{"id":16137,"date":"2024-01-21T07:09:23","date_gmt":"2024-01-21T03:39:23","guid":{"rendered":"https:\/\/rasanegar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%aa%d8%ac%d8%b2%db%8c%d9%87-%d9%88-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3%d8%a7%d8%aa-%d9%81%db%8c%d9%84\/"},"modified":"2024-01-21T07:09:23","modified_gmt":"2024-01-21T03:39:23","slug":"%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%aa%d8%ac%d8%b2%db%8c%d9%87-%d9%88-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3%d8%a7%d8%aa-%d9%81%db%8c%d9%84","status":"publish","type":"post","link":"https:\/\/rasanegaar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%aa%d8%ac%d8%b2%db%8c%d9%87-%d9%88-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3%d8%a7%d8%aa-%d9%81%db%8c%d9%84\/","title":{"rendered":"\u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc NLP: \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0641\u06cc\u0644\u0645 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u062f\u0631 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\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%aa%d8%ac%d8%b2%db%8c%d9%87-%d9%88-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3%d8%a7%d8%aa-%d9%81%db%8c%d9%84\/#%d9%85%d8%ac%d9%85%d9%88%d8%b9%d9%87_%d8%af%d8%a7%d8%af%d9%87\" >\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647<\/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%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%aa%d8%ac%d8%b2%db%8c%d9%87-%d9%88-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3%d8%a7%d8%aa-%d9%81%db%8c%d9%84\/#%d9%88%d8%a7%d8%b1%d8%af%d8%a7%d8%aa_%da%a9%d8%aa%d8%a7%d8%a8%d8%ae%d8%a7%d9%86%d9%87_%d9%87%d8%a7%db%8c_%d9%85%d9%88%d8%b1%d8%af_%d9%86%db%8c%d8%a7%d8%b2\" >\u0648\u0627\u0631\u062f\u0627\u062a \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632<\/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%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%aa%d8%ac%d8%b2%db%8c%d9%87-%d9%88-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3%d8%a7%d8%aa-%d9%81%db%8c%d9%84\/#%d9%88%d8%a7%d8%b1%d8%af_%da%a9%d8%b1%d8%af%d9%86_%d9%88_%d8%aa%d8%ac%d8%b2%db%8c%d9%87_%d9%88_%d8%aa%d8%ad%d9%84%db%8c%d9%84_%d9%85%d8%ac%d9%85%d9%88%d8%b9%d9%87_%d8%af%d8%a7%d8%af%d9%87\" >\u0648\u0627\u0631\u062f \u06a9\u0631\u062f\u0646 \u0648 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647<\/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%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%aa%d8%ac%d8%b2%db%8c%d9%87-%d9%88-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3%d8%a7%d8%aa-%d9%81%db%8c%d9%84\/#%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-5\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%aa%d8%ac%d8%b2%db%8c%d9%87-%d9%88-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3%d8%a7%d8%aa-%d9%81%db%8c%d9%84\/#%d8%a2%d9%85%d8%a7%d8%af%d9%87_%d8%b3%d8%a7%d8%b2%db%8c_%d9%84%d8%a7%db%8c%d9%87_%d8%ac%d8%a7%d8%b3%d8%a7%d8%b2%db%8c\" >\u0622\u0645\u0627\u062f\u0647 \u0633\u0627\u0632\u06cc \u0644\u0627\u06cc\u0647 \u062c\u0627\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-6\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%aa%d8%ac%d8%b2%db%8c%d9%87-%d9%88-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3%d8%a7%d8%aa-%d9%81%db%8c%d9%84\/#%d8%b7%d8%a8%d9%82%d9%87_%d8%a8%d9%86%d8%af%db%8c_%d9%85%d8%aa%d9%86_%d8%a8%d8%a7_%d8%b4%d8%a8%da%a9%d9%87_%d8%b9%d8%b5%d8%a8%db%8c_%d8%b3%d8%a7%d8%af%d9%87\" >\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0628\u0627 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0633\u0627\u062f\u0647<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%aa%d8%ac%d8%b2%db%8c%d9%87-%d9%88-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3%d8%a7%d8%aa-%d9%81%db%8c%d9%84\/#%d8%b7%d8%a8%d9%82%d9%87_%d8%a8%d9%86%d8%af%db%8c_%d9%85%d8%aa%d9%86_%d8%a8%d8%a7_%d8%b4%d8%a8%da%a9%d9%87_%d8%b9%d8%b5%d8%a8%db%8c_%da%a9%d8%a7%d9%86%d9%88%d9%84%d9%88%d8%b4%d9%86%d8%a7%d9%84\" >\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0628\u0627 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u0627\u0644<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%aa%d8%ac%d8%b2%db%8c%d9%87-%d9%88-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3%d8%a7%d8%aa-%d9%81%db%8c%d9%84\/#%d8%b7%d8%a8%d9%82%d9%87_%d8%a8%d9%86%d8%af%db%8c_%d9%85%d8%aa%d9%86_%d8%a8%d8%a7_%d8%b4%d8%a8%da%a9%d9%87_%d8%b9%d8%b5%d8%a8%db%8c_%d8%aa%da%a9%d8%b1%d8%a7%d8%b1%db%8c_lstm\" >\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0628\u0627 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u062a\u06a9\u0631\u0627\u0631\u06cc (LSTM)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%aa%d8%ac%d8%b2%db%8c%d9%87-%d9%88-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3%d8%a7%d8%aa-%d9%81%db%8c%d9%84\/#%d9%be%db%8c%d8%b4%da%af%d9%88%db%8c%db%8c_%d8%b1%d9%88%db%8c_%d9%86%d9%85%d9%88%d9%86%d9%87_%d9%88%d8%a7%d8%ad%d8%af\" >\u067e\u06cc\u0634\u06af\u0648\u06cc\u06cc \u0631\u0648\u06cc \u0646\u0645\u0648\u0646\u0647 \u0648\u0627\u062d\u062f<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%aa%d8%ac%d8%b2%db%8c%d9%87-%d9%88-%d8%aa%d8%ad%d9%84%db%8c%d9%84-%d8%a7%d8%ad%d8%b3%d8%a7%d8%b3%d8%a7%d8%aa-%d9%81%db%8c%d9%84\/#%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\"> 14<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span><p> <br \/>\n<\/p>\n<div><noscript><\/noscript><\/p>\n<p>\u0627\u06cc\u0646 \u0647\u0641\u062f\u0647\u0645\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0632 \u0633\u0631\u06cc \u0645\u0642\u0627\u0644\u0627\u062a \u0645\u0646 \u0627\u0633\u062a \u0631\u0648\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc NLP.  \u062f\u0631 \u0622\u062e\u0631\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647\u060c \u0628\u062d\u062b \u062e\u0648\u062f \u0631\u0627 \u062f\u0631 \u0645\u0648\u0631\u062f \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0631\u0627\u06cc \u067e\u0631\u062f\u0627\u0632\u0634 \u0632\u0628\u0627\u0646 \u0637\u0628\u06cc\u0639\u06cc \u0622\u063a\u0627\u0632 \u06a9\u0631\u062f\u06cc\u0645.<\/p>\n<p>\u0645\u0642\u0627\u0644\u0647 \u0642\u0628\u0644\u06cc \u0639\u0645\u062f\u062a\u0627\u064b \u0628\u0631 \u0631\u0648\u06cc \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0644\u0645\u0627\u062a \u0645\u062a\u0645\u0631\u06a9\u0632 \u0628\u0648\u062f\u060c \u062c\u0627\u06cc\u06cc \u06a9\u0647 \u062f\u06cc\u062f\u06cc\u0645 \u0686\u06af\u0648\u0646\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0627\u0632 \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0644\u0645\u0627\u062a \u0628\u0631\u0627\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0645\u062a\u0646 \u0628\u0647 \u06cc\u06a9 \u0628\u0631\u062f\u0627\u0631 \u0645\u062a\u0631\u0627\u06a9\u0645 \u0645\u062a\u0646\u0627\u0638\u0631 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f\u060c \u06a9\u0647 \u0645\u062a\u0639\u0627\u0642\u0628\u0627\u064b \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0648\u0631\u0648\u062f\u06cc \u0628\u0631\u0627\u06cc \u0647\u0631 \u0645\u062f\u0644 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0634\u0648\u062f.  \u0645\u0627 \u0648\u0638\u0627\u06cc\u0641 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0627\u0648\u0644\u06cc\u0647 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0644\u0645\u0647 \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc \u062f\u0647\u06cc\u0645.  \u0645\u0627 \u0627\u0632 \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0633\u0641\u0627\u0631\u0634\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f\u06cc\u0645 \u06a9\u0647 \u0634\u0627\u0645\u0644 16 \u0628\u0631\u0631\u0633\u06cc \u062e\u06cc\u0627\u0644\u06cc \u062f\u0631\u0628\u0627\u0631\u0647 \u0641\u06cc\u0644\u0645 \u0647\u0627 \u0628\u0648\u062f.  \u0639\u0644\u0627\u0648\u0647 \u0628\u0631 \u0627\u06cc\u0646\u060c \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0622\u0645\u0648\u0632\u0634 \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634 \u0634\u062f\u0646\u062f \u0631\u0648\u06cc \u0647\u0645\u0627\u0646 \u062f\u0627\u062f\u0647 \u0647\u0627  \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a\u060c \u0645\u0627 \u0641\u0642\u0637 \u0627\u0632 \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0645\u062a\u0635\u0644 \u0645\u062a\u0631\u0627\u06a9\u0645 \u0628\u0631\u0627\u06cc \u0622\u0632\u0645\u0627\u06cc\u0634 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u062e\u0648\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f\u06cc\u0645.<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647\u060c \u0645\u0641\u0627\u0647\u06cc\u0645\u06cc \u0631\u0627 \u06a9\u0647 \u062f\u0631 \u0645\u0642\u0627\u0644\u0647 \u0642\u0628\u0644\u06cc \u0645\u0648\u0631\u062f \u0645\u0637\u0627\u0644\u0639\u0647 \u0642\u0631\u0627\u0631 \u062f\u0627\u062f\u06cc\u0645 \u0627\u0633\u062a\u0648\u0627\u0631 \u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645 \u0648 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u062f\u0646\u06cc\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc\u060c \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc \u0631\u0627 \u0628\u0627 \u062c\u0632\u0626\u06cc\u0627\u062a \u0628\u06cc\u0634\u062a\u0631\u06cc \u0645\u0634\u0627\u0647\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f.  \u0645\u0627 \u0627\u0632 \u0633\u0647 \u0646\u0648\u0639 \u0645\u062e\u062a\u0644\u0641 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0639\u0645\u06cc\u0642 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f: \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0628\u0627 \u0627\u062a\u0635\u0627\u0644 \u0645\u062a\u0631\u0627\u06a9\u0645 (\u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u067e\u0627\u06cc\u0647)\u060c <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/en.wikipedia.org\/wiki\/Convolutional_neural_network\">\u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u0627\u0644<\/a> (\u0633\u06cc \u0627\u0646 \u0627\u0646) \u0648 <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/en.wikipedia.org\/wiki\/Long_short-term_memory\">\u0634\u0628\u06a9\u0647 \u062d\u0627\u0641\u0638\u0647 \u06a9\u0648\u062a\u0627\u0647 \u0645\u062f\u062a \u0628\u0644\u0646\u062f \u0645\u062f\u062a<\/a> (LSTM)\u060c \u06a9\u0647 \u06cc\u06a9 \u0646\u0648\u0639 \u0627\u0632 <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/en.wikipedia.org\/wiki\/Recurrent_neural_network\">\u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u0645\u06a9\u0631\u0631<\/a>.  \u0639\u0644\u0627\u0648\u0647 \u0628\u0631 \u0627\u06cc\u0646\u060c \u0631\u0648\u0634 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0645\u062f\u0644 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0631\u0627 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u06cc\u062f \u0631\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u06a9\u0627\u0645\u0644\u0627\u064b \u062f\u06cc\u062f\u0647 \u0646\u0634\u062f\u0647<\/p>\n<p><strong>\u062a\u0648\u062c\u0647 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f<\/strong>: \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0632 \u062c\u0627\u0633\u0627\u0632\u06cc\u200c\u0647\u0627\u06cc \u06a9\u0644\u0645\u0647 Keras Embedding Layer \u0648 GloVe \u0628\u0631\u0627\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0645\u062a\u0646 \u0628\u0647 \u0634\u06a9\u0644 \u0639\u062f\u062f\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc\u200c\u06a9\u0646\u062f.  \u0645\u0647\u0645 \u0627\u0633\u062a \u06a9\u0647 \u0642\u0628\u0644\u0627\u064b \u0627\u06cc\u0646 \u0645\u0641\u0627\u0647\u06cc\u0645 \u0631\u0627 \u062f\u0631\u06a9 \u06a9\u0631\u062f\u0647 \u0628\u0627\u0634\u06cc\u062f.  \u062f\u0631 \u063a\u06cc\u0631 \u0627\u06cc\u0646 \u0635\u0648\u0631\u062a\u060c \u0634\u0645\u0627 \u0628\u0627\u06cc\u062f \u0645\u0642\u0627\u0644\u0647 \u0642\u0628\u0644\u06cc \u0645\u0646 \u0631\u0627 \u0628\u062e\u0648\u0627\u0646\u06cc\u062f \u0648 \u0633\u067e\u0633 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0631\u06af\u0631\u062f\u06cc\u062f \u0648 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0631\u0627 \u0627\u062f\u0627\u0645\u0647 \u062f\u0647\u06cc\u062f.<\/p>\n<h2 id=\"thedataset\"><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\"><\/span>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647<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\u06cc \u062a\u0648\u0627\u0646 \u0627\u0632 \u0627\u06cc\u0646 \u062f\u0627\u0646\u0644\u0648\u062f \u06a9\u0631\u062f <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.kaggle.com\/lakshmi25npathi\/imdb-dataset-of-50k-movie-reviews\">\u0644\u06cc\u0646\u06a9 \u06a9\u0627\u06af\u0644<\/a>.<\/p>\n<p>\u0627\u06af\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u062f\u0627\u0646\u0644\u0648\u062f \u06a9\u0646\u06cc\u062f \u0648 \u0641\u0627\u06cc\u0644 \u0641\u0634\u0631\u062f\u0647 \u0631\u0627 \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u06a9\u0646\u06cc\u062f\u060c \u06cc\u06a9 \u0641\u0627\u06cc\u0644 CSV \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f.  \u0627\u06cc\u0646 \u0641\u0627\u06cc\u0644 \u0634\u0627\u0645\u0644 50000 \u0631\u06a9\u0648\u0631\u062f \u0648 \u062f\u0648 \u0633\u062a\u0648\u0646 \u0627\u0633\u062a: \u0628\u0631\u0631\u0633\u06cc \u0648 \u0627\u062d\u0633\u0627\u0633.  \u0633\u062a\u0648\u0646 \u0628\u0631\u0631\u0633\u06cc \u062d\u0627\u0648\u06cc \u0645\u062a\u0646\u06cc \u0628\u0631\u0627\u06cc \u0628\u0631\u0631\u0633\u06cc \u0648 \u0633\u062a\u0648\u0646 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u062d\u0627\u0648\u06cc \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0628\u0631\u0627\u06cc \u0628\u0631\u0631\u0633\u06cc \u0627\u0633\u062a.  \u0633\u062a\u0648\u0646 \u0627\u062d\u0633\u0627\u0633 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u062f\u0648 \u0645\u0642\u062f\u0627\u0631 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f \u06cc\u0639\u0646\u06cc &#8220;\u0645\u062b\u0628\u062a&#8221; \u0648 &#8220;\u0645\u0646\u0641\u06cc&#8221; \u06a9\u0647 \u0645\u0634\u06a9\u0644 \u0645\u0627 \u0631\u0627 \u0628\u0647 \u06cc\u06a9 \u0645\u0634\u06a9\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0628\u0627\u06cc\u0646\u0631\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<h2 id=\"importingrequiredlibraries\"><span class=\"ez-toc-section\" id=\"%d9%88%d8%a7%d8%b1%d8%af%d8%a7%d8%aa_%da%a9%d8%aa%d8%a7%d8%a8%d8%ae%d8%a7%d9%86%d9%87_%d9%87%d8%a7%db%8c_%d9%85%d9%88%d8%b1%d8%af_%d9%86%db%8c%d8%a7%d8%b2\"><\/span>\u0648\u0627\u0631\u062f\u0627\u062a \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632 \u0631\u0627 \u0648\u0627\u0631\u062f \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n<span class=\"hljs-keyword\">import<\/span> numpy <span class=\"hljs-keyword\">as<\/span> np\n<span class=\"hljs-keyword\">import<\/span> re\n<span class=\"hljs-keyword\">import<\/span> nltk\n<span class=\"hljs-keyword\">from<\/span> nltk.corpus <span class=\"hljs-keyword\">import<\/span> stopwords\n\n<span class=\"hljs-keyword\">from<\/span> numpy <span class=\"hljs-keyword\">import<\/span> array\n<span class=\"hljs-keyword\">from<\/span> keras.preprocessing.text <span class=\"hljs-keyword\">import<\/span> one_hot\n<span class=\"hljs-keyword\">from<\/span> keras.preprocessing.sequence <span class=\"hljs-keyword\">import<\/span> pad_sequences\n<span class=\"hljs-keyword\">from<\/span> keras.models <span class=\"hljs-keyword\">import<\/span> Sequential\n<span class=\"hljs-keyword\">from<\/span> keras.layers.core <span class=\"hljs-keyword\">import<\/span> Activation, Dropout, Dense\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> Flatten\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> GlobalMaxPooling1D\n<span class=\"hljs-keyword\">from<\/span> keras.layers.embeddings <span class=\"hljs-keyword\">import<\/span> Embedding\n<span class=\"hljs-keyword\">from<\/span> sklearn.model_selection <span class=\"hljs-keyword\">import<\/span> train_test_split\n<span class=\"hljs-keyword\">from<\/span> keras.preprocessing.text <span class=\"hljs-keyword\">import<\/span> Tokenizer\n<\/code><\/pre>\n<h2 id=\"importingandanalyzingthedataset\"><span class=\"ez-toc-section\" id=\"%d9%88%d8%a7%d8%b1%d8%af_%da%a9%d8%b1%d8%af%d9%86_%d9%88_%d8%aa%d8%ac%d8%b2%db%8c%d9%87_%d9%88_%d8%aa%d8%ad%d9%84%db%8c%d9%84_%d9%85%d8%ac%d9%85%d9%88%d8%b9%d9%87_%d8%af%d8%a7%d8%af%d9%87\"><\/span>\u0648\u0627\u0631\u062f \u06a9\u0631\u062f\u0646 \u0648 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f import \u0648 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0627 \u0631\u0627 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u06a9\u0646\u06cc\u062f.  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">movie_reviews = pd.read_csv(<span class=\"hljs-string\">\"E:\\Datasets\\IMDB Dataset.csv\"<\/span>)\n\nmovie_reviews.isnull().values.<span class=\"hljs-built_in\">any<\/span>()\n\nmovie_reviews.shape\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0627\u0644\u0627 \u0645\u0627 \u0627\u0632 <code>read_csv()<\/code> \u0631\u0648\u0634 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u067e\u0627\u0646\u062f\u0627\u0647\u0627 \u0628\u0631\u0627\u06cc \u062e\u0648\u0627\u0646\u062f\u0646 \u0641\u0627\u06cc\u0644 CSV \u062d\u0627\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0627.  \u062f\u0631 \u062e\u0637 \u0628\u0639\u062f\u06cc\u060c \u0628\u0631\u0631\u0633\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0622\u06cc\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u062d\u0627\u0648\u06cc \u0645\u0642\u062f\u0627\u0631 NULL \u0627\u0633\u062a \u06cc\u0627 \u062e\u06cc\u0631.  \u0628\u0627\u0644\u0627\u062e\u0631\u0647 \u0645\u0627 print \u0634\u06a9\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0627<\/p>\n<p>\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f print 5 \u0631\u062f\u06cc\u0641 \u0627\u0648\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 <code>head()<\/code> \u0631\u0648\u0634.<\/p>\n<pre><code class=\"hljs\">movie_reviews.head()\n<\/code><\/pre>\n<p>\u062f\u0631 \u062e\u0631\u0648\u062c\u06cc \u062f\u06cc\u062a\u0627\u0641\u0631\u06cc\u0645 \u0632\u06cc\u0631 \u0631\u0627 \u0645\u0634\u0627\u0647\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u062f \u06a9\u0631\u062f:<\/p>\n<p><img decoding=\"async\" class=\"img-responsive\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/python-nlp-movie-sentiment-analysis-deep-learning-keras-1.png\" alt=\"\u0633\u0631\" title=\"\"><\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u06a9\u0646\u0648\u0646 \u0628\u0647 \u0647\u0631 \u06cc\u06a9 \u0627\u0632 \u0628\u0631\u0631\u0633\u06cc \u0647\u0627 \u0646\u06af\u0627\u0647\u06cc \u0628\u06cc\u0646\u062f\u0627\u0632\u06cc\u0645 \u062a\u0627 \u0627\u06cc\u062f\u0647 \u0627\u06cc \u062f\u0631 \u0645\u0648\u0631\u062f \u0645\u062a\u0646\u06cc \u06a9\u0647 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 process.  \u0628\u0647 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0646\u06af\u0627\u0647 \u06a9\u0646\u06cc\u062f.<\/p>\n<pre><code class=\"hljs\">movie_reviews(<span class=\"hljs-string\">\"review\"<\/span>)(<span class=\"hljs-number\">3<\/span>)\n<\/code><\/pre>\n<p>\u0634\u0645\u0627 \u0628\u0627\u06cc\u062f \u0628\u0631\u0631\u0633\u06cc \u0632\u06cc\u0631 \u0631\u0627 \u0628\u0628\u06cc\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">\"Basically there's a family where a little boy (Jake) thinks there's a zombie in his closet &amp; his parents are fighting all the time.&lt;br \/&gt;&lt;br \/&gt;This movie is slower than a soap opera... and suddenly, Jake decides to become Rambo and kill the zombie.&lt;br \/&gt;&lt;br \/&gt;OK, first of all when you're going to make a film you must Decide if its a thriller or a drama! As a drama the movie is watchable. Parents are divorcing &amp; arguing like in real life. And then we have Jake with his closet which totally ruins all the film! I expected to see a BOOGEYMAN similar movie, and instead i watched a drama with some meaningless thriller spots.&lt;br \/&gt;&lt;br \/&gt;3 out of 10 just for the well playing parents &amp; descent dialogs. As for the shots with Jake: just ignore them.\"\n<\/code><\/pre>\n<p>\u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0645\u062a\u0646 \u0645\u0627 \u062d\u0627\u0648\u06cc \u0639\u0644\u0627\u0626\u0645 \u0646\u06af\u0627\u0631\u0634\u06cc\u060c \u0628\u0631\u0627\u06a9\u062a \u0648 \u0686\u0646\u062f \u062a\u06af HTML \u0646\u06cc\u0632 \u0647\u0633\u062a.  \u062f\u0631 \u0628\u062e\u0634 \u0628\u0639\u062f\u06cc \u0627\u06cc\u0646 \u0645\u062a\u0646 \u0631\u0627 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f.<\/p>\n<p>\u062f\u0631 \u0646\u0647\u0627\u06cc\u062a\u060c \u0628\u06cc\u0627\u06cc\u06cc\u062f \u062a\u0648\u0632\u06cc\u0639 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0645\u062b\u0628\u062a \u0648 \u0645\u0646\u0641\u06cc \u0631\u0627 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u062e\u0648\u062f \u0628\u0628\u06cc\u0646\u06cc\u0645.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> seaborn <span class=\"hljs-keyword\">as<\/span> sns\n\nsns.countplot(x=<span class=\"hljs-string\">'sentiment'<\/span>, data=movie_reviews)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<p><img decoding=\"async\" class=\"img-responsive\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/python-nlp-movie-sentiment-analysis-deep-learning-keras-2.png\" alt=\"\u062a\u0648\u0632\u06cc\u0639 \u0627\u062d\u0633\u0627\u0633\u0627\u062a\" title=\"\"><\/p>\n<p>\u0627\u0632 \u062e\u0631\u0648\u062c\u06cc\u060c \u0645\u0634\u062e\u0635 \u0627\u0633\u062a \u06a9\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0634\u0627\u0645\u0644 \u062a\u0639\u062f\u0627\u062f \u0645\u0633\u0627\u0648\u06cc \u0628\u0631\u0631\u0633\u06cc \u0645\u062b\u0628\u062a \u0648 \u0645\u0646\u0641\u06cc \u0627\u0633\u062a<\/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>\u062f\u06cc\u062f\u06cc\u0645 \u06a9\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0627 \u062d\u0627\u0648\u06cc \u0639\u0644\u0627\u0626\u0645 \u0646\u0642\u0637\u0647 \u06af\u0630\u0627\u0631\u06cc \u0648 \u062a\u06af \u0647\u0627\u06cc HTML \u0627\u0633\u062a.  \u062f\u0631 \u0627\u06cc\u0646 \u0628\u062e\u0634 \u062a\u0627\u0628\u0639\u06cc \u0631\u0627 \u062a\u0639\u0631\u06cc\u0641 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u06cc\u06a9 \u0631\u0634\u062a\u0647 \u0645\u062a\u0646 \u0631\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u0645\u06cc \u06af\u06cc\u0631\u062f \u0648 \u0633\u067e\u0633 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc \u062f\u0647\u062f \u0631\u0648\u06cc \u0631\u0634\u062a\u0647 \u0628\u0631\u0627\u06cc \u062d\u0630\u0641 \u06a9\u0627\u0631\u0627\u06a9\u062a\u0631\u0647\u0627\u06cc \u062e\u0627\u0635 \u0648 \u062a\u06af \u0647\u0627\u06cc HTML \u0627\u0632 \u0631\u0634\u062a\u0647.  \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a \u0631\u0634\u062a\u0647 \u0628\u0647 \u062a\u0627\u0628\u0639 \u0641\u0631\u0627\u062e\u0648\u0627\u0646\u06cc \u0628\u0627\u0632 \u0645\u06cc \u06af\u0631\u062f\u062f.  \u0628\u0647 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0646\u06af\u0627\u0647 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">preprocess_text<\/span>(<span class=\"hljs-params\">sen<\/span>):<\/span>\n    \n    sentence = remove_tags(sen)\n\n    \n    sentence = re.sub(<span class=\"hljs-string\">'(^a-zA-Z)'<\/span>, <span class=\"hljs-string\">' '<\/span>, sentence)\n\n    \n    sentence = re.sub(<span class=\"hljs-string\">r\"\\s+(a-zA-Z)\\s+\"<\/span>, <span class=\"hljs-string\">' '<\/span>, sentence)\n\n    \n    sentence = re.sub(<span class=\"hljs-string\">r'\\s+'<\/span>, <span class=\"hljs-string\">' '<\/span>, sentence)\n\n    <span class=\"hljs-keyword\">return<\/span> sentence\n<\/code><\/pre>\n<pre><code class=\"hljs\">TAG_RE = re.<span class=\"hljs-built_in\">compile<\/span>(<span class=\"hljs-string\">r'&lt;(^&gt;)+&gt;'<\/span>)\n\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">remove_tags<\/span>(<span class=\"hljs-params\">text<\/span>):<\/span>\n    <span class=\"hljs-keyword\">return<\/span> TAG_RE.sub(<span class=\"hljs-string\">''<\/span>, text)\n<\/code><\/pre>\n<p>\u062f\u0631 <code>preprocess_text()<\/code> \u0631\u0648\u0634 \u06af\u0627\u0645 \u0627\u0648\u0644 \u062d\u0630\u0641 \u062a\u06af \u0647\u0627\u06cc HTML \u0627\u0633\u062a.  \u0628\u0631\u0627\u06cc \u062d\u0630\u0641 \u062a\u06af \u0647\u0627\u06cc HTML\u060c <code>remove_tags()<\/code> \u062a\u0627\u0628\u0639 \u062a\u0639\u0631\u06cc\u0641 \u0634\u062f\u0647 \u0627\u0633\u062a.  \u0627\u06cc\u0646 <code>remove_tags<\/code> \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u0647 \u0633\u0627\u062f\u06af\u06cc \u062c\u0627\u06cc\u06af\u0632\u06cc\u0646 \u0647\u0631 \u0686\u06cc\u0632\u06cc \u0628\u06cc\u0646 \u0628\u0627\u0632 \u0648 \u0628\u0633\u062a\u0647 \u0634\u062f\u0646 \u0645\u06cc \u0634\u0648\u062f <code>&lt;&gt;<\/code> \u0628\u0627 \u0641\u0636\u0627\u06cc \u062e\u0627\u0644\u06cc<\/p>\n<p>\u0628\u0639\u062f\u060c \u062f\u0631 <code>preprocess_text<\/code> \u0639\u0645\u0644\u06a9\u0631\u062f\u060c \u0647\u0645\u0647 \u0686\u06cc\u0632 \u062d\u0630\u0641 \u0645\u06cc \u0634\u0648\u062f \u0628\u0647 \u062c\u0632 \u062d\u0631\u0648\u0641 \u0628\u0632\u0631\u06af \u0648 \u06a9\u0648\u0686\u06a9 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc\u060c \u06a9\u0647 \u0645\u0646\u062c\u0631 \u0628\u0647 \u0627\u06cc\u062c\u0627\u062f \u0646\u0648\u06cc\u0633\u0647 \u0647\u0627\u06cc \u0645\u0646\u0641\u0631\u062f \u0645\u06cc \u0634\u0648\u062f \u06a9\u0647 \u0645\u0639\u0646\u06cc \u0646\u062f\u0627\u0631\u062f.  \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u0647\u0646\u06af\u0627\u0645\u06cc \u06a9\u0647 \u06cc\u06a9 \u0622\u067e\u0627\u0633\u062a\u0631\u0648\u0641 \u0631\u0627 \u0627\u0632 \u06a9\u0644\u0645\u0647 &#8220;Mark&#8217;s&#8221; \u062d\u0630\u0641 \u0645\u06cc \u06a9\u0646\u06cc\u062f\u060c \u0622\u067e\u0627\u0633\u062a\u0631\u0648\u0641 \u0628\u0627 \u06cc\u06a9 \u0641\u0636\u0627\u06cc \u062e\u0627\u0644\u06cc \u062c\u0627\u06cc\u06af\u0632\u06cc\u0646 \u0645\u06cc \u0634\u0648\u062f.  \u0627\u0632 \u0627\u06cc\u0646 \u0631\u0648\u060c \u0645\u0627 \u0628\u0627 \u06cc\u06a9 \u0634\u062e\u0635\u06cc\u062a \u00abs\u00bb \u0628\u0627\u0642\u06cc \u0645\u06cc\u200c\u0645\u0627\u0646\u06cc\u0645.<\/p>\n<p>\u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u060c \u062a\u0645\u0627\u0645 \u06a9\u0627\u0631\u0627\u06a9\u062a\u0631\u0647\u0627\u06cc \u0645\u0646\u0641\u0631\u062f \u0631\u0627 \u062d\u0630\u0641 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u0628\u0627 \u0641\u0627\u0635\u0644\u0647 \u0627\u06cc \u062c\u0627\u06cc\u06af\u0632\u06cc\u0646 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0686\u0646\u062f\u06cc\u0646 \u0641\u0627\u0635\u0644\u0647 \u062f\u0631 \u0645\u062a\u0646 \u0645\u0627 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u062f.  \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a\u060c \u0641\u0627\u0635\u0644\u0647 \u0647\u0627\u06cc \u0645\u062a\u0639\u062f\u062f \u0631\u0627 \u0646\u06cc\u0632 \u0627\u0632 \u0645\u062a\u0646 \u062e\u0648\u062f \u062d\u0630\u0641 \u0645\u06cc \u06a9\u0646\u06cc\u0645.<\/p>\n<p>\u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u060c \u0628\u0631\u0631\u0633\u06cc \u0647\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u0627\u0632 \u0642\u0628\u0644 \u067e\u0631\u062f\u0627\u0632\u0634 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u0622\u0646\u0647\u0627 \u0631\u0627 \u062f\u0631 \u06cc\u06a9 \u0644\u06cc\u0633\u062a \u062c\u062f\u06cc\u062f \u0630\u062e\u06cc\u0631\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \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\">X = ()\nsentences = <span class=\"hljs-built_in\">list<\/span>(movie_reviews(<span class=\"hljs-string\">'review'<\/span>))\n<span class=\"hljs-keyword\">for<\/span> sen <span class=\"hljs-keyword\">in<\/span> sentences:\n    X.append(preprocess_text(sen))\n<\/code><\/pre>\n<p>\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u062f\u0648\u0628\u0627\u0631\u0647 \u0628\u0631\u0631\u0633\u06cc \u0686\u0647\u0627\u0631\u0645 \u0631\u0627 \u0628\u0628\u06cc\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">X(<span class=\"hljs-number\">3<\/span>)\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0628\u0647 \u0634\u06a9\u0644 \u0632\u06cc\u0631 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">'Basically there a family where little boy Jake thinks there a zombie in his closet his parents are fighting all the time This movie is slower than soap opera and suddenly Jake decides to become Rambo and kill the zombie OK first of all when you re going to make film you must Decide if its thriller or drama As drama the movie is watchable Parents are divorcing arguing like in real life And then we have Jake with his closet which totally ruins all the film expected to see BOOGEYMAN similar movie and instead watched drama with some meaningless thriller spots out of just for the well playing parents descent dialogs As for the shots with Jake just ignore them '\n<\/code><\/pre>\n<p>\u0627\u0632 \u062e\u0631\u0648\u062c\u06cc \u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u062a\u06af \u0647\u0627\u060c \u0639\u0644\u0627\u0626\u0645 \u0646\u06af\u0627\u0631\u0634\u06cc \u0648 \u0627\u0639\u062f\u0627\u062f HTML \u062d\u0630\u0641 \u0634\u062f\u0647 \u0627\u0646\u062f.  \u0645\u0627 \u0641\u0642\u0637 \u0628\u0627 \u062d\u0631\u0648\u0641 \u0627\u0644\u0641\u0628\u0627 \u0645\u0627\u0646\u062f\u0647 \u0627\u06cc\u0645.<\/p>\n<p>\u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u060c \u0628\u0627\u06cc\u062f \u0628\u0631\u0686\u0633\u0628 \u0647\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u0628\u0647 \u0631\u0642\u0645 \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u06cc\u0645.  \u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0645\u0627 \u0641\u0642\u0637 \u062f\u0648 \u0628\u0631\u0686\u0633\u0628 \u062f\u0631 \u062e\u0631\u0648\u062c\u06cc \u062f\u0627\u0631\u06cc\u0645 \u06cc\u0639\u0646\u06cc &#8220;\u0645\u062b\u0628\u062a&#8221; \u0648 &#8220;\u0645\u0646\u0641\u06cc&#8221;.  \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\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0622\u0646\u200c\u0647\u0627 \u0631\u0627 \u0628\u0627 \u062c\u0627\u06cc\u06af\u0632\u06cc\u0646 \u06a9\u0631\u062f\u0646 \u00ab\u0645\u062b\u0628\u062a\u00bb \u0628\u0627 \u0631\u0642\u0645 1 \u0648 \u0645\u0646\u0641\u06cc \u0628\u0627 \u0631\u0642\u0645 0 \u0628\u0647 \u0627\u0639\u062f\u0627\u062f \u0635\u062d\u06cc\u062d \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">y = movie_reviews(<span class=\"hljs-string\">'sentiment'<\/span>)\n\ny = np.array(<span class=\"hljs-built_in\">list<\/span>(<span class=\"hljs-built_in\">map<\/span>(<span class=\"hljs-keyword\">lambda<\/span> x: <span class=\"hljs-number\">1<\/span> <span class=\"hljs-keyword\">if<\/span> x==<span class=\"hljs-string\">\"positive\"<\/span> <span class=\"hljs-keyword\">else<\/span> <span class=\"hljs-number\">0<\/span>, y)))\n<\/code><\/pre>\n<p>\u062f\u0631 \u0646\u0647\u0627\u06cc\u062a\u060c \u0628\u0627\u06cc\u062f \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 \u0642\u0637\u0627\u0631 \u0648 \u062a\u0633\u062a \u062a\u0642\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u0645.  \u0645\u062c\u0645\u0648\u0639\u0647 \u0642\u0637\u0627\u0631 \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0645\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \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 \u0645\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u0645\u0627 \u0645\u06cc\u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645 <code>train_test_split<\/code> \u0631\u0648\u0634 \u0627\u0632 <code>sklearn.model.selection<\/code> \u0645\u0627\u0698\u0648\u0644\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\">X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=<span class=\"hljs-number\">0.20<\/span>, random_state=<span class=\"hljs-number\">42<\/span>)\n<\/code><\/pre>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0627\u0644\u0627 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u0627 \u0631\u0627 \u0628\u0647 80% \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 20% \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a \u062a\u0642\u0633\u06cc\u0645 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0644\u0627\u06cc\u0647 \u062c\u0627\u0633\u0627\u0632\u06cc \u062e\u0648\u062f \u0631\u0627 \u0628\u0646\u0648\u06cc\u0633\u06cc\u0645.  \u0644\u0627\u06cc\u0647 embedding \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u062a\u0646\u06cc \u0645\u0627 \u0631\u0627 \u0628\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0639\u062f\u062f\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0627\u0648\u0644\u06cc\u0646 \u0644\u0627\u06cc\u0647 \u0628\u0631\u0627\u06cc \u0645\u062f\u0644 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u062f\u0631 Keras \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<h2 id=\"preparingtheembeddinglayer\"><span class=\"ez-toc-section\" id=\"%d8%a2%d9%85%d8%a7%d8%af%d9%87_%d8%b3%d8%a7%d8%b2%db%8c_%d9%84%d8%a7%db%8c%d9%87_%d8%ac%d8%a7%d8%b3%d8%a7%d8%b2%db%8c\"><\/span>\u0622\u0645\u0627\u062f\u0647 \u0633\u0627\u0632\u06cc \u0644\u0627\u06cc\u0647 \u062c\u0627\u0633\u0627\u0632\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0627\u0648\u0644\u06cc\u0646 \u0642\u062f\u0645\u060c \u0645\u0627 \u0627\u0632 <code>Tokenizer<\/code> \u06a9\u0644\u0627\u0633 \u0627\u0632 <code>keras.preprocessing.text<\/code> \u0645\u0627\u0698\u0648\u0644 \u0628\u0631\u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u0641\u0631\u0647\u0646\u06af \u0644\u063a\u062a \u06a9\u0644\u0645\u0647 \u0628\u0647 \u0641\u0647\u0631\u0633\u062a.  \u062f\u0631 \u0641\u0631\u0647\u0646\u06af \u0644\u063a\u062a \u06a9\u0644\u0645\u0647 \u0628\u0647 \u0641\u0647\u0631\u0633\u062a\u060c \u0647\u0631 \u06a9\u0644\u0645\u0647 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u06cc\u06a9 \u06a9\u0644\u06cc\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f\u060c \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u06cc\u06a9 \u0634\u0627\u062e\u0635 \u0645\u0646\u062d\u0635\u0631 \u0628\u0647 \u0641\u0631\u062f \u0645\u0631\u0628\u0648\u0637\u0647 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u062f\u0627\u0631 \u06a9\u0644\u06cc\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">tokenizer = Tokenizer(num_words=<span class=\"hljs-number\">5000<\/span>)\ntokenizer.fit_on_texts(X_train)\n\nX_train = tokenizer.texts_to_sequences(X_train)\nX_test = tokenizer.texts_to_sequences(X_test)\n<\/code><\/pre>\n<p>\u0627\u06af\u0631 \u0634\u0645\u0627 \u0645\u0634\u0627\u0647\u062f\u0647 \u06a9\u0646\u06cc\u062f <code>X_train<\/code> \u0645\u062a\u063a\u06cc\u0631 \u062f\u0631 \u0627\u06a9\u0633\u067e\u0644\u0648\u0631\u0631 \u0645\u062a\u063a\u06cc\u0631\u060c \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f \u06a9\u0647 \u0634\u0627\u0645\u0644 40000 \u0644\u06cc\u0633\u062a \u0627\u0633\u062a \u06a9\u0647 \u0647\u0631 \u0644\u06cc\u0633\u062a \u0634\u0627\u0645\u0644 \u0627\u0639\u062f\u0627\u062f \u0635\u062d\u06cc\u062d \u0627\u0633\u062a.  \u0647\u0631 \u0644\u06cc\u0633\u062a \u062f\u0631 \u0648\u0627\u0642\u0639 \u0628\u0627 \u0647\u0631 \u062c\u0645\u0644\u0647 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0645\u0637\u0627\u0628\u0642\u062a \u062f\u0627\u0631\u062f.  \u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u062a\u0648\u062c\u0647 \u062e\u0648\u0627\u0647\u06cc\u062f \u0634\u062f \u06a9\u0647 \u0627\u0646\u062f\u0627\u0632\u0647 \u0647\u0631 \u0644\u06cc\u0633\u062a \u0645\u062a\u0641\u0627\u0648\u062a \u0627\u0633\u062a.  \u0627\u06cc\u0646 \u0628\u0647 \u0627\u06cc\u0646 \u062f\u0644\u06cc\u0644 \u0627\u0633\u062a \u06a9\u0647 \u0637\u0648\u0644 \u062c\u0645\u0644\u0627\u062a \u0645\u062a\u0641\u0627\u0648\u062a \u0627\u0633\u062a.<\/p>\n<p>\u0645\u0627 \u062d\u062f\u0627\u06a9\u062b\u0631 \u0627\u0646\u062f\u0627\u0632\u0647 \u0647\u0631 \u0644\u06cc\u0633\u062a \u0631\u0627 100 \u062a\u0646\u0638\u06cc\u0645 \u0645\u06cc \u06a9\u0646\u06cc\u0645. \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0646\u062f\u0627\u0632\u0647 \u062f\u06cc\u06af\u0631\u06cc \u0631\u0627 \u0627\u0645\u062a\u062d\u0627\u0646 \u06a9\u0646\u06cc\u062f.  \u0644\u06cc\u0633\u062a \u0647\u0627\u06cc\u06cc \u0628\u0627 \u0627\u0646\u062f\u0627\u0632\u0647 \u0628\u0632\u0631\u06af\u062a\u0631 \u0627\u0632 100 \u0628\u0647 100 \u06a9\u0648\u062a\u0627\u0647 \u0645\u06cc \u0634\u0648\u0646\u062f. \u0628\u0631\u0627\u06cc \u0644\u06cc\u0633\u062a \u0647\u0627\u06cc\u06cc \u06a9\u0647 \u0637\u0648\u0644 \u0622\u0646\u0647\u0627 \u06a9\u0645\u062a\u0631 \u0627\u0632 100 \u0627\u0633\u062a\u060c 0 \u0631\u0627 \u062f\u0631 \u0627\u0646\u062a\u0647\u0627\u06cc \u0644\u06cc\u0633\u062a \u0627\u0636\u0627\u0641\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u062a\u0627 \u0628\u0647 \u062d\u062f\u0627\u06a9\u062b\u0631 \u0637\u0648\u0644 \u0628\u0631\u0633\u062f.  \u0627\u06cc\u0646 process padding \u0646\u0627\u0645\u06cc\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0627\u0646\u062f\u0627\u0632\u0647 \u0648\u0627\u0698\u06af\u0627\u0646 \u0631\u0627 \u067e\u06cc\u062f\u0627 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u0633\u067e\u0633 padding \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc \u062f\u0647\u062f \u0631\u0648\u06cc \u0647\u0645 \u0642\u0637\u0627\u0631 \u0648 \u0647\u0645 \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a<\/p>\n<pre><code class=\"hljs\">\nvocab_size = <span class=\"hljs-built_in\">len<\/span>(tokenizer.word_index) + <span class=\"hljs-number\">1<\/span>\n\nmaxlen = <span class=\"hljs-number\">100<\/span>\n\nX_train = pad_sequences(X_train, padding=<span class=\"hljs-string\">'post'<\/span>, maxlen=maxlen)\nX_test = pad_sequences(X_test, padding=<span class=\"hljs-string\">'post'<\/span>, maxlen=maxlen)\n<\/code><\/pre>\n<p>\u062d\u0627\u0644\u0627 \u0627\u06af\u0631 \u0634\u0645\u0627 \u0645\u0634\u0627\u0647\u062f\u0647 \u06a9\u0646\u06cc\u062f <code>X_train<\/code> \u06cc\u0627 <code>X_test<\/code>\u060c \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f \u06a9\u0647 \u062a\u0645\u0627\u0645 \u0644\u06cc\u0633\u062a \u0647\u0627 \u062f\u0627\u0631\u0627\u06cc \u0637\u0648\u0644 \u06cc\u06a9\u0633\u0627\u0646\u06cc \u0647\u0633\u062a\u0646\u062f \u06cc\u0639\u0646\u06cc 100. \u0647\u0645\u0686\u0646\u06cc\u0646\u060c the <code>vocabulary_size<\/code> \u0645\u062a\u063a\u06cc\u0631 \u0627\u06a9\u0646\u0648\u0646 \u062d\u0627\u0648\u06cc \u0645\u0642\u062f\u0627\u0631 92547 \u0627\u0633\u062a \u06a9\u0647 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0645\u0627 \u062f\u0627\u0631\u0627\u06cc 92547 \u06a9\u0644\u0645\u0647 \u0645\u0646\u062d\u0635\u0631 \u0628\u0647 \u0641\u0631\u062f \u0627\u0633\u062a.<\/p>\n<p>\u0645\u0627 \u0627\u0632 \u062c\u0627\u0633\u0627\u0632\u06cc \u0647\u0627\u06cc GloVe \u0628\u0631\u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u0627\u062a\u0631\u06cc\u0633 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u062e\u0648\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f.  \u062f\u0631 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631\u060c \u062c\u0627\u0633\u0627\u0632\u06cc\u200c\u0647\u0627\u06cc \u06a9\u0644\u0645\u0647 GloVe \u0631\u0627 \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645 \u0648 \u0641\u0631\u0647\u0646\u06af \u0644\u063a\u062a\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u062d\u0627\u0648\u06cc \u06a9\u0644\u0645\u0627\u062a \u0628\u0647\u200c\u0639\u0646\u0648\u0627\u0646 \u06a9\u0644\u06cc\u062f \u0648 \u0641\u0647\u0631\u0633\u062a \u062c\u0627\u0633\u0627\u0632\u06cc \u0645\u062a\u0646\u0627\u0638\u0631 \u0622\u0646\u200c\u0647\u0627 \u0628\u0647\u200c\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0628\u0627\u0634\u062f.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> numpy <span class=\"hljs-keyword\">import<\/span> array\n<span class=\"hljs-keyword\">from<\/span> numpy <span class=\"hljs-keyword\">import<\/span> asarray\n<span class=\"hljs-keyword\">from<\/span> numpy <span class=\"hljs-keyword\">import<\/span> zeros\n\nembeddings_dictionary = <span class=\"hljs-built_in\">dict<\/span>()\nglove_file = <span class=\"hljs-built_in\">open<\/span>(<span class=\"hljs-string\">'E:\/Datasets\/Word Embeddings\/glove.6B.100d.txt'<\/span>, encoding=<span class=\"hljs-string\">\"utf8\"<\/span>)\n\n<span class=\"hljs-keyword\">for<\/span> line <span class=\"hljs-keyword\">in<\/span> glove_file:\n    records = line.split()\n    word = records(<span class=\"hljs-number\">0<\/span>)\n    vector_dimensions = asarray(records(<span class=\"hljs-number\">1<\/span>:), dtype=<span class=\"hljs-string\">'float32'<\/span>)\n    embeddings_dictionary (word) = vector_dimensions\nglove_file.close()\n<\/code><\/pre>\n<p>\u062f\u0631 \u0646\u0647\u0627\u06cc\u062a\u060c \u06cc\u06a9 \u0645\u0627\u062a\u0631\u06cc\u0633 \u062a\u0639\u0628\u06cc\u0647\u200c\u0633\u0627\u0632\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0647\u0631 \u0634\u0645\u0627\u0631\u0647 \u0631\u062f\u06cc\u0641 \u0628\u0627 \u0634\u0627\u062e\u0635 \u06a9\u0644\u0645\u0647 \u062f\u0631 \u0628\u062f\u0646\u0647 \u0645\u0637\u0627\u0628\u0642\u062a \u062f\u0627\u0631\u062f.  \u0645\u0627\u062a\u0631\u06cc\u0633 \u062f\u0627\u0631\u0627\u06cc 100 \u0633\u062a\u0648\u0646 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0647\u0631 \u0633\u062a\u0648\u0646 \u062d\u0627\u0648\u06cc \u06a9\u0644\u0645\u0627\u062a \u062c\u0627\u0633\u0627\u0632\u06cc \u0634\u062f\u0647 GloVe \u0628\u0631\u0627\u06cc \u06a9\u0644\u0645\u0627\u062a \u0645\u0648\u062c\u0648\u062f \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u0645\u0627 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.<\/p>\n<pre><code class=\"hljs\">embedding_matrix = zeros((vocab_size, <span class=\"hljs-number\">100<\/span>))\n<span class=\"hljs-keyword\">for<\/span> word, index <span class=\"hljs-keyword\">in<\/span> tokenizer.word_index.items():\n    embedding_vector = embeddings_dictionary.get(word)\n    <span class=\"hljs-keyword\">if<\/span> embedding_vector <span class=\"hljs-keyword\">is<\/span> <span class=\"hljs-keyword\">not<\/span> <span class=\"hljs-literal\">None<\/span>:\n        embedding_matrix(index) = embedding_vector\n<\/code><\/pre>\n<p>\u067e\u0633 \u0627\u0632 \u0627\u062c\u0631\u0627\u06cc \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0627\u0644\u0627\u060c \u0622\u0646 \u0631\u0627 \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f <code>embedding_matrix<\/code> \u0634\u0627\u0645\u0644 92547 \u0631\u062f\u06cc\u0641 (\u06cc\u06a9 \u0631\u062f\u06cc\u0641 \u0628\u0631\u0627\u06cc \u0647\u0631 \u06a9\u0644\u0645\u0647 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647) \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.  \u0627\u06a9\u0646\u0648\u0646 \u0645\u0627 \u0622\u0645\u0627\u062f\u0647 \u0627\u06cc\u0645 \u062a\u0627 \u0645\u062f\u0644 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u062e\u0648\u062f \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645.<\/p>\n<h2 id=\"textclassificationwithsimpleneuralnetwork\"><span class=\"ez-toc-section\" id=\"%d8%b7%d8%a8%d9%82%d9%87_%d8%a8%d9%86%d8%af%db%8c_%d9%85%d8%aa%d9%86_%d8%a8%d8%a7_%d8%b4%d8%a8%da%a9%d9%87_%d8%b9%d8%b5%d8%a8%db%8c_%d8%b3%d8%a7%d8%af%d9%87\"><\/span>\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0628\u0627 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0633\u0627\u062f\u0647<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u0648\u0644\u06cc\u0646 \u0645\u062f\u0644 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642\u06cc \u06a9\u0647 \u0642\u0631\u0627\u0631 \u0627\u0633\u062a \u062a\u0648\u0633\u0639\u0647 \u062f\u0647\u06cc\u0645 \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0639\u0645\u06cc\u0642 \u0633\u0627\u062f\u0647 \u0627\u0633\u062a.  \u0628\u0647 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0646\u06af\u0627\u0647 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">model = Sequential()\nembedding_layer = Embedding(vocab_size, <span class=\"hljs-number\">100<\/span>, weights=(embedding_matrix), input_length=maxlen , trainable=<span class=\"hljs-literal\">False<\/span>)\nmodel.add(embedding_layer)\n\nmodel.add(Flatten())\nmodel.add(Dense(<span class=\"hljs-number\">1<\/span>, activation=<span class=\"hljs-string\">'sigmoid'<\/span>))\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0627\u0644\u0627\u060c a \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645 <code>Sequential()<\/code> \u0645\u062f\u0644.  \u0628\u0639\u062f\u060c \u0644\u0627\u06cc\u0647 embedding \u062e\u0648\u062f \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u0644\u0627\u06cc\u0647 \u062c\u0627\u0633\u0627\u0632\u06cc \u062f\u0627\u0631\u0627\u06cc \u0637\u0648\u0644 \u0648\u0631\u0648\u062f\u06cc 100 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f\u060c \u0628\u0639\u062f \u0628\u0631\u062f\u0627\u0631 \u062e\u0631\u0648\u062c\u06cc \u0646\u06cc\u0632 100 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f. \u0627\u0646\u062f\u0627\u0632\u0647 \u0648\u0627\u0698\u06af\u0627\u0646 92547 \u06a9\u0644\u0645\u0647 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.  \u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0645\u0627 \u062a\u0639\u0628\u06cc\u0647\u200c\u0647\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u0646\u0645\u06cc\u200c\u062f\u0647\u06cc\u0645 \u0648 \u0627\u0632 \u062c\u0627\u0633\u0627\u0632\u06cc GloVe \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0646\u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645\u060c \u062a\u0646\u0638\u06cc\u0645 \u06a9\u0631\u062f\u06cc\u0645 <code>trainable<\/code> \u0628\u0647 <code>False<\/code> \u0648 \u062f\u0631 <code>weights<\/code> \u0645\u0627\u062a\u0631\u06cc\u0633 \u062a\u0639\u0628\u06cc\u0647 \u0634\u062f\u0647 \u062e\u0648\u062f \u0631\u0627 \u067e\u0627\u0633 \u0645\u06cc \u06a9\u0646\u06cc\u0645.<\/p>\n<p>\u0633\u067e\u0633 \u0644\u0627\u06cc\u0647 embedding \u0628\u0647 \u0645\u062f\u0644 \u0645\u0627 \u0627\u0636\u0627\u0641\u0647 \u0645\u06cc \u0634\u0648\u062f.  \u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u060c \u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0644\u0627\u06cc\u0647 \u062c\u0627\u0633\u0627\u0632\u06cc \u062e\u0648\u062f \u0631\u0627 \u0645\u0633\u062a\u0642\u06cc\u0645\u0627\u064b \u0628\u0647 \u0644\u0627\u06cc\u0647 \u0627\u06cc \u0645\u062a\u0631\u0627\u06a9\u0645 \u0645\u062a\u0635\u0644 \u0645\u06cc \u06a9\u0646\u06cc\u0645\u060c \u0644\u0627\u06cc\u0647 \u062c\u0627\u0633\u0627\u0632\u06cc \u0631\u0627 \u0635\u0627\u0641 \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u0645\u062a\u0631\u0627\u06a9\u0645 \u0628\u0627 \u0627\u0636\u0627\u0641\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645 <code>sigmoid<\/code> \u0639\u0645\u0644\u06a9\u0631\u062f \u0641\u0639\u0627\u0644 \u0633\u0627\u0632\u06cc<\/p>\n<p>\u0628\u0631\u0627\u06cc \u06a9\u0627\u0645\u067e\u0627\u06cc\u0644 \u06a9\u0631\u062f\u0646 \u0645\u062f\u0644 \u062e\u0648\u062f\u060c \u0627\u0632 \u0622\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f <code>adam<\/code> \u0628\u0647\u06cc\u0646\u0647 \u0633\u0627\u0632\u060c <code>binary_crossentropy<\/code> \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u062a\u0627\u0628\u0639 \u0636\u0631\u0631 \u0645\u0627 \u0648 <code>accuracy<\/code> \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u0639\u06cc\u0627\u0631\u0647\u0627 \u0648 \u0633\u067e\u0633 \u0645\u0627 print \u062e\u0644\u0627\u0635\u0647 \u0645\u062f\u0644 \u0645\u0627:<\/p>\n<pre><code class=\"hljs\">model.<span class=\"hljs-built_in\">compile<\/span>(optimizer=<span class=\"hljs-string\">'adam'<\/span>, loss=<span class=\"hljs-string\">'binary_crossentropy'<\/span>, metrics=(<span class=\"hljs-string\">'acc'<\/span>))\n\n<span class=\"hljs-built_in\">print<\/span>(model.summary())\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0628\u0647 \u0634\u06a9\u0644 \u0632\u06cc\u0631 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">Layer (type)                 Output Shape              Param #\n=================================================================\nembedding_1 (Embedding)      (None, 100, 100)          9254700\n_________________________________________________________________\nflatten_1 (Flatten)          (None, 10000)             0\n_________________________________________________________________\ndense_1 (Dense)              (None, 1)                 10001\n=================================================================\nTotal params: 9,264,701\nTrainable params: 10,001\nNon-trainable params: 9,254,700\n<\/code><\/pre>\n<p>\u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 92547 \u06a9\u0644\u0645\u0647 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u0645\u0627 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f \u0648 \u0647\u0631 \u06a9\u0644\u0645\u0647 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u06cc\u06a9 \u0628\u0631\u062f\u0627\u0631 100 \u0628\u0639\u062f\u06cc \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f\u060c \u062a\u0639\u062f\u0627\u062f \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0642\u0627\u0628\u0644 \u0622\u0645\u0648\u0632\u0634 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f. <code>92547x100<\/code> \u062f\u0631 \u0644\u0627\u06cc\u0647 \u062c\u0627\u0633\u0627\u0632\u06cc  \u062f\u0631 \u0644\u0627\u06cc\u0647 \u0645\u0633\u0637\u062d\u060c \u0628\u0647 \u0633\u0627\u062f\u06af\u06cc \u0633\u0637\u0631\u0647\u0627 \u0648 \u0633\u062a\u0648\u0646 \u0647\u0627 \u0631\u0627 \u0636\u0631\u0628 \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a \u062f\u0631 \u0644\u0627\u06cc\u0647 \u0645\u062a\u0631\u0627\u06a9\u0645 \u062a\u0639\u062f\u0627\u062f \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 10000 (\u0627\u0632 \u0644\u0627\u06cc\u0647 \u0645\u0633\u0637\u062d) \u0648 1 \u0628\u0631\u0627\u06cc \u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u0628\u0627\u06cc\u0627\u0633\u060c \u062f\u0631 \u0645\u062c\u0645\u0648\u0639 10001 \u0627\u0633\u062a.<\/p>\n<p>\u062d\u0627\u0644 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0645\u062f\u0644 \u062e\u0648\u062f \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">history = model.fit(X_train, y_train, batch_size=<span class=\"hljs-number\">128<\/span>, epochs=<span class=\"hljs-number\">6<\/span>, verbose=<span class=\"hljs-number\">1<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>)\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0627\u0644\u0627 \u0627\u0632 <code>fit<\/code> \u0631\u0648\u0634\u06cc \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0645\u0627  \u062a\u0648\u062c\u0647 \u06a9\u0646\u06cc\u062f \u062f\u0631 \u062d\u0627\u0644 \u062a\u0645\u0631\u06cc\u0646 \u0647\u0633\u062a\u06cc\u0645 \u0631\u0648\u06cc \u0641\u0642\u0637 \u0645\u062c\u0645\u0648\u0639\u0647 \u0642\u0637\u0627\u0631 \u0645\u0627  \u0627\u06cc\u0646 <code>validation_split<\/code> \u0627\u0632 0.2 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 20\u066a \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0628\u0631\u0627\u06cc \u06cc\u0627\u0641\u062a\u0646 \u062f\u0642\u062a \u0622\u0645\u0648\u0632\u0634 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u062f\u0631 \u067e\u0627\u06cc\u0627\u0646 \u0622\u0645\u0648\u0632\u0634 \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f \u06a9\u0647 \u062f\u0642\u062a \u062a\u0645\u0631\u06cc\u0646 \u062f\u0631 \u062d\u062f\u0648\u062f 85.52 \u062f\u0631\u0635\u062f \u0627\u0633\u062a.<\/p>\n<p>\u0628\u0631\u0627\u06cc \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0639\u0645\u0644\u06a9\u0631\u062f \u0645\u062f\u0644\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0628\u0647 \u0633\u0627\u062f\u06af\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a \u0631\u0627 \u0628\u0647 \u0622\u0646 \u067e\u0627\u0633 \u06a9\u0646\u06cc\u0645 <code>evaluate<\/code> \u0631\u0648\u0634 \u0645\u062f\u0644 \u0645\u0627<\/p>\n<pre><code class=\"hljs\">score = model.evaluate(X_test, y_test, verbose=<span class=\"hljs-number\">1<\/span>)\n<\/code><\/pre>\n<p>\u0628\u0631\u0627\u06cc \u0628\u0631\u0631\u0633\u06cc \u062f\u0642\u062a \u0648 \u0627\u0632 \u062f\u0633\u062a \u062f\u0627\u062f\u0646 \u062a\u0633\u062a\u060c \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Test Score:\"<\/span>, score(<span class=\"hljs-number\">0<\/span>))\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Test Accuracy:\"<\/span>, score(<span class=\"hljs-number\">1<\/span>))\n<\/code><\/pre>\n<p>\u0648\u0642\u062a\u06cc \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0627\u0644\u0627 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0631\u062f\u06cc\u062f\u060c \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f \u06a9\u0647 \u062f\u0642\u062a \u062a\u0633\u062a 74.68% \u0628\u062f\u0633\u062a \u0645\u06cc \u0622\u06cc\u062f.  \u062f\u0642\u062a \u062a\u0645\u0631\u06cc\u0646 \u0645\u0627 85.52 \u062f\u0631\u0635\u062f \u0628\u0648\u062f.  \u0627\u06cc\u0646 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0645\u062f\u0644 \u0645\u0627 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0645\u0646\u0627\u0633\u0628 \u0627\u0633\u062a \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc  \u0627\u0636\u0627\u0641\u0647 \u06a9\u0631\u062f\u0646 \u0632\u0645\u0627\u0646\u06cc \u0627\u062a\u0641\u0627\u0642 \u0645\u06cc \u0627\u0641\u062a\u062f \u06a9\u0647 \u0645\u062f\u0644 \u0634\u0645\u0627 \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u0647\u062a\u0631\u06cc \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0646\u0633\u0628\u062a \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a.  \u062f\u0631 \u062d\u0627\u0644\u062a \u0627\u06cc\u062f\u0647 \u0622\u0644\u060c \u062a\u0641\u0627\u0648\u062a \u0639\u0645\u0644\u06a9\u0631\u062f \u0628\u06cc\u0646 \u062a\u0645\u0631\u06cc\u0646 \u0648 \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a \u0628\u0627\u06cc\u062f \u062d\u062f\u0627\u0642\u0644 \u0628\u0627\u0634\u062f.<\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0633\u0639\u06cc \u06a9\u0646\u06cc\u0645 \u062a\u0641\u0627\u0648\u062a \u0647\u0627\u06cc \u0628\u0627\u062e\u062a \u0648 \u062f\u0642\u062a \u0631\u0627 \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u062a\u0633\u062a \u062a\u0631\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u0645.  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">plt.title(<span class=\"hljs-string\">'model accuracy'<\/span>)\nplt.ylabel(<span class=\"hljs-string\">'accuracy'<\/span>)\nplt.xlabel(<span class=\"hljs-string\">'epoch'<\/span>)\nplt.legend((<span class=\"hljs-string\">'train'<\/span>,<span class=\"hljs-string\">'test'<\/span>), loc=<span class=\"hljs-string\">'upper left'<\/span>)\nplt.show()\n\nplt.plot(history.history(<span class=\"hljs-string\">'loss'<\/span>))\nplt.plot(history.history(<span class=\"hljs-string\">'val_loss'<\/span>))\n\nplt.title(<span class=\"hljs-string\">'model loss'<\/span>)\nplt.ylabel(<span class=\"hljs-string\">'loss'<\/span>)\nplt.xlabel(<span class=\"hljs-string\">'epoch'<\/span>)\nplt.legend((<span class=\"hljs-string\">'train'<\/span>,<span class=\"hljs-string\">'test'<\/span>), loc=<span class=\"hljs-string\">'upper left'<\/span>)\nplt.show()\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<p><img decoding=\"async\" class=\"img-responsive\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/python-nlp-movie-sentiment-analysis-deep-learning-keras-3.png\" alt=\"\" title=\"\"><\/p>\n<p>\u0634\u0645\u0627 \u0628\u0647 \u0648\u0636\u0648\u062d \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u062a\u0641\u0627\u0648\u062a \u0647\u0627\u06cc \u0628\u0627\u062e\u062a \u0648 \u062f\u0642\u062a \u0631\u0627 \u0628\u06cc\u0646 \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u062a\u0633\u062a \u0645\u0634\u0627\u0647\u062f\u0647 \u06a9\u0646\u06cc\u062f.<\/p>\n<h2 id=\"textclassificationwithaconvolutionalneuralnetwork\"><span class=\"ez-toc-section\" id=\"%d8%b7%d8%a8%d9%82%d9%87_%d8%a8%d9%86%d8%af%db%8c_%d9%85%d8%aa%d9%86_%d8%a8%d8%a7_%d8%b4%d8%a8%da%a9%d9%87_%d8%b9%d8%b5%d8%a8%db%8c_%da%a9%d8%a7%d9%86%d9%88%d9%84%d9%88%d8%b4%d9%86%d8%a7%d9%84\"><\/span>\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0628\u0627 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u0627\u0644<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u0627\u0644 \u0646\u0648\u0639\u06cc \u0634\u0628\u06a9\u0647 \u0627\u0633\u062a \u06a9\u0647 \u062f\u0631 \u062f\u0631\u062c\u0647 \u0627\u0648\u0644 \u0628\u0631\u0627\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062f\u0648 \u0628\u0639\u062f\u06cc \u0645\u0627\u0646\u0646\u062f \u062a\u0635\u0627\u0648\u06cc\u0631 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.  \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646 \u0633\u0639\u06cc \u0645\u06cc \u06a9\u0646\u062f \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u062e\u0627\u0635\u06cc \u0631\u0627 \u062f\u0631 \u06cc\u06a9 \u062a\u0635\u0648\u06cc\u0631 \u062f\u0631 \u0644\u0627\u06cc\u0647 \u0627\u0648\u0644 \u067e\u06cc\u062f\u0627 \u06a9\u0646\u062f.  \u062f\u0631 \u0644\u0627\u06cc\u0647\u200c\u0647\u0627\u06cc \u0628\u0639\u062f\u06cc\u060c \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627\u06cc \u0634\u0646\u0627\u0633\u0627\u06cc\u06cc \u0634\u062f\u0647 \u0627\u0648\u0644\u06cc\u0647 \u0628\u0647 \u06cc\u06a9\u062f\u06cc\u06af\u0631 \u0645\u062a\u0635\u0644 \u0645\u06cc\u200c\u0634\u0648\u0646\u062f \u062a\u0627 \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627\u06cc \u0628\u0632\u0631\u06af\u200c\u062a\u0631\u06cc \u0631\u0627 \u062a\u0634\u06a9\u06cc\u0644 \u062f\u0647\u0646\u062f.  \u0628\u0647 \u0627\u06cc\u0646 \u062a\u0631\u062a\u06cc\u0628 \u06a9\u0644 \u062a\u0635\u0648\u06cc\u0631 \u0634\u0646\u0627\u0633\u0627\u06cc\u06cc \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u0627\u0644 \u0628\u0627 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0645\u062a\u0646\u06cc \u0646\u06cc\u0632 \u0628\u0647 \u062e\u0648\u0628\u06cc \u06a9\u0627\u0631 \u0645\u06cc\u200c\u06a9\u0646\u0646\u062f.  \u0627\u06af\u0631\u0686\u0647 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0645\u062a\u0646\u06cc \u062a\u06a9 \u0628\u0639\u062f\u06cc \u0647\u0633\u062a\u0646\u062f\u060c \u0627\u0645\u0627 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u0632 \u0634\u0628\u06a9\u0647\u200c\u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u06cc\u06a9 \u06cc\u06a9 \u0628\u0639\u062f\u06cc \u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0648\u06cc\u0698\u06af\u06cc\u200c\u0647\u0627 \u0627\u0632 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc\u0645\u0627\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645.  \u0628\u0631\u0627\u06cc \u06a9\u0633\u0628 \u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0628\u06cc\u0634\u062a\u0631 \u062f\u0631 \u0645\u0648\u0631\u062f \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u060c \u0644\u0637\u0641\u0627\u064b \u0628\u0647 <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/ujjwalkarn.me\/2016\/08\/11\/intuitive-explanation-convnets\/\">\u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647<\/a>.<\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u0627\u0644 \u0633\u0627\u062f\u0647 \u0628\u0627 1 \u0644\u0627\u06cc\u0647 \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646 \u0648 1 \u0644\u0627\u06cc\u0647 \u0627\u062f\u063a\u0627\u0645 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645.  \u0628\u0647 \u06cc\u0627\u062f \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f\u060c \u06a9\u062f \u062a\u0627 \u0627\u06cc\u062c\u0627\u062f \u0644\u0627\u06cc\u0647 embedding \u06cc\u06a9\u0633\u0627\u0646 \u0628\u0627\u0642\u06cc \u0645\u06cc \u0645\u0627\u0646\u062f\u060c \u067e\u0633 \u0627\u0632 \u0627\u06cc\u062c\u0627\u062f \u0644\u0627\u06cc\u0647 embedding\u060c \u06a9\u062f \u0632\u06cc\u0631 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">model = Sequential()\n\nembedding_layer = Embedding(vocab_size, <span class=\"hljs-number\">100<\/span>, weights=(embedding_matrix), input_length=maxlen , trainable=<span class=\"hljs-literal\">False<\/span>)\nmodel.add(embedding_layer)\n\nmodel.add(Conv1D(<span class=\"hljs-number\">128<\/span>, <span class=\"hljs-number\">5<\/span>, activation=<span class=\"hljs-string\">'relu'<\/span>))\nmodel.add(GlobalMaxPooling1D())\nmodel.add(Dense(<span class=\"hljs-number\">1<\/span>, activation=<span class=\"hljs-string\">'sigmoid'<\/span>))\nmodel.<span class=\"hljs-built_in\">compile<\/span>(optimizer=<span class=\"hljs-string\">'adam'<\/span>, loss=<span class=\"hljs-string\">'binary_crossentropy'<\/span>, metrics=(<span class=\"hljs-string\">'acc'<\/span>))\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0627\u0644\u0627 \u06cc\u06a9 \u0645\u062f\u0644 \u0645\u062a\u0648\u0627\u0644\u06cc \u0648 \u0628\u0647 \u062f\u0646\u0628\u0627\u0644 \u0622\u0646 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u062c\u0627\u0633\u0627\u0632\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u0627\u06cc\u0646 \u0645\u0631\u062d\u0644\u0647 \u0645\u0634\u0627\u0628\u0647 \u06a9\u0627\u0631\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0642\u0628\u0644\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0627\u062f\u0647 \u0628\u0648\u062f\u06cc\u0645.  \u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u060c \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u06cc\u06a9 \u06cc\u06a9 \u0628\u0639\u062f\u06cc \u0628\u0627 128 \u0648\u06cc\u0698\u06af\u06cc \u06cc\u0627 \u0647\u0633\u062a\u0647 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u0627\u0646\u062f\u0627\u0632\u0647 \u0647\u0633\u062a\u0647 5 \u0648 \u062a\u0627\u0628\u0639 \u0641\u0639\u0627\u0644 \u0633\u0627\u0632\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a <code>sigmoid<\/code>.  \u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u060c \u0628\u0631\u0627\u06cc \u06a9\u0627\u0647\u0634 \u0627\u0646\u062f\u0627\u0632\u0647 \u0648\u06cc\u0698\u06af\u06cc\u060c \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u062c\u0647\u0627\u0646\u06cc max pooling \u0627\u0636\u0627\u0641\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u0645\u062a\u0631\u0627\u06a9\u0645 \u0628\u0627 \u0641\u0639\u0627\u0644 \u0633\u0627\u0632\u06cc \u0633\u06cc\u06af\u0645\u0648\u0626\u06cc\u062f \u0627\u0636\u0627\u0641\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u062a\u0627\u0644\u06cc\u0641 process \u0647\u0645\u0627\u0646 \u0627\u0633\u062a \u06a9\u0647 \u062f\u0631 \u0628\u062e\u0634 \u0642\u0628\u0644 \u0628\u0648\u062f.<\/p>\n<p>\u062d\u0627\u0644 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u062e\u0644\u0627\u0635\u0647 \u0645\u062f\u0644 \u062e\u0648\u062f \u0631\u0627 \u0628\u0628\u06cc\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(model.summary())\n<\/code><\/pre>\n<pre><code class=\"hljs\">_________________________________________________________________\nLayer (type)                 Output Shape              Param #\n=================================================================\nembedding_2 (Embedding)      (None, 100, 100)          9254700\n_________________________________________________________________\nconv1d_1 (Conv1D)            (None, 96, 128)           64128\n_________________________________________________________________\nglobal_max_pooling1d_1 (Glob (None, 128)               0\n_________________________________________________________________\ndense_2 (Dense)              (None, 1)                 129\n=================================================================\nTotal params: 9,318,957\nTrainable params: 64,257\nNon-trainable params: 9,254,700\n<\/code><\/pre>\n<p>\u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u062f\u0631 \u0645\u0648\u0631\u062f \u0628\u0627\u0644\u0627 \u0646\u06cc\u0627\u0632\u06cc \u0646\u06cc\u0633\u062a \u0644\u0627\u06cc\u0647 \u062c\u0627\u0633\u0627\u0632\u06cc \u062e\u0648\u062f \u0631\u0627 \u0635\u0627\u0641 \u06a9\u0646\u06cc\u0645.  \u0634\u0645\u0627 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0645\u062a\u0648\u062c\u0647 \u0634\u0648\u06cc\u062f \u06a9\u0647 \u0627\u0646\u062f\u0627\u0632\u0647 \u0648\u06cc\u0698\u06af\u06cc \u0627\u06a9\u0646\u0648\u0646 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0644\u0627\u06cc\u0647 \u0627\u062f\u063a\u0627\u0645 \u06a9\u0627\u0647\u0634 \u06cc\u0627\u0641\u062a\u0647 \u0627\u0633\u062a.<\/p>\n<p>\u062d\u0627\u0644 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0645\u062f\u0644 \u062e\u0648\u062f \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645 \u0648 \u0622\u0646 \u0631\u0627 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u06a9\u0646\u06cc\u0645 \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc  \u0627\u06cc\u0646 process \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634 \u0645\u062f\u0644 \u0645\u0627 \u062b\u0627\u0628\u062a \u0645\u06cc \u0645\u0627\u0646\u062f.  \u0628\u0631\u0627\u06cc \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u0632 <code>fit<\/code> \u0648 <code>evaluate<\/code> \u0631\u0648\u0634 \u0647\u0627\u060c \u0628\u0647 \u062a\u0631\u062a\u06cc\u0628.<\/p>\n<pre><code class=\"hljs\">history = model.fit(X_train, y_train, batch_size=<span class=\"hljs-number\">128<\/span>, epochs=<span class=\"hljs-number\">6<\/span>, verbose=<span class=\"hljs-number\">1<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>)\n\nscore = model.evaluate(X_test, y_test, verbose=<span class=\"hljs-number\">1<\/span>)\n<\/code><\/pre>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0646\u062a\u0627\u06cc\u062c \u0631\u0627 \u0686\u0627\u067e \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Test Score:\"<\/span>, score(<span class=\"hljs-number\">0<\/span>))\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Test Accuracy:\"<\/span>, score(<span class=\"hljs-number\">1<\/span>))\n<\/code><\/pre>\n<p>\u0627\u06af\u0631 \u062f\u0642\u062a \u0622\u0645\u0648\u0632\u0634 \u0648 \u062a\u0633\u062a \u0631\u0627 \u0628\u0627 \u0647\u0645 \u0645\u0642\u0627\u06cc\u0633\u0647 \u06a9\u0646\u06cc\u062f\u060c \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f \u06a9\u0647 \u062f\u0642\u062a \u0622\u0645\u0648\u0632\u0634 \u0628\u0631\u0627\u06cc CNN \u062d\u062f\u0648\u062f 92 \u062f\u0631\u0635\u062f \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f \u06a9\u0647 \u0627\u0632 \u062f\u0642\u062a \u0622\u0645\u0648\u0632\u0634 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0633\u0627\u062f\u0647 \u0628\u06cc\u0634\u062a\u0631 \u0627\u0633\u062a.  \u062f\u0642\u062a \u062a\u0633\u062a \u0628\u0631\u0627\u06cc CNN \u062d\u062f\u0648\u062f 82\u066a \u0627\u0633\u062a\u060c \u06a9\u0647 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0627\u0632 \u062f\u0642\u062a \u062a\u0633\u062a \u0628\u0631\u0627\u06cc \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0633\u0627\u062f\u0647 \u06a9\u0647 \u062d\u062f\u0648\u062f 74\u066a \u0628\u0648\u062f \u0628\u06cc\u0634\u062a\u0631 \u0627\u0633\u062a.<\/p>\n<p>\u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u0645\u062f\u0644 CNN \u0645\u0627 \u0647\u0646\u0648\u0632 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0645\u0646\u0627\u0633\u0628 \u0627\u0633\u062a \u0632\u06cc\u0631\u0627 \u062a\u0641\u0627\u0648\u062a \u0632\u06cc\u0627\u062f\u06cc \u0628\u06cc\u0646 \u062f\u0642\u062a \u0622\u0645\u0648\u0632\u0634 \u0648 \u0622\u0632\u0645\u0648\u0646 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f.  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u062a\u0641\u0627\u0648\u062a \u0627\u0632 \u062f\u0633\u062a \u062f\u0627\u062f\u0646 \u0648 \u062f\u0642\u062a \u0628\u06cc\u0646 \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u062a\u0633\u062a \u0631\u0627 \u062a\u0631\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u0645.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n\nplt.plot(history.history(<span class=\"hljs-string\">'acc'<\/span>))\nplt.plot(history.history(<span class=\"hljs-string\">'val_acc'<\/span>))\n\nplt.title(<span class=\"hljs-string\">'model accuracy'<\/span>)\nplt.ylabel(<span class=\"hljs-string\">'accuracy'<\/span>)\nplt.xlabel(<span class=\"hljs-string\">'epoch'<\/span>)\nplt.legend((<span class=\"hljs-string\">'train'<\/span>,<span class=\"hljs-string\">'test'<\/span>), loc = <span class=\"hljs-string\">'upper left'<\/span>)\nplt.show()\n\nplt.plot(history.history(<span class=\"hljs-string\">'loss'<\/span>))\nplt.plot(history.history(<span class=\"hljs-string\">'val_loss'<\/span>))\n\nplt.title(<span class=\"hljs-string\">'model loss'<\/span>)\nplt.ylabel(<span class=\"hljs-string\">'loss'<\/span>)\nplt.xlabel(<span class=\"hljs-string\">'epoch'<\/span>)\nplt.legend((<span class=\"hljs-string\">'train'<\/span>,<span class=\"hljs-string\">'test'<\/span>), loc = <span class=\"hljs-string\">'upper left'<\/span>)\nplt.show()\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<p><img decoding=\"async\" class=\"img-responsive\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/python-nlp-movie-sentiment-analysis-deep-learning-keras-4.png\" alt=\"\" title=\"\"><\/p>\n<p>\u0634\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0647 \u0648\u0636\u0648\u062d \u062a\u0641\u0627\u0648\u062a \u0627\u0632 \u062f\u0633\u062a \u062f\u0627\u062f\u0646 \u0648 \u062f\u0642\u062a \u0628\u06cc\u0646 \u0642\u0637\u0627\u0631 \u0648 \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a \u0631\u0627 \u0628\u0628\u06cc\u0646\u06cc\u062f.