (N, 1, C), dim=0) =&gt; (C, C)\n        e_data = (_x[:, :, None] @ _x[:, None, :]).mean(dim=0)\n        # \u30b5\u30f3\u30d7\u30eb\u306b\u3064\u3044\u3066\u3082\u540c\u69d8\u306b\u6c42\u3081\u308b: mean((N, C, 1) x (N, 1, C), dim=0) =&gt; (C, C)\n        e_model = (samples[:, :, None] @ samples[:, None, :]).mean(dim=0)\n        # \u91cd\u307f\u306e\u52fe\u914d\u3092\u8fd4\u3059\n        return e_data - e_model\n    \n    # \u30ae\u30d6\u30b9\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3092\u884c\u3046\u30e1\u30bd\u30c3\u30c9\n    def sampling(self, y):\n        &quot;&quot;&quot;\n        y: \u6587\u5b57\u306e\u7a2e\u985e\u3092\u8868\u3059One-Hot\u30d9\u30af\u30c8\u30eb\n        &quot;&quot;&quot;\n        # \u30b5\u30f3\u30d7\u30eb\u3092\u66f4\u65b0\u3059\u308b\u30a4\u30f3\u30ca\u30fc\u95a2\u6570\n        def _sample(x, b, w, y):\n            # \u30d0\u30a4\u30a2\u30b9 + \u91cd\u307f x \u7279\u5fb4\u91cf: (1, C, 1) + (1, C, C) x (N, C, 1) =&gt; (N, C, 1)\n            _x = b[None, :, None] + w[None, :, :] @ x[:, :, None]\n            # x\u306e\u8981\u7d20\u304c1\u306e\u78ba\u7387 p(x = 1 | \\theta)\u3092\u6c42\u3081\u308b\n            p = torch.sigmoid(_x.squeeze(-1))\n            rand = torch.rand(_x.size(0), self.dim, device=self.device)\n            # U(0, 1) &lt; p(x = 1 | \\theta)\u306e\u66421\uff0c \u305d\u308c\u4ee5\u5916\u30920\n            x = (rand &lt; p).float()\n            x[:, -self.n_select:] = y\n            return x\n        # \u6700\u521d\u306e\u30b5\u30f3\u30d7\u30eb\u3068\u3057\u3066\u30e9\u30f3\u30c0\u30e0\u306b\u521d\u671f\u5316\n        x = (torch.rand(y.size(0), self.dim, device=self.device) &gt; 0.5).float()\n        x[:, -self.n_select:] = y\n        w = self.w + self.w.T\n        # \u30d0\u30fc\u30f3\u30a4\u30f3\u3060\u3051\u66f4\u65b0\n        for _ in range(self.burn_in):\n            x = _sample(x, self.b, w, y)\n        # \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u9593\u9694\u3060\u3051\u66f4\u65b0\u3057\uff0c\u305d\u308c\u3092\u30b5\u30f3\u30d7\u30eb\u3068\u3059\u308b\uff0e\n        for _ in range(self.sampling_interval):\n            x = _sample(x, self.b, w, y)\n        return x\n    \n    # \u5c24\u5ea6\u8a08\u7b97\u7528\u30e1\u30bd\u30c3\u30c9\n    @torch.no_grad()\n    def calc_likelihood(self, data, batch_size=2**14):\n        &quot;&quot;&quot;\n        data: \u8a08\u7b97\u306b\u4f7f\u7528\u3059\u308b\u30c7\u30fc\u30bf\n        batch_size: (default: 2^14) \u5c24\u5ea6\u8a08\u7b97\u306e\u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\n        &quot;&quot;&quot;\n        if isinstance(data, np.ndarray):\n            data = torch.tensor(data, dtype=torch.float, device=self.device)\n        w = self.w + self.w.T\n\n        # \u7b2c1\u9805\u76ee\n        # (1, 1, C) x (N, C, 1) + (N, 1, C) x (1, C, C) x (N, C, 1) =&gt; (N, 1, 1) =&gt; `mean` =&gt; (1,)\n        first = (self.b[None, None, :] @ data[:, :, None] + data[:, None, :] @ w[None, :, :] @ data[:, :, None]).mean()\n        # \u7b2c2\u9805\u76ee\n        Z = 0\n        for num in [5, 6, 7, 8, 9]:\n            z_x = itertools.product([0, 1], repeat=self.img_dim)\n            chunked_z_x = more_itertools.chunked(z_x, batch_size)\n            for d_x in chunked_z_x:\n                d = torch.zeros(len(d_x), self.dim, device=self.device)\n                d[:, :-self.n_select] = torch.tensor(d_x, device=self.device, dtype=torch.float)\n                d[:, num-10] = 1\n                # (1, 1, C) x (N, C, 1) + (N, 1, C) x (1, C, C) x (N, C, 1) =&gt; (N, 1, 1)\n                minus_phi = (self.b[None, None, :] @ d[:, :, None] + d[:, None, :] @ w @ d[:, :, None])\n                Z += torch.exp(minus_phi).sum()\n        second = torch.log(Z)\n        # \u5c24\u5ea6 = \u7b2c1\u9805\u76ee - \u7b2c2\u9805\u76ee\n        likelihood = first - second\n        return float(likelihood.cpu().numpy())<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h3><span class=\"ez-toc-section\" id=\"%E3%83%9C%E3%83%AB%E3%83%84%E3%83%9E%E3%83%B3%E3%83%9E%E3%82%B7%E3%83%B3%E3%81%AE%E5%AD%A6%E7%BF%92\"><\/span>\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u306e\u5b66\u7fd2<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u5b9f\u969b\u306b\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u3092\u5b66\u7fd2\u3057\u3066\u3044\u304d\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code># \u5404\u7a2e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u8a2d\u5b9a\nN_EPOCHS = 4000\nexp1_dir = Path(&#39;.