{"id":16065,"date":"2024-01-20T09:50:16","date_gmt":"2024-01-20T06:20:16","guid":{"rendered":"https:\/\/rasanegar.com\/blog\/%d8%ad%d9%84-%d9%85%d8%b3%d8%a7%d8%a6%d9%84-%d8%aa%d9%88%d8%a7%d9%84%db%8c-%d8%a8%d8%a7-lstm-%d8%af%d8%b1-keras-%d9%82%d8%b3%d9%85%d8%aa-2\/"},"modified":"2024-01-20T09:50:16","modified_gmt":"2024-01-20T06:20:16","slug":"%d8%ad%d9%84-%d9%85%d8%b3%d8%a7%d8%a6%d9%84-%d8%aa%d9%88%d8%a7%d9%84%db%8c-%d8%a8%d8%a7-lstm-%d8%af%d8%b1-keras-%d9%82%d8%b3%d9%85%d8%aa-2","status":"publish","type":"post","link":"https:\/\/rasanegaar.com\/blog\/%d8%ad%d9%84-%d9%85%d8%b3%d8%a7%d8%a6%d9%84-%d8%aa%d9%88%d8%a7%d9%84%db%8c-%d8%a8%d8%a7-lstm-%d8%af%d8%b1-keras-%d9%82%d8%b3%d9%85%d8%aa-2\/","title":{"rendered":"\u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u0628\u0627 LSTM \u062f\u0631 Keras: \u0642\u0633\u0645\u062a 2"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\"><p class=\"ez-toc-title\" style=\"cursor:inherit\">\u0633\u0631\u0641\u0635\u0644\u0647\u0627\u06cc \u0645\u0637\u0644\u0628<\/p>\n<\/div><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:\/\/rasanegaar.com\/blog\/%d8%ad%d9%84-%d9%85%d8%b3%d8%a7%d8%a6%d9%84-%d8%aa%d9%88%d8%a7%d9%84%db%8c-%d8%a8%d8%a7-lstm-%d8%af%d8%b1-keras-%d9%82%d8%b3%d9%85%d8%aa-2\/#%d9%85%d8%b4%da%a9%d9%84%d8%a7%d8%aa_%d8%aa%d9%88%d8%a7%d9%84%db%8c_%db%8c%da%a9_%d8%a8%d9%87_%da%86%d9%86%d8%af\" >\u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ad%d9%84-%d9%85%d8%b3%d8%a7%d8%a6%d9%84-%d8%aa%d9%88%d8%a7%d9%84%db%8c-%d8%a8%d8%a7-lstm-%d8%af%d8%b1-keras-%d9%82%d8%b3%d9%85%d8%aa-2\/#%d9%85%d8%b4%da%a9%d9%84%d8%a7%d8%aa_%d8%aa%d9%88%d8%a7%d9%84%db%8c_%db%8c%da%a9_%d8%a8%d9%87_%da%86%d9%86%d8%af_%d8%a8%d8%a7_%db%8c%da%a9_%d9%88%db%8c%da%98%da%af%db%8c_%d9%88%d8%a7%d8%ad%d8%af\" >\u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f \u0628\u0627 \u06cc\u06a9 \u0648\u06cc\u0698\u06af\u06cc \u0648\u0627\u062d\u062f<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ad%d9%84-%d9%85%d8%b3%d8%a7%d8%a6%d9%84-%d8%aa%d9%88%d8%a7%d9%84%db%8c-%d8%a8%d8%a7-lstm-%d8%af%d8%b1-keras-%d9%82%d8%b3%d9%85%d8%aa-2\/#%d9%85%d8%b4%da%a9%d9%84%d8%a7%d8%aa_%d8%aa%d9%88%d8%a7%d9%84%db%8c_%db%8c%da%a9_%d8%a8%d9%87_%da%86%d9%86%d8%af_%d8%a8%d8%a7_%da%86%d9%86%d8%af%db%8c%d9%86_%d9%88%db%8c%da%98%da%af%db%8c\" >\u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f \u0628\u0627 \u0686\u0646\u062f\u06cc\u0646 \u0648\u06cc\u0698\u06af\u06cc<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ad%d9%84-%d9%85%d8%b3%d8%a7%d8%a6%d9%84-%d8%aa%d9%88%d8%a7%d9%84%db%8c-%d8%a8%d8%a7-lstm-%d8%af%d8%b1-keras-%d9%82%d8%b3%d9%85%d8%aa-2\/#%d9%85%d8%b4%da%a9%d9%84%d8%a7%d8%aa_%d8%aa%d9%88%d8%a7%d9%84%db%8c_%da%86%d9%86%d8%af_%d8%a8%d9%87_%da%86%d9%86%d8%af\" >\u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u0686\u0646\u062f \u0628\u0647 \u0686\u0646\u062f<\/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:\/\/rasanegaar.com\/blog\/%d8%ad%d9%84-%d9%85%d8%b3%d8%a7%d8%a6%d9%84-%d8%aa%d9%88%d8%a7%d9%84%db%8c-%d8%a8%d8%a7-lstm-%d8%af%d8%b1-keras-%d9%82%d8%b3%d9%85%d8%aa-2\/#%d9%85%d8%af%d9%84_%d8%b1%d9%85%d8%b2%da%af%d8%b0%d8%a7%d8%b1-%d8%b1%d9%85%d8%b2%da%af%d8%b4%d8%a7\" >\u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ad%d9%84-%d9%85%d8%b3%d8%a7%d8%a6%d9%84-%d8%aa%d9%88%d8%a7%d9%84%db%8c-%d8%a8%d8%a7-lstm-%d8%af%d8%b1-keras-%d9%82%d8%b3%d9%85%d8%aa-2\/#%d9%85%d8%b4%da%a9%d9%84%d8%a7%d8%aa_%d8%aa%d9%88%d8%a7%d9%84%db%8c_%da%86%d9%86%d8%af_%d8%a8%d9%87_%da%86%d9%86%d8%af_%d8%a8%d8%a7_%db%8c%da%a9_%d9%88%db%8c%da%98%da%af%db%8c\" >\u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u0686\u0646\u062f \u0628\u0647 \u0686\u0646\u062f \u0628\u0627 \u06cc\u06a9 \u0648\u06cc\u0698\u06af\u06cc<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ad%d9%84-%d9%85%d8%b3%d8%a7%d8%a6%d9%84-%d8%aa%d9%88%d8%a7%d9%84%db%8c-%d8%a8%d8%a7-lstm-%d8%af%d8%b1-keras-%d9%82%d8%b3%d9%85%d8%aa-2\/#%d9%85%d8%b4%da%a9%d9%84%d8%a7%d8%aa_%d8%aa%d9%88%d8%a7%d9%84%db%8c_%da%86%d9%86%d8%af_%d8%a8%d9%87_%da%86%d9%86%d8%af_%d8%a8%d8%a7_%da%86%d9%86%d8%af%db%8c%d9%86_%d9%88%db%8c%da%98%da%af%db%8c\" >\u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u0686\u0646\u062f \u0628\u0647 \u0686\u0646\u062f \u0628\u0627 \u0686\u0646\u062f\u06cc\u0646 \u0648\u06cc\u0698\u06af\u06cc<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%ad%d9%84-%d9%85%d8%b3%d8%a7%d8%a6%d9%84-%d8%aa%d9%88%d8%a7%d9%84%db%8c-%d8%a8%d8%a7-lstm-%d8%af%d8%b1-keras-%d9%82%d8%b3%d9%85%d8%aa-2\/#%d9%86%d8%aa%db%8c%d8%ac%d9%87\" >\u0646\u062a\u06cc\u062c\u0647<\/a><\/li><\/ul><\/nav><\/div>\n<span class=\"span-reading-time rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">\u0632\u0645\u0627\u0646 \u0644\u0627\u0632\u0645 \u0628\u0631\u0627\u06cc \u0645\u0637\u0627\u0644\u0639\u0647: <\/span> <span class=\"rt-time\"> 10<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span><p> <br \/>\n<\/p>\n<div><noscript><\/noscript><\/p>\n<p>\u0627\u06cc\u0646 \u0642\u0633\u0645\u062a \u062f\u0648\u0645 \u0648 \u067e\u0627\u06cc\u0627\u0646\u06cc \u0627\u0632 \u0633\u0631\u06cc \u0645\u0642\u0627\u0644\u0627\u062a \u062f\u0648 \u0642\u0633\u0645\u062a\u06cc \u0627\u0633\u062a \u0631\u0648\u06cc \u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u0628\u0627 LSTM  \u062f\u0631 \u0642\u0633\u0645\u062a 1 \u0645\u062c\u0645\u0648\u0639\u0647\u060c \u0631\u0648\u0634 \u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u06cc\u06a9 \u0648 \u0686\u0646\u062f \u0628\u0647 \u06cc\u06a9 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 LSTM \u062a\u0648\u0636\u06cc\u062d \u062f\u0627\u062f\u0645.  \u062f\u0631 \u0627\u06cc\u0646 \u0642\u0633\u0645\u062a \u0631\u0648\u0634 \u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f \u0648 \u0686\u0646\u062f \u0628\u0647 \u0686\u0646\u062f \u0631\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 LSTM \u062f\u0631 Keras \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f.<\/p>\n<p>\u0632\u06cc\u0631\u0646\u0648\u06cc\u0633 \u062a\u0635\u0648\u06cc\u0631 \u06cc\u06a9 \u0645\u062b\u0627\u0644 \u06a9\u0644\u0627\u0633\u06cc\u06a9 \u0627\u0632 \u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f \u0627\u0633\u062a \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0634\u0645\u0627 \u06cc\u06a9 \u062a\u0635\u0648\u06cc\u0631 \u0648\u0627\u062d\u062f \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0648\u0631\u0648\u062f\u06cc \u062f\u0627\u0631\u06cc\u062f \u0648 \u0628\u0627\u06cc\u062f \u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u062a\u0635\u0648\u06cc\u0631 \u0631\u0627 \u062f\u0631 \u0642\u0627\u0644\u0628 \u06cc\u06a9 \u062f\u0646\u0628\u0627\u0644\u0647 \u06a9\u0644\u0645\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u06a9\u0646\u06cc\u062f.  \u0628\u0647 \u0637\u0648\u0631 \u0645\u0634\u0627\u0628\u0647\u060c \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u0628\u0627\u0632\u0627\u0631 \u0633\u0647\u0627\u0645 \u0628\u0631\u0627\u06cc \u0631\u0648\u0632\u0647\u0627\u06cc X \u0628\u0639\u062f\u06cc\u060c \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0648\u0631\u0648\u062f\u06cc \u0642\u06cc\u0645\u062a \u0633\u0647\u0627\u0645 \u0631\u0648\u0632\u0647\u0627\u06cc Y \u0642\u0628\u0644\u06cc \u0627\u0633\u062a\u060c \u06cc\u06a9 \u0645\u062b\u0627\u0644 \u06a9\u0644\u0627\u0633\u06cc\u06a9 \u0627\u0632 \u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u0686\u0646\u062f \u0628\u0647 \u0686\u0646\u062f \u0627\u0633\u062a.<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0646\u0645\u0648\u0646\u0647 \u0647\u0627\u06cc \u0628\u0633\u06cc\u0627\u0631 \u0627\u0628\u062a\u062f\u0627\u06cc\u06cc \u0645\u0634\u06a9\u0644\u0627\u062a \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f \u0648 \u0686\u0646\u062f \u0628\u0647 \u0686\u0646\u062f \u0631\u0627 \u0645\u0634\u0627\u0647\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u062f \u06a9\u0631\u062f.  \u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u0645\u0641\u0627\u0647\u06cc\u0645 \u0622\u0645\u0648\u062e\u062a\u0647 \u0634\u062f\u0647 \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647\u060c \u067e\u0627\u06cc\u0647 \u0648 \u0627\u0633\u0627\u0633 \u062d\u0644 \u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647\u060c \u0645\u0627\u0646\u0646\u062f \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0642\u06cc\u0645\u062a \u0633\u0647\u0627\u0645 \u0648 \u0634\u0631\u062d \u062e\u0648\u062f\u06a9\u0627\u0631 \u062a\u0635\u0627\u0648\u06cc\u0631 \u0631\u0627 \u06a9\u0647 \u062f\u0631 \u0645\u0642\u0627\u0644\u0627\u062a \u0622\u06cc\u0646\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u06cc\u062f\u060c \u0627\u06cc\u062c\u0627\u062f \u062e\u0648\u0627\u0647\u062f \u06a9\u0631\u062f.<\/p>\n<h2 id=\"onetomanysequenceproblems\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%b4%da%a9%d9%84%d8%a7%d8%aa_%d8%aa%d9%88%d8%a7%d9%84%db%8c_%db%8c%da%a9_%d8%a8%d9%87_%da%86%d9%86%d8%af\"><\/span>\u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f \u0646\u0648\u0639 \u0645\u0634\u06a9\u0644\u0627\u062a \u062f\u0646\u0628\u0627\u0644\u0647 \u0627\u06cc \u0647\u0633\u062a\u0646\u062f \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0648\u0631\u0648\u062f\u06cc \u062f\u0627\u0631\u0627\u06cc \u06cc\u06a9 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u0647\u0633\u062a\u0646\u062f \u0648 \u062e\u0631\u0648\u062c\u06cc \u0634\u0627\u0645\u0644 \u0628\u0631\u062f\u0627\u0631 \u0686\u0646\u062f \u0645\u0642\u062f\u0627\u0631 \u06cc\u0627 \u0686\u0646\u062f\u06cc\u0646 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u0627\u0633\u062a.  \u062f\u0631 \u0627\u06cc\u0646 \u0628\u062e\u0634\u060c \u0631\u0648\u0634 \u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f \u0631\u0627 \u062f\u0631 \u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0648\u0631\u0648\u062f\u06cc \u062f\u0627\u0631\u0627\u06cc \u06cc\u06a9 \u0648\u06cc\u0698\u06af\u06cc \u0648\u0627\u062d\u062f \u0627\u0633\u062a\u060c \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u06cc\u062f.  \u0633\u067e\u0633 \u062d\u0631\u06a9\u062a \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f \u0631\u0648\u06cc \u0628\u0631\u0627\u06cc \u0645\u0634\u0627\u0647\u062f\u0647 \u0631\u0648\u0634 \u06a9\u0627\u0631 \u0628\u0627 \u0648\u0631\u0648\u062f\u06cc \u0686\u0646\u062f \u0648\u06cc\u0698\u06af\u06cc \u0628\u0631\u0627\u06cc \u062d\u0644 \u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f.<\/p>\n<h3 id=\"onetomanysequenceproblemswithasinglefeature\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%b4%da%a9%d9%84%d8%a7%d8%aa_%d8%aa%d9%88%d8%a7%d9%84%db%8c_%db%8c%da%a9_%d8%a8%d9%87_%da%86%d9%86%d8%af_%d8%a8%d8%a7_%db%8c%da%a9_%d9%88%db%8c%da%98%da%af%db%8c_%d9%88%d8%a7%d8%ad%d8%af\"><\/span>\u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f \u0628\u0627 \u06cc\u06a9 \u0648\u06cc\u0698\u06af\u06cc \u0648\u0627\u062d\u062f<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u0628\u062a\u062f\u0627 \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645 \u0648 \u0645\u0634\u06a9\u0644\u06cc \u0631\u0627 \u06a9\u0647 \u062f\u0631 \u0627\u06cc\u0646 \u0628\u062e\u0634 \u062d\u0644 \u0645\u06cc \u06a9\u0646\u06cc\u0645\u060c \u062f\u0631\u06a9 \u06a9\u0646\u06cc\u0645.<\/p>\n<h4 id=\"creatingthedataset\">\u0627\u06cc\u062c\u0627\u062f \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647<\/h4>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632 \u0631\u0627 \u0648\u0627\u0631\u062f \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> numpy <span class=\"hljs-keyword\">import<\/span> array\n<span class=\"hljs-keyword\">from<\/span> keras.preprocessing.text <span class=\"hljs-keyword\">import<\/span> one_hot\n<span class=\"hljs-keyword\">from<\/span> keras.preprocessing.sequence <span class=\"hljs-keyword\">import<\/span> pad_sequences\n<span class=\"hljs-keyword\">from<\/span> keras.models <span class=\"hljs-keyword\">import<\/span> Sequential\n<span class=\"hljs-keyword\">from<\/span> keras.layers.core <span class=\"hljs-keyword\">import<\/span> Activation, Dropout, Dense\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> Flatten, LSTM\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> GlobalMaxPooling1D\n<span class=\"hljs-keyword\">from<\/span> keras.models <span class=\"hljs-keyword\">import<\/span> Model\n<span class=\"hljs-keyword\">from<\/span> keras.layers.embeddings <span class=\"hljs-keyword\">import<\/span> Embedding\n<span class=\"hljs-keyword\">from<\/span> sklearn.model_selection <span class=\"hljs-keyword\">import<\/span> train_test_split\n<span class=\"hljs-keyword\">from<\/span> keras.preprocessing.text <span class=\"hljs-keyword\">import<\/span> Tokenizer\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> Input\n<span class=\"hljs-keyword\">from<\/span> keras.layers.merge <span class=\"hljs-keyword\">import<\/span> Concatenate\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> Bidirectional\n\n<span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n<span class=\"hljs-keyword\">import<\/span> numpy <span class=\"hljs-keyword\">as<\/span> np\n<span class=\"hljs-keyword\">import<\/span> re\n\n<span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n<\/code><\/pre>\n<p>\u0648 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">X = <span class=\"hljs-built_in\">list<\/span>()\nY = <span class=\"hljs-built_in\">list<\/span>()\nX = (x+<span class=\"hljs-number\">3<\/span> <span class=\"hljs-keyword\">for<\/span> x <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(-<span class=\"hljs-number\">2<\/span>, <span class=\"hljs-number\">43<\/span>, <span class=\"hljs-number\">3<\/span>))\n\n<span class=\"hljs-keyword\">for<\/span> i <span class=\"hljs-keyword\">in<\/span> X:\n    output_vector = <span class=\"hljs-built_in\">list<\/span>()\n    output_vector.append(i+<span class=\"hljs-number\">1<\/span>)\n    output_vector.append(i+<span class=\"hljs-number\">2<\/span>)\n    Y.append(output_vector)\n\n<span class=\"hljs-built_in\">print<\/span>(X)\n<span class=\"hljs-built_in\">print<\/span>(Y)\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u062e\u0631\u0648\u062c\u06cc \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">(1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43)\n((2, 3), (5, 6), (8, 9), (11, 12), (14, 15), (17, 18), (20, 21), (23, 24), (26, 27), (29, 30), (32, 33), (35, 36), (38, 39), (41, 42), (44, 45))\n<\/code><\/pre>\n<p>\u0648\u0631\u0648\u062f\u06cc \u0645\u0627 \u0634\u0627\u0645\u0644 15 \u0646\u0645\u0648\u0646\u0647 \u0628\u0627 \u06cc\u06a9 \u06af\u0627\u0645 \u0632\u0645\u0627\u0646\u06cc \u0648 \u06cc\u06a9 \u0645\u0642\u062f\u0627\u0631 \u0648\u06cc\u0698\u06af\u06cc \u0627\u0633\u062a.  \u0628\u0631\u0627\u06cc \u0647\u0631 \u0645\u0642\u062f\u0627\u0631 \u062f\u0631 \u0646\u0645\u0648\u0646\u0647 \u0648\u0631\u0648\u062f\u06cc\u060c \u0628\u0631\u062f\u0627\u0631 \u062e\u0631\u0648\u062c\u06cc \u0645\u0631\u0628\u0648\u0637\u0647 \u0634\u0627\u0645\u0644 \u062f\u0648 \u0639\u062f\u062f \u0635\u062d\u06cc\u062d \u0628\u0639\u062f\u06cc \u0627\u0633\u062a.  \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u0627\u06af\u0631 \u0648\u0631\u0648\u062f\u06cc 4 \u0628\u0627\u0634\u062f\u060c \u0628\u0631\u062f\u0627\u0631 \u062e\u0631\u0648\u062c\u06cc \u062d\u0627\u0648\u06cc \u0645\u0642\u0627\u062f\u06cc\u0631 5 \u0648 6 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f. \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646\u060c \u0645\u0633\u0626\u0644\u0647 \u06cc\u06a9 \u0645\u0633\u0626\u0644\u0647 \u0633\u0627\u062f\u0647 \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f \u0627\u0633\u062a.<\/p>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u0627 \u0631\u0627 \u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u062a\u0648\u0633\u0637 LSTM \u0644\u0627\u0632\u0645 \u0627\u0633\u062a \u062a\u063a\u06cc\u06cc\u0631 \u0634\u06a9\u0644 \u0645\u06cc \u062f\u0647\u062f:<\/p>\n<pre><code class=\"hljs\">X = np.array(X).reshape(<span class=\"hljs-number\">15<\/span>, <span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>)\nY = np.array(Y)\n<\/code><\/pre>\n<p>\u0627\u06a9\u0646\u0648\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0645\u062f\u0644 \u0647\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645.  \u0645\u0627 LSTM \u0647\u0627\u06cc \u0633\u0627\u062f\u0647 \u0648 \u067e\u0634\u062a\u0647 \u0627\u06cc \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u062f.<\/p>\n<h4 id=\"solutionviasimplelstm\">\u0631\u0627\u0647 \u062d\u0644 \u0627\u0632 \u0637\u0631\u06cc\u0642 LSTM \u0633\u0627\u062f\u0647<\/h4>\n<pre><code class=\"hljs\">model = Sequential()\nmodel.add(LSTM(<span class=\"hljs-number\">50<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>, input_shape=(<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>)))\nmodel.add(Dense(<span class=\"hljs-number\">2<\/span>))\nmodel.<span class=\"hljs-built_in\">compile<\/span>(optimizer=<span class=\"hljs-string\">'adam'<\/span>, loss=<span class=\"hljs-string\">'mse'<\/span>)\nmodel.fit(X, Y, epochs=<span class=\"hljs-number\">1000<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>, batch_size=<span class=\"hljs-number\">3<\/span>)\n<\/code><\/pre>\n<p>\u0632\u0645\u0627\u0646\u06cc \u06a9\u0647 \u0645\u062f\u0644 \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0634\u062f\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u06a9\u0646\u06cc\u0645 \u0631\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062a\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">test_input = array((<span class=\"hljs-number\">10<\/span>))\ntest_input = test_input.reshape((<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>))\ntest_output = model.predict(test_input, verbose=<span class=\"hljs-number\">0<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(test_output)\n<\/code><\/pre>\n<p>\u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062a\u0633\u062a \u062d\u0627\u0648\u06cc \u0645\u0642\u062f\u0627\u0631 10 \u0627\u0633\u062a. \u062f\u0631 \u062e\u0631\u0648\u062c\u06cc \u0628\u0627\u06cc\u062f \u0628\u0631\u062f\u0627\u0631 \u062d\u0627\u0648\u06cc 11 \u0648 12 \u0631\u0627 \u062f\u0631\u06cc\u0627\u0641\u062a \u06a9\u0646\u06cc\u0645. \u062e\u0631\u0648\u062c\u06cc \u06a9\u0647 \u062f\u0631\u06cc\u0627\u0641\u062a \u06a9\u0631\u062f\u0645 (10.982891 12.109697) \u0627\u0633\u062a \u06a9\u0647 \u062f\u0631 \u0648\u0627\u0642\u0639 \u0628\u0633\u06cc\u0627\u0631 \u0646\u0632\u062f\u06cc\u06a9 \u0628\u0647 \u062e\u0631\u0648\u062c\u06cc \u0645\u0648\u0631\u062f \u0627\u0646\u062a\u0638\u0627\u0631 \u0627\u0633\u062a.