<\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u06a9\u0646\u0648\u0646 \u0633\u0648\u0645\u06cc\u0646 \u0645\u062f\u0644 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u062e\u0648\u062f \u0631\u0627 \u06a9\u0647 \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u062a\u06a9\u0631\u0627\u0631\u06cc \u0627\u0633\u062a \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645 \u0648 \u0628\u0628\u06cc\u0646\u06cc\u0645 \u06a9\u0647 \u0622\u06cc\u0627 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u0632 \u0634\u0631 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0628\u0631\u0627\u0632\u0634 \u062e\u0644\u0627\u0635 \u0634\u0648\u06cc\u0645.<\/p>\n<h2 id=\"textclassificationwithrecurrentneuralnetworklstm\"><span class=\"ez-toc-section\" id=\"%d8%b7%d8%a8%d9%82%d9%87_%d8%a8%d9%86%d8%af%db%8c_%d9%85%d8%aa%d9%86_%d8%a8%d8%a7_%d8%b4%d8%a8%da%a9%d9%87_%d8%b9%d8%b5%d8%a8%db%8c_%d8%aa%da%a9%d8%b1%d8%a7%d8%b1%db%8c_lstm\"><\/span>\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0628\u0627 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u062a\u06a9\u0631\u0627\u0631\u06cc (LSTM)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0628\u0627\u0632\u06af\u0634\u062a\u06cc \u0646\u0648\u0639\u06cc \u0627\u0632 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0627\u0633\u062a \u06a9\u0647 \u062b\u0627\u0628\u062a \u0634\u062f\u0647 \u0627\u0633\u062a \u06a9\u0647 \u0628\u0627 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062a\u0648\u0627\u0644\u06cc \u0628\u0647 \u062e\u0648\u0628\u06cc \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f.  \u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0645\u062a\u0646 \u062f\u0631 \u0648\u0627\u0642\u0639 \u062f\u0646\u0628\u0627\u0644\u0647 \u0627\u06cc \u0627\u0632 \u06a9\u0644\u0645\u0627\u062a \u0627\u0633\u062a\u060c \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u062a\u06a9\u0631\u0627\u0631\u06cc \u06cc\u06a9 \u0627\u0646\u062a\u062e\u0627\u0628 \u062e\u0648\u062f\u06a9\u0627\u0631 \u0628\u0631\u0627\u06cc \u062d\u0644 \u0645\u0634\u06a9\u0644\u0627\u062a \u0645\u0631\u0628\u0648\u0637 \u0628\u0647 \u0645\u062a\u0646 \u0627\u0633\u062a.  \u062f\u0631 \u0627\u06cc\u0646 \u0628\u062e\u0634 \u0627\u0632 LSTM (\u0634\u0628\u06a9\u0647 \u062d\u0627\u0641\u0638\u0647 \u06a9\u0648\u062a\u0627\u0647 \u0645\u062f\u062a \u0628\u0644\u0646\u062f\u0645\u062f\u062a) \u06a9\u0647 \u0646\u0648\u0639\u06cc \u0627\u0632 RNN \u0627\u0633\u062a \u0628\u0631\u0627\u06cc \u062d\u0644 \u0645\u0633\u0627\u0626\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f.<\/p>\n<p>\u06cc\u06a9 \u0628\u0627\u0631 \u062f\u06cc\u06af\u0631 \u06a9\u062f \u0631\u0627 \u062a\u0627 \u0642\u0633\u0645\u062a \u0648\u0627\u0698\u0647 embedding \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f \u0648 \u067e\u0633 \u0627\u0632 \u0622\u0646 \u06a9\u062f \u0632\u06cc\u0631 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f.<\/p>\n<pre><code class=\"hljs\">model = Sequential()\nembedding_layer = Embedding(vocab_size, <span class=\"hljs-number\">100<\/span>, weights=(embedding_matrix), input_length=maxlen , trainable=<span class=\"hljs-literal\">False<\/span>)\nmodel.add(embedding_layer)\nmodel.add(LSTM(<span class=\"hljs-number\">128<\/span>))\n\nmodel.add(Dense(<span class=\"hljs-number\">1<\/span>, activation=<span class=\"hljs-string\">'sigmoid'<\/span>))\nmodel.<span class=\"hljs-built_in\">compile<\/span>(optimizer=<span class=\"hljs-string\">'adam'<\/span>, loss=<span class=\"hljs-string\">'binary_crossentropy'<\/span>, metrics=(<span class=\"hljs-string\">'acc'<\/span>))\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0627\u0644\u0627\u060c \u0645\u0627 \u0628\u0627 \u0645\u0642\u062f\u0627\u0631\u062f\u0647\u06cc \u0627\u0648\u0644\u06cc\u0647 \u06cc\u06a9 \u0645\u062f\u0644 \u0645\u062a\u0648\u0627\u0644\u06cc \u0648 \u0633\u067e\u0633 \u0627\u06cc\u062c\u0627\u062f \u0644\u0627\u06cc\u0647 embedding \u0634\u0631\u0648\u0639 \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u0628\u0639\u062f\u060c \u06cc\u06a9 \u0644\u0627\u06cc\u0647 LSTM \u0628\u0627 128 \u0646\u0648\u0631\u0648\u0646 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645 (\u0634\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0627 \u062a\u0639\u062f\u0627\u062f \u0646\u0648\u0631\u0648\u0646 \u0647\u0627 \u0628\u0627\u0632\u06cc \u06a9\u0646\u06cc\u062f).  \u0645\u0627\u0628\u0642\u06cc \u06a9\u062f \u0647\u0645\u0627\u0646 \u0686\u06cc\u0632\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0628\u0631\u0627\u06cc CNN \u0628\u0648\u062f.<\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u062e\u0644\u0627\u0635\u0647 \u0645\u062f\u0644 \u062e\u0648\u062f \u0631\u0627 \u062a\u0631\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u0645.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(model.summary())\n<\/code><\/pre>\n<p>\u062e\u0644\u0627\u0635\u0647 \u0645\u062f\u0644 \u0628\u0647 \u0627\u06cc\u0646 \u0635\u0648\u0631\u062a \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">_________________________________________________________________\nLayer (type)                 Output Shape              Param #\n=================================================================\nembedding_3 (Embedding)      (None, 100, 100)          9254700\n_________________________________________________________________\nlstm_1 (LSTM)                (None, 128)               117248\n_________________________________________________________________\ndense_3 (Dense)              (None, 1)                 129\n=================================================================\nTotal params: 9,372,077\nTrainable params: 117,377\nNon-trainable params: 9,254,700\n<\/code><\/pre>\n<p>\u0642\u062f\u0645 \u0628\u0639\u062f\u06cc \u0645\u0627 \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0627\u0633\u062a \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0639\u0645\u0644\u06a9\u0631\u062f \u0622\u0646 \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a<\/p>\n<pre><code class=\"hljs\">history = model.fit(X_train, y_train, batch_size=<span class=\"hljs-number\">128<\/span>, epochs=<span class=\"hljs-number\">6<\/span>, verbose=<span class=\"hljs-number\">1<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>)\n\nscore = model.evaluate(X_test, y_test, verbose=<span class=\"hljs-number\">1<\/span>)\n<\/code><\/pre>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0627\u0644\u0627 \u0645\u062f\u0644 \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u0645\u06cc \u062f\u0647\u062f \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a  \u0627\u0646\u062f\u0627\u0632\u0647 \u062f\u0633\u062a\u0647 128 \u0627\u0633\u062a \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u062a\u0639\u062f\u0627\u062f \u062f\u0648\u0631\u0647 \u0647\u0627 6 \u0627\u0633\u062a. \u062f\u0631 \u067e\u0627\u06cc\u0627\u0646 \u0622\u0645\u0648\u0632\u0634 \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f \u06a9\u0647 \u062f\u0642\u062a \u0622\u0645\u0648\u0632\u0634 \u062d\u062f\u0648\u062f 85.40 \u062f\u0631\u0635\u062f \u0627\u0633\u062a.<\/p>\n<p>\u0647\u0646\u06af\u0627\u0645\u06cc \u06a9\u0647 \u0645\u062f\u0644 \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0634\u062f\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0646\u062a\u0627\u06cc\u062c \u0645\u062f\u0644 \u0631\u0627 \u0645\u0634\u0627\u0647\u062f\u0647 \u06a9\u0646\u06cc\u0645 \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a \u0628\u0627 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Test Score:\"<\/span>, score(<span class=\"hljs-number\">0<\/span>))\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Test Accuracy:\"<\/span>, score(<span class=\"hljs-number\">1<\/span>))\n<\/code><\/pre>\n<p>\u062f\u0631 \u062e\u0631\u0648\u062c\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f \u06a9\u0647 \u062f\u0642\u062a \u062a\u0633\u062a \u0645\u0627 \u062d\u062f\u0648\u062f 85.04 \u062f\u0631\u0635\u062f \u0627\u0633\u062a.  \u062f\u0642\u062a \u062a\u0633\u062a \u0647\u0645 \u0627\u0632 CNN \u0648 \u0647\u0645 \u0627\u0632 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0628\u0627 \u0627\u062a\u0635\u0627\u0644 \u0645\u062a\u0631\u0627\u06a9\u0645 \u0628\u0647\u062a\u0631 \u0627\u0633\u062a.  \u0647\u0645\u0686\u0646\u06cc\u0646\u060c \u0645\u06cc \u0628\u06cc\u0646\u06cc\u0645 \u06a9\u0647 \u062a\u0641\u0627\u0648\u062a \u0628\u0633\u06cc\u0627\u0631 \u06a9\u0645\u06cc \u0628\u06cc\u0646 \u062f\u0642\u062a \u062a\u0645\u0631\u06cc\u0646 \u0648 \u062f\u0642\u062a \u062a\u0633\u062a \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f \u06a9\u0647 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0645\u062f\u0644 \u0645\u0627 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0645\u0646\u0627\u0633\u0628 \u0646\u06cc\u0633\u062a.<\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u062a\u0641\u0627\u0648\u062a\u200c\u0647\u0627\u06cc \u0627\u0632 \u062f\u0633\u062a \u062f\u0627\u062f\u0646 \u0648 \u062f\u0642\u062a \u0628\u06cc\u0646 \u0645\u062c\u0645\u0648\u0639\u0647\u200c\u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u062a\u0633\u062a \u0631\u0627 \u062a\u0631\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u0645.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n\nplt.plot(history.history(<span class=\"hljs-string\">'acc'<\/span>))\nplt.plot(history.history(<span class=\"hljs-string\">'val_acc'<\/span>))\n\nplt.title(<span class=\"hljs-string\">'model accuracy'<\/span>)\nplt.ylabel(<span class=\"hljs-string\">'accuracy'<\/span>)\nplt.xlabel(<span class=\"hljs-string\">'epoch'<\/span>)\nplt.legend((<span class=\"hljs-string\">'train'<\/span>,<span class=\"hljs-string\">'test'<\/span>), loc=<span class=\"hljs-string\">'upper left'<\/span>)\nplt.show()\n\nplt.plot(history.history(<span class=\"hljs-string\">'loss'<\/span>))\nplt.plot(history.history(<span class=\"hljs-string\">'val_loss'<\/span>))\n\nplt.title(<span class=\"hljs-string\">'model loss'<\/span>)\nplt.ylabel(<span class=\"hljs-string\">'loss'<\/span>)\nplt.xlabel(<span class=\"hljs-string\">'epoch'<\/span>)\nplt.legend((<span class=\"hljs-string\">'train'<\/span>,<span class=\"hljs-string\">'test'<\/span>), loc=<span class=\"hljs-string\">'upper left'<\/span>)\nplt.show()\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<p><img decoding=\"async\" class=\"img-responsive\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/python-nlp-movie-sentiment-analysis-deep-learning-keras-5.png\" alt=\"\" title=\"\"><\/p>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u06a9\u0647 \u062a\u0641\u0627\u0648\u062a \u0628\u06cc\u0646 \u0645\u0642\u0627\u062f\u06cc\u0631 \u062f\u0642\u062a \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u062f\u0631 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0628\u0627 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0633\u0627\u062f\u0647 \u0648 CNN \u0628\u0633\u06cc\u0627\u0631 \u06a9\u0648\u0686\u06a9\u062a\u0631 \u0627\u0633\u062a.  \u0628\u0647 \u0637\u0648\u0631 \u0645\u0634\u0627\u0628\u0647\u060c \u062a\u0641\u0627\u0648\u062a \u0628\u06cc\u0646 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0636\u0631\u0631 \u0646\u06cc\u0632 \u0646\u0627\u0686\u06cc\u0632 \u0627\u0633\u062a\u060c \u06a9\u0647 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u0645\u062f\u0644 \u0645\u0627 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0645\u0646\u0627\u0633\u0628 \u0646\u06cc\u0633\u062a.  \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0646\u062a\u06cc\u062c\u0647 \u0628\u06af\u06cc\u0631\u06cc\u0645 \u06a9\u0647 \u0628\u0631\u0627\u06cc \u0645\u0634\u06a9\u0644 \u0645\u0627\u060c RNN \u0628\u0647\u062a\u0631\u06cc\u0646 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0627\u0633\u062a.