{"id":14178,"date":"2024-01-04T08:58:07","date_gmt":"2024-01-04T05:28:07","guid":{"rendered":"https:\/\/rasanegar.com\/blog\/%d8%aa%d8%b1%d8%a7%d9%86%d8%b3%d9%81%d9%88%d8%b1%d9%85%d8%a7%d8%aa%d9%88%d8%b1-%d8%aa%d9%88%da%a9%d9%86-%d9%88-%d8%ac%d8%a7%d8%b3%d8%a7%d8%b2%db%8c-%d9%85%d9%88%d9%82%d8%b9%db%8c%d8%aa-%d8%a8%d8%a7-ke\/"},"modified":"2024-01-04T08:58:07","modified_gmt":"2024-01-04T05:28:07","slug":"%d8%aa%d8%b1%d8%a7%d9%86%d8%b3%d9%81%d9%88%d8%b1%d9%85%d8%a7%d8%aa%d9%88%d8%b1-%d8%aa%d9%88%da%a9%d9%86-%d9%88-%d8%ac%d8%a7%d8%b3%d8%a7%d8%b2%db%8c-%d9%85%d9%88%d9%82%d8%b9%db%8c%d8%aa-%d8%a8%d8%a7-ke","status":"publish","type":"post","link":"https:\/\/rasanegaar.com\/blog\/%d8%aa%d8%b1%d8%a7%d9%86%d8%b3%d9%81%d9%88%d8%b1%d9%85%d8%a7%d8%aa%d9%88%d8%b1-%d8%aa%d9%88%da%a9%d9%86-%d9%88-%d8%ac%d8%a7%d8%b3%d8%a7%d8%b2%db%8c-%d9%85%d9%88%d9%82%d8%b9%db%8c%d8%aa-%d8%a8%d8%a7-ke\/","title":{"rendered":"\u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631 \u062a\u0648\u06a9\u0646 \u0648 \u062c\u0627\u0633\u0627\u0632\u06cc \u0645\u0648\u0642\u0639\u06cc\u062a \u0628\u0627 Keras"},"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%aa%d8%b1%d8%a7%d9%86%d8%b3%d9%81%d9%88%d8%b1%d9%85%d8%a7%d8%aa%d9%88%d8%b1-%d8%aa%d9%88%da%a9%d9%86-%d9%88-%d8%ac%d8%a7%d8%b3%d8%a7%d8%b2%db%8c-%d9%85%d9%88%d9%82%d8%b9%db%8c%d8%aa-%d8%a8%d8%a7-ke\/#%d9%85%d8%b9%d8%b1%d9%81%db%8c\" >\u0645\u0639\u0631\u0641\u06cc<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%aa%d8%b1%d8%a7%d9%86%d8%b3%d9%81%d9%88%d8%b1%d9%85%d8%a7%d8%aa%d9%88%d8%b1-%d8%aa%d9%88%da%a9%d9%86-%d9%88-%d8%ac%d8%a7%d8%b3%d8%a7%d8%b2%db%8c-%d9%85%d9%88%d9%82%d8%b9%db%8c%d8%aa-%d8%a8%d8%a7-ke\/#kerasnlp\" >KerasNLP<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%aa%d8%b1%d8%a7%d9%86%d8%b3%d9%81%d9%88%d8%b1%d9%85%d8%a7%d8%aa%d9%88%d8%b1-%d8%aa%d9%88%da%a9%d9%86-%d9%88-%d8%ac%d8%a7%d8%b3%d8%a7%d8%b2%db%8c-%d9%85%d9%88%d9%82%d8%b9%db%8c%d8%aa-%d8%a8%d8%a7-ke\/#%d8%aa%d9%88%da%a9%d9%86_%d8%b3%d8%a7%d8%b2%db%8c\" >\u062a\u0648\u06a9\u0646 \u0633\u0627\u0632\u06cc<\/a><\/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%aa%d8%b1%d8%a7%d9%86%d8%b3%d9%81%d9%88%d8%b1%d9%85%d8%a7%d8%aa%d9%88%d8%b1-%d8%aa%d9%88%da%a9%d9%86-%d9%88-%d8%ac%d8%a7%d8%b3%d8%a7%d8%b2%db%8c-%d9%85%d9%88%d9%82%d8%b9%db%8c%d8%aa-%d8%a8%d8%a7-ke\/#%d8%ac%d8%a7%d8%b3%d8%a7%d8%b2%db%8c_%d9%85%d9%88%d9%82%d8%b9%db%8c%d8%aa\" >\u062c\u0627\u0633\u0627\u0632\u06cc \u0645\u0648\u0642\u0639\u06cc\u062a<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%aa%d8%b1%d8%a7%d9%86%d8%b3%d9%81%d9%88%d8%b1%d9%85%d8%a7%d8%aa%d9%88%d8%b1-%d8%aa%d9%88%da%a9%d9%86-%d9%88-%d8%ac%d8%a7%d8%b3%d8%a7%d8%b2%db%8c-%d9%85%d9%88%d9%82%d8%b9%db%8c%d8%aa-%d8%a8%d8%a7-ke\/#tokenandpositionembedding\" >TokenAndPositionEmbedding<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%aa%d8%b1%d8%a7%d9%86%d8%b3%d9%81%d9%88%d8%b1%d9%85%d8%a7%d8%aa%d9%88%d8%b1-%d8%aa%d9%88%da%a9%d9%86-%d9%88-%d8%ac%d8%a7%d8%b3%d8%a7%d8%b2%db%8c-%d9%85%d9%88%d9%82%d8%b9%db%8c%d8%aa-%d8%a8%d8%a7-ke\/#%d9%86%d8%aa%db%8c%d8%ac%d9%87_%da%af%db%8c%d8%b1%db%8c\" >\u0646\u062a\u06cc\u062c\u0647 \u06af\u06cc\u0631\u06cc<\/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\"> 4<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span><p> <br \/>\n<\/p>\n<div><noscript><\/noscript><\/p>\n<h2 id=\"introduction\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%b9%d8%b1%d9%81%db%8c\"><\/span>\u0645\u0639\u0631\u0641\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0631\u0627\u0647\u0646\u0645\u0627\u0647\u0627\u06cc \u0632\u06cc\u0627\u062f\u06cc \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f \u06a9\u0647 \u0631\u0648\u0634 \u0639\u0645\u0644\u06a9\u0631\u062f \u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631\u0647\u0627 \u0648 \u0627\u06cc\u062c\u0627\u062f \u0634\u0647\u0648\u062f \u0631\u0627 \u062a\u0648\u0636\u06cc\u062d \u0645\u06cc \u062f\u0647\u062f \u0631\u0648\u06cc \u06cc\u06a9 \u0639\u0646\u0635\u0631 \u06a9\u0644\u06cc\u062f\u06cc \u0627\u0632 \u0622\u0646\u0647\u0627 &#8211; \u062a\u0648\u06a9\u0646 \u0648 \u062c\u0627\u0633\u0627\u0632\u06cc \u0645\u0648\u0642\u0639\u06cc\u062a.