\/exp1&#39;)\nexp1_dir.mkdir(parents=True, exist_ok=True)\n# \u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u521d\u671f\u5316\nbm = BolzmannMachine(img_dim=IMG_DIM, n_select=N_SELECT, output_dir=exp1_dir)\n# \u5b66\u7fd2\u958b\u59cb\nbm.train(N_EPOCHS, data)<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-plain\" data-line=\"Output\"><code>Epoch : 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4000\/4000 [07:41&lt;00:00,  8.66it\/s]<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u6b21\u306b\uff0c100\u30a8\u30dd\u30c3\u30af\u3054\u3068\u306e\u751f\u6210\u7d50\u679c\u3092\u898b\u3066\u3044\u304d\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code># 100\u30a8\u30dd\u30c3\u30af\u6bce\u306e\u751f\u6210\u7d50\u679c\u3092\u30d7\u30ed\u30c3\u30c8\nn_gen = N_EPOCHS \/\/ 100\nplt.figure(figsize=(8, 2 * n_gen))\nfor n in range(n_gen):\n    bm.load_state_dict(torch.load(exp1_dir \/ f&#39;ckpt_{(n + 1) * 100:04d}.pth&#39;))\n    for i in range(5):\n        num = i + N_SELECT\n        gen = bm.generate(num)\n        plt.subplot(n_gen, N_SELECT, N_SELECT * n + i + 1)\n        plt.imshow(gen.reshape((HEIGHT, WIDTH)), aspect=&#39;auto&#39;)<\/code><\/pre><\/div>\n\n\n\n<figure><img decoding=\"async\" src=\"data:image\/png;base64,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\" class=\"aligncenter\"><\/figure>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<ul>\n<li>\u5e8f\u76e4\u306f\u5b66\u7fd2\u304c\u9032\u3080\u306b\u9023\u308c\u3066\u751f\u6210\u753b\u50cf\u3082\u6570\u5b57\u306b\u8fd1\u3065\u3044\u3066\u3044\u305d\u3046\u3067\u3059\u304c\uff0c\u9014\u4e2d\u3067\u5d29\u58ca\u3057\u3066\u3044\u308b\u305f\u3081\uff0c\u5b89\u5b9a\u6027\u306b\u4e4f\u3057\u3044\u3068\u3044\u3048\u307e\u3059\uff0e<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h3><span class=\"ez-toc-section\" id=\"%E5%88%86%E9%A1%9E%E3%83%A2%E3%83%87%E3%83%AB%E3%81%AE%E5%AD%A6%E7%BF%92\"><\/span>\u5206\u985e\u30e2\u30c7\u30eb\u306e\u5b66\u7fd2<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u6b21\u306b\uff0c\u5206\u985e\u30e2\u30c7\u30eb\u3067\u8a55\u4fa1\u3092\u884c\u3046\u305f\u3081\u306f\u3058\u3081\u306b\u5206\u985e\u30e2\u30c7\u30eb\u3092\u5b66\u7fd2\u3055\u305b\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code># \u5206\u985e\u30e2\u30c7\u30eb\u7528\u306b\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\nfrom sklearn.model_selection  import train_test_split\n\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.models import Sequential, load_model\nfrom keras.layers import Dense, Dropout, InputLayer<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code># \u518d\u5ea6\u30c7\u30fc\u30bf\u3092\u7528\u610f\nX = data[:, :IMG_DIM]\ny = data[:, IMG_DIM:]\nX_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.1, shuffle=True, random_state=SEED)\n\nX_train = X_train.astype(np.float32)\nX_valid = X_valid.astype(np.float32)<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u5206\u985e\u30e2\u30c7\u30eb\u306e\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3092\u5b9a\u7fa9\u3057\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>def build_model(img_dim, n_select):\n    model = Sequential()\n    model.add(InputLayer(input_shape=(img_dim,)))\n    model.add(Dense(512, activation=&#39;relu&#39;))\n    model.add(Dropout(0.4))\n    model.add(Dense(512, activation=&#39;relu&#39;))\n    model.add(Dropout(0.4))\n    model.add(Dense(n_select, activation=&#39;softmax&#39;))\n    return model<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u5b9f\u969b\u306b\u5206\u985e\u30e2\u30c7\u30eb\u3092\u5b66\u7fd2\u3057\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code># \u5b66\u7fd2\u30a8\u30dd\u30c3\u30af\u6570\nN_EPOCHS_CLS = 20\n# \u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\nBATCH_SIZE = 32\n\n# \u30e2\u30c7\u30eb\u521d\u671f\u5316\nmodel = build_model(IMG_DIM, N_SELECT)\nmodel.compile(loss=&#39;categorical_crossentropy&#39;, optimizer=&#39;adam&#39;, metrics=[&#39;accuracy&#39;])\nckpt_callback = ModelCheckpoint(\n    filepath=exp1_dir \/ &#39;best.h5&#39;,\n    monitor=&#39;val_accuracy&#39;,\n    mode=&#39;max&#39;,\n    save_best_only=True\n)\n# \u5b66\u7fd2\nhistory = model.fit(\n    X_train,\n    y_train,\n    batch_size=BATCH_SIZE, \n    epochs=N_EPOCHS_CLS, \n    verbose=0, \n    validation_data=(X_valid, y_valid),\n    callbacks=[ckpt_callback]\n)<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Loss\u3068Accuracy\u3092\u30a8\u30dd\u30c3\u30af\u3054\u3068\u306e\u63a8\u79fb\u3067\u898b\u3066\u3044\u304d\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>plt.plot(range(1, N_EPOCHS_CLS+1), history.history[&#39;loss&#39;],  marker=&#39;.&#39;, label=&#39;train&#39;)\nplt.plot(range(1, N_EPOCHS_CLS+1), history.history[&#39;val_loss&#39;], marker=&#39;.&#39;, label=&#39;valid&#39;)\nplt.legend(loc=&#39;best&#39;, fontsize=10)\nplt.grid()\nplt.xlabel(&#39;Epoch&#39;)\nplt.ylabel(&#39;Loss&#39;)\nplt.show()<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><img decoding=\"async\" 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\" style=\"background-color: #ffffff; color: initial; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen-Sans, Ubuntu, Cantarell, 'Helvetica Neue', sans-serif; font-size: 18px;\" class=\"aligncenter\"><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>plt.plot(range(1, N_EPOCHS_CLS+1), history.history[&#39;accuracy&#39;], marker=&#39;.&#39;, label=&#39;train&#39;)\nplt.plot(range(1, N_EPOCHS_CLS+1), history.history[&#39;val_accuracy&#39;], marker=&#39;.&#39;, label=&#39;valid&#39;)\nplt.legend(loc=&#39;best&#39;, fontsize=10)\nplt.grid()\nplt.xlabel(&#39;Epoch&#39;)\nplt.ylabel(&#39;Accuracy&#39;)\nplt.show()<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\" style=\"text-align: center;\">\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<figure><img loading=\"lazy\" decoding=\"async\" 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\" width=\"440\" height=\"294\" class=\"\"><\/figure>\n<div class=\"output_png output_subarea \"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<ul>\n<li>Loss\u3068Accuracy\u306e\u63a8\u79fb\u3092\u898b\u3066\u3082\u8a13\u7df4(train)\uff0c\u691c\u8a3c(valid)\u9593\u3067\u5dee\u306f\u5927\u304d\u304f\u306a\u304f\u554f\u984c\u306a\u304f\u5b66\u7fd2\u3067\u304d\u3066\u3044\u308b\u3068\u3044\u3048\u307e\u3059\uff0e<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h3><span class=\"ez-toc-section\" id=\"%E5%88%86%E9%A1%9E%E3%83%A2%E3%83%87%E3%83%AB%E3%81%AB%E3%82%88%E3%82%8B%E8%A9%95%E4%BE%A1\"><\/span>\u5206\u985e\u30e2\u30c7\u30eb\u306b\u3088\u308b\u8a55\u4fa1<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u3067\u306f\uff0c\u5b9f\u969b\u306b\u8a55\u4fa1\u3057\u3066\u3044\u304d\u307e\u3059\uff0e<br>\u8ad6\u6587\u306e\u8a55\u4fa1\u6307\u6a19\\(R_a\\)\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u8868\u3055\u308c\u308b\u306e\u3067\u3057\u305f\uff0e<\/p>\n<p>$$<br>R_a = \\frac{D_m}{D_t}<br>$$<\/p>\n<ul>\n<li>\\(D_m\\): \u751f\u6210\u3067\u6307\u5b9a\u3057\u305f\u6570\u5b57\u3068\u5206\u985e\u7d50\u679c\u304c\u4e00\u81f4\u3057\u305f\u6570<\/li>\n<li>\\(D_t\\): \u751f\u6210\u3057\u305f\u753b\u50cf\u306e\u7dcf\u6570<\/li>\n<\/ul>\n<p>\u8ad6\u6587\u306e\u8a55\u4fa1\u6307\u6a19\u306710\u30a8\u30dd\u30c3\u30af\u305a\u3064\uff0c\u30e2\u30c7\u30eb\u3092\u8a55\u4fa1\u3057\u3066\u307f\u307e\u3057\u3087\u3046\uff0e<\/p>\n<ul>\n<li>5 &#8211; 9\u307e\u3067\u306e\u6570\u5b57\u3092100\u679a\u305a\u3064\u751f\u6210\u3057\uff0c\u5206\u985e\u30e2\u30c7\u30eb\u3067Accuracy\u3092\u8a08\u7b97\u3057\u307e\u3059\uff0e<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>n_gen = N_EPOCHS \/\/ 10\nepoch_list = list()\nscore_list = list()\nbm = BolzmannMachine(img_dim=IMG_DIM, n_select=N_SELECT, output_dir=exp1_dir)\nmodel = load_model(exp1_dir \/ &#39;best.h5&#39;)\nfor n in range(n_gen):\n    bm.load_state_dict(torch.load(exp1_dir \/ f&#39;ckpt_{(n + 1) * 10:04d}.pth&#39;))\n    num_list = torch.LongTensor([5] * 100 + [6] * 100 + [7] * 100 + [8] * 100 + [9] * 100)\n    gen, y = bm.generate_multi(num_list)\n    epoch_list.append((n + 1) * 10)\n    score = model.evaluate(gen, y, verbose=0)\n    score_list.append(score)<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>_, scores = tuple(zip(*score_list))\nplt.plot(epoch_list, scores, marker=&#39;.