<\/p>\n<h4 id=\"solutionviastackedlstm\">\u0631\u0627\u0647 \u062d\u0644 \u0627\u0632 \u0637\u0631\u06cc\u0642 Stacked LSTM<\/h4>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0647\u0627\u06cc \u0632\u06cc\u0631 LSTM \u0647\u0627 \u0631\u0627 \u0631\u0648\u06cc \u0647\u0645 \u0686\u06cc\u062f\u0647 \u0627\u0646\u062f \u0631\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u0627 \u0631\u0627 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0645\u06cc \u06a9\u0646\u062f \u0631\u0648\u06cc \u0646\u06a9\u0627\u062a \u0622\u0632\u0645\u0648\u0646:<\/p>\n<pre><code class=\"hljs\">model = Sequential()\nmodel.add(LSTM(<span class=\"hljs-number\">50<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>, return_sequences=<span class=\"hljs-literal\">True<\/span>, input_shape=(<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>)))\nmodel.add(LSTM(<span class=\"hljs-number\">50<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>))\nmodel.add(Dense(<span class=\"hljs-number\">2<\/span>))\nmodel.<span class=\"hljs-built_in\">compile<\/span>(optimizer=<span class=\"hljs-string\">'adam'<\/span>, loss=<span class=\"hljs-string\">'mse'<\/span>)\nhistory = model.fit(X, Y, epochs=<span class=\"hljs-number\">1000<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>, verbose=<span class=\"hljs-number\">1<\/span>, batch_size=<span class=\"hljs-number\">3<\/span>)\n\ntest_output = model.predict(test_input, verbose=<span class=\"hljs-number\">0<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(test_output)\n<\/code><\/pre>\n<p>\u067e\u0627\u0633\u062e (11.00432 11.99205) \u0627\u0633\u062a \u06a9\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0646\u0632\u062f\u06cc\u06a9 \u0628\u0647 \u062e\u0631\u0648\u062c\u06cc \u0648\u0627\u0642\u0639\u06cc \u0627\u0633\u062a.<\/p>\n<h4 id=\"solutionviabidirectionallstm\">\u0631\u0627\u0647 \u062d\u0644 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u062f\u0648 \u062c\u0647\u062a\u0647 LSTM<\/h4>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u06cc\u06a9 LSTM \u062f\u0648 \u0637\u0631\u0641\u0647 \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u0645\u06cc \u062f\u0647\u062f \u0631\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u0627 \u0648 \u0633\u067e\u0633 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0645\u06cc \u06a9\u0646\u062f \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> Bidirectional\n\nmodel = Sequential()\nmodel.add(Bidirectional(LSTM(<span class=\"hljs-number\">50<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>), input_shape=(<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>)))\nmodel.add(Dense(<span class=\"hljs-number\">2<\/span>))\nmodel.<span class=\"hljs-built_in\">compile<\/span>(optimizer=<span class=\"hljs-string\">'adam'<\/span>, loss=<span class=\"hljs-string\">'mse'<\/span>)\n\nhistory = model.fit(X, Y, epochs=<span class=\"hljs-number\">1000<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>, verbose=<span class=\"hljs-number\">1<\/span>, batch_size=<span class=\"hljs-number\">3<\/span>)\ntest_output = model.predict(test_input, verbose=<span class=\"hljs-number\">0<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(test_output)\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0627\u06cc \u06a9\u0647 \u062f\u0631\u06cc\u0627\u0641\u062a \u06a9\u0631\u062f\u0645 (11.035181 12.082813) \u0627\u0633\u062a.<\/p>\n<h3 id=\"onetomanysequenceproblemswithmultiplefeatures\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%b4%da%a9%d9%84%d8%a7%d8%aa_%d8%aa%d9%88%d8%a7%d9%84%db%8c_%db%8c%da%a9_%d8%a8%d9%87_%da%86%d9%86%d8%af_%d8%a8%d8%a7_%da%86%d9%86%d8%af%db%8c%d9%86_%d9%88%db%8c%da%98%da%af%db%8c\"><\/span>\u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f \u0628\u0627 \u0686\u0646\u062f\u06cc\u0646 \u0648\u06cc\u0698\u06af\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0628\u062e\u0634 \u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f \u0631\u0627 \u0645\u0634\u0627\u0647\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0646\u0645\u0648\u0646\u0647 \u0647\u0627\u06cc \u0648\u0631\u0648\u062f\u06cc \u06cc\u06a9 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc\u060c \u0627\u0645\u0627 \u062f\u0648 \u0648\u06cc\u0698\u06af\u06cc \u062f\u0627\u0631\u0646\u062f.  \u062e\u0631\u0648\u062c\u06cc \u0628\u0631\u062f\u0627\u0631 \u062f\u0648 \u0639\u0646\u0635\u0631 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.<\/p>\n<h4 id=\"creatingthedataset\">\u0627\u06cc\u062c\u0627\u062f \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647<\/h4>\n<p>\u0645\u062b\u0644 \u0647\u0645\u06cc\u0634\u0647\u060c \u0627\u0648\u0644\u06cc\u0646 \u0642\u062f\u0645 \u0627\u06cc\u062c\u0627\u062f \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">nums = <span class=\"hljs-number\">25<\/span>\n\nX1 = <span class=\"hljs-built_in\">list<\/span>()\nX2 = <span class=\"hljs-built_in\">list<\/span>()\nX = <span class=\"hljs-built_in\">list<\/span>()\nY = <span class=\"hljs-built_in\">list<\/span>()\n\nX1 = ((x+<span class=\"hljs-number\">1<\/span>)*<span class=\"hljs-number\">2<\/span> <span class=\"hljs-keyword\">for<\/span> x <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(<span class=\"hljs-number\">25<\/span>))\nX2 = ((x+<span class=\"hljs-number\">1<\/span>)*<span class=\"hljs-number\">3<\/span> <span class=\"hljs-keyword\">for<\/span> x <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(<span class=\"hljs-number\">25<\/span>))\n\n<span class=\"hljs-keyword\">for<\/span> x1, x2 <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">zip<\/span>(X1, X2):\n    output_vector = <span class=\"hljs-built_in\">list<\/span>()\n    output_vector.append(x1+<span class=\"hljs-number\">1<\/span>)\n    output_vector.append(x2+<span class=\"hljs-number\">1<\/span>)\n    Y.append(output_vector)\n\nX = np.column_stack((X1, X2))\n<span class=\"hljs-built_in\">print<\/span>(X)\n<\/code><\/pre>\n<p>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0648\u0631\u0648\u062f\u06cc \u0645\u0627 \u0628\u0647 \u0627\u06cc\u0646 \u0635\u0648\u0631\u062a \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">(( 2  3)\n ( 4  6)\n ( 6  9)\n ( 8 12)\n (10 15)\n (12 18)\n (14 21)\n (16 24)\n (18 27)\n (20 30)\n (22 33)\n (24 36)\n (26 39)\n (28 42)\n (30 45)\n (32 48)\n (34 51)\n (36 54)\n (38 57)\n (40 60)\n (42 63)\n (44 66)\n (46 69)\n (48 72)\n (50 75))\n<\/code><\/pre>\n<p>\u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0647\u0631 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u0648\u0631\u0648\u062f\u06cc \u0634\u0627\u0645\u0644 \u062f\u0648 \u0648\u06cc\u0698\u06af\u06cc \u0627\u0633\u062a.  \u062e\u0631\u0648\u062c\u06cc \u06cc\u06a9 \u0628\u0631\u062f\u0627\u0631 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f \u06a9\u0647 \u0634\u0627\u0645\u0644 \u062f\u0648 \u0639\u0646\u0635\u0631 \u0628\u0639\u062f\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0628\u0627 \u062f\u0648 \u0648\u06cc\u0698\u06af\u06cc \u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u0646\u0645\u0648\u0646\u0647 \u0648\u0631\u0648\u062f\u06cc \u0645\u0637\u0627\u0628\u0642\u062a \u062f\u0627\u0631\u0646\u062f.  \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u0628\u0631\u0627\u06cc \u0646\u0645\u0648\u0646\u0647 \u0648\u0631\u0648\u062f\u06cc <code>(2, 3)<\/code>\u060c \u062e\u0631\u0648\u062c\u06cc \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f <code>(3, 4)<\/code>\u060c \u0648 \u063a\u06cc\u0631\u0647 \u0631\u0648\u06cc.<\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u062f\u0648\u0628\u0627\u0631\u0647 \u0634\u06a9\u0644 \u062f\u0647\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">X = np.array(X).reshape(<span class=\"hljs-number\">25<\/span>, <span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">2<\/span>)\nY = np.array(Y)\n<\/code><\/pre>\n<h4 id=\"solutionviasimplelstm\">\u0631\u0627\u0647 \u062d\u0644 \u0627\u0632 \u0637\u0631\u06cc\u0642 LSTM \u0633\u0627\u062f\u0647<\/h4>\n<pre><code class=\"hljs\">model = Sequential()\nmodel.add(LSTM(<span class=\"hljs-number\">50<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>, input_shape=(<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">2<\/span>)))\nmodel.add(Dense(<span class=\"hljs-number\">2<\/span>))\nmodel.<span class=\"hljs-built_in\">compile<\/span>(optimizer=<span class=\"hljs-string\">'adam'<\/span>, loss=<span class=\"hljs-string\">'mse'<\/span>)\nmodel.fit(X, Y, epochs=<span class=\"hljs-number\">1000<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>, batch_size=<span class=\"hljs-number\">3<\/span>)\n<\/code><\/pre>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u06a9\u0646\u0648\u0646 \u0646\u0642\u0637\u0647 \u0622\u0632\u0645\u0627\u06cc\u0634 \u062e\u0648\u062f \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645 \u0648 \u0628\u0628\u06cc\u0646\u06cc\u0645 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0645\u0627 \u0686\u0642\u062f\u0631 \u062e\u0648\u0628 \u0639\u0645\u0644 \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">test_input = array((<span class=\"hljs-number\">40<\/span>, <span class=\"hljs-number\">60<\/span>))\ntest_input = test_input.reshape((<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">2<\/span>))\ntest_output = model.predict(test_input, verbose=<span class=\"hljs-number\">0<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(test_output)\n<\/code><\/pre>\n<p>\u0648\u0631\u0648\u062f\u06cc (40\u060c 60)\u060c \u062e\u0631\u0648\u062c\u06cc \u0628\u0627\u06cc\u062f (41\u060c 61) \u0628\u0627\u0634\u062f.  \u062e\u0631\u0648\u062c\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0634\u062f\u0647 \u062a\u0648\u0633\u0637 LSTM \u0633\u0627\u062f\u0647 \u0645\u0627 (40.946873 60.941723) \u0627\u0633\u062a \u06a9\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0646\u0632\u062f\u06cc\u06a9 \u0628\u0647 \u062e\u0631\u0648\u062c\u06cc \u0645\u0648\u0631\u062f \u0627\u0646\u062a\u0638\u0627\u0631 \u0627\u0633\u062a.<\/p>\n<h4 id=\"solutionviastackedlstm\">\u0631\u0627\u0647 \u062d\u0644 \u0627\u0632 \u0637\u0631\u06cc\u0642 Stacked LSTM<\/h4>\n<pre><code class=\"hljs\">model = Sequential()\nmodel.add(LSTM(<span class=\"hljs-number\">50<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>, return_sequences=<span class=\"hljs-literal\">True<\/span>, input_shape=(<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">2<\/span>)))\nmodel.add(LSTM(<span class=\"hljs-number\">50<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>))\nmodel.add(Dense(<span class=\"hljs-number\">2<\/span>))\nmodel.<span class=\"hljs-built_in\">compile<\/span>(optimizer=<span class=\"hljs-string\">'adam'<\/span>, loss=<span class=\"hljs-string\">'mse'<\/span>)\nhistory = model.fit(X, Y, epochs=<span class=\"hljs-number\">1000<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>, verbose=<span class=\"hljs-number\">1<\/span>, batch_size=<span class=\"hljs-number\">3<\/span>)\n\ntest_input = array((<span class=\"hljs-number\">40<\/span>, <span class=\"hljs-number\">60<\/span>))\ntest_input = test_input.reshape((<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">2<\/span>))\ntest_output = model.predict(test_input, verbose=<span class=\"hljs-number\">0<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(test_output)\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0648\u0631\u062f: (40.978477 60.994644)<\/p>\n<h4 id=\"solutionviabidirectionallstm\">\u0631\u0627\u0647 \u062d\u0644 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u062f\u0648 \u062c\u0647\u062a\u0647 LSTM<\/h4>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> Bidirectional\n\nmodel = Sequential()\nmodel.add(Bidirectional(LSTM(<span class=\"hljs-number\">50<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>), input_shape=(<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">2<\/span>)))\nmodel.add(Dense(<span class=\"hljs-number\">2<\/span>))\nmodel.<span class=\"hljs-built_in\">compile<\/span>(optimizer=<span class=\"hljs-string\">'adam'<\/span>, loss=<span class=\"hljs-string\">'mse'<\/span>)\n\nhistory = model.fit(X, Y, epochs=<span class=\"hljs-number\">1000<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>, verbose=<span class=\"hljs-number\">1<\/span>, batch_size=<span class=\"hljs-number\">3<\/span>)\ntest_output = model.predict(test_input, verbose=<span class=\"hljs-number\">0<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(test_output)\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0628\u0647 \u062f\u0633\u062a \u0622\u0645\u062f\u0647: (41.0975 61.159065)<\/p>\n<h2 id=\"manytomanysequenceproblems\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%b4%da%a9%d9%84%d8%a7%d8%aa_%d8%aa%d9%88%d8%a7%d9%84%db%8c_%da%86%d9%86%d8%af_%d8%a8%d9%87_%da%86%d9%86%d8%af\"><\/span>\u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u0686\u0646\u062f \u0628\u0647 \u0686\u0646\u062f<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u062f\u0631 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f \u0648 \u0686\u0646\u062f \u0628\u0647 \u06cc\u06a9\u060c \u062f\u06cc\u062f\u06cc\u0645 \u06a9\u0647 \u0628\u0631\u062f\u0627\u0631 \u062e\u0631\u0648\u062c\u06cc \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0686\u0646\u062f\u06cc\u0646 \u0645\u0642\u062f\u0627\u0631 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f.  \u0628\u0633\u062a\u0647 \u0628\u0647 \u0645\u0633\u0626\u0644\u0647\u060c \u06cc\u06a9 \u0628\u0631\u062f\u0627\u0631 \u062e\u0631\u0648\u062c\u06cc \u062d\u0627\u0648\u06cc \u0645\u0642\u0627\u062f\u06cc\u0631 \u0645\u062a\u0639\u062f\u062f \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u062f\u0627\u0631\u0627\u06cc \u062e\u0631\u0648\u062c\u06cc \u0647\u0627\u06cc \u062a\u06a9\u06cc (\u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u062e\u0631\u0648\u062c\u06cc \u0634\u0627\u0645\u0644 \u06cc\u06a9 \u062f\u0627\u062f\u0647 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u0628\u0627 \u0634\u0631\u0627\u06cc\u0637 \u062f\u0642\u06cc\u0642 \u0627\u0633\u062a) \u06cc\u0627 \u0686\u0646\u062f\u06af\u0627\u0646\u0647 (\u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u06cc\u06a9 \u0628\u0631\u062f\u0627\u0631 \u062d\u0627\u0648\u06cc \u0686\u0646\u062f\u06cc\u0646 \u0645\u0642\u062f\u0627\u0631 \u0627\u0633\u062a) \u062f\u0631 \u0646\u0638\u0631 \u06af\u0631\u0641\u062a.<\/p>\n<p>\u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u062f\u0631 \u0628\u0631\u062e\u06cc \u0627\u0632 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc\u060c \u0645\u0627 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u062e\u0631\u0648\u062c\u06cc \u0647\u0627\u06cc \u0645\u062a\u0639\u062f\u062f\u06cc \u0631\u0627 \u062f\u0631 \u0645\u0631\u0627\u062d\u0644 \u0632\u0645\u0627\u0646\u06cc \u062a\u0642\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u0645.  \u0628\u0647 \u0639\u0628\u0627\u0631\u062a \u062f\u06cc\u06af\u0631\u060c \u0628\u0631\u0627\u06cc \u0647\u0631 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u062f\u0631 \u0648\u0631\u0648\u062f\u06cc\u060c \u06cc\u06a9 \u06af\u0627\u0645 \u0632\u0645\u0627\u0646\u06cc \u0645\u0631\u0628\u0648\u0637\u0647 \u0631\u0627 \u062f\u0631 \u062e\u0631\u0648\u062c\u06cc \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645.  \u0686\u0646\u06cc\u0646 \u0645\u062f\u0644 \u0647\u0627\u06cc\u06cc \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0628\u0631\u0627\u06cc \u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u0686\u0646\u062f \u0628\u0647 \u0686\u0646\u062f \u0628\u0627 \u0637\u0648\u0644 \u0647\u0627\u06cc \u0645\u062a\u063a\u06cc\u0631 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f.<\/p>\n<h3 id=\"encoderdecodermodel\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%af%d9%84_%d8%b1%d9%85%d8%b2%da%af%d8%b0%d8%a7%d8%b1-%d8%b1%d9%85%d8%b2%da%af%d8%b4%d8%a7\"><\/span>\u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u0628\u0631\u0627\u06cc \u062d\u0644 \u0686\u0646\u06cc\u0646 \u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc\u060c \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631 &#8211; \u0631\u0645\u0632\u06af\u0634\u0627 \u0637\u0631\u0627\u062d\u06cc \u0634\u062f\u0647 \u0627\u0633\u062a.  \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627 \u0627\u0633\u0627\u0633\u0627\u064b \u06cc\u06a9 \u0646\u0627\u0645 \u0641\u0627\u0646\u062a\u0632\u06cc \u0628\u0631\u0627\u06cc \u0645\u0639\u0645\u0627\u0631\u06cc \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0628\u0627 \u062f\u0648 \u0644\u0627\u06cc\u0647 LSTM \u0627\u0633\u062a.<\/p>\n<p>\u0644\u0627\u06cc\u0647 \u0627\u0648\u0644 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u0631\u0645\u0632\u06af\u0630\u0627\u0631 \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u062f\u0646\u0628\u0627\u0644\u0647 \u0648\u0631\u0648\u062f\u06cc \u0631\u0627 \u0631\u0645\u0632\u06af\u0630\u0627\u0631\u06cc \u0645\u06cc \u06a9\u0646\u062f.  \u0631\u0645\u0632\u06af\u0634\u0627 \u0647\u0645\u0686\u0646\u06cc\u0646 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 LSTM \u0627\u0633\u062a \u06a9\u0647 \u0633\u0647 \u0648\u0631\u0648\u062f\u06cc \u0631\u0627 \u0645\u06cc \u067e\u0630\u06cc\u0631\u062f: \u062f\u0646\u0628\u0627\u0644\u0647 \u06a9\u062f\u06af\u0630\u0627\u0631\u06cc \u0634\u062f\u0647 \u0627\u0632 \u0631\u0645\u0632\u06af\u0630\u0627\u0631 LSTM\u060c \u062d\u0627\u0644\u062a \u067e\u0646\u0647\u0627\u0646 \u0642\u0628\u0644\u06cc \u0648 \u0648\u0631\u0648\u062f\u06cc \u0641\u0639\u0644\u06cc.  \u062f\u0631 \u0637\u0648\u0644 \u0622\u0645\u0648\u0632\u0634\u060c \u062e\u0631\u0648\u062c\u06cc \u0648\u0627\u0642\u0639\u06cc \u062f\u0631 \u0647\u0631 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.  \u0647\u0646\u06af\u0627\u0645 \u0627\u0646\u062c\u0627\u0645 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0647\u0627\u060c \u062e\u0631\u0648\u062c\u06cc \u0631\u0645\u0632\u06af\u0630\u0627\u0631\u060c \u062d\u0627\u0644\u062a \u067e\u0646\u0647\u0627\u0646 \u0641\u0639\u0644\u06cc \u0648 \u062e\u0631\u0648\u062c\u06cc \u0642\u0628\u0644\u06cc \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0648\u0631\u0648\u062f\u06cc \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062f\u0631 \u0647\u0631 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.  \u0627\u06cc\u0646 \u0645\u0641\u0627\u0647\u06cc\u0645 \u0632\u0645\u0627\u0646\u06cc \u0642\u0627\u0628\u0644 \u062f\u0631\u06a9 \u062a\u0631 \u0645\u06cc \u0634\u0648\u0646\u062f \u06a9\u0647 \u0622\u0646\u0647\u0627 \u0631\u0627 \u062f\u0631 \u0639\u0645\u0644 \u062f\u0631 \u0628\u062e\u0634 \u0628\u0639\u062f\u06cc \u0645\u0634\u0627\u0647\u062f\u0647 \u06a9\u0646\u06cc\u062f.<\/p>\n<h3 id=\"manytomanysequenceproblemswithsinglefeature\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%b4%da%a9%d9%84%d8%a7%d8%aa_%d8%aa%d9%88%d8%a7%d9%84%db%8c_%da%86%d9%86%d8%af_%d8%a8%d9%87_%da%86%d9%86%d8%af_%d8%a8%d8%a7_%db%8c%da%a9_%d9%88%db%8c%da%98%da%af%db%8c\"><\/span>\u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u0686\u0646\u062f \u0628\u0647 \u0686\u0646\u062f \u0628\u0627 \u06cc\u06a9 \u0648\u06cc\u0698\u06af\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0628\u062e\u0634\u060c \u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u0686\u0646\u062f \u0628\u0647 \u0686\u0646\u062f \u0631\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627 \u062d\u0644 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f\u060c \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0647\u0631 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u062f\u0631 \u0646\u0645\u0648\u0646\u0647 \u0648\u0631\u0648\u062f\u06cc \u062f\u0627\u0631\u0627\u06cc \u06cc\u06a9 \u0648\u06cc\u0698\u06af\u06cc \u0627\u0633\u062a.<\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u0628\u062a\u062f\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u062e\u0648\u062f \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645.<\/p>\n<h4 id=\"creatingthedataset\">\u0627\u06cc\u062c\u0627\u062f \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647<\/h4>\n<pre><code class=\"hljs\">X = <span class=\"hljs-built_in\">list<\/span>()\nY = <span class=\"hljs-built_in\">list<\/span>()\nX = (x <span class=\"hljs-keyword\">for<\/span> x <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(<span class=\"hljs-number\">5<\/span>, <span class=\"hljs-number\">301<\/span>, <span class=\"hljs-number\">5<\/span>))\nY = (y <span class=\"hljs-keyword\">for<\/span> y <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">316<\/span>, <span class=\"hljs-number\">5<\/span>))\n\nX = np.array(X).reshape(<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">1<\/span>)\nY = np.array(Y).reshape(<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">1<\/span>)\n<\/code><\/pre>\n<p>\u0648\u0631\u0648\u062f\u06cc <code>X<\/code> \u0634\u0627\u0645\u0644 20 \u0646\u0645\u0648\u0646\u0647 \u0627\u0633\u062a \u06a9\u0647 \u0647\u0631 \u0646\u0645\u0648\u0646\u0647 \u0634\u0627\u0645\u0644 3 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u0628\u0627 \u06cc\u06a9 \u0648\u06cc\u0698\u06af\u06cc \u0627\u0633\u062a.  \u06cc\u06a9 \u0646\u0645\u0648\u0646\u0647 \u0648\u0631\u0648\u062f\u06cc \u0628\u0647 \u0627\u06cc\u0646 \u0635\u0648\u0631\u062a \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">(((  5)\n  ( 10)\n  ( 15))\n<\/code><\/pre>\n<p>\u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0646\u0645\u0648\u0646\u0647 \u0648\u0631\u0648\u062f\u06cc \u0634\u0627\u0645\u0644 3 \u0645\u0642\u062f\u0627\u0631 \u0627\u0633\u062a \u06a9\u0647 \u0627\u0633\u0627\u0633\u0627\u064b 3 \u0645\u0636\u0631\u0628 5 \u0645\u062a\u0648\u0627\u0644\u06cc \u0647\u0633\u062a\u0646\u062f. \u062f\u0646\u0628\u0627\u0644\u0647 \u062e\u0631\u0648\u062c\u06cc \u0645\u0631\u0628\u0648\u0637\u0647 \u0628\u0631\u0627\u06cc \u0646\u0645\u0648\u0646\u0647 \u0648\u0631\u0648\u062f\u06cc \u0628\u0627\u0644\u0627 \u0628\u0647 \u0634\u0631\u062d \u0632\u06cc\u0631 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">((( 20)\n  ( 25)\n  ( 30))\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0634\u0627\u0645\u0644 \u0633\u0647 \u0645\u0636\u0631\u0628 5 \u0645\u062a\u0648\u0627\u0644\u06cc \u0628\u0639\u062f\u06cc \u0627\u0633\u062a. \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u062e\u0631\u0648\u062c\u06cc \u062f\u0631 \u0627\u06cc\u0646 \u062d\u0627\u0644\u062a \u0628\u0627 \u0622\u0646\u0686\u0647 \u062f\u0631 \u0642\u0633\u0645\u062a \u0647\u0627\u06cc \u0642\u0628\u0644\u06cc \u062f\u06cc\u062f\u0647 \u0627\u06cc\u0645 \u0645\u062a\u0641\u0627\u0648\u062a \u0627\u0633\u062a.  \u0628\u0631\u0627\u06cc \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627\u060c \u062e\u0631\u0648\u062c\u06cc \u0628\u0627\u06cc\u062f \u0628\u0647 \u0641\u0631\u0645\u062a \u0633\u0647 \u0628\u0639\u062f\u06cc \u062d\u0627\u0648\u06cc \u062a\u0639\u062f\u0627\u062f \u0646\u0645\u0648\u0646\u0647\u060c \u0645\u0631\u0627\u062d\u0644 \u0632\u0645\u0627\u0646\u06cc \u0648 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0646\u06cc\u0632 \u062a\u0628\u062f\u06cc\u0644 \u0634\u0648\u062f.  \u0627\u06cc\u0646 \u0628\u0647 \u0627\u06cc\u0646 \u062f\u0644\u06cc\u0644 \u0627\u0633\u062a \u06a9\u0647 \u0631\u0645\u0632\u06af\u0634\u0627 \u062f\u0631 \u0647\u0631 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u06cc\u06a9 \u062e\u0631\u0648\u062c\u06cc \u062a\u0648\u0644\u06cc\u062f \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u0645\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u062e\u0648\u062f \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0631\u062f\u0647 \u0627\u06cc\u0645.  \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0647\u0627\u06cc \u0645\u0627 \u0627\u0633\u062a.  \u0645\u0627 \u0645\u062f\u0644 \u0647\u0627\u06cc LSTM \u067e\u0634\u062a\u0647 \u0627\u06cc \u0648 LSTM \u062f\u0648 \u0637\u0631\u0641\u0647 \u0631\u0627 \u062f\u0631 \u0628\u062e\u0634 \u0647\u0627\u06cc \u0632\u06cc\u0631 \u0622\u0645\u0648\u0632\u0634 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u062f.<\/p>\n<h4 id=\"solutionviastackedlstm\">\u0631\u0627\u0647 \u062d\u0644 \u0627\u0632 \u0637\u0631\u06cc\u0642 Stacked LSTM<\/h4>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 LSTM \u0647\u0627\u06cc \u067e\u0634\u062a\u0647 \u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> RepeatVector\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> TimeDistributed\n\nmodel = Sequential()\n\n\nmodel.add(LSTM(<span class=\"hljs-number\">100<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>, input_shape=(<span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">1<\/span>)))\n\n\nmodel.add(RepeatVector(<span class=\"hljs-number\">3<\/span>))\n\n\nmodel.add(LSTM(<span class=\"hljs-number\">100<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>, return_sequences=<span class=\"hljs-literal\">True<\/span>))\n\nmodel.add(TimeDistributed(Dense(<span class=\"hljs-number\">1<\/span>)))\nmodel.<span class=\"hljs-built_in\">compile<\/span>(optimizer=<span class=\"hljs-string\">'adam'<\/span>, loss=<span class=\"hljs-string\">'mse'<\/span>)\n\n<span class=\"hljs-built_in\">print<\/span>(model.summary())\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0641\u0648\u0642 \u0627\u0648\u0644\u06cc\u0646 \u0644\u0627\u06cc\u0647 LSTM \u0644\u0627\u06cc\u0647 \u0631\u0645\u0632\u06af\u0630\u0627\u0631 \u0627\u0633\u062a.<\/p>\n<p>\u0633\u067e\u0633 \u0628\u0631\u062f\u0627\u0631 \u062a\u06a9\u0631\u0627\u0631 \u0631\u0627 \u0628\u0647 \u0645\u062f\u0644 \u062e\u0648\u062f \u0627\u0636\u0627\u0641\u0647 \u06a9\u0631\u062f\u0647 \u0627\u06cc\u0645.  \u0628\u0631\u062f\u0627\u0631 \u062a\u06a9\u0631\u0627\u0631 \u062e\u0631\u0648\u062c\u06cc \u0631\u0627 \u0627\u0632 \u0631\u0645\u0632\u06af\u0630\u0627\u0631 \u0645\u06cc \u06af\u06cc\u0631\u062f \u0648 \u0622\u0646 \u0631\u0627 \u0628\u0647 \u0637\u0648\u0631 \u0645\u06a9\u0631\u0631 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0648\u0631\u0648\u062f\u06cc \u062f\u0631 \u0647\u0631 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u0628\u0647 \u0631\u0645\u0632\u06af\u0634\u0627 \u0645\u06cc \u062f\u0647\u062f.  \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u062f\u0631 \u062e\u0631\u0648\u062c\u06cc \u0645\u0627 \u0633\u0647 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u062f\u0627\u0631\u06cc\u0645.  \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0647\u0631 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u062e\u0631\u0648\u062c\u06cc\u060c \u0631\u0645\u0632\u06af\u0634\u0627 \u0627\u0632 \u0645\u0642\u062f\u0627\u0631 \u0628\u0631\u062f\u0627\u0631 \u062a\u06a9\u0631\u0627\u0631\u060c \u062d\u0627\u0644\u062a \u067e\u0646\u0647\u0627\u0646 \u0627\u0632 \u062e\u0631\u0648\u062c\u06cc \u0642\u0628\u0644\u06cc \u0648 \u0648\u0631\u0648\u062f\u06cc \u0641\u0639\u0644\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u0628\u0639\u062f \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u0631\u0645\u0632\u06af\u0634\u0627 \u062f\u0627\u0631\u06cc\u0645.  \u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u062e\u0631\u0648\u062c\u06cc \u0628\u0647 \u0635\u0648\u0631\u062a \u06cc\u06a9 \u06af\u0627\u0645 \u0632\u0645\u0627\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0641\u0631\u0645\u062a \u0633\u0647 \u0628\u0639\u062f\u06cc \u0627\u0633\u062a <code>return_sequences<\/code> \u0628\u0631\u0627\u06cc \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0634\u0627 \u062a\u0646\u0638\u06cc\u0645 \u0634\u062f\u0647 \u0627\u0633\u062a <code>True<\/code>.  \u0631\u0627 <code>TimeDistributed<\/code> \u0644\u0627\u06cc\u0647 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062c\u062f\u0627\u06af\u0627\u0646\u0647 \u062e\u0631\u0648\u062c\u06cc \u0628\u0631\u0627\u06cc \u0647\u0631 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u062e\u0644\u0627\u0635\u0647 \u0645\u062f\u0644 \u0628\u0631\u0627\u06cc \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627 \u0627\u06cc\u062c\u0627\u062f \u0634\u062f\u0647 \u062f\u0631 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0627\u0644\u0627 \u0628\u0647 \u0634\u0631\u062d \u0632\u06cc\u0631 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">Layer (type)                 Output Shape              Param #\n=================================================================\nlstm_40 (LSTM)               (None, 100)               40800\n_________________________________________________________________\nrepeat_vector_7 (RepeatVecto (None, 3, 100)            0\n_________________________________________________________________\nlstm_41 (LSTM)               (None, 3, 100)            80400\n_________________________________________________________________\ntime_distributed_7 (TimeDist (None, 3, 1)              101\n=================================================================\nTotal params: 121,301\nTrainable params: 121,301\nNon-trainable params: 0\n<\/code><\/pre>\n<p>\u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0628\u0631\u062f\u0627\u0631 \u062a\u06a9\u0631\u0627\u0631 \u0641\u0642\u0637 \u062e\u0631\u0648\u062c\u06cc \u0631\u0645\u0632\u06af\u0630\u0627\u0631 \u0631\u0627 \u062a\u06a9\u0631\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u0647\u06cc\u0686 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u06cc \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0646\u062f\u0627\u0631\u062f.<\/p>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627\u06cc \u0641\u0648\u0642 \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u0645\u06cc \u062f\u0647\u062f.<\/p>\n<pre><code class=\"hljs\">history = model.fit(X, Y, epochs=<span class=\"hljs-number\">1000<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>, verbose=<span class=\"hljs-number\">1<\/span>, batch_size=<span class=\"hljs-number\">3<\/span>)\n<\/code><\/pre>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u06cc\u06a9 \u0646\u0642\u0637\u0647 \u0622\u0632\u0645\u0627\u06cc\u0634 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645 \u0648 \u0628\u0628\u06cc\u0646\u06cc\u0645 \u0622\u06cc\u0627 \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627 \u0645\u0627 \u0642\u0627\u062f\u0631 \u0628\u0647 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u062e\u0631\u0648\u062c\u06cc \u0686\u0646\u062f \u0645\u0631\u062d\u0644\u0647\u200c\u0627\u06cc \u0627\u0633\u062a \u06cc\u0627 \u062e\u06cc\u0631.  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">test_input = array((<span class=\"hljs-number\">300<\/span>, <span class=\"hljs-number\">305<\/span>, <span class=\"hljs-number\">310<\/span>))\ntest_input = test_input.reshape((<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">1<\/span>))\ntest_output = model.predict(test_input, verbose=<span class=\"hljs-number\">0<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(test_output)\n<\/code><\/pre>\n<p>\u062f\u0646\u0628\u0627\u0644\u0647 \u0648\u0631\u0648\u062f\u06cc \u0645\u0627 \u0634\u0627\u0645\u0644 \u0633\u0647 \u0645\u0642\u062f\u0627\u0631 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc 300\u060c 305 \u0648 310 \u0627\u0633\u062a. \u062e\u0631\u0648\u062c\u06cc \u0628\u0627\u06cc\u062f \u0633\u0647 \u0645\u0636\u0631\u0628 \u0628\u0639\u062f\u06cc 5 \u06cc\u0639\u0646\u06cc 315\u060c 320 \u0648 325 \u0628\u0627\u0634\u062f. \u0645\u0646 \u062e\u0631\u0648\u062c\u06cc \u0632\u06cc\u0631 \u0631\u0627 \u062f\u0631\u06cc\u0627\u0641\u062a \u06a9\u0631\u062f\u0645:<\/p>\n<pre><code class=\"hljs\">(((316.02878)\n  (322.27145)\n  (328.5536 )))\n<\/code><\/pre>\n<p>\u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u062e\u0631\u0648\u062c\u06cc \u0628\u0647 \u0635\u0648\u0631\u062a \u0633\u0647 \u0628\u0639\u062f\u06cc \u0627\u0633\u062a.<\/p>\n<h4 id=\"solutionviabidirectionallstm\">\u0631\u0627\u0647 \u062d\u0644 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u062f\u0648 \u062c\u0647\u062a\u0647 LSTM<\/h4>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u06a9\u0646\u0648\u0646 \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627 \u0631\u0627 \u0628\u0627 LSTM \u0647\u0627\u06cc \u062f\u0648 \u0637\u0631\u0641\u0647 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645 \u0648 \u0628\u0628\u06cc\u0646\u06cc\u0645 \u0622\u06cc\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0646\u062a\u0627\u06cc\u062c \u0628\u0647\u062a\u0631\u06cc \u0628\u06af\u06cc\u0631\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> RepeatVector\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> TimeDistributed\n\nmodel = Sequential()\nmodel.add(Bidirectional(LSTM(<span class=\"hljs-number\">100<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>, input_shape=(<span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">1<\/span>))))\nmodel.add(RepeatVector(<span class=\"hljs-number\">3<\/span>))\nmodel.add(Bidirectional(LSTM(<span class=\"hljs-number\">100<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>, return_sequences=<span class=\"hljs-literal\">True<\/span>)))\nmodel.add(TimeDistributed(Dense(<span class=\"hljs-number\">1<\/span>)))\nmodel.<span class=\"hljs-built_in\">compile<\/span>(optimizer=<span class=\"hljs-string\">'adam'<\/span>, loss=<span class=\"hljs-string\">'mse'<\/span>)\n\nhistory = model.fit(X, Y, epochs=<span class=\"hljs-number\">1000<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>, verbose=<span class=\"hljs-number\">1<\/span>, batch_size=<span class=\"hljs-number\">3<\/span>)\n<\/code><\/pre>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0641\u0648\u0642 \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627 \u0631\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 LSTM \u062f\u0648\u0637\u0631\u0641\u0647 \u0622\u0645\u0648\u0632\u0634 \u0645\u06cc \u062f\u0647\u062f.  \u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u06a9\u0646\u06cc\u0645 \u0631\u0648\u06cc \u0646\u0642\u0637\u0647 \u0622\u0632\u0645\u0648\u0646 \u06cc\u0639\u0646\u06cc (300\u060c 305\u060c 310).<\/p>\n<pre><code class=\"hljs\">test_output = model.predict(test_input, verbose=<span class=\"hljs-number\">0<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(test_output)\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u062e\u0631\u0648\u062c\u06cc \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">(((315.7526 )\n  (321.47153)\n  (327.94025)))\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0627\u06cc \u06a9\u0647 \u0645\u0646 \u0627\u0632 \u0637\u0631\u06cc\u0642 LSTM \u0647\u0627\u06cc \u062f\u0648\u0637\u0631\u0641\u0647 \u062f\u0631\u06cc\u0627\u0641\u062a \u06a9\u0631\u062f\u0645 \u0628\u0647\u062a\u0631 \u0627\u0632 \u0622\u0646 \u0686\u06cc\u0632\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627 \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 LSTM \u0627\u0646\u0628\u0627\u0634\u062a\u0647 \u0634\u062f\u0647 \u062f\u0631\u06cc\u0627\u0641\u062a \u06a9\u0631\u062f\u0645.<\/p>\n<h3 id=\"manytomanysequenceproblemswithmultiplefeatures\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%b4%da%a9%d9%84%d8%a7%d8%aa_%d8%aa%d9%88%d8%a7%d9%84%db%8c_%da%86%d9%86%d8%af_%d8%a8%d9%87_%da%86%d9%86%d8%af_%d8%a8%d8%a7_%da%86%d9%86%d8%af%db%8c%d9%86_%d9%88%db%8c%da%98%da%af%db%8c\"><\/span>\u0645\u0634\u06a9\u0644\u0627\u062a \u062a\u0648\u0627\u0644\u06cc \u0686\u0646\u062f \u0628\u0647 \u0686\u0646\u062f \u0628\u0627 \u0686\u0646\u062f\u06cc\u0646 \u0648\u06cc\u0698\u06af\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u062a\u0627 \u0628\u0647 \u062d\u0627\u0644 \u062d\u062f\u0633 \u0632\u062f\u0647 \u0628\u0627\u0634\u06cc\u062f\u060c \u062f\u0631 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u0686\u0646\u062f \u0628\u0647 \u0686\u0646\u062f\u060c \u0647\u0631 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u062f\u0631 \u0646\u0645\u0648\u0646\u0647 \u0648\u0631\u0648\u062f\u06cc \u062d\u0627\u0648\u06cc \u0686\u0646\u062f\u06cc\u0646 \u0648\u06cc\u0698\u06af\u06cc \u0627\u0633\u062a.<\/p>\n<h4 id=\"creatingthedataset\">\u0627\u06cc\u062c\u0627\u062f \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647<\/h4>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0633\u0627\u062f\u0647 \u0628\u0631\u0627\u06cc \u0645\u0634\u06a9\u0644 \u062e\u0648\u062f \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">X = <span class=\"hljs-built_in\">list<\/span>()\nY = <span class=\"hljs-built_in\">list<\/span>()\nX1 = (x1 <span class=\"hljs-keyword\">for<\/span> x1 <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(<span class=\"hljs-number\">5<\/span>, <span class=\"hljs-number\">301<\/span>, <span class=\"hljs-number\">5<\/span>))\nX2 = (x2 <span class=\"hljs-keyword\">for<\/span> x2 <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">316<\/span>, <span class=\"hljs-number\">5<\/span>))\nY = (y <span class=\"hljs-keyword\">for<\/span> y <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(<span class=\"hljs-number\">35<\/span>, <span class=\"hljs-number\">331<\/span>, <span class=\"hljs-number\">5<\/span>))\n\nX = np.column_stack((X1, X2))\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0627\u0644\u0627 \u062f\u0648 \u0644\u06cc\u0633\u062a \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645 <code>X1<\/code> \u0648 <code>X2<\/code>.  \u0644\u06cc\u0633\u062a <code>X1<\/code> \u0634\u0627\u0645\u0644 \u062a\u0645\u0627\u0645 \u0645\u0636\u0631\u0628 \u0647\u0627\u06cc 5 \u0627\u0632 5 \u062a\u0627 300 (\u0634\u0627\u0645\u0644) \u0648 \u0644\u06cc\u0633\u062a \u0627\u0633\u062a <code>X2<\/code> \u0634\u0627\u0645\u0644 \u062a\u0645\u0627\u0645 \u0645\u0636\u0631\u0628 \u0647\u0627\u06cc 5 \u0627\u0632 20 \u062a\u0627 315 (\u0634\u0627\u0645\u0644).  \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a\u060c \u0644\u06cc\u0633\u062a <code>Y<\/code>\u060c \u06a9\u0647 \u0627\u062a\u0641\u0627\u0642\u0627\u064b \u062e\u0631\u0648\u062c\u06cc \u0634\u0627\u0645\u0644 \u062a\u0645\u0627\u0645 \u0645\u0636\u0631\u0628 \u0647\u0627\u06cc 5 \u0628\u06cc\u0646 35 \u0648 330 (\u0634\u0627\u0645\u0644) \u0627\u0633\u062a.  \u0644\u06cc\u0633\u062a \u0648\u0631\u0648\u062f\u06cc \u0646\u0647\u0627\u06cc\u06cc <code>X<\/code> \u06cc\u06a9 \u0627\u062f\u063a\u0627\u0645 \u0633\u062a\u0648\u0646\u06cc \u0627\u0632 <code>X1<\/code> \u0648 <code>X2<\/code>.<\/p>\n<p>\u0645\u062b\u0644 \u0647\u0645\u06cc\u0634\u0647\u060c \u0628\u0627\u06cc\u062f \u0648\u0631\u0648\u062f\u06cc \u062e\u0648\u062f \u0631\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u062f\u0647\u06cc\u0645 <code>X<\/code> \u0648 \u062e\u0631\u0648\u062c\u06cc <code>Y<\/code> \u0642\u0628\u0644 \u0627\u0632 \u0627\u06cc\u0646\u06a9\u0647 \u0628\u062a\u0648\u0627\u0646 \u0627\u0632 \u0622\u0646\u0647\u0627 \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 LSTM \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f.<\/p>\n<pre><code class=\"hljs\">X = np.array(X).reshape(<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">2<\/span>)\nY = np.array(Y).reshape(<span class=\"hljs-number\">20<\/span>, <span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">1<\/span>)\n<\/code><\/pre>\n<p>\u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0648\u0631\u0648\u062f\u06cc \u0631\u0627 \u0628\u0628\u06cc\u0646\u06cc\u062f <code>X<\/code> \u0628\u0647 20 \u0646\u0645\u0648\u0646\u0647 \u0627\u0632 \u0633\u0647 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u0628\u0627 2 \u0648\u06cc\u0698\u06af\u06cc \u062a\u063a\u06cc\u06cc\u0631 \u0634\u06a9\u0644 \u062f\u0627\u062f\u0647 \u0627\u0633\u062a \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u062e\u0631\u0648\u062c\u06cc \u0628\u0647 \u0627\u0628\u0639\u0627\u062f \u0645\u0634\u0627\u0628\u0647 \u0627\u0645\u0627 \u0628\u0627 1 \u0648\u06cc\u0698\u06af\u06cc \u062a\u063a\u06cc\u06cc\u0631 \u0634\u06a9\u0644 \u062f\u0627\u062f\u0647 \u0627\u0633\u062a.<\/p>\n<p>\u0627\u0648\u0644\u06cc\u0646 \u0646\u0645\u0648\u0646\u0647 \u0627\u0632 \u0648\u0631\u0648\u062f\u06cc \u0628\u0647 \u0627\u06cc\u0646 \u0635\u0648\u0631\u062a \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">(( 5  20)\n( 10  25)\n( 15  30))\n<\/code><\/pre>\n<p>\u0648\u0631\u0648\u062f\u06cc \u0634\u0627\u0645\u0644 6 \u0645\u0636\u0631\u0628 \u0645\u062a\u0648\u0627\u0644\u06cc \u0627\u0632 \u0639\u062f\u062f \u0635\u062d\u06cc\u062d 5 \u0627\u0633\u062a \u06a9\u0647 \u0647\u0631 \u06a9\u062f\u0627\u0645 \u0633\u0647 \u0645\u0636\u0631\u0628 \u062f\u0631 \u062f\u0648 \u0633\u062a\u0648\u0646 \u0647\u0633\u062a\u0646\u062f.  \u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u062e\u0631\u0648\u062c\u06cc \u0645\u0631\u0628\u0648\u0637 \u0628\u0647 \u0646\u0645\u0648\u0646\u0647 \u0648\u0631\u0648\u062f\u06cc \u0628\u0627\u0644\u0627 \u0622\u0645\u062f\u0647 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">(( 35)\n( 40)\n( 45))\n<\/code><\/pre>\n<p>\u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f\u060c \u062e\u0631\u0648\u062c\u06cc \u0634\u0627\u0645\u0644 \u0633\u0647 \u0645\u0636\u0631\u0628 5 \u0645\u062a\u0648\u0627\u0644\u06cc \u0628\u0639\u062f\u06cc \u0627\u0633\u062a.<\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u06a9\u0646\u0648\u0646 \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062f\u0646\u0628\u0627\u0644\u0647 \u0628\u0627\u0644\u0627 \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645.  \u0627\u0628\u062a\u062f\u0627 \u06cc\u06a9 \u0631\u0645\u0632\u06af\u0630\u0627\u0631 \u0631\u0645\u0632\u06af\u0630\u0627\u0631 \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 LSTM \u0627\u0646\u0628\u0627\u0634\u062a\u0647 \u0634\u062f\u0647 \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u062f.<\/p>\n<h4 id=\"solutionviastackedlstm\">\u0631\u0627\u0647 \u062d\u0644 \u0627\u0632 \u0637\u0631\u06cc\u0642 Stacked LSTM<\/h4>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0645\u062f\u0644 LSTM \u0627\u0646\u0628\u0627\u0634\u062a\u0647 \u0634\u062f\u0647 \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u0645\u06cc \u062f\u0647\u062f.  \u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0634\u06a9\u0644 \u0648\u0631\u0648\u062f\u06cc \u0627\u06a9\u0646\u0648\u0646 (3\u060c 2) \u0645\u0631\u0628\u0648\u0637 \u0628\u0647 \u0633\u0647 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u0648 \u062f\u0648 \u0648\u06cc\u0698\u06af\u06cc \u062f\u0631 \u0648\u0631\u0648\u062f\u06cc \u0627\u0633\u062a.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> RepeatVector\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> TimeDistributed\n\nmodel = Sequential()\nmodel.add(LSTM(<span class=\"hljs-number\">100<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>, input_shape=(<span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">2<\/span>)))\nmodel.add(RepeatVector(<span class=\"hljs-number\">3<\/span>))\nmodel.add(LSTM(<span class=\"hljs-number\">100<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>, return_sequences=<span class=\"hljs-literal\">True<\/span>))\nmodel.add(TimeDistributed(Dense(<span class=\"hljs-number\">1<\/span>)))\nmodel.<span class=\"hljs-built_in\">compile<\/span>(optimizer=<span class=\"hljs-string\">'adam'<\/span>, loss=<span class=\"hljs-string\">'mse'<\/span>)\n\nhistory = model.fit(X, Y, epochs=<span class=\"hljs-number\">1000<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>, verbose=<span class=\"hljs-number\">1<\/span>, batch_size=<span class=\"hljs-number\">3<\/span>)\n<\/code><\/pre>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u06a9\u0646\u0648\u0646 \u06cc\u06a9 \u0646\u0642\u0637\u0647 \u0622\u0632\u0645\u0627\u06cc\u0634 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<pre><code class=\"hljs\">X1 = (<span class=\"hljs-number\">300<\/span>, <span class=\"hljs-number\">305<\/span>, <span class=\"hljs-number\">310<\/span>)\nX2 = (<span class=\"hljs-number\">315<\/span>, <span class=\"hljs-number\">320<\/span>, <span class=\"hljs-number\">325<\/span>)\n\ntest_input = np.column_stack((X1, X2))\n\ntest_input = test_input.reshape((<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">2<\/span>))\n<span class=\"hljs-built_in\">print<\/span>(test_input)\n<\/code><\/pre>\n<p>\u0646\u0642\u0637\u0647 \u062a\u0633\u062a \u0628\u0647 \u0627\u06cc\u0646 \u0635\u0648\u0631\u062a \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">(((300 315)\n  (305 320)\n  (310 325)))\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0648\u0627\u0642\u0639\u06cc \u0646\u0642\u0637\u0647 \u062a\u0633\u062a \u0628\u0627\u0644\u0627 (330\u060c 335\u060c 340) \u0627\u0633\u062a.  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0628\u0628\u06cc\u0646\u06cc\u0645 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0647\u0627\u06cc \u0645\u062f\u0644 \u0686\u06cc\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">test_output = model.predict(test_input, verbose=<span class=\"hljs-number\">0<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(test_output)\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0634\u062f\u0647 \u0627\u06cc\u0646 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">(((324.5786 )\n  (328.89658)\n  (335.67603)))\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0627\u0632 \u062f\u0631\u0633\u062a \u0628\u0648\u062f\u0646 \u0641\u0627\u0635\u0644\u0647 \u0632\u06cc\u0627\u062f\u06cc \u062f\u0627\u0631\u062f.<\/p>\n<h4 id=\"solutionviabidirectionallstm\">\u0631\u0627\u0647 \u062d\u0644 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u062f\u0648 \u062c\u0647\u062a\u0647 LSTM<\/h4>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u06a9\u0646\u0648\u0646 \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627 \u0631\u0627 \u0628\u0631 \u0627\u0633\u0627\u0633 \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645 \u0631\u0648\u06cc LSTM \u0647\u0627\u06cc \u062f\u0648 \u0637\u0631\u0641\u0647 \u0648 \u0628\u0628\u06cc\u0646\u06cc\u062f \u0622\u06cc\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0646\u062a\u0627\u06cc\u062c \u0628\u0647\u0628\u0648\u062f \u06cc\u0627\u0641\u062a\u0647 \u0627\u06cc \u062f\u0631\u06cc\u0627\u0641\u062a \u06a9\u0646\u06cc\u0645.  