{"id":16020,"date":"2024-01-19T21:39:27","date_gmt":"2024-01-19T18:09:27","guid":{"rendered":"https:\/\/rasanegar.com\/blog\/%d9%be%db%8c%d8%b4%d8%a8%db%8c%d9%86%db%8c-%d8%b3%d8%b1%db%8c-%d8%b2%d9%85%d8%a7%d9%86%db%8c-%d8%a8%d8%a7-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-lstm-%d8%a8%d8%a7-pytorch\/"},"modified":"2024-01-19T21:39:27","modified_gmt":"2024-01-19T18:09:27","slug":"%d9%be%db%8c%d8%b4%d8%a8%db%8c%d9%86%db%8c-%d8%b3%d8%b1%db%8c-%d8%b2%d9%85%d8%a7%d9%86%db%8c-%d8%a8%d8%a7-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-lstm-%d8%a8%d8%a7-pytorch","status":"publish","type":"post","link":"https:\/\/rasanegaar.com\/blog\/%d9%be%db%8c%d8%b4%d8%a8%db%8c%d9%86%db%8c-%d8%b3%d8%b1%db%8c-%d8%b2%d9%85%d8%a7%d9%86%db%8c-%d8%a8%d8%a7-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-lstm-%d8%a8%d8%a7-pytorch\/","title":{"rendered":"\u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 LSTM \u0628\u0627 PyTorch \u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646"},"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\/%d9%be%db%8c%d8%b4%d8%a8%db%8c%d9%86%db%8c-%d8%b3%d8%b1%db%8c-%d8%b2%d9%85%d8%a7%d9%86%db%8c-%d8%a8%d8%a7-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-lstm-%d8%a8%d8%a7-pytorch\/#%d9%85%d8%ac%d9%85%d9%88%d8%b9%d9%87_%d8%af%d8%a7%d8%af%d9%87_%d9%88_%d8%aa%d8%b9%d8%b1%db%8c%d9%81_%d9%85%d8%b4%da%a9%d9%84\" >\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0648 \u062a\u0639\u0631\u06cc\u0641 \u0645\u0634\u06a9\u0644<\/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\/%d9%be%db%8c%d8%b4%d8%a8%db%8c%d9%86%db%8c-%d8%b3%d8%b1%db%8c-%d8%b2%d9%85%d8%a7%d9%86%db%8c-%d8%a8%d8%a7-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-lstm-%d8%a8%d8%a7-pytorch\/#%d9%be%db%8c%d8%b4_%d9%be%d8%b1%d8%af%d8%a7%d8%b2%d8%b4_%d8%af%d8%a7%d8%af%d9%87_%d9%87%d8%a7\" >\u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u062f\u0627\u062f\u0647 \u0647\u0627<\/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\/%d9%be%db%8c%d8%b4%d8%a8%db%8c%d9%86%db%8c-%d8%b3%d8%b1%db%8c-%d8%b2%d9%85%d8%a7%d9%86%db%8c-%d8%a8%d8%a7-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-lstm-%d8%a8%d8%a7-pytorch\/#%d8%a7%db%8c%d8%ac%d8%a7%d8%af_%d9%85%d8%af%d9%84_lstm\" >\u0627\u06cc\u062c\u0627\u062f \u0645\u062f\u0644 LSTM<\/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\/%d9%be%db%8c%d8%b4%d8%a8%db%8c%d9%86%db%8c-%d8%b3%d8%b1%db%8c-%d8%b2%d9%85%d8%a7%d9%86%db%8c-%d8%a8%d8%a7-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-lstm-%d8%a8%d8%a7-pytorch\/#%d8%a2%d9%85%d9%88%d8%b2%d8%b4_%d9%85%d8%af%d9%84\" >\u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644<\/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\/%d9%be%db%8c%d8%b4%d8%a8%db%8c%d9%86%db%8c-%d8%b3%d8%b1%db%8c-%d8%b2%d9%85%d8%a7%d9%86%db%8c-%d8%a8%d8%a7-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-lstm-%d8%a8%d8%a7-pytorch\/#%d9%be%db%8c%d8%b4%da%af%d9%88%db%8c%db%8c\" >\u067e\u06cc\u0634\u06af\u0648\u06cc\u06cc<\/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\/%d9%be%db%8c%d8%b4%d8%a8%db%8c%d9%86%db%8c-%d8%b3%d8%b1%db%8c-%d8%b2%d9%85%d8%a7%d9%86%db%8c-%d8%a8%d8%a7-%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87-%d8%a7%d8%b2-lstm-%d8%a8%d8%a7-pytorch\/#%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>\u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc\u060c \u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u0627\u0632 \u0646\u0627\u0645 \u0622\u0646 \u067e\u06cc\u062f\u0627\u0633\u062a\u060c \u0646\u0648\u0639\u06cc \u062f\u0627\u062f\u0647 \u0627\u0633\u062a \u06a9\u0647 \u0628\u0627 \u0632\u0645\u0627\u0646 \u062a\u063a\u06cc\u06cc\u0631 \u0645\u06cc \u06a9\u0646\u062f.  \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u062f\u0645\u0627 \u062f\u0631 \u06cc\u06a9 \u062f\u0648\u0631\u0647 \u0632\u0645\u0627\u0646\u06cc 24 \u0633\u0627\u0639\u062a\u0647\u060c \u0642\u06cc\u0645\u062a \u0645\u062d\u0635\u0648\u0644\u0627\u062a \u0645\u062e\u062a\u0644\u0641 \u062f\u0631 \u06cc\u06a9 \u0645\u0627\u0647\u060c \u0642\u06cc\u0645\u062a \u0633\u0647\u0627\u0645 \u06cc\u06a9 \u0634\u0631\u06a9\u062a \u062e\u0627\u0635 \u062f\u0631 \u06cc\u06a9 \u0633\u0627\u0644.  \u0645\u062f\u0644 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0645\u0627\u0646\u0646\u062f <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/en.wikipedia.org\/wiki\/Long_short-term_memory\">\u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u062d\u0627\u0641\u0638\u0647 \u06a9\u0648\u062a\u0627\u0647 \u0645\u062f\u062a \u0628\u0644\u0646\u062f \u0645\u062f\u062a<\/a> (LSTM)\u060c \u0642\u0627\u062f\u0631 \u0628\u0647 \u06af\u0631\u0641\u062a\u0646 \u0627\u0644\u06af\u0648\u0647\u0627 \u062f\u0631 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc \u0647\u0633\u062a\u0646\u062f \u0648 \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0627\u0632 \u0622\u0646\u0647\u0627 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0631\u0648\u0646\u062f \u0622\u06cc\u0646\u062f\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f.  \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0631\u0648\u0634 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 LSTM \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0647\u0627\u06cc \u0622\u06cc\u0646\u062f\u0647 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc \u0631\u0627 \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f.<\/p>\n<p>\u062f\u0631 \u06cc\u06a9\u06cc \u0627\u0632 \u0645\u0642\u0627\u0644\u0627\u062a \u0642\u0628\u0644\u06cc \u062e\u0648\u062f\u060c \u0631\u0648\u0634 \u0627\u0646\u062c\u0627\u0645 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0633\u0631\u06cc \u0647\u0627\u06cc \u0632\u0645\u0627\u0646\u06cc \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 LSTM \u062f\u0631 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 Keras \u0628\u0647 \u0645\u0646\u0638\u0648\u0631 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0642\u06cc\u0645\u062a \u0633\u0647\u0627\u0645 \u062f\u0631 \u0622\u06cc\u0646\u062f\u0647 \u062a\u0648\u0636\u06cc\u062d \u062f\u0627\u062f\u0645.  \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647\u060c \u0645\u0627 \u0627\u0632 \u0622\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/pytorch.org\/\">PyTorch<\/a> \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u06a9\u0647 \u06cc\u06a9\u06cc \u0627\u0632 \u067e\u0631\u06a9\u0627\u0631\u0628\u0631\u062f\u062a\u0631\u06cc\u0646 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0627\u0633\u062a.<\/p>\n<p>\u0642\u0628\u0644 \u0627\u0632 \u0627\u062f\u0627\u0645\u0647\u060c \u0641\u0631\u0636 \u0628\u0631 \u0627\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0633\u0637\u062d \u0645\u062a\u0648\u0633\u0637\u06cc \u0627\u0632 \u0632\u0628\u0627\u0646 \u0628\u0631\u0646\u0627\u0645\u0647 \u0646\u0648\u06cc\u0633\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u062f\u0627\u0631\u06cc\u062f \u0648 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 PyTorch \u0631\u0627 \u0646\u0635\u0628 \u06a9\u0631\u062f\u0647 \u0627\u06cc\u062f.  \u0647\u0645\u0686\u0646\u06cc\u0646\u060c \u062f\u0627\u0646\u0634 \u0645\u0641\u0627\u0647\u06cc\u0645 \u0627\u0648\u0644\u06cc\u0647 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0648 \u0645\u0641\u0627\u0647\u06cc\u0645 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u06a9\u0645\u06a9 \u062e\u0648\u0627\u0647\u062f \u06a9\u0631\u062f.  \u0627\u06af\u0631 PyTorch \u0631\u0627 \u0646\u0635\u0628 \u0646\u06a9\u0631\u062f\u0647 \u0627\u06cc\u062f\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0627 \u0645\u0648\u0627\u0631\u062f \u0632\u06cc\u0631 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u062f pip \u062f\u0633\u062a\u0648\u0631:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-meta\">$<\/span><span class=\"bash\"> pip install pytorch<\/span>\n<\/code><\/pre>\n<h2 id=\"datasetandproblemdefinition\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%ac%d9%85%d9%88%d8%b9%d9%87_%d8%af%d8%a7%d8%af%d9%87_%d9%88_%d8%aa%d8%b9%d8%b1%db%8c%d9%81_%d9%85%d8%b4%da%a9%d9%84\"><\/span>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0648 \u062a\u0639\u0631\u06cc\u0641 \u0645\u0634\u06a9\u0644<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0627\u06cc \u06a9\u0647 \u0645\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f \u0647\u0645\u0631\u0627\u0647 \u0628\u0627 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 Seaborn Python \u0633\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a.  \u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f import \u0627\u0628\u062a\u062f\u0627 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632 \u0648 \u0633\u067e\u0633 \u062e\u0648\u0627\u0647\u062f \u0634\u062f import \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> torch\n<span class=\"hljs-keyword\">import<\/span> torch.nn <span class=\"hljs-keyword\">as<\/span> nn\n\n<span class=\"hljs-keyword\">import<\/span> seaborn <span class=\"hljs-keyword\">as<\/span> sns\n<span class=\"hljs-keyword\">import<\/span> numpy <span class=\"hljs-keyword\">as<\/span> np\n<span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n<span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n%matplotlib inline\n<\/code><\/pre>\n<p>\u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f print \u0644\u06cc\u0633\u062a \u062a\u0645\u0627\u0645 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc\u06cc \u06a9\u0647 \u0628\u0627 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 Seaborn \u0633\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u0627\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">sns.get_dataset_names()\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">('anscombe',\n 'attention',\n 'brain_networks',\n 'car_crashes',\n 'diamonds',\n 'dots',\n 'exercise',\n 'flights',\n 'fmri',\n 'gammas',\n 'iris',\n 'mpg',\n 'planets',\n 'tips',\n 'titanic')\n<\/code><\/pre>\n<p>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0627\u06cc \u06a9\u0647 \u0645\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f \u0639\u0628\u0627\u0631\u062a \u0627\u0633\u062a \u0627\u0632 <code>flights<\/code> \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u062f\u0631 \u0628\u0631\u0646\u0627\u0645\u0647 \u062e\u0648\u062f \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u0628\u0628\u06cc\u0646\u06cc\u0645 \u0686\u06af\u0648\u0646\u0647 \u0628\u0647 \u0646\u0638\u0631 \u0645\u06cc \u0631\u0633\u062f:<\/p>\n<pre><code class=\"hljs\">flight_data = sns.load_dataset(<span class=\"hljs-string\">\"flights\"<\/span>)\nflight_data.head()\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/Time-Series-Prediction-Using-LSTM-Pytorch-1.PNG\" alt=\"\u0631\u0626\u06cc\u0633 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u067e\u0631\u0648\u0627\u0632\u0647\u0627\" title=\"\"><\/p>\n<p>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u062f\u0627\u0631\u0627\u06cc \u0633\u0647 \u0633\u062a\u0648\u0646 \u0627\u0633\u062a: <code>year<\/code>\u060c <code>month<\/code>\u060c \u0648 <code>passengers<\/code>.  \u0631\u0627 <code>passengers<\/code> \u0633\u062a\u0648\u0646 \u0634\u0627\u0645\u0644 \u062a\u0639\u062f\u0627\u062f \u06a9\u0644 \u0645\u0633\u0627\u0641\u0631\u0627\u0646 \u0645\u0633\u0627\u0641\u0631 \u062f\u0631 \u06cc\u06a9 \u0645\u0627\u0647 \u0645\u0634\u062e\u0635 \u0627\u0633\u062a.  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0634\u06a9\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u062e\u0648\u062f \u0631\u0627 \u0631\u0633\u0645 \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">flight_data.shape\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">(144, 3)\n<\/code><\/pre>\n<p>\u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 144 \u0631\u062f\u06cc\u0641 \u0648 3 \u0633\u062a\u0648\u0646 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f\u060c \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u06a9\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0634\u0627\u0645\u0644 \u0631\u06a9\u0648\u0631\u062f 12 \u0633\u0627\u0644 \u0633\u0641\u0631 \u0645\u0633\u0627\u0641\u0631\u0627\u0646 \u0627\u0633\u062a.<\/p>\n<p>\u0648\u0638\u06cc\u0641\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062a\u0639\u062f\u0627\u062f \u0645\u0633\u0627\u0641\u0631\u0627\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u062f\u0631 12 \u0645\u0627\u0647 \u06af\u0630\u0634\u062a\u0647 \u0633\u0641\u0631 \u06a9\u0631\u062f\u0647 \u0627\u0646\u062f \u0631\u0648\u06cc 132 \u0645\u0627\u0647 \u0627\u0648\u0644  \u0628\u0647 \u06cc\u0627\u062f \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f \u06a9\u0647 \u0645\u0627 \u0633\u0627\u0628\u0642\u0647 144 \u0645\u0627\u0647\u0647 \u062f\u0627\u0631\u06cc\u0645\u060c \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u06a9\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc 132 \u0645\u0627\u0647 \u0627\u0648\u0644 \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 LSTM \u0645\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f\u060c \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u0639\u0645\u0644\u06a9\u0631\u062f \u0645\u062f\u0644 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0645\u0642\u0627\u062f\u06cc\u0631 12 \u0645\u0627\u0647 \u06af\u0630\u0634\u062a\u0647 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u062a\u0639\u062f\u0627\u062f \u0645\u0633\u0627\u0641\u0631\u0627\u0646\u06cc \u06a9\u0647 \u062f\u0631 \u0645\u0627\u0647 \u0633\u0641\u0631 \u0645\u06cc \u06a9\u0646\u0646\u062f \u0631\u0627 \u062a\u0631\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u0645.  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0627\u0646\u062f\u0627\u0632\u0647 \u0637\u0631\u062d \u067e\u06cc\u0634 \u0641\u0631\u0636 \u0631\u0627 \u0627\u0641\u0632\u0627\u06cc\u0634 \u0645\u06cc \u062f\u0647\u062f:<\/p>\n<pre><code class=\"hljs\">fig_size = plt.rcParams(<span class=\"hljs-string\">\"figure.figsize\"<\/span>)\nfig_size(<span class=\"hljs-number\">0<\/span>) = <span class=\"hljs-number\">15<\/span>\nfig_size(<span class=\"hljs-number\">1<\/span>) = <span class=\"hljs-number\">5<\/span>\nplt.rcParams(<span class=\"hljs-string\">\"figure.figsize\"<\/span>) = fig_size\n<\/code><\/pre>\n<p>\u0648 \u0627\u06cc\u0646 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0639\u062f\u06cc \u0641\u0631\u06a9\u0627\u0646\u0633 \u0645\u0627\u0647\u0627\u0646\u0647 \u062a\u0639\u062f\u0627\u062f \u0645\u0633\u0627\u0641\u0631\u0627\u0646 \u0631\u0627 \u062a\u0631\u0633\u06cc\u0645 \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">plt.title(<span class=\"hljs-string\">'Month vs Passenger'<\/span>)\nplt.ylabel(<span class=\"hljs-string\">'Total Passengers'<\/span>)\nplt.xlabel(<span class=\"hljs-string\">'Months'<\/span>)\nplt.grid(<span class=\"hljs-literal\">True<\/span>)\nplt.autoscale(axis=<span class=\"hljs-string\">'x'<\/span>,tight=<span class=\"hljs-literal\">True<\/span>)\nplt.plot(flight_data(<span class=\"hljs-string\">'passengers'<\/span>))\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/Time-Series-Prediction-Using-LSTM-Pytorch-2.PNG\" alt=\"\u062a\u0631\u0633\u06cc\u0645 \u062a\u0639\u062f\u0627\u062f \u0645\u0627\u0647\u0627\u0646\u0647 \u0645\u0633\u0627\u0641\u0631\u0627\u0646\" title=\"\"><\/p>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u06a9\u0647 \u062f\u0631 \u0637\u0648\u0644 \u0633\u0627\u0644 \u0647\u0627 \u0645\u06cc\u0627\u0646\u06af\u06cc\u0646 \u062a\u0639\u062f\u0627\u062f \u0645\u0633\u0627\u0641\u0631\u0627\u0646\u06cc \u06a9\u0647 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0647\u0648\u0627\u06cc\u06cc \u0633\u0641\u0631 \u0645\u06cc \u06a9\u0646\u0646\u062f \u0627\u0641\u0632\u0627\u06cc\u0634 \u06cc\u0627\u0641\u062a\u0647 \u0627\u0633\u062a.  \u062a\u0639\u062f\u0627\u062f \u0645\u0633\u0627\u0641\u0631\u0627\u0646\u06cc \u06a9\u0647 \u062f\u0631 \u0637\u0648\u0644 \u06cc\u06a9 \u0633\u0627\u0644 \u0633\u0641\u0631 \u0645\u06cc \u06a9\u0646\u0646\u062f \u062f\u0631 \u0646\u0648\u0633\u0627\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0645\u0646\u0637\u0642\u06cc \u0627\u0633\u062a \u0632\u06cc\u0631\u0627 \u062f\u0631 \u062a\u0639\u0637\u06cc\u0644\u0627\u062a \u062a\u0627\u0628\u0633\u062a\u0627\u0646\u06cc \u06cc\u0627 \u0632\u0645\u0633\u062a\u0627\u0646\u06cc \u062a\u0639\u062f\u0627\u062f \u0645\u0633\u0627\u0641\u0631\u0627\u0646 \u0645\u0633\u0627\u0641\u0631 \u0646\u0633\u0628\u062a \u0628\u0647 \u0633\u0627\u06cc\u0631 \u0642\u0633\u0645\u062a \u0647\u0627\u06cc \u0633\u0627\u0644 \u0627\u0641\u0632\u0627\u06cc\u0634 \u0645\u06cc \u06cc\u0627\u0628\u062f.<\/p>\n<h2 id=\"datapreprocessing\"><span class=\"ez-toc-section\" id=\"%d9%be%db%8c%d8%b4_%d9%be%d8%b1%d8%af%d8%a7%d8%b2%d8%b4_%d8%af%d8%a7%d8%af%d9%87_%d9%87%d8%a7\"><\/span>\u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u062f\u0627\u062f\u0647 \u0647\u0627<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u0646\u0648\u0627\u0639 \u0633\u062a\u0648\u0646 \u0647\u0627 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0627 \u0627\u0633\u062a <code>object<\/code>\u060c \u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u062a\u0648\u0633\u0637 \u06a9\u062f \u0632\u06cc\u0631 \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">flight_data.columns\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">Index(('year', 'month', 'passengers'), dtype='object')\n<\/code><\/pre>\n<p>\u0627\u0648\u0644\u06cc\u0646 \u0645\u0631\u062d\u0644\u0647 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634\u060c \u062a\u063a\u06cc\u06cc\u0631 \u0646\u0648\u0639 \u0627\u0633\u062a <code>passengers<\/code> \u0633\u062a\u0648\u0646 \u0628\u0647 <code>float<\/code>.