&#39;, label=&#39;train&#39;)\nplt.grid()\nplt.xlabel(&#39;Epoch&#39;)\nplt.ylabel(&#39;R_a&#39;)\nplt.show()<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><img decoding=\"async\" src=\"data:image\/png;base64,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\" 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rendered_html\">\n<ul>\n<li>\u751f\u6210\u753b\u50cf\u3067\u307f\u3066\u3044\u305f\u3088\u3046\u306b\u9014\u4e2d\u3067\u5b66\u7fd2\u304c\u5d29\u58ca\u3057\uff0c\u632f\u52d5\u3057\u3066\u3044\u308b\u69d8\u5b50\u304c\u898b\u3066\u53d6\u308c\u307e\u3059\uff0e<\/li>\n<li>\u5b66\u7fd2\u3092\u7d9a\u3051\u308c\u3070\u8a55\u4fa1\u6307\u6a19\u306e\u5024\u304c\u4e0a\u6607\u3057\u7d9a\u3051\u3066\u3044\u304f\u308f\u3051\u3067\u306f\u306a\u3055\u305d\u3046\u3067\u3059\uff0e<\/li>\n<li>\u6700\u9ad8\u5024\u306f\u8ad6\u6587\u3088\u308a\u9ad8\u304f\u306a\u3063\u3066\u3044\u307e\u3059\u304c\uff0c\u8ad6\u6587\u306e\u7d50\u679c\u306f\u5b89\u5b9a\u3057\u3066\u8a55\u4fa1\u6307\u6a19\u304c\u63a8\u79fb\u3057\u3066\u3044\u305f\u305f\u3081\uff0c\u305d\u3046\u3044\u3063\u305f\u70b9\u3067\u306f\u518d\u73fe\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u305b\u3093\u3067\u3057\u305f\uff0e<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u30a8\u30dd\u30c3\u30af\u3054\u3068\u306e\u8a55\u4fa1\u6307\u6a19\u306e\u5024\u304c\u308f\u304b\u3063\u305f\u305f\u3081\uff0c\u4e00\u756a\u8a55\u4fa1\u6307\u6a19\u306e\u5024\u304c\u826f\u3044\u30e2\u30c7\u30eb\u306e\u751f\u6210\u753b\u50cf\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>best_epoch = epoch_list[np.argmax(scores)]\nprint(f&#39;Best Epoch : {best_epoch}&#39;)<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-plain\" data-file=\"Output\"><code>Best Epoch : 1690<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>plt.figure(figsize=(8, 6))\nbm = BolzmannMachine(img_dim=IMG_DIM, n_select=N_SELECT, output_dir=exp1_dir)\nbm.load_state_dict(torch.load(exp1_dir \/ f&#39;ckpt_{best_epoch:04d}.pth&#39;))\nfor i in range(3): \n    for j in range(N_SELECT):\n        num = j + N_SELECT\n        gen = bm.generate(num)\n        plt.subplot(3, N_SELECT, 5 * i + j + 1)\n        plt.imshow(gen.reshape((HEIGHT, WIDTH)), aspect=&#39;auto&#39;)<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><img decoding=\"async\" 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\" 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rendered_html\">\n<p>\u6b21\u306b\uff0c\u5bfe\u6570\u5c24\u5ea6\u3092\u6c42\u3081\u3066\u3044\u304f\u306e\u3067\u3059\u304c\uff0c\u3053\u3053\u3067\u554f\u984c\u304c\u3042\u308a\u307e\u3059\uff0e<\/p>\n<p>\u5bfe\u6570\u5c24\u5ea6\u3092\u6c42\u3081\u308b\u305f\u3081\u306b\u306f\u30dc\u30eb\u30c4\u30de\u30f3\u5206\u5e03\u306e\u5206\u914d\u95a2\u6570(\\(Z(\\theta)\\))\u3092\u6b63\u78ba\u306b\u8a55\u4fa1\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\uff0e<br>\u3053\u3053\u3067\uff0c\u753b\u50cf\u306e\u6b21\u5143\u6570\u306f48\u6b21\u5143\u306a\u306e\u3067\uff0c\u4e00\u56de\u306e\u5c24\u5ea6\u8a08\u7b97\u306b\\(2^{48}\\)\u500b\u306e\u5834\u5408\u5206\u3051\u3092\u8a08\u7b97\u3059\u308b\u5fc5\u8981\u304c\u51fa\u3066\u304d\u307e\u3059\uff0e<br>\u3053\u308c\u306f\uff0c\u305f\u3068\u3048GPU\u3067\u8a08\u7b97\u3057\u305f\u3068\u3057\u3066\u3082\u73fe\u5b9f\u6642\u9593\u3067\u7d42\u308f\u3089\u305b\u308b\u3053\u3068\u304c\u96e3\u3057\u3044\u3067\u3059\uff0e<\/p>\n<p>\u305d\u3053\u3067\uff0c\u672c\u8a18\u4e8b\u3067\u306f\u6b21\u5143\u6570\u3092\u5c0f\u3055\u304f\u3057\u305f\u30c7\u30fc\u30bf\u3092\u4eba\u5de5\u7684\u306b\u4f5c\u6210\u3057\uff0c\u305d\u306e\u30c7\u30fc\u30bf\u3092\u7528\u3044\u308b\u3053\u3068\u3067\u5c24\u5ea6\u8a08\u7b97\u3092\u884c\u3044\u307e\u3059\uff0e<br>\u305d\u306e\u305f\u3081\uff0c\u65b0\u3057\u3044\u30c7\u30fc\u30bf\u306b\u5bfe\u3057\u3066\u3082\u3046\u4e00\u5ea6\u5b66\u7fd2\u53ca\u3073\u8a55\u4fa1\u3092\u884c\u3063\u3066\u3044\u304d\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2><span