<\/p>\n<p>\u062a\u0648\u06a9\u0646\u200c\u0647\u0627\u06cc \u062c\u0627\u0633\u0627\u0632\u06cc \u0645\u0648\u0642\u0639\u06cc\u062a\u06cc \u0628\u0647 \u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631\u0647\u0627 \u0627\u062c\u0627\u0632\u0647 \u0645\u06cc\u200c\u062f\u0647\u062f \u062a\u0627 \u0631\u0648\u0627\u0628\u0637 \u063a\u06cc\u0631 \u0635\u0644\u0628 \u0628\u06cc\u0646 \u0646\u0634\u0627\u0646\u0647\u200c\u0647\u0627 (\u0645\u0639\u0645\u0648\u0644\u0627\u064b \u06a9\u0644\u0645\u0627\u062a) \u0631\u0627 \u0646\u0634\u0627\u0646 \u062f\u0647\u0646\u062f\u060c \u06a9\u0647 \u062f\u0631 \u0645\u062f\u0644\u200c\u0633\u0627\u0632\u06cc \u06af\u0641\u062a\u0627\u0631 \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u0632\u0645\u06cc\u0646\u0647 \u0645\u0627 \u062f\u0631 \u0645\u062f\u0644\u200c\u0633\u0627\u0632\u06cc \u0632\u0628\u0627\u0646 \u0628\u0633\u06cc\u0627\u0631 \u0628\u0647\u062a\u0631 \u0627\u0633\u062a.  \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 process \u0646\u0633\u0628\u062a\u0627 \u0633\u0627\u062f\u0647 \u0627\u0633\u062a\u060c \u0646\u0633\u0628\u062a\u0627\u064b \u0639\u0645\u0648\u0645\u06cc \u0627\u0633\u062a \u0648 \u067e\u06cc\u0627\u062f\u0647 \u0633\u0627\u0632\u06cc \u0647\u0627 \u0628\u0647 \u0633\u0631\u0639\u062a \u062a\u0628\u062f\u06cc\u0644 \u0628\u0647 \u062f\u06cc\u06af \u0628\u062e\u0627\u0631 \u0645\u06cc \u0634\u0648\u0646\u062f.<\/p>\n<blockquote>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0631\u0627\u0647\u0646\u0645\u0627\u06cc \u06a9\u0648\u062a\u0627\u0647\u060c \u0631\u0648\u0634 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 KerasNLP\u060c \u0627\u0641\u0632\u0648\u0646\u0647 \u0631\u0633\u0645\u06cc Keras \u0631\u0627 \u0628\u0631\u0631\u0633\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f.\u0631\u0648\u06cc\u060c \u0627\u062c\u0631\u0627 \u06a9\u0631\u062f\u0646 <code>PositionEmbedding<\/code> \u0648 <code>TokenAndPositionEmbedding<\/code>.<\/p>\n<\/blockquote>\n<h2 id=\"kerasnlp\"><span class=\"ez-toc-section\" id=\"kerasnlp\"><\/span>KerasNLP<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>KerasNLP \u0627\u0641\u0632\u0648\u062f\u0646\u06cc \u0627\u0641\u0642\u06cc \u0628\u0631\u0627\u06cc NLP \u0627\u0633\u062a.  \u062f\u0631 \u0632\u0645\u0627\u0646 \u0646\u06af\u0627\u0631\u0634\u060c \u0647\u0646\u0648\u0632 \u062e\u06cc\u0644\u06cc \u062c\u0648\u0627\u0646 \u0627\u0633\u062a\u060c \u062f\u0631 \u0646\u0633\u062e\u0647 0.3\u060c \u0648 \u0645\u0633\u062a\u0646\u062f\u0627\u062a \u0647\u0646\u0648\u0632 \u0646\u0633\u0628\u062a\u0627\u064b \u0645\u062e\u062a\u0635\u0631 \u0627\u0633\u062a\u060c \u0627\u0645\u0627 \u0628\u0633\u062a\u0647 \u0641\u0631\u0627\u062a\u0631 \u0627\u0632 \u0627\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0641\u0642\u0637 \u0642\u0627\u0628\u0644 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0628\u0627\u0634\u062f.<\/p>\n<p>\u062f\u0633\u062a\u0631\u0633\u06cc \u0628\u0647 \u0644\u0627\u06cc\u0647 \u0647\u0627\u06cc Keras \u0631\u0627 \u0641\u0631\u0627\u0647\u0645 \u0645\u06cc \u06a9\u0646\u062f\u060c \u0645\u0627\u0646\u0646\u062f <code>TokenAndPositionEmbedding<\/code>\u060c <code>TransformerEncoder<\/code> \u0648 <code>TransformerDecoder<\/code>\u060c \u06a9\u0647 \u0633\u0627\u062e\u062a \u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631\u0647\u0627\u06cc \u0633\u0641\u0627\u0631\u0634\u06cc \u0631\u0627 \u0622\u0633\u0627\u0646 \u062a\u0631 \u0627\u0632 \u0647\u0645\u06cc\u0634\u0647 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 KerasNLP \u062f\u0631 \u067e\u0631\u0648\u0698\u0647 \u0645\u0627\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0622\u0646 \u0631\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0646\u0635\u0628 \u06a9\u0646\u06cc\u062f <code>pip<\/code>:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-meta\">$<\/span><span class=\"bash\"> pip install keras_nlp<\/span>\n<\/code><\/pre>\n<p>\u067e\u0633 \u0627\u0632 \u0648\u0627\u0631\u062f \u0634\u062f\u0646 \u0628\u0647 \u067e\u0631\u0648\u0698\u0647\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0632 \u0647\u0631 \u06a9\u062f\u0627\u0645 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f <code>keras_nlp<\/code> \u0644\u0627\u06cc\u0647 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f Keras.<\/p>\n<h2 id=\"tokenization\"><span class=\"ez-toc-section\" id=\"%d8%aa%d9%88%da%a9%d9%86_%d8%b3%d8%a7%d8%b2%db%8c\"><\/span>\u062a\u0648\u06a9\u0646 \u0633\u0627\u0632\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u06a9\u0627\u0645\u067e\u06cc\u0648\u062a\u0631\u0647\u0627 \u0628\u0627 \u0627\u0639\u062f\u0627\u062f \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u0646\u062f.  \u0645\u0627 \u0627\u0641\u06a9\u0627\u0631\u0645\u0627\u0646 \u0631\u0627 \u0628\u0627 \u06a9\u0644\u0645\u0627\u062a \u0628\u06cc\u0627\u0646 \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u0628\u0631\u0627\u06cc \u0627\u06cc\u0646\u06a9\u0647 \u0628\u0647 \u0631\u0627\u06cc\u0627\u0646\u0647\u200c\u0647\u0627 \u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u0645 \u0622\u0646\u0647\u0627 \u0631\u0627 \u0628\u0647 \u0647\u0645 \u0628\u0631\u06cc\u0632\u0646\u062f\u060c \u0628\u0627\u06cc\u062f \u06a9\u0644\u0645\u0627\u062a \u0631\u0627 \u0628\u0647 \u0634\u06a9\u0644\u06cc \u0628\u0647 \u0627\u0639\u062f\u0627\u062f \u0646\u06af\u0627\u0634\u062a \u06a9\u0646\u06cc\u0645.<\/p>\n<p>\u06cc\u06a9 \u0631\u0627\u0647 \u0645\u0639\u0645\u0648\u0644 \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0627\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0628\u0647 \u0633\u0627\u062f\u06af\u06cc \u06a9\u0644\u0645\u0627\u062a \u0631\u0627 \u0628\u0647 \u0627\u0639\u062f\u0627\u062f\u06cc \u06a9\u0647 \u0647\u0631 \u0639\u062f\u062f \u0635\u062d\u06cc\u062d \u0646\u0634\u0627\u0646 \u062f\u0647\u0646\u062f\u0647 \u06cc\u06a9 \u06a9\u0644\u0645\u0647 \u0627\u0633\u062a\u060c \u0646\u06af\u0627\u0634\u062a \u06a9\u0646\u06cc\u062f.  \u0645\u062c\u0645\u0648\u0639\u0647 \u0627\u06cc \u0627\u0632 \u06a9\u0644\u0645\u0627\u062a \u06cc\u06a9 \u0648\u0627\u0698\u06af\u0627\u0646 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u062f \u0648 \u0647\u0631 \u06a9\u0644\u0645\u0647 \u062f\u0631 \u0648\u0627\u0698\u06af\u0627\u0646 \u06cc\u06a9 \u0646\u0645\u0627\u06cc\u0647 \u0645\u06cc \u06af\u06cc\u0631\u062f.  \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u062f \u062f\u0646\u0628\u0627\u0644\u0647\u200c\u0627\u06cc \u0627\u0632 \u06a9\u0644\u0645\u0627\u062a \u0631\u0627 \u0628\u0647 \u062f\u0646\u0628\u0627\u0644\u0647\u200c\u0627\u06cc \u0627\u0632 \u0634\u0627\u062e\u0635\u200c\u0647\u0627 \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u06cc\u062f <em>\u062a\u0648\u06a9\u0646 \u0647\u0627<\/em>:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">tokenize<\/span>(<span class=\"hljs-params\">sequence<\/span>):<\/span>\n    \n    <span class=\"hljs-keyword\">return<\/span> tokenized_sequence\n\nsequence = (<span class=\"hljs-string\">'I'<\/span>, <span class=\"hljs-string\">'am'<\/span>, <span class=\"hljs-string\">'Wall-E'<\/span>)\nsequence = tokenize(sequence)\n<span class=\"hljs-built_in\">print<\/span>(sequence) \n<\/code><\/pre>\n<p>\u0628\u0627 Keras\u060c \u062a\u0648\u06a9\u0646\u200c\u0633\u0627\u0632\u06cc \u0645\u0639\u0645\u0648\u0644\u0627\u064b \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0622\u0646 \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc\u200c\u0634\u0648\u062f <code>TextVectorization<\/code> \u0644\u0627\u06cc\u0647\u060c \u06a9\u0647 \u0628\u0647 \u0637\u0631\u0632 \u0634\u06af\u0641\u062a \u0627\u0646\u06af\u06cc\u0632\u06cc \u0628\u0631\u0627\u06cc \u0637\u06cc\u0641 \u06af\u0633\u062a\u0631\u062f\u0647 \u0627\u06cc \u0627\u0632 \u0648\u0631\u0648\u062f\u06cc \u0647\u0627 \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u0686\u0646\u062f\u06cc\u0646 \u062d\u0627\u0644\u062a \u062e\u0631\u0648\u062c\u06cc \u0631\u0627 \u067e\u0634\u062a\u06cc\u0628\u0627\u0646\u06cc \u0645\u06cc \u06a9\u0646\u062f (\u06a9\u0647 \u062d\u0627\u0644\u062a \u067e\u06cc\u0634 \u0641\u0631\u0636 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f <code>int<\/code> \u06a9\u0647 \u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u0642\u0628\u0644\u0627 \u062a\u0648\u0636\u06cc\u062d \u062f\u0627\u062f\u0647 \u0634\u062f \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f):<\/p>\n<pre><code class=\"hljs\">\nvectorize = keras.layers.TextVectorization(\n    max_tokens=max_features,\n    output_mode=<span class=\"hljs-string\">'int'<\/span>,\n    output_sequence_length=max_len)\n\n\nvectorize.adapt(text_dataset)\n\nvectorized_text = vectorize((<span class=\"hljs-string\">'some input'<\/span>))\n<\/code><\/pre>\n<p>\u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0632 \u0627\u06cc\u0646 \u0644\u0627\u06cc\u0647 \u0628\u0647\u200c\u0639\u0646\u0648\u0627\u0646 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u0645\u0633\u062a\u0642\u0644 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634\u200c\u067e\u0631\u062f\u0627\u0632\u0634 \u06cc\u0627 \u0628\u0647\u200c\u0639\u0646\u0648\u0627\u0646 \u0628\u062e\u0634\u06cc \u0627\u0632 \u0645\u062f\u0644 Keras \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f \u062a\u0627 \u067e\u06cc\u0634\u200c\u067e\u0631\u062f\u0627\u0632\u0634 \u0648\u0627\u0642\u0639\u0627\u064b \u0633\u0631\u062a\u0627\u0633\u0631 \u0628\u0627\u0634\u062f \u0648 \u0648\u0631\u0648\u062f\u06cc \u062e\u0627\u0645 \u0628\u0647 \u0645\u062f\u0644 \u0627\u0631\u0627\u0626\u0647 \u0634\u0648\u062f.  \u0647\u062f\u0641 \u0627\u06cc\u0646 \u0631\u0627\u0647\u0646\u0645\u0627 \u0646\u0634\u0627\u0646\u0647 \u0627\u0633\u062a <em>\u062a\u0639\u0628\u06cc\u0647 \u06a9\u0631\u062f\u0646<\/em>\u060c \u0646\u0647 \u0646\u0634\u0627\u0646\u0647 \u06af\u0630\u0627\u0631\u06cc\u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0645\u0646 \u0628\u06cc\u0634\u062a\u0631 \u0628\u0647 \u0644\u0627\u06cc\u0647\u060c \u06a9\u0647 \u0645\u0648\u0636\u0648\u0639 \u0627\u0635\u0644\u06cc \u0631\u0627\u0647\u0646\u0645\u0627\u06cc \u062f\u06cc\u06af\u0631\u06cc \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f\u060c \u0641\u0631\u0648 \u0646\u062e\u0648\u0627\u0647\u0645 \u0631\u0641\u062a.<\/p>\n<p>\u0633\u067e\u0633 \u0627\u06cc\u0646 \u062f\u0646\u0628\u0627\u0644\u0647 \u0627\u0632 \u0646\u0634\u0627\u0646\u0647 \u0647\u0627 \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u062f\u0631 \u06cc\u06a9 \u0628\u0631\u062f\u0627\u0631 \u0645\u062a\u0631\u0627\u06a9\u0645 \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0631\u062f \u06a9\u0647 \u062a\u0648\u06a9\u0646 \u0647\u0627 \u0631\u0627 \u062f\u0631 \u0641\u0636\u0627\u06cc \u067e\u0646\u0647\u0627\u0646 \u062a\u0639\u0631\u06cc\u0641 \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">((4), (26), (472)) -&gt; ((0.5, 0.25), (0.73, 0.2), (0.1, -0.75))\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0645\u0639\u0645\u0648\u0644\u0627 \u0628\u0627 <code>Embedding<\/code> \u0644\u0627\u06cc\u0647 \u062f\u0631 \u06a9\u0631\u0627\u0633.  \u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631\u0647\u0627 \u0641\u0642\u0637 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u06a9\u062f\u06af\u0630\u0627\u0631\u06cc \u0646\u0645\u06cc \u06a9\u0646\u0646\u062f <code>Embedding<\/code> \u0644\u0627\u06cc\u0647.  \u0627\u062c\u0631\u0627 \u0645\u06cc \u06a9\u0646\u0646\u062f <code>Embedding<\/code> \u0648 <code>PositionEmbedding<\/code>\u060c \u0648 \u0622\u0646\u0647\u0627 \u0631\u0627 \u0628\u0627 \u0647\u0645 \u062c\u0645\u0639 \u06a9\u0646\u06cc\u062f\u060c \u062c\u0627\u0633\u0627\u0632\u06cc \u0647\u0627\u06cc \u0645\u0646\u0638\u0645 \u0631\u0627 \u0628\u0627 \u0645\u0648\u0642\u0639\u06cc\u062a \u0622\u0646\u0647\u0627 \u062f\u0631 \u0641\u0636\u0627\u06cc \u067e\u0646\u0647\u0627\u0646 \u062c\u0627\u0628\u0647 \u062c\u0627 \u06a9\u0646\u06cc\u062f.<\/p>\n<p>\u0628\u0627 KerasNLP &#8211; \u0627\u062c\u0631\u0627 <code>TokenAndPositionEmbedding<\/code> \u062a\u0631\u06a9\u06cc\u0628\u06cc \u0627\u0632 \u062a\u0639\u0628\u06cc\u0647 \u0646\u0634\u0627\u0646\u0647 \u0647\u0627\u06cc \u0645\u0646\u0638\u0645 (<code>Embedding<\/code>) \u0628\u0627 \u062a\u0639\u0628\u06cc\u0647 \u0645\u0648\u0642\u0639\u06cc\u062a\u06cc (<code>PositionEmbedding<\/code>).<\/p>\n<h2 id=\"positionembedding\"><span class=\"ez-toc-section\" id=\"%d8%ac%d8%a7%d8%b3%d8%a7%d8%b2%db%8c_%d9%85%d9%88%d9%82%d8%b9%db%8c%d8%aa\"><\/span>\u062c\u0627\u0633\u0627\u0632\u06cc \u0645\u0648\u0642\u0639\u06cc\u062a<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 <code>PositionEmbedding<\/code> \u0627\u0648\u0644\u06cc\u0646.  \u062a\u0627\u0646\u0633\u0648\u0631\u0647\u0627 \u0648 \u062a\u0627\u0646\u0633\u0648\u0631\u0647\u0627\u06cc \u0698\u0646\u062f\u0647\u200c\u062f\u0627\u0631 \u0631\u0627 \u0645\u06cc\u200c\u067e\u0630\u06cc\u0631\u062f \u0648 \u0641\u0631\u0636 \u0645\u06cc\u200c\u06a9\u0646\u062f \u06a9\u0647 \u0628\u0639\u062f \u0646\u0647\u0627\u06cc\u06cc \u0646\u0645\u0627\u06cc\u0627\u0646\u06af\u0631 \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627 \u0627\u0633\u062a\u060c \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u0628\u0639\u062f \u062f\u0648\u0645 \u062a\u0627 \u0622\u062e\u0631 \u0646\u0634\u0627\u0646\u200c\u062f\u0647\u0646\u062f\u0647 \u062f\u0646\u0628\u0627\u0644\u0647 \u0627\u0633\u062a.<\/p>\n<pre><code class=\"hljs\"># Seq\n(5, 10)\n     # Features\n<\/code><\/pre>\n<p>\u0644\u0627\u06cc\u0647 a \u0631\u0627 \u0645\u06cc \u067e\u0630\u06cc\u0631\u062f <code>sequence_length<\/code> \u0622\u0631\u06af\u0648\u0645\u0627\u0646\u060c \u0646\u0634\u0627\u0646 \u062f\u0647\u0646\u062f\u0647 \u0637\u0648\u0644 \u062a\u0648\u0627\u0644\u06cc \u0648\u0631\u0648\u062f\u06cc \u0648 \u062e\u0631\u0648\u062c\u06cc \u0627\u0633\u062a.  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u062c\u0644\u0648 \u0628\u0631\u0648\u06cc\u0645 \u0648 \u06cc\u06a9 \u062a\u0627\u0646\u0633\u0648\u0631 \u06cc\u06a9\u0646\u0648\u0627\u062e\u062a \u062a\u0635\u0627\u062f\u0641\u06cc \u0631\u0627 \u0628\u0647 \u0635\u0648\u0631\u062a \u0645\u0648\u0642\u0639\u06cc\u062a\u06cc \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">seq_length = <span class=\"hljs-number\">5<\/span>\ninput_data = tf.random.uniform(shape=(<span class=\"hljs-number\">5<\/span>, <span class=\"hljs-number\">10<\/span>))\n\ninput_tensor = keras.Input(shape=(<span class=\"hljs-literal\">None<\/span>, <span class=\"hljs-number\">5<\/span>, <span class=\"hljs-number\">10<\/span>))\noutput = keras_nlp.layers.PositionEmbedding(sequence_length=seq_length)(input_tensor)\nmodel = keras.Model(inputs=input_tensor, outputs=output)\n    \nmodel(input_data)\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0646\u062a\u06cc\u062c\u0647 \u062f\u0631:<\/p>\n<pre><code class=\"hljs\">&lt;tf.Tensor: shape=(5, 10), dtype=float32, numpy=\narray((( 0.23758471, -0.16798696, -0.15070847,  0.208067  , -0.5123104 ,\n        -0.36670157,  0.27487397,  0.14939266,  0.23843127, -0.23328197),\n       (-0.51353353, -0.4293166 , -0.30189738, -0.140344  , -0.15444171,\n        -0.27691704,  0.14078277, -0.22552207, -0.5952263 , -0.5982155 ),\n       (-0.265581  , -0.12168896,  0.46075982,  0.61768025, -0.36352775,\n        -0.14212841, -0.26831496, -0.34448475,  0.4418767 ,  0.05758983),\n       (-0.46500492, -0.19256318, -0.23447984,  0.17891657, -0.01812166,\n        -0.58293337, -0.36404118,  0.54269964,  0.3727749 ,  0.33238482),\n       (-0.2965023 , -0.3390794 ,  0.4949159 ,  0.32005525,  0.02882379,\n        -0.15913549,  0.27996767,  0.4387421 , -0.09119213,  0.1294356 )),\n      dtype=float32)&gt;\n<\/code><\/pre>\n<h2 id=\"tokenandpositionembedding\"><span class=\"ez-toc-section\" id=\"tokenandpositionembedding\"><\/span>TokenAndPositionEmbedding<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u062a\u0648\u06a9\u0646 \u0648 \u062c\u0627\u0633\u0627\u0632\u06cc \u0645\u0648\u0642\u0639\u06cc\u062a \u0628\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0644\u0627\u0635\u0647 \u0645\u06cc \u0634\u0648\u062f <code>Embedding<\/code> \u0631\u0648\u06cc  \u062f\u0646\u0628\u0627\u0644\u0647 \u0648\u0631\u0648\u062f\u06cc\u060c <code>PositionEmbedding<\/code> \u0631\u0648\u06cc  \u0646\u0634\u0627\u0646\u0647\u200c\u0647\u0627\u06cc \u062a\u0639\u0628\u06cc\u0647\u200c\u0634\u062f\u0647\u060c \u0648 \u0633\u067e\u0633 \u0627\u0641\u0632\u0648\u062f\u0646 \u0627\u06cc\u0646 \u062f\u0648 \u0646\u062a\u06cc\u062c\u0647 \u0628\u0627 \u0647\u0645\u060c \u0628\u0647 \u0637\u0648\u0631 \u0645\u0648\u062b\u0631 \u062c\u0627\u0633\u0627\u0632\u06cc\u200c\u0647\u0627\u06cc \u0646\u0634\u0627\u0646\u0647 \u0631\u0627 \u062f\u0631 \u0641\u0636\u0627 \u062c\u0627\u0628\u0647\u200c\u062c\u0627 \u0645\u06cc\u200c\u06a9\u0646\u0646\u062f \u062a\u0627 \u0631\u0648\u0627\u0628\u0637 \u0645\u0639\u0646\u06cc\u200c\u062f\u0627\u0631 \u0646\u0633\u0628\u06cc \u0622\u0646\u0647\u0627 \u0631\u0627 \u0631\u0645\u0632\u06af\u0630\u0627\u0631\u06cc \u06a9\u0646\u0646\u062f.<\/p>\n<p>\u0627\u06cc\u0646 \u0627\u0632 \u0646\u0638\u0631 \u0641\u0646\u06cc \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u0647 \u0635\u0648\u0631\u062a \u0632\u06cc\u0631 \u0627\u0646\u062c\u0627\u0645 \u0634\u0648\u062f:<\/p>\n<pre><code class=\"hljs\">seq_length = <span class=\"hljs-number\">10<\/span>\nvocab_size = <span class=\"hljs-number\">25<\/span>\nembed_dim = <span class=\"hljs-number\">10<\/span>\n\ninput_data = tf.random.uniform(shape=(<span class=\"hljs-number\">5<\/span>, <span class=\"hljs-number\">10<\/span>))\n\ninput_tensor = keras.Input(shape=(<span class=\"hljs-literal\">None<\/span>, <span class=\"hljs-number\">5<\/span>, <span class=\"hljs-number\">10<\/span>))\nembedding = keras.layers.Embedding(vocab_size, embed_dim)(input_tensor)\nposition = keras_nlp.layers.PositionEmbedding(seq_length)(embedding)\noutput = keras.layers.add((embedding, position))\nmodel = keras.Model(inputs=input_tensor, outputs=output)\n    \nmodel(input_data).shape \n<\/code><\/pre>\n<p>\u0648\u0631\u0648\u062f\u06cc \u0647\u0627 \u062a\u0639\u0628\u06cc\u0647 \u0645\u06cc \u0634\u0648\u0646\u062f \u0648 \u0633\u067e\u0633 \u0628\u0647 \u0635\u0648\u0631\u062a \u0645\u0648\u0642\u0639\u06cc\u062a\u06cc \u062c\u0627\u0633\u0627\u0632\u06cc \u0645\u06cc \u0634\u0648\u0646\u062f\u060c \u067e\u0633 \u0627\u0632 \u0622\u0646 \u0628\u0627 \u0647\u0645 \u062c\u0645\u0639 \u0645\u06cc \u0634\u0648\u0646\u062f \u0648 \u06cc\u06a9 \u0634\u06a9\u0644 \u062c\u0627\u0633\u0627\u0632\u06cc \u0634\u062f\u0647 \u062f\u0631 \u0645\u0648\u0642\u0639\u06cc\u062a \u062c\u062f\u06cc\u062f \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u0646\u062f.  \u0628\u0647 \u0637\u0648\u0631 \u0645\u062a\u0646\u0627\u0648\u0628\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0632 \u0627\u0647\u0631\u0645 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f <code>TokenAndPositionEmbedding<\/code> \u0644\u0627\u06cc\u0647\u060c \u06a9\u0647 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u062f\u0631 \u0632\u06cc\u0631 \u0647\u0648\u062f \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc \u062f\u0647\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-meta\">... <\/span>\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">call<\/span>(<span class=\"hljs-params\">self, inputs<\/span>):<\/span>\n        embedded_tokens = self.token_embedding(inputs)\n        embedded_positions = self.position_embedding(embedded_tokens)\n        outputs = embedded_tokens + embedded_positions\n        <span class=\"hljs-keyword\">return<\/span> outputs\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0628\u0627\u0639\u062b \u0645\u06cc \u0634\u0648\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u0622\u0646 \u0628\u0633\u06cc\u0627\u0631 \u062a\u0645\u06cc\u0632\u062a\u0631 \u0634\u0648\u062f <code>TokenAndPositionEmbedding<\/code>:<\/p>\n<pre><code class=\"hljs\">seq_length = <span class=\"hljs-number\">10<\/span>\nvocab_size = <span class=\"hljs-number\">25<\/span>\nembed_dim = <span class=\"hljs-number\">10<\/span>\n\ninput_data = tf.random.uniform(shape=(<span class=\"hljs-number\">5<\/span>, <span class=\"hljs-number\">10<\/span>))\n\ninput_tensor = keras.Input(shape=(<span class=\"hljs-literal\">None<\/span>, <span class=\"hljs-number\">5<\/span>, <span class=\"hljs-number\">10<\/span>))\noutput = keras_nlp.layers.TokenAndPositionEmbedding(vocabulary_size=vocab_size, \n                                                     sequence_length=seq_length, \n                                                     embedding_dim=embed_dim)(input_tensor)\nmodel = keras.Model(inputs=input_tensor, outputs=output)\n    \nmodel(input_data).shape \n<\/code><\/pre>\n<p>\u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc\u06cc \u06a9\u0647 \u0628\u0647 \u0644\u0627\u06cc\u0647 \u0645\u0646\u062a\u0642\u0644 \u06a9\u0631\u062f\u0647\u200c\u0627\u06cc\u0645 \u0627\u06a9\u0646\u0648\u0646 \u0628\u0647 \u0635\u0648\u0631\u062a \u0645\u0648\u0642\u0639\u06cc\u062a\u06cc \u062f\u0631 \u06cc\u06a9 \u0641\u0636\u0627\u06cc \u067e\u0646\u0647\u0627\u0646 \u0627\u0632 10 \u0628\u0639\u062f \u062c\u0627\u0633\u0627\u0632\u06cc \u0634\u062f\u0647\u200c\u0627\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">model(input_data)\n<\/code><\/pre>\n<pre><code class=\"hljs\">&lt;tf.Tensor: shape=(5, 10, 10), dtype=float32, numpy=\narray((((-0.01695484,  0.7656435 , -0.84340465,  0.50211895,\n         -0.3162892 ,  0.16375223, -0.3774369 , -0.10028353,\n         -0.00136751, -0.14690581),\n        (-0.05646318,  0.00225556, -0.7745967 ,  0.5233861 ,\n         -0.22601983,  0.07024342,  0.0905793 , -0.46133494,\n         -0.30130145,  0.451248  ),\n         ...\n<\/code><\/pre>\n<h2 id=\"conclusions\"><span class=\"ez-toc-section\" id=\"%d9%86%d8%aa%db%8c%d8%ac%d9%87_%da%af%db%8c%d8%b1%db%8c\"><\/span>\u0646\u062a\u06cc\u062c\u0647 \u06af\u06cc\u0631\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631\u0647\u0627 \u0627\u0632 \u0633\u0627\u0644 2017 \u0645\u0648\u062c \u0628\u0632\u0631\u06af\u06cc \u0627\u06cc\u062c\u0627\u062f \u06a9\u0631\u062f\u0647\u200c\u0627\u0646\u062f\u060c \u0648 \u0628\u0633\u06cc\u0627\u0631\u06cc \u0627\u0632 \u0631\u0627\u0647\u0646\u0645\u0627\u0647\u0627\u06cc \u0639\u0627\u0644\u06cc \u0628\u06cc\u0646\u0634\u06cc \u062f\u0631 \u0645\u0648\u0631\u062f \u0631\u0648\u0634 \u06a9\u0627\u0631 \u0622\u0646\u0647\u0627 \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc\u200c\u062f\u0647\u0646\u062f\u060c \u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u0628\u0647 \u062f\u0644\u06cc\u0644 \u0647\u0632\u06cc\u0646\u0647\u200c\u0647\u0627\u06cc \u0628\u0627\u0644\u0627\u06cc \u067e\u06cc\u0627\u062f\u0647\u200c\u0633\u0627\u0632\u06cc \u0633\u0641\u0627\u0631\u0634\u06cc\u060c \u0647\u0646\u0648\u0632 \u0628\u0631\u0627\u06cc \u0628\u0633\u06cc\u0627\u0631\u06cc \u06af\u0631\u06cc\u0632\u0627\u0646 \u0647\u0633\u062a\u0646\u062f.  