<\/p>\n<pre><code class=\"hljs\">all_data = flight_data(<span class=\"hljs-string\">'passengers'<\/span>).values.astype(<span class=\"hljs-built_in\">float<\/span>)\n<\/code><\/pre>\n<p>\u062d\u0627\u0644\u0627 \u0627\u06af\u0631 \u0634\u0645\u0627 print \u0631\u0627 <code>all_data<\/code> \u0622\u0631\u0627\u06cc\u0647 NumPy\u060c \u0628\u0627\u06cc\u062f \u0645\u0642\u0627\u062f\u06cc\u0631 \u0646\u0648\u0639 \u0634\u0646\u0627\u0648\u0631 \u0632\u06cc\u0631 \u0631\u0627 \u0628\u0628\u06cc\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(all_data)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">(112. 118. 132. 129. 121. 135. 148. 148. 136. 119. 104. 118. 115. 126.\n 141. 135. 125. 149. 170. 170. 158. 133. 114. 140. 145. 150. 178. 163.\n 172. 178. 199. 199. 184. 162. 146. 166. 171. 180. 193. 181. 183. 218.\n 230. 242. 209. 191. 172. 194. 196. 196. 236. 235. 229. 243. 264. 272.\n 237. 211. 180. 201. 204. 188. 235. 227. 234. 264. 302. 293. 259. 229.\n 203. 229. 242. 233. 267. 269. 270. 315. 364. 347. 312. 274. 237. 278.\n 284. 277. 317. 313. 318. 374. 413. 405. 355. 306. 271. 306. 315. 301.\n 356. 348. 355. 422. 465. 467. 404. 347. 305. 336. 340. 318. 362. 348.\n 363. 435. 491. 505. 404. 359. 310. 337. 360. 342. 406. 396. 420. 472.\n 548. 559. 463. 407. 362. 405. 417. 391. 419. 461. 472. 535. 622. 606.\n 508. 461. 390. 432.)\n<\/code><\/pre>\n<p>\u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u060c \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u062a\u0642\u0633\u06cc\u0645 \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 LSTM \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u062f \u0634\u062f \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc  \u0633\u067e\u0633 \u0627\u0632 \u0645\u062f\u0644 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a  \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0647\u0627 \u0628\u0627 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0648\u0627\u0642\u0639\u06cc \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a \u0628\u0631\u0627\u06cc \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0639\u0645\u0644\u06a9\u0631\u062f \u0645\u062f\u0644 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0645\u06cc \u0634\u0648\u0646\u062f.<\/p>\n<p>132 \u0631\u06a9\u0648\u0631\u062f \u0627\u0648\u0644 \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0648 12 \u0631\u06a9\u0648\u0631\u062f \u0622\u062e\u0631 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u062f \u0634\u062f.  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0631\u0627 \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u062a\u0642\u0633\u06cc\u0645 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<pre><code class=\"hljs\">test_data_size = <span class=\"hljs-number\">12<\/span>\n\ntrain_data = all_data(:-test_data_size)\ntest_data = all_data(-test_data_size:)\n<\/code><\/pre>\n<p>\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f print \u0637\u0648\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a \u0648 \u0642\u0637\u0627\u0631:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-built_in\">len<\/span>(train_data))\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-built_in\">len<\/span>(test_data))\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">132\n12\n<\/code><\/pre>\n<p>\u0627\u06af\u0631 \u0634\u0645\u0627 \u062f\u0631 \u062d\u0627\u0644 \u062d\u0627\u0636\u0631 print \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062a\u0633\u062a\u060c \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f \u06a9\u0647 \u062d\u0627\u0648\u06cc 12 \u0631\u06a9\u0648\u0631\u062f \u0622\u062e\u0631 \u0627\u0632 <code>all_data<\/code> \u0622\u0631\u0627\u06cc\u0647 NumPy:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(test_data)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">(417. 391. 419. 461. 472. 535. 622. 606. 508. 461. 390. 432.)\n<\/code><\/pre>\n<p>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0627 \u062f\u0631 \u062d\u0627\u0644 \u062d\u0627\u0636\u0631 \u0639\u0627\u062f\u06cc \u0646\u06cc\u0633\u062a.  \u062a\u0639\u062f\u0627\u062f \u06a9\u0644 \u0645\u0633\u0627\u0641\u0631\u0627\u0646 \u062f\u0631 \u0633\u0627\u0644 \u0647\u0627\u06cc \u0627\u0648\u0644\u06cc\u0647 \u062f\u0631 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0628\u0627 \u062a\u0639\u062f\u0627\u062f \u06a9\u0644 \u0645\u0633\u0627\u0641\u0631\u0627\u0646 \u0633\u0627\u0644 \u0647\u0627\u06cc \u0628\u0639\u062f \u0628\u0647 \u0645\u0631\u0627\u062a\u0628 \u06a9\u0645\u062a\u0631 \u0627\u0633\u062a.  \u0639\u0627\u062f\u06cc \u0633\u0627\u0632\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0647\u0627\u06cc \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc \u0628\u0633\u06cc\u0627\u0631 \u0645\u0647\u0645 \u0627\u0633\u062a.  \u0645\u0627 \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u062d\u062f\u0627\u0642\u0644\/\u062d\u062f\u0627\u06a9\u062b\u0631 \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u062f \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0627\u06cc \u06a9\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0631\u0627 \u062f\u0631 \u0645\u062d\u062f\u0648\u062f\u0647 \u0645\u0639\u06cc\u0646\u06cc \u0627\u0632 \u0645\u0642\u0627\u062f\u06cc\u0631 \u062d\u062f\u0627\u0642\u0644 \u0648 \u062d\u062f\u0627\u06a9\u062b\u0631 \u0646\u0631\u0645\u0627\u0644 \u0645\u06cc \u06a9\u0646\u062f.  \u0645\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f <code>MinMaxScaler<\/code> \u06a9\u0644\u0627\u0633 \u0627\u0632 <code>sklearn.preprocessing<\/code> \u0645\u0627\u0698\u0648\u0644 \u0628\u0631\u0627\u06cc \u0645\u0642\u06cc\u0627\u0633 \u0628\u0646\u062f\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u0627.  \u0628\u0631\u0627\u06cc \u062c\u0632\u0626\u06cc\u0627\u062a \u0628\u06cc\u0634\u062a\u0631 \u062f\u0631 \u0645\u0648\u0631\u062f \u0627\u062c\u0631\u0627\u06cc \u0645\u0642\u06cc\u0627\u0633\u200c\u06a9\u0646\u0646\u062f\u0647 \u062d\u062f\u0627\u0642\u0644\/\u062d\u062f\u0627\u06a9\u062b\u0631\u060c \u0645\u0631\u0627\u062c\u0639\u0647 \u06a9\u0646\u06cc\u062f <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.preprocessing.MinMaxScaler.html\">\u0627\u06cc\u0646 \u0644\u06cc\u0646\u06a9<\/a>.<\/p>\n<p>\u06a9\u062f \u0632\u06cc\u0631 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0645\u0642\u06cc\u0627\u0633\u200c\u06a9\u0646\u0646\u062f\u0647 min\/max \u0628\u0627 \u062d\u062f\u0627\u0642\u0644 \u0648 \u062d\u062f\u0627\u06a9\u062b\u0631 \u0645\u0642\u0627\u062f\u06cc\u0631 -1 \u0648 1\u060c \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0645\u0627 \u0631\u0627 \u0639\u0627\u062f\u06cc \u0645\u06cc\u200c\u06a9\u0646\u062f.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> sklearn.preprocessing <span class=\"hljs-keyword\">import<\/span> MinMaxScaler\n\nscaler = MinMaxScaler(feature_range=(-<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>))\ntrain_data_normalized = scaler.fit_transform(train_data .reshape(-<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>))\n<\/code><\/pre>\n<p>\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f print \u0627\u0648\u0644\u06cc\u0646 5 \u0648 5 \u0631\u06a9\u0648\u0631\u062f \u0622\u062e\u0631 \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0639\u0627\u062f\u06cc \u0642\u0637\u0627\u0631 \u0645\u0627.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(train_data_normalized(:<span class=\"hljs-number\">5<\/span>))\n<span class=\"hljs-built_in\">print<\/span>(train_data_normalized(-<span class=\"hljs-number\">5<\/span>:))\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">((-0.96483516)\n (-0.93846154)\n (-0.87692308)\n (-0.89010989)\n (-0.92527473))\n((1.        )\n (0.57802198)\n (0.33186813)\n (0.13406593)\n (0.32307692))\n<\/code><\/pre>\n<p>\u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0645\u0642\u0627\u062f\u06cc\u0631 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0627\u06a9\u0646\u0648\u0646 \u0628\u06cc\u0646 -1 \u0648 1 \u0647\u0633\u062a\u0646\u062f.