class=\"ez-toc-section\" id=\"%E5%AE%9F%E9%A8%932\"><\/span>\u5b9f\u9a132<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u4e0a\u8ff0\u3057\u305f\u3088\u3046\u306b\uff0c\u8ad6\u6587\u30c7\u30fc\u30bf\u306e\u5bfe\u6570\u5c24\u5ea6\u3092\u8a08\u7b97\u3059\u308b\u306b\u306f\u8a08\u7b97\u6642\u9593\u306e\u554f\u984c\u304c\u3042\u308a\u307e\u3057\u305f\uff0e<br>\u305d\u3053\u3067\uff0c\u5b9f\u9a132\u3068\u3057\u3066\u8ad6\u6587\u30c7\u30fc\u30bf\u3088\u308a\u3082\u5c0f\u3055\u3044\u30c7\u30fc\u30bf\u306b\u5bfe\u3057\u3066\u540c\u3058\u5b9f\u9a13\u3092\u884c\u3044\uff0c\u8a55\u4fa1\u6307\u6a19\\(R_a\\)\u3068\u5bfe\u6570\u5c24\u5ea6\u306e\u8a08\u7b97\u3092\u884c\u3063\u3066\u3044\u304d\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h3><span class=\"ez-toc-section\" id=\"%E3%83%87%E3%83%BC%E3%82%BF%E6%BA%96%E5%82%99-2\"><\/span>\u30c7\u30fc\u30bf\u6e96\u5099<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>5-9\u306e\u6570\u5b57\u306b\u3064\u3044\u3066\uff0c20\u6b21\u5143(5&#215;4)\u306e\u30c7\u30fc\u30bf\u3092\u4f5c\u6210\u3057\u3066\u3044\u304d\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>d5 = np.array([[0, 1, 1, 0],\n               [1, 0, 0, 0],\n               [0, 1, 1, 0],\n               [0, 0, 0, 1],\n               [0, 1, 1, 0]])\nedge_flag5 = [1, 1, 1, 1, 1, 1]\nplt.imshow(d5)<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span class=\"n\"><\/span><img decoding=\"async\" src=\"data:image\/png;base64,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\" class=\"aligncenter\"><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>d6 = np.array([[0, 1, 1, 0],\n               [1, 0, 0, 0],\n               [0, 1, 1, 0],\n               [1, 0, 0, 1],\n               [0, 1, 1, 0]])\nedge_flag6 = [1, 1, 1, 1, 1, 1]\nplt.imshow(d6)<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<p><\/p>\n<pre><span class=\"n\"><\/span><img decoding=\"async\" src=\"data:image\/png;base64,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\" class=\"aligncenter\"><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>d7 = np.array([[0, 1, 1, 0],\n               [1, 0, 0, 1],\n               [0, 0, 0, 1],\n               [0, 0, 0, 1],\n               [0, 0, 0, 1]])\nedge_flag7 = [1, 1, 0, 1, 0, 1]\nplt.imshow(d7)<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span class=\"n\"><\/span> <img decoding=\"async\" src=\"data:image\/png;base64,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\" style=\"background-color: #ffffff; color: initial; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen-Sans, Ubuntu, Cantarell, 'Helvetica Neue', sans-serif; font-size: 18px;\" class=\"aligncenter\"><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"output_png output_subarea \"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>d8 = np.array([[0, 1, 1, 0],\n               [1, 0, 0, 1],\n               [0, 1, 1, 0],\n               [1, 0, 0, 1],\n               [0, 1, 1, 0]])\nedge_flag8 = [1, 1, 1, 1, 1, 1]\nplt.imshow(d8)<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span class=\"n\"><\/span><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<figure><img decoding=\"async\" src=\"data:image\/png;base64,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\" class=\"aligncenter\"><\/figure>\n<div class=\"output_png output_subarea \"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>d9 = np.array([[0, 1, 1, 0],\n               [1, 0, 0, 1],\n               [0, 1, 1, 0],\n               [0, 0, 0, 1],\n               [0, 1, 1, 0]])\nedge_flag9 = [1, 1, 1, 1, 1, 1]\nplt.imshow(d9)<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<figure><img decoding=\"async\" src=\"data:image\/png;base64,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\" class=\"aligncenter\"><\/figure>\n<div class=\"output_png output_subarea \"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>5\u679a\u3067\u306f\u8db3\u308a\u306a\u3044\u306e\u3067\uff0c\u4e0d\u81ea\u7136\u306b\u306a\u3089\u306a\u3044\u3088\u3046\u306b\u753b\u7d20\u3092\u5897\u3084\u3057\u3066\u679a\u6570\u3092\u5897\u3084\u3057\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>_data = list()\nnum_list = [d5, d6, d7, d8, d9]\nedge_flag_list = [edge_flag5, edge_flag6, edge_flag7, edge_flag8, edge_flag9]\nedge_idx_list = [(0, 0), (0, 3), (2, 0), (2, 3), (4, 0), (4, 3)]\nlabels = np.eye(5)\nfor i in range(5):\n  num = num_list[i]\n  edge_flag = edge_flag_list[i]\n  for j, flag in enumerate(edge_flag):\n    d = num.copy()\n    if flag:\n      d[edge_idx_list[j]] = 1\n    d = np.concatenate([d.reshape(-1), labels[i]], axis=-1)\n    _data.append(d)\n  d = np.concatenate([num.reshape(-1), labels[i]], axis=-1)\n  _data.append(d)\ndata = np.stack(_data, axis=0)\ndata.shape<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-plain\" data-file=\"Output\"><code>(35, 25)<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code># \u5404\u7a2e\u5b9a\u6570\u3092\u5b9a\u7fa9\u3057\u3066\u304a\u304f\nIMG_DIM = 20\nN_SELECT = 5\nHEIGHT = 5\nWIDTH = 4<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u30c7\u30fc\u30bf\u306b\u3064\u3044\u3066\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u307f\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>plt.figure(figsize=(8, 12))\nfor i in range(data.shape[0]):\n  plt.subplot(7, 5, i+1)\n  plt.imshow(data[i, :IMG_DIM].reshape((HEIGHT, WIDTH)))<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span class=\"n\"><\/span> <img decoding=\"async\" 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\" style=\"background-color: #ffffff; color: initial; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen-Sans, Ubuntu, Cantarell, 'Helvetica Neue', sans-serif; font-size: 18px;\" class=\"aligncenter\"><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<div class=\"output_png output_subarea \"><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h3><span class=\"ez-toc-section\" id=\"%E3%83%9C%E3%83%AB%E3%83%84%E3%83%9E%E3%83%B3%E3%83%9E%E3%82%B7%E3%83%B3%E3%81%AE%E5%AD%A6%E7%BF%92-2\"><\/span>\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u306e\u5b66\u7fd2<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u3059\u3067\u306b\u5b9f\u9a131\u3067\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u306f\u5b9a\u7fa9\u3057\u305f\u306e\u3067\uff0c\u305d\u308c\u3092\u4e0a\u3067\u4f5c\u6210\u3057\u305f\u30c7\u30fc\u30bf\u3067\u5b66\u7fd2\u3057\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>N_EPOCHS = 4000\nexp2_dir = Path(&#39;.\/exp2&#39;)\nexp2_dir.mkdir(parents=True, exist_ok=True)\nbm = BolzmannMachine(img_dim=IMG_DIM, n_select=N_SELECT, output_dir=exp2_dir)\nbm.train(N_EPOCHS, data)<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-plain\" data-file=\"Output\"><code>Epoch : 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4000\/4000 [02:01&lt;00:00, 32.91it\/s]<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u540c\u69d8\u306b100\u30a8\u30dd\u30c3\u30af\u3054\u3068\u306e\u751f\u6210\u753b\u50cf\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code># 100\u30a8\u30dd\u30c3\u30af\u6bce\u306e\u751f\u6210\u7d50\u679c\u3092\u30d7\u30ed\u30c3\u30c8\nn_gen = N_EPOCHS \/\/ 100\nplt.figure(figsize=(7, 2 * n_gen))\nfor n in range(n_gen):\n    bm.load_state_dict(torch.load(exp2_dir \/ f&#39;ckpt_{(n+1) * 100:04d}.pth&#39;))\n    for i in range(5):\n        num = i + N_SELECT\n        gen = bm.generate(num)\n        plt.subplot(n_gen, N_SELECT, N_SELECT * n + i + 1)\n        plt.imshow(gen.reshape((HEIGHT, WIDTH)), aspect=&#39;auto&#39;)<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span class=\"c1\"><\/span><img decoding=\"async\" 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\" class=\"aligncenter\"><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<ul>\n<li>5, 6, 8, 9\u306f\u975e\u5e38\u306b\u4f3c\u3066\u3044\u308b\u305f\u3081\uff0c\u3068\u3053\u308d\u3069\u3053\u308d\u6df7\u3056\u3063\u3066\u3057\u307e\u3063\u3066\u3044\u308b\u3082\u306e\u304c\u3042\u308a\u307e\u3059\uff0e<\/li>\n<li>7\u306b\u3064\u3044\u3066\u306f\u6bd4\u8f03\u7684\u554f\u984c\u306a\u304f\u5b66\u7fd2\u3067\u304d\u3066\u3044\u305d\u3046\u3067\u3059\uff0e<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h3><span