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0645\u062f\u0644 \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u0645\u06cc \u062f\u0647\u062f.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> RepeatVector\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> TimeDistributed\n\nmodel = Sequential()\nmodel.add(Bidirectional(LSTM(<span class=\"hljs-number\">100<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>, input_shape=(<span class=\"hljs-number\">3<\/span>, <span class=\"hljs-number\">2<\/span>))))\nmodel.add(RepeatVector(<span class=\"hljs-number\">3<\/span>))\nmodel.add(Bidirectional(LSTM(<span class=\"hljs-number\">100<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>, return_sequences=<span class=\"hljs-literal\">True<\/span>)))\nmodel.add(TimeDistributed(Dense(<span class=\"hljs-number\">1<\/span>)))\nmodel.<span class=\"hljs-built_in\">compile<\/span>(optimizer=<span class=\"hljs-string\">'adam'<\/span>, loss=<span class=\"hljs-string\">'mse'<\/span>)\n\nhistory = model.fit(X, Y, epochs=<span class=\"hljs-number\">1000<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>, verbose=<span class=\"hljs-number\">1<\/span>, batch_size=<span class=\"hljs-number\">3<\/span>)\n<\/code><\/pre>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0645\u06cc \u06a9\u0646\u062f \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">test_output = model.predict(test_input, verbose=<span class=\"hljs-number\">0<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(test_output)\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u062e\u0631\u0648\u062c\u06cc \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">(((330.49133)\n  (335.35327)\n  (339.64398)))\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0628\u0647 \u062f\u0633\u062a \u0622\u0645\u062f\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0646\u0632\u062f\u06cc\u06a9 \u0628\u0647 \u062e\u0631\u0648\u062c\u06cc \u0648\u0627\u0642\u0639\u06cc \u0627\u0633\u062a (330\u060c 335\u060c 340).  \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 LSTM \u062f\u0648\u0637\u0631\u0641\u0647 \u0645\u0627 \u0627\u0632 LSTM \u0633\u0627\u062f\u0647 \u0628\u0647\u062a\u0631 \u0639\u0645\u0644 \u06a9\u0631\u062f.<\/p>\n<h2 id=\"conclusion\"><span class=\"ez-toc-section\" id=\"%d9%86%d8%aa%db%8c%d8%ac%d9%87\"><\/span>\u0646\u062a\u06cc\u062c\u0647<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u06cc\u0646 \u0642\u0633\u0645\u062a \u062f\u0648\u0645 \u0645\u0642\u0627\u0644\u0647 \u0645\u0646 \u0627\u0633\u062a \u0631\u0648\u06cc &#8220;\u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u0628\u0627 LSTM \u062f\u0631 \u06a9\u0631\u0627\u0633&#8221; (\u0642\u0633\u0645\u062a 1 \u0627\u06cc\u0646\u062c\u0627).  \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0631\u0648\u0634 \u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f \u0648 \u0686\u0646\u062f \u0628\u0647 \u0686\u0646\u062f \u0631\u0627 \u062f\u0631 LSTM \u062f\u06cc\u062f\u06cc\u062f.  \u0647\u0645\u0686\u0646\u06cc\u0646 \u062f\u06cc\u062f\u06cc\u062f \u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0627\u0632 \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062e\u0631\u0648\u062c\u06cc \u0647\u0627\u06cc \u0686\u0646\u062f \u0645\u0631\u062d\u0644\u0647 \u0627\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f.  \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627 \u062f\u0631 \u0627\u0646\u0648\u0627\u0639 \u0628\u0631\u0646\u0627\u0645\u0647 \u0647\u0627\u06cc \u067e\u0631\u062f\u0627\u0632\u0634 \u0632\u0628\u0627\u0646 \u0637\u0628\u06cc\u0639\u06cc \u0645\u0627\u0646\u0646\u062f \u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646 \u0639\u0635\u0628\u06cc \u0648 \u062a\u0648\u0633\u0639\u0647 \u0631\u0628\u0627\u062a \u0686\u062a \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u062f\u0631 \u0645\u0642\u0627\u0644\u0647 \u067e\u06cc\u0634 \u0631\u0648 \u0634\u0627\u0647\u062f \u06a9\u0627\u0631\u0628\u0631\u062f \u0645\u062f\u0644 \u0631\u0645\u0632\u06af\u0630\u0627\u0631-\u0631\u0645\u0632\u06af\u0634\u0627 \u062f\u0631 NLP \u062e\u0648\u0627\u0647\u06cc\u0645 \u0628\u0648\u062f.<\/p>\n<\/div>\n<p><script>\n                        !function(f,b,e,v,n,t,s)\n                        {if(f.fbq)return;n=f.fbq=function(){n.callMethod?\n                        n.callMethod.apply(n,arguments):n.queue.push(arguments)};\n                        if(!f._fbq)f._fbq=n;n.push=n;n.loaded=!0;n.version='2.0';\n                        n.queue=();t=b.createElement(e);t.async=!0;\n                        t.src=v;s=b.getElementsByTagName(e)(0);\n                        s.parentNode.insertBefore(t,s)}(window, document,'script',\n                        'https:\/\/connect.facebook.net\/en_US\/fbevents.js');\n                        fbq('init', '525232124909042');\n                        fbq('track', 'PageView');\n                    <\/script>    (\u0628\u0631\u0686\u0633\u0628\u200c\u0647\u0627 \u0628\u0647 \u062a\u0631\u062c\u0645\u0647)# python<br \/>\n<br \/><br \/>\n<br \/>\u0645\u0646\u062a\u0634\u0631 \u0634\u062f\u0647 \u062f\u0631 1403-01-20 09:50:04<br \/>\n<\/p>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-center kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;center&quot;,&quot;id&quot;:&quot;16065&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;bottom&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;0&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;0&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;\u0627\u0645\u062a\u06cc\u0627\u0632 \u0634\u0645\u0627 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0637\u0644\u0628&quot;,&quot;legend&quot;:&quot;0\\\/5 (0 \u0631\u0627\u06cc)&quot;,&quot;size&quot;:&quot;30&quot;,&quot;title&quot;:&quot;\u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u0628\u0627 LSTM \u062f\u0631 Keras: \u0642\u0633\u0645\u062a 2&quot;,&quot;width&quot;:&quot;0&quot;,&quot;_legend&quot;:&quot;{score}\\\/{best} ({count} \u0631\u0627\u06cc)&quot;,&quot;font_factor&quot;:&quot;1.25&quot;}'>\n            \n<div class=\"kksr-stars\">\n    \n<div class=\"kksr-stars-inactive\">\n            <div class=\"kksr-star\" data-star=\"1\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"2\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"3\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"4\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"5\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n    <\/div>\n    \n<div class=\"kksr-stars-active\" style=\"width: 0px;\">\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n    <\/div>\n<\/div>\n                \n\n<div class=\"kksr-legend\" style=\"font-size: 24px;\">\n            <span class=\"kksr-muted\">\u0627\u0645\u062a\u06cc\u0627\u0632 \u0634\u0645\u0627 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0637\u0644\u0628<\/span>\n    <\/div>\n    <\/div>\n","protected":false},"excerpt":{"rendered":"<p><span class=\"span-reading-time rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">\u0632\u0645\u0627\u0646 \u0644\u0627\u0632\u0645 \u0628\u0631\u0627\u06cc \u0645\u0637\u0627\u0644\u0639\u0647: <\/span> <span class=\"rt-time\"> 10<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span>\u0627\u06cc\u0646 \u0642\u0633\u0645\u062a \u062f\u0648\u0645 \u0648 \u067e\u0627\u06cc\u0627\u0646\u06cc \u0627\u0632 \u0633\u0631\u06cc \u0645\u0642\u0627\u0644\u0627\u062a \u062f\u0648 \u0642\u0633\u0645\u062a\u06cc \u0627\u0633\u062a \u0631\u0648\u06cc \u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u0628\u0627 LSTM \u062f\u0631 \u0642\u0633\u0645\u062a 1 \u0645\u062c\u0645\u0648\u0639\u0647\u060c \u0631\u0648\u0634 \u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u06cc\u06a9 \u0648 \u0686\u0646\u062f \u0628\u0647 \u06cc\u06a9 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 LSTM \u062a\u0648\u0636\u06cc\u062d \u062f\u0627\u062f\u0645. \u062f\u0631 \u0627\u06cc\u0646 \u0642\u0633\u0645\u062a \u0631\u0648\u0634 \u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u06cc\u06a9 \u0628\u0647 \u0686\u0646\u062f \u0648 \u0686\u0646\u062f \u0628\u0647 \u0686\u0646\u062f \u0631\u0627 \u0627\u0632 [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":9162,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1743,620],"tags":[],"class_list":["post-16065","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","category-programming"],"acf":[],"_links":{"self":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/16065","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/comments?post=16065"}],"version-history":[{"count":0,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/16065\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media\/9162"}],"wp:attachment":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media?parent=16065"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/categories?post=16065"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/tags?post=16065"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}