<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u0630\u06a9\u0631 \u0627\u06cc\u0646 \u0646\u06a9\u062a\u0647 \u0636\u0631\u0648\u0631\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0646\u0631\u0645\u0627\u0644 \u0633\u0627\u0632\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u0641\u0642\u0637 \u0627\u0639\u0645\u0627\u0644 \u0645\u06cc \u0634\u0648\u062f \u0631\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u0646\u0647 \u0631\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062a\u0633\u062a  \u0627\u06af\u0631 \u0646\u0631\u0645\u0627\u0644 \u0633\u0627\u0632\u06cc \u0627\u0639\u0645\u0627\u0644 \u0634\u0648\u062f \u0631\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062a\u0633\u062a\u060c \u0627\u06cc\u0646 \u0627\u062d\u062a\u0645\u0627\u0644 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f \u06a9\u0647 \u0628\u0631\u062e\u06cc \u0627\u0632 \u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0627\u0632 \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a \u0646\u0634\u062a \u06a9\u0646\u062f.<\/p>\n<p>\u06af\u0627\u0645 \u0628\u0639\u062f\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0627 \u0628\u0647 \u062a\u0627\u0646\u0633\u0648\u0631 \u0627\u0633\u062a \u0632\u06cc\u0631\u0627 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc PyTorch \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0627\u0646\u0633\u0648\u0631 \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.  \u0628\u0631\u0627\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0628\u0647 \u062a\u0627\u0646\u0633\u0648\u0631\u060c \u0628\u0647 \u0633\u0627\u062f\u06af\u06cc \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u062e\u0648\u062f \u0631\u0627 \u0628\u0647 \u0633\u0627\u0632\u0646\u062f\u0647 \u0622\u0646 \u0627\u0631\u0633\u0627\u0644 \u06a9\u0646\u06cc\u0645 <code>FloatTensor<\/code> \u0634\u06cc\u0621\u060c \u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u062f\u0631 \u0632\u06cc\u0631 \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">train_data_normalized = torch.FloatTensor(train_data_normalized).view(-<span class=\"hljs-number\">1<\/span>)\n<\/code><\/pre>\n<p>\u0622\u062e\u0631\u06cc\u0646 \u0645\u0631\u062d\u0644\u0647 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u062a\u0628\u062f\u06cc\u0644 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0628\u0647 \u062f\u0646\u0628\u0627\u0644\u0647 \u0647\u0627 \u0648 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627\u06cc \u0645\u0631\u0628\u0648\u0637\u0647 \u0627\u0633\u062a.<\/p>\n<p>\u0634\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0632 \u0647\u0631 \u0637\u0648\u0644 \u062f\u0646\u0628\u0627\u0644\u0647 \u0627\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f \u0648 \u0627\u06cc\u0646 \u0628\u0647 \u062f\u0627\u0646\u0634 \u062f\u0627\u0645\u0646\u0647 \u0628\u0633\u062a\u06af\u06cc \u062f\u0627\u0631\u062f.  \u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0637\u0648\u0644 \u062f\u0646\u0628\u0627\u0644\u0647 12 \u0631\u0627\u062d\u062a \u0627\u0633\u062a \u0632\u06cc\u0631\u0627 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u0627\u0647\u0627\u0646\u0647 \u062f\u0627\u0631\u06cc\u0645 \u0648 12 \u0645\u0627\u0647 \u062f\u0631 \u0633\u0627\u0644 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f.  \u0627\u06af\u0631 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0631\u0648\u0632\u0627\u0646\u0647 \u062f\u0627\u0634\u062a\u06cc\u0645\u060c \u0637\u0648\u0644 \u062f\u0646\u0628\u0627\u0644\u0647 \u0628\u0647\u062a\u0631 365 \u0628\u0648\u062f\u060c \u06cc\u0639\u0646\u06cc \u062a\u0639\u062f\u0627\u062f \u0631\u0648\u0632\u0647\u0627\u06cc \u06cc\u06a9 \u0633\u0627\u0644.  \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646\u060c \u0637\u0648\u0644 \u062a\u0648\u0627\u0644\u06cc \u0648\u0631\u0648\u062f\u06cc \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0631\u0627 \u0631\u0648\u06cc 12 \u0642\u0631\u0627\u0631 \u0645\u06cc \u062f\u0647\u06cc\u0645.<\/p>\n<pre><code class=\"hljs\">train_window = <span class=\"hljs-number\">12<\/span>\n<\/code><\/pre>\n<p>\u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f \u062a\u0627\u0628\u0639\u06cc \u0628\u0647 \u0646\u0627\u0645 \u062a\u0639\u0631\u06cc\u0641 \u0645\u06cc \u06a9\u0646\u06cc\u0645 <code>create_inout_sequences<\/code>.  \u062a\u0627\u0628\u0639 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0648\u0631\u0648\u062f\u06cc \u062e\u0627\u0645 \u0631\u0627 \u0645\u06cc \u067e\u0630\u06cc\u0631\u062f \u0648 \u0644\u06cc\u0633\u062a\u06cc \u0627\u0632 \u062a\u0627\u067e\u0644 \u0647\u0627 \u0631\u0627 \u0628\u0631\u0645\u06cc \u06af\u0631\u062f\u0627\u0646\u062f.  \u062f\u0631 \u0647\u0631 \u062a\u0627\u067e\u0644\u060c \u0627\u0644\u0645\u0627\u0646 \u0627\u0648\u0644 \u0634\u0627\u0645\u0644 \u0641\u0647\u0631\u0633\u062a\u06cc \u0627\u0632 12 \u0622\u06cc\u062a\u0645 \u0645\u0631\u0628\u0648\u0637 \u0628\u0647 \u062a\u0639\u062f\u0627\u062f \u0645\u0633\u0627\u0641\u0631\u0627\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u062f\u0631 12 \u0645\u0627\u0647 \u0633\u0641\u0631 \u0645\u06cc \u06a9\u0646\u0646\u062f\u060c \u0627\u0644\u0645\u0627\u0646 \u062a\u0627\u067e\u0644 \u062f\u0648\u0645 \u0634\u0627\u0645\u0644 \u06cc\u06a9 \u0622\u06cc\u062a\u0645 \u06cc\u0639\u0646\u06cc \u062a\u0639\u062f\u0627\u062f \u0645\u0633\u0627\u0641\u0631\u0627\u0646 \u062f\u0631 \u0645\u0627\u0647 12+1 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">create_inout_sequences<\/span>(<span class=\"hljs-params\">input_data, tw<\/span>):<\/span>\n    inout_seq = ()\n    L = <span class=\"hljs-built_in\">len<\/span>(input_data)\n    <span class=\"hljs-keyword\">for<\/span> i <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(L-tw):\n        train_seq = input_data(i:i+tw)\n        train_label = input_data(i+tw:i+tw+<span class=\"hljs-number\">1<\/span>)\n        inout_seq.append((train_seq ,train_label))\n    <span class=\"hljs-keyword\">return<\/span> inout_seq\n<\/code><\/pre>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0631\u0627 \u0628\u0631\u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u062f\u0646\u0628\u0627\u0644\u0647 \u0647\u0627 \u0648 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627\u06cc \u0645\u0631\u0628\u0648\u0637 \u0628\u0647 \u0622\u0645\u0648\u0632\u0634 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">train_inout_seq = create_inout_sequences(train_data_normalized, train_window)\n<\/code><\/pre>\n<p>\u0627\u06af\u0631 \u0634\u0645\u0627 print \u0637\u0648\u0644 <code>train_inout_seq<\/code> \u0644\u06cc\u0633\u062a\u060c \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f \u06a9\u0647 \u0634\u0627\u0645\u0644 120 \u0645\u0648\u0631\u062f \u0627\u0633\u062a.  \u0627\u06cc\u0646 \u0628\u0647 \u0627\u06cc\u0646 \u062f\u0644\u06cc\u0644 \u0627\u0633\u062a \u06a9\u0647 \u0627\u06af\u0631\u0686\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0634\u0627\u0645\u0644 132 \u0639\u0646\u0635\u0631 \u0627\u0633\u062a\u060c \u0637\u0648\u0644 \u062f\u0646\u0628\u0627\u0644\u0647 \u0622\u0646 12 \u0627\u0633\u062a\u060c \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u06a9\u0647 \u0633\u06a9\u0627\u0646\u0633 \u0627\u0648\u0644 \u0627\u0632 12 \u0645\u0648\u0631\u062f \u0627\u0648\u0644 \u062a\u0634\u06a9\u06cc\u0644 \u0634\u062f\u0647 \u0627\u0633\u062a \u0648 \u0622\u06cc\u062a\u0645 \u0633\u06cc\u0632\u062f\u0647\u0645 \u0628\u0631\u0686\u0633\u0628 \u0633\u06a9\u0627\u0646\u0633 \u0627\u0648\u0644 \u0627\u0633\u062a.  \u0628\u0647 \u0647\u0645\u06cc\u0646 \u062a\u0631\u062a\u06cc\u0628\u060c \u0633\u06a9\u0627\u0646\u0633 \u062f\u0648\u0645 \u0627\u0632 \u0622\u06cc\u062a\u0645 \u062f\u0648\u0645 \u0634\u0631\u0648\u0639 \u0645\u06cc \u0634\u0648\u062f \u0648 \u0628\u0647 \u0622\u06cc\u062a\u0645 \u0633\u06cc\u0632\u062f\u0647\u0645 \u062e\u062a\u0645 \u0645\u06cc \u0634\u0648\u062f\u060c \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u0622\u06cc\u062a\u0645 \u0686\u0647\u0627\u0631\u062f\u0647\u0645 \u0628\u0631\u0686\u0633\u0628 \u0633\u06a9\u0627\u0646\u0633 \u062f\u0648\u0645 \u0627\u0633\u062a \u0648 \u0628\u0647 \u0647\u0645\u06cc\u0646 \u062a\u0631\u062a\u06cc\u0628 \u0631\u0648\u06cc.