KerasNLP \u0628\u0627 \u0627\u0631\u0627\u0626\u0647 \u0628\u0644\u0648\u06a9\u200c\u0647\u0627\u06cc \u0633\u0627\u062e\u062a\u0645\u0627\u0646\u06cc \u06a9\u0647 \u0628\u0647 \u0634\u0645\u0627 \u0627\u0645\u06a9\u0627\u0646 \u0645\u06cc\u200c\u062f\u0647\u062f \u0633\u06cc\u0633\u062a\u0645\u200c\u0647\u0627\u06cc NLP \u0627\u0646\u0639\u0637\u0627\u0641\u200c\u067e\u0630\u06cc\u0631 \u0648 \u0642\u062f\u0631\u062a\u0645\u0646\u062f \u0628\u0633\u0627\u0632\u06cc\u062f\u060c \u0628\u0647 \u062c\u0627\u06cc \u0627\u0631\u0627\u0626\u0647 \u0631\u0627\u0647\u200c\u062d\u0644\u200c\u0647\u0627\u06cc \u0627\u0632 \u067e\u06cc\u0634 \u0628\u0633\u062a\u0647\u200c\u0628\u0646\u062f\u06cc \u0634\u062f\u0647\u060c \u0627\u06cc\u0646 \u0645\u0634\u06a9\u0644 \u0631\u0627 \u0628\u0631\u0637\u0631\u0641 \u0645\u06cc\u200c\u06a9\u0646\u062f.<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0631\u0627\u0647\u0646\u0645\u0627\u060c \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 \u062a\u0648\u06a9\u0646 \u0648 \u062c\u0627\u0633\u0627\u0632\u06cc \u0645\u0648\u0642\u0639\u06cc\u062a \u0628\u0627 Keras \u0648 KerasNLP \u0627\u0646\u062f\u0627\u062e\u062a\u0647\u200c\u0627\u06cc\u0645.<\/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 1403-01-04 08:58:03<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;14178&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;\u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631 \u062a\u0648\u06a9\u0646 \u0648 \u062c\u0627\u0633\u0627\u0632\u06cc \u0645\u0648\u0642\u0639\u06cc\u062a \u0628\u0627 Keras&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\"> 4<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span>\u0645\u0639\u0631\u0641\u06cc \u0631\u0627\u0647\u0646\u0645\u0627\u0647\u0627\u06cc \u0632\u06cc\u0627\u062f\u06cc \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f \u06a9\u0647 \u0631\u0648\u0634 \u0639\u0645\u0644\u06a9\u0631\u062f \u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631\u0647\u0627 \u0648 \u0627\u06cc\u062c\u0627\u062f \u0634\u0647\u0648\u062f \u0631\u0627 \u062a\u0648\u0636\u06cc\u062d \u0645\u06cc \u062f\u0647\u062f \u0631\u0648\u06cc \u06cc\u06a9 \u0639\u0646\u0635\u0631 \u06a9\u0644\u06cc\u062f\u06cc \u0627\u0632 \u0622\u0646\u0647\u0627 &#8211; \u062a\u0648\u06a9\u0646 \u0648 \u062c\u0627\u0633\u0627\u0632\u06cc \u0645\u0648\u0642\u0639\u06cc\u062a. \u062a\u0648\u06a9\u0646\u200c\u0647\u0627\u06cc \u062c\u0627\u0633\u0627\u0632\u06cc \u0645\u0648\u0642\u0639\u06cc\u062a\u06cc \u0628\u0647 \u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631\u0647\u0627 \u0627\u062c\u0627\u0632\u0647 \u0645\u06cc\u200c\u062f\u0647\u062f \u062a\u0627 \u0631\u0648\u0627\u0628\u0637 \u063a\u06cc\u0631 \u0635\u0644\u0628 \u0628\u06cc\u0646 \u0646\u0634\u0627\u0646\u0647\u200c\u0647\u0627 (\u0645\u0639\u0645\u0648\u0644\u0627\u064b \u06a9\u0644\u0645\u0627\u062a) \u0631\u0627 \u0646\u0634\u0627\u0646 \u062f\u0647\u0646\u062f\u060c \u06a9\u0647 \u062f\u0631 \u0645\u062f\u0644\u200c\u0633\u0627\u0632\u06cc \u06af\u0641\u062a\u0627\u0631 \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u0632\u0645\u06cc\u0646\u0647 \u0645\u0627 \u062f\u0631 \u0645\u062f\u0644\u200c\u0633\u0627\u0632\u06cc [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":9398,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1743,620],"tags":[],"class_list":["post-14178","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\/14178","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=14178"}],"version-history":[{"count":0,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/14178\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media\/9398"}],"wp:attachment":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media?parent=14178"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/categories?post=14178"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/tags?post=14178"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}