<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0637\u0648\u0631 \u062a\u0635\u0627\u062f\u0641\u06cc \u062a\u0639\u062f\u0627\u062f \u0644\u0627\u06cc\u0647 \u0647\u0627\u060c \u0646\u0648\u0631\u0648\u0646 \u0647\u0627\u060c \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0647\u0627\u06cc\u067e\u0631 \u0648 &#8230; \u0631\u0627 \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u0631\u062f\u06cc\u0645. \u067e\u06cc\u0634\u0646\u0647\u0627\u062f \u0645\u06cc \u06a9\u0646\u0645 \u0633\u0639\u06cc \u06a9\u0646\u06cc\u062f \u062a\u0639\u062f\u0627\u062f \u0644\u0627\u06cc\u0647 \u0647\u0627\u060c \u062a\u0639\u062f\u0627\u062f \u0646\u0648\u0631\u0648\u0646 \u0647\u0627 \u0648 \u062a\u0648\u0627\u0628\u0639 \u0641\u0639\u0627\u0644 \u0633\u0627\u0632\u06cc \u0631\u0627 \u0628\u0631\u0627\u06cc \u0647\u0631 \u0633\u0647 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0645\u0648\u0631\u062f \u0628\u062d\u062b \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u062a\u063a\u06cc\u06cc\u0631 \u062f\u0647\u06cc\u062f \u0648 \u0628\u0628\u06cc\u0646\u06cc\u062f \u06a9\u062f\u0627\u0645 \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0628\u0647\u062a\u0631\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u0628\u0631\u0627\u06cc \u0634\u0645\u0627 \u062f\u0627\u0631\u062f.<\/p>\n<h2 id=\"makingpredictionsonsingleinstance\"><span class=\"ez-toc-section\" id=\"%d9%be%db%8c%d8%b4%da%af%d9%88%db%8c%db%8c_%d8%b1%d9%88%db%8c_%d9%86%d9%85%d9%88%d9%86%d9%87_%d9%88%d8%a7%d8%ad%d8%af\"><\/span>\u067e\u06cc\u0634\u06af\u0648\u06cc\u06cc \u0631\u0648\u06cc \u0646\u0645\u0648\u0646\u0647 \u0648\u0627\u062d\u062f<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u06cc\u0646 \u0628\u062e\u0634 \u067e\u0627\u06cc\u0627\u0646\u06cc \u0645\u0642\u0627\u0644\u0647 \u0627\u0633\u062a \u0648 \u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u0631\u0648\u0634 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u0631\u0627 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u06cc\u062f \u0631\u0648\u06cc \u06cc\u06a9 \u0646\u0645\u0648\u0646\u0647 \u06cc\u0627 \u0627\u062d\u0633\u0627\u0633 \u0648\u0627\u062d\u062f.  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0647\u0631 \u0628\u0631\u0631\u0633\u06cc \u0631\u0627 \u0627\u0632 \u0645\u062c\u0645\u0648\u0639\u0647 \u062e\u0648\u062f \u0628\u0627\u0632\u06cc\u0627\u0628\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u0633\u067e\u0633 \u0633\u0639\u06cc \u06a9\u0646\u06cc\u0645 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0622\u0646 \u0631\u0627 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u06a9\u0646\u06cc\u0645.<\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u0628\u062a\u062f\u0627 \u0628\u0647 \u0637\u0648\u0631 \u062a\u0635\u0627\u062f\u0641\u06cc \u0647\u0631 \u0628\u0631\u0631\u0633\u06cc \u0631\u0627 \u0627\u0632 \u0645\u062c\u0645\u0648\u0639\u0647 \u062e\u0648\u062f \u0627\u0646\u062a\u062e\u0627\u0628 \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">instance = X(<span class=\"hljs-number\">57<\/span>)\n<span class=\"hljs-built_in\">print<\/span>(instance)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">I laughed all the way through this rotten movie It so unbelievable woman leaves her husband after many years of marriage has breakdown in front of real estate office What happens The office manager comes outside and offers her job Hilarious Next thing you know the two women are going at it Yep they re lesbians Nothing rings true in this Lifetime for Women with nothing better to do movie Clunky dialogue like don want to spend the rest of my life feeling like had chance to be happy and didn take it doesn help There a wealthy distant mother who disapproves of her daughter new relationship sassy black maid unbelievable that in the year film gets made in which there a sassy black maid Hattie McDaniel must be turning in her grave The woman has husband who freaks out and wants custody of the snotty teenage kids Sheesh No cliche is left unturned\n<\/code><\/pre>\n<p>\u0634\u0645\u0627 \u0628\u0647 \u0648\u0636\u0648\u062d \u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0627\u06cc\u0646 \u06cc\u06a9 \u0628\u0631\u0631\u0633\u06cc \u0645\u0646\u0641\u06cc \u0627\u0633\u062a.  \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0627\u062d\u0633\u0627\u0633 \u0627\u06cc\u0646 \u0628\u0631\u0631\u0633\u06cc\u060c \u0628\u0627\u06cc\u062f \u0627\u06cc\u0646 \u0628\u0631\u0631\u0633\u06cc \u0631\u0627 \u0628\u0647 \u0634\u06a9\u0644 \u0639\u062f\u062f\u06cc \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u06cc\u0645.  \u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 <code>tokenizer<\/code> \u06a9\u0647 \u062f\u0631 \u0642\u0633\u0645\u062a word embedding \u0627\u06cc\u062c\u0627\u062f \u06a9\u0631\u062f\u06cc\u0645.  \u0627\u06cc\u0646 <code>text_to_sequences<\/code> \u0631\u0648\u0634 \u062c\u0645\u0644\u0647 \u0631\u0627 \u0628\u0647 \u0647\u0645\u062a\u0627\u06cc \u0639\u062f\u062f\u06cc \u062e\u0648\u062f \u062a\u0628\u062f\u06cc\u0644 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u060c \u0628\u0627\u06cc\u062f \u062f\u0646\u0628\u0627\u0644\u0647 \u0648\u0631\u0648\u062f\u06cc \u062e\u0648\u062f \u0631\u0627 \u0645\u0627\u0646\u0646\u062f \u067e\u06cc\u06a9\u0631\u0647 \u062e\u0648\u062f \u0627\u0636\u0627\u0641\u0647 \u06a9\u0646\u06cc\u0645.  \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u0632 <code>predict<\/code> \u0631\u0648\u0634 \u0645\u062f\u0644 \u0645\u0627 \u0648 \u0622\u0646 \u0631\u0627 \u0628\u0647 \u062f\u0646\u0628\u0627\u0644\u0647 \u0648\u0631\u0648\u062f\u06cc \u067e\u0631\u062f\u0627\u0632\u0634 \u0634\u062f\u0647 \u0645\u0627 \u0627\u0631\u0633\u0627\u0644 \u06a9\u0646\u06cc\u062f.  \u0628\u0647 \u06a9\u062f \u0632\u06cc\u0631 \u0646\u06af\u0627\u0647 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">instance = tokenizer.texts_to_sequences(instance)\n\nflat_list = ()\n<span class=\"hljs-keyword\">for<\/span> sublist <span class=\"hljs-keyword\">in<\/span> instance:\n    <span class=\"hljs-keyword\">for<\/span> item <span class=\"hljs-keyword\">in<\/span> sublist:\n        flat_list.append(item)\n\nflat_list = (flat_list)\n\ninstance = pad_sequences(flat_list, padding=<span class=\"hljs-string\">'post'<\/span>, maxlen=maxlen)\n\nmodel.predict(instance)\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0628\u0647 \u0634\u06a9\u0644 \u0632\u06cc\u0631 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">array(((0.3304276)), dtype=float32)\n<\/code><\/pre>\n<p>\u0628\u0647 \u06cc\u0627\u062f \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f\u060c \u0645\u0627 \u062e\u0631\u0648\u062c\u06cc \u0647\u0627\u06cc \u0645\u062b\u0628\u062a \u0631\u0627 \u0628\u0647 1 \u0648 \u062e\u0631\u0648\u062c\u06cc \u0647\u0627\u06cc \u0645\u0646\u0641\u06cc \u0631\u0627 \u0628\u0647 0 \u0646\u06af\u0627\u0634\u062a \u06a9\u0631\u062f\u06cc\u0645. \u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u062a\u0627\u0628\u0639 \u0633\u06cc\u06af\u0645\u0648\u0626\u06cc\u062f \u0645\u0642\u0627\u062f\u06cc\u0631 \u0634\u0646\u0627\u0648\u0631 \u0628\u06cc\u0646 0 \u0648 1 \u0631\u0627 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0645\u06cc \u06a9\u0646\u062f. \u0627\u06af\u0631 \u0645\u0642\u062f\u0627\u0631 \u06a9\u0645\u062a\u0631 \u0627\u0632 0.5 \u0628\u0627\u0634\u062f\u060c \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0645\u0646\u0641\u06cc \u062f\u0631 \u0646\u0638\u0631 \u06af\u0631\u0641\u062a\u0647 \u0645\u06cc \u0634\u0648\u062f \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u0627\u06af\u0631 \u0645\u0642\u062f\u0627\u0631 \u0628\u06cc\u0634\u062a\u0631 \u0627\u0632 0.5 \u0628\u0627\u0634\u062f. \u060c \u0627\u06cc\u0646 \u0627\u062d\u0633\u0627\u0633 \u0645\u062b\u0628\u062a \u062f\u0631 \u0646\u0638\u0631 \u06af\u0631\u0641\u062a\u0647 \u0645\u06cc \u0634\u0648\u062f.  \u0645\u0642\u062f\u0627\u0631 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0628\u0631\u0627\u06cc \u0646\u0645\u0648\u0646\u0647 \u0648\u0627\u062d\u062f \u0645\u0627 0.33 \u0627\u0633\u062a \u06a9\u0647 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0645\u0627 \u0645\u0646\u0641\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0645\u06cc \u0634\u0648\u062f\u060c \u06a9\u0647 \u062f\u0631 \u0648\u0627\u0642\u0639 \u0647\u0645\u06cc\u0646\u0637\u0648\u0631 \u0627\u0633\u062a.<\/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>\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0627\u06cc\u062c \u062a\u0631\u06cc\u0646 \u06a9\u0627\u0631\u0647\u0627\u06cc \u067e\u0631\u062f\u0627\u0632\u0634 \u0632\u0628\u0627\u0646 \u0637\u0628\u06cc\u0639\u06cc \u0627\u0633\u062a.  \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0631\u0648\u0634 \u0627\u0646\u062c\u0627\u0645 \u062a\u062d\u0644\u06cc\u0644 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0631\u0627 \u062f\u06cc\u062f\u06cc\u0645 \u06a9\u0647 \u0646\u0648\u0639\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 Keras \u0627\u0633\u062a.  \u0645\u0627 \u0627\u0632 \u0633\u0647 \u0646\u0648\u0639 \u0645\u062e\u062a\u0644\u0641 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0628\u0631\u0627\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0639\u0645\u0648\u0645\u06cc \u062f\u0631 \u0645\u0648\u0631\u062f \u0641\u06cc\u0644\u0645 \u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f\u06cc\u0645.  \u0646\u062a\u0627\u06cc\u062c \u0646\u0634\u0627\u0646 \u0645\u06cc\u200c\u062f\u0647\u062f \u06a9\u0647 LSTM\u060c \u06a9\u0647 \u0646\u0648\u0639\u06cc \u0627\u0632 RNN \u0627\u0633\u062a\u060c \u0647\u0645 \u0627\u0632 CNN \u0648 \u0647\u0645 \u0627\u0632 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0633\u0627\u062f\u0647 \u0628\u0647\u062a\u0631 \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 1403-01-21 07:09:03<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;16137&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\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc NLP: \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0641\u06cc\u0644\u0645 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u062f\u0631 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\"> 14<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span>\u0627\u06cc\u0646 \u0647\u0641\u062f\u0647\u0645\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0632 \u0633\u0631\u06cc \u0645\u0642\u0627\u0644\u0627\u062a \u0645\u0646 \u0627\u0633\u062a \u0631\u0648\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc NLP. \u062f\u0631 \u0622\u062e\u0631\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647\u060c \u0628\u062d\u062b \u062e\u0648\u062f \u0631\u0627 \u062f\u0631 \u0645\u0648\u0631\u062f \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0631\u0627\u06cc \u067e\u0631\u062f\u0627\u0632\u0634 \u0632\u0628\u0627\u0646 \u0637\u0628\u06cc\u0639\u06cc \u0622\u063a\u0627\u0632 \u06a9\u0631\u062f\u06cc\u0645. \u0645\u0642\u0627\u0644\u0647 \u0642\u0628\u0644\u06cc \u0639\u0645\u062f\u062a\u0627\u064b \u0628\u0631 \u0631\u0648\u06cc \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0644\u0645\u0627\u062a \u0645\u062a\u0645\u0631\u06a9\u0632 \u0628\u0648\u062f\u060c \u062c\u0627\u06cc\u06cc \u06a9\u0647 \u062f\u06cc\u062f\u06cc\u0645 \u0686\u06af\u0648\u0646\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0627\u0632 \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0644\u0645\u0627\u062a \u0628\u0631\u0627\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0645\u062a\u0646 \u0628\u0647 \u06cc\u06a9 \u0628\u0631\u062f\u0627\u0631 \u0645\u062a\u0631\u0627\u06a9\u0645 \u0645\u062a\u0646\u0627\u0638\u0631 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 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