<\/p>\n<p>\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f print 5 \u0645\u0648\u0631\u062f \u0627\u0648\u0644 \u0627\u0632 <code>train_inout_seq<\/code> \u0644\u06cc\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">train_inout_seq(:<span class=\"hljs-number\">5<\/span>)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">((tensor((-<span class=\"hljs-number\">0.9648<\/span>, -<span class=\"hljs-number\">0.9385<\/span>, -<span class=\"hljs-number\">0.8769<\/span>, -<span class=\"hljs-number\">0.8901<\/span>, -<span class=\"hljs-number\">0.9253<\/span>, -<span class=\"hljs-number\">0.8637<\/span>, -<span class=\"hljs-number\">0.8066<\/span>, -<span class=\"hljs-number\">0.8066<\/span>,\n          -<span class=\"hljs-number\">0.8593<\/span>, -<span class=\"hljs-number\">0.9341<\/span>, -<span class=\"hljs-number\">1.0000<\/span>, -<span class=\"hljs-number\">0.9385<\/span>)), tensor((-<span class=\"hljs-number\">0.9516<\/span>))),\n (tensor((-<span class=\"hljs-number\">0.9385<\/span>, -<span class=\"hljs-number\">0.8769<\/span>, -<span class=\"hljs-number\">0.8901<\/span>, -<span class=\"hljs-number\">0.9253<\/span>, -<span class=\"hljs-number\">0.8637<\/span>, -<span class=\"hljs-number\">0.8066<\/span>, -<span class=\"hljs-number\">0.8066<\/span>, -<span class=\"hljs-number\">0.8593<\/span>,\n          -<span class=\"hljs-number\">0.9341<\/span>, -<span class=\"hljs-number\">1.0000<\/span>, -<span class=\"hljs-number\">0.9385<\/span>, -<span class=\"hljs-number\">0.9516<\/span>)),\n  tensor((-<span class=\"hljs-number\">0.9033<\/span>))),\n (tensor((-<span class=\"hljs-number\">0.8769<\/span>, -<span class=\"hljs-number\">0.8901<\/span>, -<span class=\"hljs-number\">0.9253<\/span>, -<span class=\"hljs-number\">0.8637<\/span>, -<span class=\"hljs-number\">0.8066<\/span>, -<span class=\"hljs-number\">0.8066<\/span>, -<span class=\"hljs-number\">0.8593<\/span>, -<span class=\"hljs-number\">0.9341<\/span>,\n          -<span class=\"hljs-number\">1.0000<\/span>, -<span class=\"hljs-number\">0.9385<\/span>, -<span class=\"hljs-number\">0.9516<\/span>, -<span class=\"hljs-number\">0.9033<\/span>)), tensor((-<span class=\"hljs-number\">0.8374<\/span>))),\n (tensor((-<span class=\"hljs-number\">0.8901<\/span>, -<span class=\"hljs-number\">0.9253<\/span>, -<span class=\"hljs-number\">0.8637<\/span>, -<span class=\"hljs-number\">0.8066<\/span>, -<span class=\"hljs-number\">0.8066<\/span>, -<span class=\"hljs-number\">0.8593<\/span>, -<span class=\"hljs-number\">0.9341<\/span>, -<span class=\"hljs-number\">1.0000<\/span>,\n          -<span class=\"hljs-number\">0.9385<\/span>, -<span class=\"hljs-number\">0.9516<\/span>, -<span class=\"hljs-number\">0.9033<\/span>, -<span class=\"hljs-number\">0.8374<\/span>)), tensor((-<span class=\"hljs-number\">0.8637<\/span>))),\n (tensor((-<span class=\"hljs-number\">0.9253<\/span>, -<span class=\"hljs-number\">0.8637<\/span>, -<span class=\"hljs-number\">0.8066<\/span>, -<span class=\"hljs-number\">0.8066<\/span>, -<span class=\"hljs-number\">0.8593<\/span>, -<span class=\"hljs-number\">0.9341<\/span>, -<span class=\"hljs-number\">1.0000<\/span>, -<span class=\"hljs-number\">0.9385<\/span>,\n          -<span class=\"hljs-number\">0.9516<\/span>, -<span class=\"hljs-number\">0.9033<\/span>, -<span class=\"hljs-number\">0.8374<\/span>, -<span class=\"hljs-number\">0.8637<\/span>)), tensor((-<span class=\"hljs-number\">0.9077<\/span>))))\n<\/code><\/pre>\n<p>\u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0647\u0631 \u0622\u06cc\u062a\u0645 \u06cc\u06a9 \u062a\u0627\u067e\u0644 \u0627\u0633\u062a \u06a9\u0647 \u0639\u0646\u0635\u0631 \u0627\u0648\u0644 \u0627\u0632 12 \u0645\u0648\u0631\u062f \u0627\u0632 \u06cc\u06a9 \u062f\u0646\u0628\u0627\u0644\u0647 \u062a\u0634\u06a9\u06cc\u0644 \u0634\u062f\u0647 \u0627\u0633\u062a \u0648 \u0639\u0646\u0635\u0631 \u062a\u0627\u067e\u0644 \u062f\u0648\u0645 \u062d\u0627\u0648\u06cc \u0628\u0631\u0686\u0633\u0628 \u0645\u0631\u0628\u0648\u0637\u0647 \u0627\u0633\u062a.<\/p>\n<h2 id=\"creatinglstmmodel\"><span class=\"ez-toc-section\" id=\"%d8%a7%db%8c%d8%ac%d8%a7%d8%af_%d9%85%d8%af%d9%84_lstm\"><\/span>\u0627\u06cc\u062c\u0627\u062f \u0645\u062f\u0644 LSTM<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0645\u0627 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0631\u0627 \u0627\u0632 \u0642\u0628\u0644 \u067e\u0631\u062f\u0627\u0632\u0634 \u06a9\u0631\u062f\u0647 \u0627\u06cc\u0645\u060c \u0627\u06a9\u0646\u0648\u0646 \u0632\u0645\u0627\u0646 \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0645\u0627 \u0627\u0633\u062a.  \u0645\u0627 \u06cc\u06a9 \u06a9\u0644\u0627\u0633 \u062a\u0639\u0631\u06cc\u0641 \u0645\u06cc \u06a9\u0646\u06cc\u0645 <code>LSTM<\/code>\u060c \u06a9\u0647 \u0627\u0632 \u0627\u0631\u062b \u0645\u06cc \u0628\u0631\u062f <code>nn.Module<\/code> \u06a9\u0644\u0627\u0633 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 PyTorch  \u0628\u0631\u0627\u06cc \u0645\u0634\u0627\u0647\u062f\u0647 \u0631\u0648\u0634 \u0627\u06cc\u062c\u0627\u062f \u06cc\u06a9 \u0645\u062f\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0628\u0627 PyTorch \u0622\u062e\u0631\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0645\u0646 \u0631\u0627 \u0628\u0631\u0631\u0633\u06cc \u06a9\u0646\u06cc\u062f.  \u0622\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0634\u0645\u0627 \u06a9\u0645\u06a9 \u0645\u06cc \u06a9\u0646\u062f \u062a\u0627 \u0628\u0641\u0647\u0645\u06cc\u062f \u062f\u0631 \u06a9\u062f \u0632\u06cc\u0631 \u0686\u0647 \u0627\u062a\u0641\u0627\u0642\u06cc \u0645\u06cc \u0627\u0641\u062a\u062f.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-class\"><span class=\"hljs-keyword\">class<\/span> <span class=\"hljs-title\">LSTM<\/span>(<span class=\"hljs-params\">nn.Module<\/span>):<\/span>\n    <span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">__init__<\/span>(<span class=\"hljs-params\">self, input_size=<span class=\"hljs-number\">1<\/span>, hidden_layer_size=<span class=\"hljs-number\">100<\/span>, output_size=<span class=\"hljs-number\">1<\/span><\/span>):<\/span>\n        <span class=\"hljs-built_in\">super<\/span>().__init__()\n        self.hidden_layer_size = hidden_layer_size\n\n        self.lstm = nn.LSTM(input_size, hidden_layer_size)\n\n        self.linear = nn.Linear(hidden_layer_size, output_size)\n\n        self.hidden_cell = (torch.zeros(<span class=\"hljs-number\">1<\/span>,<span class=\"hljs-number\">1<\/span>,self.hidden_layer_size),\n                            torch.zeros(<span class=\"hljs-number\">1<\/span>,<span class=\"hljs-number\">1<\/span>,self.hidden_layer_size))\n\n    <span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">forward<\/span>(<span class=\"hljs-params\">self, input_seq<\/span>):<\/span>\n        lstm_out, self.hidden_cell = self.lstm(input_seq.view(<span class=\"hljs-built_in\">len<\/span>(input_seq) ,<span class=\"hljs-number\">1<\/span>, -<span class=\"hljs-number\">1<\/span>), self.hidden_cell)\n        predictions = self.linear(lstm_out.view(<span class=\"hljs-built_in\">len<\/span>(input_seq), -<span class=\"hljs-number\">1<\/span>))\n        <span class=\"hljs-keyword\">return<\/span> predictions(-<span class=\"hljs-number\">1<\/span>)\n<\/code><\/pre>\n<p>\u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f \u0622\u0646\u0686\u0647 \u0631\u0627 \u06a9\u0647 \u062f\u0631 \u06a9\u062f \u0628\u0627\u0644\u0627 \u0627\u062a\u0641\u0627\u0642 \u0645\u06cc \u0627\u0641\u062a\u062f \u062e\u0644\u0627\u0635\u0647 \u06a9\u0646\u0645.  \u0633\u0627\u0632\u0646\u062f\u0647 \u0627\u0632 <code>LSTM<\/code> \u06a9\u0644\u0627\u0633 \u0633\u0647 \u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u0631\u0627 \u0645\u06cc \u067e\u0630\u06cc\u0631\u062f:<\/p>\n<ol>\n<li><code>input_size<\/code>: \u0645\u0631\u0628\u0648\u0637 \u0628\u0647 \u062a\u0639\u062f\u0627\u062f \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u0648\u0631\u0648\u062f\u06cc \u0627\u0633\u062a.  \u0627\u06af\u0631\u0686\u0647 \u0637\u0648\u0644 \u062a\u0648\u0627\u0644\u06cc \u0645\u0627 12 \u0627\u0633\u062a\u060c \u0627\u0645\u0627 \u0628\u0631\u0627\u06cc \u0647\u0631 \u0645\u0627\u0647 \u0641\u0642\u0637 1 \u0645\u0642\u062f\u0627\u0631 \u062f\u0627\u0631\u06cc\u0645\u060c \u06cc\u0639\u0646\u06cc \u062a\u0639\u062f\u0627\u062f \u06a9\u0644 \u0645\u0633\u0627\u0641\u0631\u0627\u0646\u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0627\u0646\u062f\u0627\u0632\u0647 \u0648\u0631\u0648\u062f\u06cc 1 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.<\/li>\n<li><code>hidden_layer_size<\/code>: \u062a\u0639\u062f\u0627\u062f \u0644\u0627\u06cc\u0647 \u0647\u0627\u06cc \u067e\u0646\u0647\u0627\u0646 \u0628\u0647 \u0647\u0645\u0631\u0627\u0647 \u062a\u0639\u062f\u0627\u062f \u0646\u0648\u0631\u0648\u0646 \u0647\u0627\u06cc \u0647\u0631 \u0644\u0627\u06cc\u0647 \u0631\u0627 \u0645\u0634\u062e\u0635 \u0645\u06cc \u06a9\u0646\u062f.  \u0645\u0627 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u0627\u0632 100 \u0646\u0648\u0631\u0648\u0646 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u0634\u062a.<\/li>\n<li><code>output_size<\/code>: \u062a\u0639\u062f\u0627\u062f \u0622\u06cc\u062a\u0645 \u0647\u0627\u06cc \u0645\u0648\u062c\u0648\u062f \u062f\u0631 \u062e\u0631\u0648\u062c\u06cc\u060c \u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u062a\u0639\u062f\u0627\u062f \u0645\u0633\u0627\u0641\u0631\u0627\u0646 \u0631\u0627 \u0628\u0631\u0627\u06cc 1 \u0645\u0627\u0647 \u0622\u06cc\u0646\u062f\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u06a9\u0646\u06cc\u0645\u060c \u0627\u0646\u062f\u0627\u0632\u0647 \u062e\u0631\u0648\u062c\u06cc 1 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.