class=\"ez-toc-section\" id=\"%E5%88%86%E9%A1%9E%E3%83%A2%E3%83%87%E3%83%AB%E3%81%AE%E5%AD%A6%E7%BF%92-2\"><\/span>\u5206\u985e\u30e2\u30c7\u30eb\u306e\u5b66\u7fd2<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u5206\u985e\u30e2\u30c7\u30eb\u306b\u3064\u3044\u3066\u3082\uff0c\u4e0a\u306e\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066\u540c\u69d8\u306e\u5b66\u7fd2\u3092\u3055\u305b\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code># \u518d\u5ea6\u30c7\u30fc\u30bf\u3092\u7528\u610f\nX = data[:, :IMG_DIM]\ny = data[:, IMG_DIM:]\nX_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.1, shuffle=True, random_state=SEED)\n\nX_train = X_train.astype(np.float32)\nX_valid = X_valid.astype(np.float32)<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>N_EPOCHS_CLS = 20\nBATCH_SIZE = 32\n\nmodel = build_model(IMG_DIM, N_SELECT)\nmodel.compile(loss=&#39;categorical_crossentropy&#39;, optimizer=&#39;adam&#39;, metrics=[&#39;accuracy&#39;])\nckpt_callback = ModelCheckpoint(\n    filepath=exp2_dir \/ &#39;best.h5&#39;,\n    monitor=&#39;val_accuracy&#39;,\n    mode=&#39;max&#39;,\n    save_best_only=True\n)\nhistory = model.fit(\n    X_train,\n    y_train,\n    batch_size=BATCH_SIZE, \n    epochs=N_EPOCHS_CLS, \n    verbose=0, \n    validation_data=(X_valid, y_valid),\n    callbacks=[ckpt_callback]\n)<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Loss\u3068Accuracy\u306e\u30a8\u30dd\u30c3\u30af\u3054\u3068\u306e\u63a8\u79fb\u3092\u898b\u3066\u307f\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>plt.plot(range(1, N_EPOCHS_CLS+1), history.history[&#39;loss&#39;],  marker=&#39;.&#39;, label=&#39;train&#39;)\nplt.plot(range(1, N_EPOCHS_CLS+1), history.history[&#39;val_loss&#39;], marker=&#39;.&#39;, label=&#39;valid&#39;)\nplt.legend(loc=&#39;best&#39;, fontsize=10)\nplt.grid()\nplt.xlabel(&#39;Epoch&#39;)\nplt.ylabel(&#39;Loss&#39;)\nplt.show()<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span class=\"n\"><\/span><img decoding=\"async\" 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\" class=\"aligncenter\"><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>plt.plot(range(1, N_EPOCHS_CLS+1), history.history[&#39;accuracy&#39;], marker=&#39;.&#39;, label=&#39;train&#39;)\nplt.plot(range(1, N_EPOCHS_CLS+1), history.history[&#39;val_accuracy&#39;], marker=&#39;.&#39;, label=&#39;valid&#39;)\nplt.legend(loc=&#39;best&#39;, fontsize=10)\nplt.grid()\nplt.xlabel(&#39;Epoch&#39;)\nplt.ylabel(&#39;Accuracy&#39;)\nplt.show()<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span class=\"n\"><\/span><img decoding=\"async\" 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\" class=\"aligncenter\"><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<ul>\n<li>\u305d\u3082\u305d\u3082\u306e\u5b66\u7fd2\u30c7\u30fc\u30bf\u6570\u304c\u5c11\u306a\u3044\u305f\u3081\uff0c\u30b9\u30b3\u30a2\u306e\u63a8\u79fb\u304c\u6975\u7aef\u306b\u306a\u3063\u3066\u3044\u307e\u3059\uff0e<\/li>\n<li>\u305f\u3060\u7d42\u76e4\u306e\u30a8\u30dd\u30c3\u30af\u3067\u306f\u554f\u984c\u306a\u304f\u5206\u985e\u3067\u304d\u3066\u3044\u305d\u3046\u3067\u3059<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h3><span class=\"ez-toc-section\" id=\"%E5%88%86%E9%A1%9E%E3%83%A2%E3%83%87%E3%83%AB%E3%81%AB%E3%82%88%E3%82%8B%E8%A9%95%E4%BE%A1-2\"><\/span>\u5206\u985e\u30e2\u30c7\u30eb\u306b\u3088\u308b\u8a55\u4fa1<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u3067\u306f10\u30a8\u30dd\u30c3\u30af\u3054\u3068\u306b\u8ad6\u6587\u306e\u8a55\u4fa1\u6307\u6a19\u306e\u63a8\u79fb\u3092\u898b\u3066\u3044\u304d\u307e\u3059\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>n_gen = N_EPOCHS \/\/ 10\nepoch_list = list()\nscore_list = list()\nbm = BolzmannMachine(img_dim=IMG_DIM, n_select=N_SELECT, output_dir=exp2_dir)\nmodel = load_model(exp2_dir \/ &#39;best.h5&#39;)\nfor n in range(n_gen):\n    bm.load_state_dict(torch.load(exp2_dir \/ f&#39;ckpt_{(n+1) * 10:04d}.pth&#39;))\n    num_list = torch.LongTensor([5] * 100 + [6] * 100 + [7] * 100 + [8] * 100 + [9] * 100)\n    gen, y = bm.generate_multi(num_list)\n    epoch_list.append((n+1) * 10)\n    score = model.evaluate(gen, y, verbose=0)\n    score_list.append(score)<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>_, scores = tuple(zip(*score_list))\nplt.figure(figsize=(8, 4))\nplt.plot(epoch_list, scores, marker=&#39;.