<\/li>\n<\/ol>\n<p>\u0628\u0639\u062f\u060c \u062f\u0631 \u0633\u0627\u0632\u0646\u062f\u0647 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627 \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645 <code>hidden_layer_size<\/code>\u060c <code>lstm<\/code>\u060c <code>linear<\/code>\u060c \u0648 <code>hidden_cell<\/code>.  \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 LSTM \u0633\u0647 \u0648\u0631\u0648\u062f\u06cc \u0631\u0627 \u0645\u06cc \u067e\u0630\u06cc\u0631\u062f: \u062d\u0627\u0644\u062a \u067e\u0646\u0647\u0627\u0646 \u0642\u0628\u0644\u06cc\u060c \u0648\u0636\u0639\u06cc\u062a \u0633\u0644\u0648\u0644 \u0642\u0628\u0644\u06cc \u0648 \u0648\u0631\u0648\u062f\u06cc \u0641\u0639\u0644\u06cc.  \u0631\u0627 <code>hidden_cell<\/code> \u0645\u062a\u063a\u06cc\u0631 \u0634\u0627\u0645\u0644 \u062d\u0627\u0644\u062a \u0645\u062e\u0641\u06cc \u0648 \u0633\u0644\u0648\u0644 \u0642\u0628\u0644\u06cc \u0627\u0633\u062a.  \u0631\u0627 <code>lstm<\/code> \u0648 <code>linear<\/code> \u0627\u0632 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u0644\u0627\u06cc\u0647 \u0628\u0631\u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u0644\u0627\u06cc\u0647 \u0647\u0627\u06cc LSTM \u0648 \u062e\u0637\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u062f\u0631\u0648\u0646 <code>forward<\/code> \u0631\u0648\u0634\u060c <code>input_seq<\/code> \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u06cc\u06a9 \u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u0627\u0631\u0633\u0627\u0644 \u0645\u06cc \u0634\u0648\u062f \u06a9\u0647 \u0627\u0628\u062a\u062f\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 <code>lstm<\/code> \u0644\u0627\u06cc\u0647.  \u062e\u0631\u0648\u062c\u06cc \u0627\u0632 <code>lstm<\/code> \u0644\u0627\u06cc\u0647 \u062d\u0627\u0644\u062a \u0647\u0627\u06cc \u0645\u062e\u0641\u06cc \u0648 \u0633\u0644\u0648\u0644\u06cc \u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0632\u0645\u0627\u0646\u06cc \u0641\u0639\u0644\u06cc \u0628\u0647 \u0647\u0645\u0631\u0627\u0647 \u062e\u0631\u0648\u062c\u06cc \u0627\u0633\u062a.  \u062e\u0631\u0648\u062c\u06cc \u0627\u0632 <code>lstm<\/code> \u0644\u0627\u06cc\u0647 \u0628\u0647 <code>linear<\/code> \u0644\u0627\u06cc\u0647.  \u062a\u0639\u062f\u0627\u062f \u0645\u0633\u0627\u0641\u0631\u0627\u0646 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0634\u062f\u0647 \u062f\u0631 \u0622\u062e\u0631\u06cc\u0646 \u0645\u0648\u0631\u062f \u0630\u062e\u06cc\u0631\u0647 \u0645\u06cc \u0634\u0648\u062f <code>predictions<\/code> \u0644\u06cc\u0633\u062a\u060c \u06a9\u0647 \u0628\u0647 \u062a\u0627\u0628\u0639 \u0641\u0631\u0627\u062e\u0648\u0627\u0646\u06cc \u0628\u0627\u0632\u06af\u0631\u062f\u0627\u0646\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u06cc \u0627\u06cc\u062c\u0627\u062f \u06cc\u06a9 \u0634\u06cc \u0627\u0632 the \u0627\u0633\u062a <code>LSTM()<\/code> \u06a9\u0644\u0627\u0633\u060c \u06cc\u06a9 \u062a\u0627\u0628\u0639 \u0636\u0631\u0631 \u0648 \u0628\u0647\u06cc\u0646\u0647 \u0633\u0627\u0632 \u0631\u0627 \u062a\u0639\u0631\u06cc\u0641 \u06a9\u0646\u06cc\u062f.  \u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0645\u0627 \u062f\u0631 \u062d\u0627\u0644 \u062d\u0644 \u06cc\u06a9 \u0645\u0634\u06a9\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0647\u0633\u062a\u06cc\u0645\u060c \u0627\u0632 \u0622\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/gombru.github.io\/2018\/05\/23\/cross_entropy_loss\/\">\u0627\u0632 \u062f\u0633\u062a \u062f\u0627\u062f\u0646 \u0622\u0646\u062a\u0631\u0648\u067e\u06cc \u0645\u062a\u0642\u0627\u0628\u0644<\/a>.  \u0628\u0631\u0627\u06cc \u062a\u0627\u0628\u0639 \u0628\u0647\u06cc\u0646\u0647 \u0633\u0627\u0632\u060c \u0627\u0632 <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/machinelearningmastery.com\/adam-optimization-algorithm-for-deep-learning\/\">Adam Optimizer<\/a>.<\/p>\n<pre><code class=\"hljs\">model = LSTM()\nloss_function = nn.MSELoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=<span class=\"hljs-number\">0.001<\/span>)\n<\/code><\/pre>\n<p>\u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f print \u0645\u062f\u0644 \u0645\u0627:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(model)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">LSTM(\n  (lstm): LSTM(1, 100)\n  (linear): Linear(in_features=100, out_features=1, bias=True)\n)\n<\/code><\/pre>\n<h2 id=\"trainingthemodel\"><span class=\"ez-toc-section\" id=\"%d8%a2%d9%85%d9%88%d8%b2%d8%b4_%d9%85%d8%af%d9%84\"><\/span>\u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0645\u0627 \u0645\u062f\u0644 \u062e\u0648\u062f \u0631\u0627 \u0628\u0631\u0627\u06cc 150 \u062f\u0648\u0631\u0647 \u0622\u0645\u0648\u0632\u0634 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u062f.  \u0627\u06af\u0631 \u0628\u062e\u0648\u0627\u0647\u06cc\u062f \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u062f\u0648\u0631\u0647 \u0647\u0627\u06cc \u0628\u06cc\u0634\u062a\u0631\u06cc \u0631\u0627 \u0627\u0645\u062a\u062d\u0627\u0646 \u06a9\u0646\u06cc\u062f.  \u0636\u0627\u06cc\u0639\u0627\u062a \u067e\u0633 \u0627\u0632 \u0647\u0631 25 \u062f\u0648\u0631\u0647 \u0686\u0627\u067e \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<pre><code class=\"hljs\">epochs = <span class=\"hljs-number\">150<\/span>\n\n<span class=\"hljs-keyword\">for<\/span> i <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(epochs):\n    <span class=\"hljs-keyword\">for<\/span> seq, labels <span class=\"hljs-keyword\">in<\/span> train_inout_seq:\n        optimizer.zero_grad()\n        model.hidden_cell = (torch.zeros(<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>, model.hidden_layer_size),\n                        torch.zeros(<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>, model.hidden_layer_size))\n\n        y_pred = model(seq)\n\n        single_loss = loss_function(y_pred, labels)\n        single_loss.backward()\n        optimizer.step()\n\n    <span class=\"hljs-keyword\">if<\/span> i%<span class=\"hljs-number\">25<\/span> == <span class=\"hljs-number\">1<\/span>:\n        <span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">f'epoch: <span class=\"hljs-subst\">{i:<span class=\"hljs-number\">3<\/span>}<\/span> loss: <span class=\"hljs-subst\">{single_loss.item():<span class=\"hljs-number\">10.8<\/span>f}<\/span>'<\/span>)\n\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">f'epoch: <span class=\"hljs-subst\">{i:<span class=\"hljs-number\">3<\/span>}<\/span> loss: <span class=\"hljs-subst\">{single_loss.item():<span class=\"hljs-number\">10.10<\/span>f}<\/span>'<\/span>)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">epoch:   1 loss: 0.00517058\nepoch:  26 loss: 0.00390285\nepoch:  51 loss: 0.00473305\nepoch:  76 loss: 0.00187001\nepoch: 101 loss: 0.00000075\nepoch: 126 loss: 0.00608046\nepoch: 149 loss: 0.0004329932\n<\/code><\/pre>\n<p>\u0634\u0645\u0627 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0645\u0642\u0627\u062f\u06cc\u0631 \u0645\u062a\u0641\u0627\u0648\u062a\u06cc \u062f\u0631\u06cc\u0627\u0641\u062a \u06a9\u0646\u06cc\u062f \u0632\u06cc\u0631\u0627 \u0628\u0647 \u0637\u0648\u0631 \u067e\u06cc\u0634 \u0641\u0631\u0636 \u0648\u0632\u0646 \u0647\u0627 \u0628\u0647 \u0637\u0648\u0631 \u062a\u0635\u0627\u062f\u0641\u06cc \u062f\u0631 \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc PyTorch \u0645\u0642\u062f\u0627\u0631\u062f\u0647\u06cc \u0627\u0648\u0644\u06cc\u0647 \u0645\u06cc \u0634\u0648\u0646\u062f.<\/p>\n<h2 id=\"makingpredictions\"><span class=\"ez-toc-section\" id=\"%d9%be%db%8c%d8%b4%da%af%d9%88%db%8c%db%8c\"><\/span>\u067e\u06cc\u0634\u06af\u0648\u06cc\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u06a9\u0646\u0648\u0646 \u06a9\u0647 \u0645\u062f\u0644 \u0645\u0627 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u0627\u0633\u062a\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0634\u0631\u0648\u0639 \u0628\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u06a9\u0646\u06cc\u0645.  \u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u0645\u0627 \u062d\u0627\u0648\u06cc \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0645\u0633\u0627\u0641\u0631 \u0628\u0631\u0627\u06cc 12 \u0645\u0627\u0647 \u06af\u0630\u0634\u062a\u0647 \u0627\u0633\u062a \u0648 \u0645\u062f\u0644 \u0645\u0627 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u0628\u0627 \u0637\u0648\u0644 \u062f\u0646\u0628\u0627\u0644\u0647 12 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u0627\u0633\u062a. \u0627\u0628\u062a\u062f\u0627 12 \u0645\u0642\u062f\u0627\u0631 \u0622\u062e\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0627 \u0641\u06cc\u0644\u062a\u0631 \u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">fut_pred = <span class=\"hljs-number\">12<\/span>\n\ntest_inputs = train_data_normalized(-train_window:).tolist()\n<span class=\"hljs-built_in\">print<\/span>(test_inputs)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">(0.12527473270893097, 0.04615384712815285, 0.3274725377559662, 0.2835164964199066, 0.3890109956264496, 0.6175824403762817, 0.9516483545303345, 1.0, 0.5780220031738281, 0.33186814188957214, 0.13406594097614288, 0.32307693362236023)\n<\/code><\/pre>\n<p>\u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0645\u0642\u0627\u062f\u06cc\u0631 \u0628\u0627\u0644\u0627 \u0631\u0627 \u0628\u0627 12 \u0645\u0642\u062f\u0627\u0631 \u0622\u062e\u0631 \u0645\u0642\u0627\u06cc\u0633\u0647 \u06a9\u0646\u06cc\u062f <code>train_data_normalized<\/code> \u0644\u06cc\u0633\u062a \u062f\u0627\u062f\u0647 \u0647\u0627<\/p>\n<p>\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 <code>test_inputs<\/code> \u0645\u0648\u0631\u062f \u0634\u0627\u0645\u0644 12 \u0645\u0648\u0631\u062f \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.  \u062f\u0627\u062e\u0644 a <code>for<\/code> \u062d\u0644\u0642\u0647 \u0627\u06cc\u0646 12 \u0645\u0648\u0631\u062f \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u0627\u0648\u0644\u06cc\u0646 \u0645\u0648\u0631\u062f \u0627\u0632 \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u06cc\u0639\u0646\u06cc \u0622\u06cc\u062a\u0645 \u0634\u0645\u0627\u0631\u0647 133 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f. \u0633\u067e\u0633 \u0645\u0642\u062f\u0627\u0631 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0634\u062f\u0647 \u0628\u0647 \u0622\u0646 \u0627\u0636\u0627\u0641\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f. <code>test_inputs<\/code> \u0641\u0647\u0631\u0633\u062a  \u062f\u0631 \u0637\u0648\u0644 \u062a\u06a9\u0631\u0627\u0631 \u062f\u0648\u0645\u060c \u062f\u0648\u0628\u0627\u0631\u0647 \u0627\u0632 12 \u0645\u0648\u0631\u062f \u0622\u062e\u0631 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0648\u0631\u0648\u062f\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f \u0648 \u06cc\u06a9 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062c\u062f\u06cc\u062f \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc \u0634\u0648\u062f \u06a9\u0647 \u0633\u067e\u0633 \u0628\u0647 <code>test_inputs<\/code> \u062f\u0648\u0628\u0627\u0631\u0647 \u0644\u06cc\u0633\u062a \u06a9\u0646\u06cc\u062f  \u0631\u0627 <code>for<\/code> \u062d\u0644\u0642\u0647 12 \u0628\u0627\u0631 \u0627\u062c\u0631\u0627 \u0645\u06cc \u0634\u0648\u062f \u0632\u06cc\u0631\u0627 12 \u0639\u0646\u0635\u0631 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f.  \u062f\u0631 \u0627\u0646\u062a\u0647\u0627\u06cc \u062d\u0644\u0642\u0647 <code>test_inputs<\/code> \u0644\u06cc\u0633\u062a \u0634\u0627\u0645\u0644 24 \u0645\u0648\u0631\u062f \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.  12 \u0645\u0648\u0631\u062f \u0622\u062e\u0631 \u0645\u0642\u0627\u062f\u06cc\u0631 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a \u062e\u0648\u0627\u0647\u0646\u062f \u0628\u0648\u062f.<\/p>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \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\">model.<span class=\"hljs-built_in\">eval<\/span>()\n\n<span class=\"hljs-keyword\">for<\/span> i <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(fut_pred):\n    seq = torch.FloatTensor(test_inputs(-train_window:))\n    <span class=\"hljs-keyword\">with<\/span> torch.no_grad():\n        model.hidden = (torch.zeros(<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>, model.hidden_layer_size),\n                        torch.zeros(<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>, model.hidden_layer_size))\n        test_inputs.append(model(seq).item())\n<\/code><\/pre>\n<p>\u0627\u06af\u0631 \u0634\u0645\u0627 print \u0637\u0648\u0644 <code>test_inputs<\/code> \u0644\u06cc\u0633\u062a\u060c \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f \u06a9\u0647 \u0634\u0627\u0645\u0644 24 \u0645\u0648\u0631\u062f \u0627\u0633\u062a.  12 \u0645\u0648\u0631\u062f \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0634\u062f\u0647 \u0622\u062e\u0631 \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0628\u0647 \u0635\u0648\u0631\u062a \u0632\u06cc\u0631 \u0686\u0627\u067e \u06a9\u0631\u062f:<\/p>\n<pre><code class=\"hljs\">test_inputs(fut_pred:)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">(0.4574652910232544,\n 0.9810629487037659,\n 1.279405951499939,\n 1.0621851682662964,\n 1.5830546617507935,\n 1.8899496793746948,\n 1.323508620262146,\n 1.8764172792434692,\n 2.1249167919158936,\n 1.7745600938796997,\n 1.7952896356582642,\n 1.977765679359436)\n<\/code><\/pre>\n<p>\u0644\u0627\u0632\u0645 \u0628\u0647 \u0630\u06a9\u0631 \u0627\u0633\u062a \u06a9\u0647 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0628\u0633\u062a\u0647 \u0628\u0647 \u0648\u0632\u0646\u0647 \u0647\u0627\u06cc\u06cc \u06a9\u0647 \u0628\u0631\u0627\u06cc \u062a\u0645\u0631\u06cc\u0646 LSTM \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f \u0645\u0642\u0627\u062f\u06cc\u0631 \u0645\u062a\u0641\u0627\u0648\u062a\u06cc \u0628\u062f\u0633\u062a \u0622\u0648\u0631\u06cc\u062f.<\/p>\n<p>\u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0639\u0627\u062f\u06cc \u06a9\u0631\u062f\u06cc\u0645\u060c \u0645\u0642\u0627\u062f\u06cc\u0631 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0634\u062f\u0647 \u0646\u06cc\u0632 \u0646\u0631\u0645\u0627\u0644 \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.  \u0645\u0627 \u0628\u0627\u06cc\u062f \u0645\u0642\u0627\u062f\u06cc\u0631 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0634\u062f\u0647 \u0646\u0631\u0645\u0627\u0644 \u0634\u062f\u0647 \u0631\u0627 \u0628\u0647 \u0645\u0642\u0627\u062f\u06cc\u0631 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0634\u062f\u0647 \u0648\u0627\u0642\u0639\u06cc \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u06cc\u0645.  \u0645\u0627 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u0628\u0627 \u0627\u0631\u0633\u0627\u0644 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0646\u0631\u0645\u0627\u0644 \u0634\u062f\u0647 \u0628\u0647 the \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u0645 <code>inverse_transform<\/code> \u0631\u0648\u0634 \u0634\u06cc \u0645\u0642\u06cc\u0627\u0633\u200c\u06a9\u0646\u0646\u062f\u0647 min\/max \u06a9\u0647 \u0628\u0631\u0627\u06cc \u0639\u0627\u062f\u06cc\u200c\u0633\u0627\u0632\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc\u0645\u0627\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f\u06cc\u0645.<\/p>\n<pre><code class=\"hljs\">actual_predictions = scaler.inverse_transform(np.array(test_inputs(train_window:) ).reshape(-<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>))\n<span class=\"hljs-built_in\">print<\/span>(actual_predictions)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">((435.57335371)\n (554.69182083)\n (622.56485397)\n (573.14712578)\n (691.64493555)\n (761.46355206)\n (632.59821111)\n (758.38493103)\n (814.91857016)\n (735.21242136)\n (739.92839211)\n (781.44169205))\n<\/code><\/pre>\n<p>\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0645\u0642\u0627\u062f\u06cc\u0631 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0634\u062f\u0647 \u0631\u0627 \u062f\u0631 \u0645\u0642\u0627\u0628\u0644 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0648\u0627\u0642\u0639\u06cc \u0631\u0633\u0645 \u06a9\u0646\u06cc\u0645.  \u0628\u0647 \u06a9\u062f \u0632\u06cc\u0631 \u0646\u06af\u0627\u0647 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">x = np.arange(<span class=\"hljs-number\">132<\/span>, <span class=\"hljs-number\">144<\/span>, <span class=\"hljs-number\">1<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(x)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">(132 133 134 135 136 137 138 139 140 141 142 143)\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0627\u0644\u0627 \u0644\u06cc\u0633\u062a\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u062d\u0627\u0648\u06cc \u0645\u0642\u0627\u062f\u06cc\u0631 \u0639\u062f\u062f\u06cc \u0628\u0631\u0627\u06cc 12 \u0645\u0627\u0647 \u06af\u0630\u0634\u062a\u0647 \u0627\u0633\u062a.  \u0645\u0627\u0647 \u0627\u0648\u0644 \u062f\u0627\u0631\u0627\u06cc \u0645\u0642\u062f\u0627\u0631 \u0634\u0627\u062e\u0635 0 \u0627\u0633\u062a\u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0645\u0627\u0647 \u0622\u062e\u0631 \u062f\u0631 \u0634\u0627\u062e\u0635 143 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.<\/p>\n<p>\u062f\u0631 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631\u060c \u062a\u0639\u062f\u0627\u062f \u06a9\u0644 \u0645\u0633\u0627\u0641\u0631\u0627\u0646 144 \u0645\u0627\u0647 \u0631\u0627 \u0628\u0647 \u0647\u0645\u0631\u0627\u0647 \u062a\u0639\u062f\u0627\u062f \u0645\u0633\u0627\u0641\u0631\u0627\u0646 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0634\u062f\u0647 12 \u0645\u0627\u0647 \u06af\u0630\u0634\u062a\u0647 \u062a\u0631\u0633\u06cc\u0645 \u0645\u06cc \u06a9\u0646\u06cc\u0645.<\/p>\n<pre><code class=\"hljs\">plt.title(<span class=\"hljs-string\">'Month vs Passenger'<\/span>)\nplt.ylabel(<span class=\"hljs-string\">'Total Passengers'<\/span>)\nplt.grid(<span class=\"hljs-literal\">True<\/span>)\nplt.autoscale(axis=<span class=\"hljs-string\">'x'<\/span>, tight=<span class=\"hljs-literal\">True<\/span>)\nplt.plot(flight_data(<span class=\"hljs-string\">'passengers'<\/span>))\nplt.plot(x,actual_predictions)\nplt.show()\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/Time-Series-Prediction-Using-LSTM-Pytorch-4.PNG\" alt=\"\u0631\u0633\u0645 \u062a\u0639\u062f\u0627\u062f \u06a9\u0644 \u0645\u0633\u0627\u0641\u0631\u0627\u0646\" title=\"\"><\/p>\n<p>\u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0647\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0634\u062f\u0647 \u062a\u0648\u0633\u0637 LSTM \u0645\u0627 \u0628\u0627 \u062e\u0637 \u0646\u0627\u0631\u0646\u062c\u06cc \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.  \u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0645\u0627 \u062e\u06cc\u0644\u06cc \u062f\u0642\u06cc\u0642 \u0646\u06cc\u0633\u062a\u060c \u0627\u0645\u0627 \u0647\u0645\u0686\u0646\u0627\u0646 \u062a\u0648\u0627\u0646\u0633\u062a\u0647 \u0627\u0633\u062a \u0631\u0648\u0646\u062f \u0635\u0639\u0648\u062f\u06cc \u0631\u0627 \u0628\u0631\u0627\u06cc \u062a\u0639\u062f\u0627\u062f \u06a9\u0644 \u0645\u0633\u0627\u0641\u0631\u0627\u0646\u06cc \u06a9\u0647 \u062f\u0631 12 \u0645\u0627\u0647 \u06af\u0630\u0634\u062a\u0647 \u0633\u0641\u0631 \u06a9\u0631\u062f\u0647 \u0627\u0646\u062f \u0647\u0645\u0631\u0627\u0647 \u0628\u0627 \u0646\u0648\u0633\u0627\u0646\u0627\u062a \u06af\u0627\u0647 \u0628\u0647 \u06af\u0627\u0647 \u062b\u0628\u062a \u06a9\u0646\u062f.  \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0627 \u062a\u0639\u062f\u0627\u062f \u0628\u06cc\u0634\u062a\u0631\u06cc \u0627\u0632 \u062f\u0648\u0631\u0647 \u0647\u0627 \u0648 \u0628\u0627 \u062a\u0639\u062f\u0627\u062f \u0633\u0644\u0648\u0644 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0628\u0627\u0644\u0627\u062a\u0631 \u062f\u0631 \u0644\u0627\u06cc\u0647 LSTM \u062a\u0644\u0627\u0634 \u06a9\u0646\u06cc\u062f \u062a\u0627 \u0628\u0628\u06cc\u0646\u06cc\u062f \u0622\u06cc\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u0647\u062a\u0631\u06cc \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f \u06cc\u0627 \u062e\u06cc\u0631.<\/p>\n<p>\u0628\u0631\u0627\u06cc \u0627\u06cc\u0646\u06a9\u0647 \u062f\u06cc\u062f \u0628\u0647\u062a\u0631\u06cc \u0627\u0632 \u062e\u0631\u0648\u062c\u06cc \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u0645 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u062a\u0639\u062f\u0627\u062f \u0648\u0627\u0642\u0639\u06cc \u0648 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0634\u062f\u0647 \u0645\u0633\u0627\u0641\u0631\u0627\u0646 12 \u0645\u0627\u0647 \u06af\u0630\u0634\u062a\u0647 \u0631\u0627 \u0628\u0647 \u0635\u0648\u0631\u062a \u0632\u06cc\u0631 \u062a\u0631\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">plt.title(<span class=\"hljs-string\">'Month vs Passenger'<\/span>)\nplt.ylabel(<span class=\"hljs-string\">'Total Passengers'<\/span>)\nplt.grid(<span class=\"hljs-literal\">True<\/span>)\nplt.autoscale(axis=<span class=\"hljs-string\">'x'<\/span>, tight=<span class=\"hljs-literal\">True<\/span>)\n\nplt.plot(flight_data(<span class=\"hljs-string\">'passengers'<\/span>)(-train_window:))\nplt.plot(x,actual_predictions)\nplt.show()\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/Time-Series-Prediction-Using-LSTM-Pytorch-5.PNG\" alt=\"\u062a\u0631\u0633\u06cc\u0645 \u062a\u0639\u062f\u0627\u062f \u0645\u0633\u0627\u0641\u0631\u0627\u0646 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0634\u062f\u0647\" title=\"\"><\/p>\n<p>\u0628\u0627\u0632 \u0647\u0645 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0647\u0627 \u062e\u06cc\u0644\u06cc \u062f\u0642\u06cc\u0642 \u0646\u06cc\u0633\u062a\u0646\u062f\u060c \u0627\u0645\u0627 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u062a\u0648\u0627\u0646\u0633\u062a \u0627\u06cc\u0646 \u0631\u0648\u0646\u062f \u0631\u0627 \u0628\u0647 \u062a\u0635\u0648\u06cc\u0631 \u0628\u06a9\u0634\u062f \u06a9\u0647 \u062a\u0639\u062f\u0627\u062f \u0645\u0633\u0627\u0641\u0631\u0627\u0646 \u062f\u0631 \u0645\u0627\u0647\u200c\u0647\u0627\u06cc \u0622\u06cc\u0646\u062f\u0647 \u0628\u0627\u06cc\u062f \u0628\u06cc\u0634\u062a\u0631 \u0627\u0632 \u0645\u0627\u0647\u200c\u0647\u0627\u06cc \u0642\u0628\u0644 \u0628\u0627 \u0646\u0648\u0633\u0627\u0646\u200c\u0647\u0627\u06cc \u06af\u0627\u0647 \u0628\u0647 \u06af\u0627\u0647 \u0628\u0627\u0634\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>LSTM \u06cc\u06a9\u06cc \u0627\u0632 \u067e\u0631\u06a9\u0627\u0631\u0628\u0631\u062f\u062a\u0631\u06cc\u0646 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627 \u0628\u0631\u0627\u06cc \u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u062a\u0648\u0627\u0644\u06cc \u0627\u0633\u062a.  \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0631\u0648\u0634 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0647\u0627\u06cc \u0622\u06cc\u0646\u062f\u0647 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc \u0628\u0627 LSTM \u062f\u06cc\u062f\u06cc\u0645.  \u0647\u0645\u0686\u0646\u06cc\u0646 \u0631\u0648\u0634 \u067e\u06cc\u0627\u062f\u0647\u200c\u0633\u0627\u0632\u06cc LSTM \u0628\u0627 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 PyTorch \u0648 \u0633\u067e\u0633 \u0631\u0648\u0634 \u0631\u0633\u0645 \u0646\u062a\u0627\u06cc\u062c \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u0634\u062f\u0647 \u0628\u0631 \u0627\u0633\u0627\u0633 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0648\u0627\u0642\u0639\u06cc \u0631\u0627 \u0645\u0634\u0627\u0647\u062f\u0647 \u06a9\u0631\u062f\u06cc\u062f \u062a\u0627 \u0628\u0628\u06cc\u0646\u06cc\u062f \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0622\u0645\u0648\u0632\u0634\u200c\u062f\u06cc\u062f\u0647 \u0686\u0642\u062f\u0631 \u062e\u0648\u0628 \u0639\u0645\u0644 \u0645\u06cc\u200c\u06a9\u0646\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 1403-01-19 21:39:04<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;16020&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;\u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 LSTM \u0628\u0627 PyTorch \u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646&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>\u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0633\u0631\u06cc \u0632\u0645\u0627\u0646\u06cc\u060c \u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u0627\u0632 \u0646\u0627\u0645 \u0622\u0646 \u067e\u06cc\u062f\u0627\u0633\u062a\u060c \u0646\u0648\u0639\u06cc \u062f\u0627\u062f\u0647 \u0627\u0633\u062a \u06a9\u0647 \u0628\u0627 \u0632\u0645\u0627\u0646 \u062a\u063a\u06cc\u06cc\u0631 \u0645\u06cc \u06a9\u0646\u062f. \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u062f\u0645\u0627 \u062f\u0631 \u06cc\u06a9 \u062f\u0648\u0631\u0647 \u0632\u0645\u0627\u0646\u06cc 24 \u0633\u0627\u0639\u062a\u0647\u060c \u0642\u06cc\u0645\u062a \u0645\u062d\u0635\u0648\u0644\u0627\u062a \u0645\u062e\u062a\u0644\u0641 \u062f\u0631 \u06cc\u06a9 \u0645\u0627\u0647\u060c \u0642\u06cc\u0645\u062a \u0633\u0647\u0627\u0645 \u06cc\u06a9 \u0634\u0631\u06a9\u062a \u062e\u0627\u0635 \u062f\u0631 \u06cc\u06a9 \u0633\u0627\u0644. \u0645\u062f\u0644 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0645\u0627\u0646\u0646\u062f \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u062d\u0627\u0641\u0638\u0647 \u06a9\u0648\u062a\u0627\u0647 \u0645\u062f\u062a \u0628\u0644\u0646\u062f [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":16021,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1743,620],"tags":[],"class_list":["post-16020","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\/16020","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=16020"}],"version-history":[{"count":0,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/16020\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media\/16021"}],"wp:attachment":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media?parent=16020"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/categories?post=16020"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/tags?post=16020"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}