&#39;)\nplt.grid()\nplt.xlabel(&#39;Epoch&#39;)\nplt.ylabel(&#39;R_a&#39;)\nplt.show()<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span class=\"n\"><\/span><img decoding=\"async\" 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\" class=\"aligncenter\"><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<ul>\n<li>\u5b9f\u9a131\u306e\u8ad6\u6587\u30c7\u30fc\u30bf\u306e\u3068\u304d\u3068\u6bd4\u8f03\u3059\u308b\u3068\u5024\u306e\u63a8\u79fb\u306f\u975e\u5e38\u306b\u5b89\u5b9a\u3057\u3066\u3044\u307e\u3059\uff0e<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u4e00\u756a\u8a55\u4fa1\u6307\u6a19\u306e\u5024\u304c\u826f\u3044\u30e2\u30c7\u30eb\u306e\u751f\u6210\u753b\u50cf\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>best_epoch = epoch_list[np.argmax(scores)]\nprint(f&#39;Best Epoch : {best_epoch}&#39;)<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-plain\" data-file=\"output\"><code>Best Epoch : 3920<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>plt.figure(figsize=(6, 7))\nbm = BolzmannMachine(img_dim=IMG_DIM, n_select=N_SELECT, output_dir=exp2_dir)\nbm.load_state_dict(torch.load(exp2_dir \/ f&#39;ckpt_{best_epoch:04d}.pth&#39;))\nfor i in range(3):\n    for j in range(N_SELECT):\n        num = j + N_SELECT\n        gen = bm.generate(num)\n        plt.subplot(3, N_SELECT, 5 * i + j + 1)\n        plt.imshow(gen.reshape((HEIGHT, WIDTH)), aspect=&#39;auto&#39;)<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span class=\"n\"><\/span><img decoding=\"async\" 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\" class=\"aligncenter\"><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<ul>\n<li>\u554f\u984c\u306a\u304f\u751f\u6210\u3067\u304d\u3066\u3044\u305d\u3046\u3067\u3059\uff0e<\/li>\n<li>\u7aef\u306e\u30d4\u30af\u30bb\u30eb\u304c\u591a\u304f\u306a\u308b\u306e\u306f\u30c7\u30fc\u30bf\u62e1\u5f35\u306e\u5f71\u97ff\u3060\u3068\u8003\u3048\u3089\u308c\u307e\u3059\uff0e<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h3><span class=\"ez-toc-section\" id=\"%E5%B0%A4%E5%BA%A6%E8%A8%88%E7%AE%97\"><\/span>\u5c24\u5ea6\u8a08\u7b97<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"cell border-box-sizing text_cell rendered\">\n<div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>\u3067\u306f\uff0c\u5b9f\u9a131\u3067\u306f\u3067\u304d\u306a\u304b\u3063\u305f\u5c24\u5ea6\u8a08\u7b97\u306b\u79fb\u308a\u307e\u3059\uff0e<\/p>\n<p>\u5c24\u5ea6\u8a08\u7b97\u306e\u5b9f\u88c5\u306f\u3059\u3067\u306b\u5b9a\u7fa9\u3057\u305f\u30dc\u30eb\u30c4\u30de\u30f3\u30de\u30b7\u30f3\u306e\u30af\u30e9\u30b9\u5185\u3067\u884c\u3063\u305f\u306e\u3067\uff0c20\u30a8\u30dd\u30c3\u30af\u305a\u3064\u5c24\u5ea6\u8a08\u7b97\u3092\u3057\u3066\u63a8\u79fb\u3092\u78ba\u8a8d\u3057\u3066\u307f\u307e\u3057\u3087\u3046\uff0e<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>N = N_EPOCHS \/\/ 20\nepoch_list = list()\nlikelihood_list = list()\nbm = BolzmannMachine(img_dim=IMG_DIM, n_select=N_SELECT, output_dir=exp2_dir)\nfor n in tqdm(range(N)):\n    epoch_list.append((n + 1) * 20)\n    bm.load_state_dict(torch.load(exp2_dir \/ f&#39;ckpt_{(n+1) * 20:04d}.pth&#39;))\n    likelihood = bm.calc_likelihood(data, batch_size=2**18)\n    likelihood_list.append(likelihood)<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-plain\" data-file=\"Output\"><code>100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200\/200 [18:39&lt;00:00,  5.60s\/it]<\/code><\/pre><\/div>\n\n\n\n<div class=\"hcb_wrap\"><pre class=\"prism line-numbers lang-python\" data-lang=\"Python\"><code>plt.plot(epoch_list, likelihood_list, marker=&#39;.&#39;)\nplt.grid()\nplt.xlabel(&#39;Epoch&#39;)\nplt.ylabel(&#39;Likelihood&#39;)\nplt.tight_layout()<\/code><\/pre><\/div>\n\n\n\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"inner_cell\">\n<div class=\"input_area\">\n<div class=\" highlight hl-python\">\n<pre><span class=\"n\"><\/span><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"output_wrapper\">\n<div class=\"output\">\n<div class=\"output_area\">\n<figure><img decoding=\"async\" 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