{"id":13926,"date":"2024-01-03T13:30:20","date_gmt":"2024-01-03T10:00:20","guid":{"rendered":"https:\/\/rasanegar.com\/blog\/randaugment-%d8%a8%d8%b1%d8%a7%db%8c-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d8%aa%d8%b5%d9%88%db%8c%d8%b1-%d8%a8%d8%a7-keras-tensorflow\/"},"modified":"2024-01-03T13:30:20","modified_gmt":"2024-01-03T10:00:20","slug":"randaugment-%d8%a8%d8%b1%d8%a7%db%8c-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d8%aa%d8%b5%d9%88%db%8c%d8%b1-%d8%a8%d8%a7-keras-tensorflow","status":"publish","type":"post","link":"https:\/\/rasanegaar.com\/blog\/randaugment-%d8%a8%d8%b1%d8%a7%db%8c-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d8%aa%d8%b5%d9%88%db%8c%d8%b1-%d8%a8%d8%a7-keras-tensorflow\/","title":{"rendered":"RandAugment \u0628\u0631\u0627\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u062a\u0635\u0648\u06cc\u0631 \u0628\u0627 Keras\/TensorFlow"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_86 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\/randaugment-%d8%a8%d8%b1%d8%a7%db%8c-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d8%aa%d8%b5%d9%88%db%8c%d8%b1-%d8%a8%d8%a7-keras-tensorflow\/#%d8%a7%d9%86%d9%88%d8%a7%d8%b9_%d8%a7%d9%81%d8%b2%d8%a7%db%8c%d8%b4\" >\u0627\u0646\u0648\u0627\u0639 \u0627\u0641\u0632\u0627\u06cc\u0634<\/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\/randaugment-%d8%a8%d8%b1%d8%a7%db%8c-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d8%aa%d8%b5%d9%88%db%8c%d8%b1-%d8%a8%d8%a7-keras-tensorflow\/#kerascv_%d9%88_randaugment\" >KerasCV \u0648 RandAugment<\/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\/randaugment-%d8%a8%d8%b1%d8%a7%db%8c-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d8%aa%d8%b5%d9%88%db%8c%d8%b1-%d8%a8%d8%a7-keras-tensorflow\/#%d8%a2%d9%85%d9%88%d8%b2%d8%b4_classifier_%d8%a8%d8%a7_%d9%88_%d8%a8%d8%af%d9%88%d9%86_randaugment\" >\u0622\u0645\u0648\u0632\u0634 Classifier \u0628\u0627 \u0648 \u0628\u062f\u0648\u0646 RandAugment<\/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\/randaugment-%d8%a8%d8%b1%d8%a7%db%8c-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d8%aa%d8%b5%d9%88%db%8c%d8%b1-%d8%a8%d8%a7-keras-tensorflow\/#%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\"> 6<\/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\u0641\u0632\u0627\u06cc\u0634 \u062f\u0627\u062f\u0647 \u0628\u0631\u0627\u06cc \u0645\u062f\u062a \u0637\u0648\u0644\u0627\u0646\u06cc \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0648\u0633\u06cc\u0644\u0647 \u0627\u06cc \u0628\u0631\u0627\u06cc \u062c\u0627\u06cc\u06af\u0632\u06cc\u0646\u06cc \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 &#8220;\u0627\u06cc\u0633\u062a\u0627&#8221; \u0628\u0627 \u0627\u0646\u0648\u0627\u0639 \u062a\u0628\u062f\u06cc\u0644 \u0634\u062f\u0647 \u0639\u0645\u0644 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u062a\u063a\u06cc\u06cc\u0631 \u0646\u0627\u067e\u0630\u06cc\u0631\u06cc \u0631\u0627 \u062a\u0642\u0648\u06cc\u062a \u0645\u06cc \u06a9\u0646\u062f. <em>\u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646 (CNN)<\/em>\u060c \u0648 \u0645\u0639\u0645\u0648\u0644\u0627\u064b \u0645\u0646\u062c\u0631 \u0628\u0647 \u0627\u0633\u062a\u062d\u06a9\u0627\u0645 \u0648\u0631\u0648\u062f\u06cc \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<div class=\"alert alert-note\">\n<div class=\"flex\">\n<div class=\"flex-shrink-0 mr-3\"><\/div>\n<div class=\"w-full\">\n<p><strong>\u062a\u0648\u062c\u0647 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f:<\/strong> \u062a\u063a\u06cc\u06cc\u0631 \u0646\u0627\u067e\u0630\u06cc\u0631\u06cc \u0628\u0647 \u06a9\u0648\u0631 \u06a9\u0631\u062f\u0646 \u0645\u062f\u0644 \u0647\u0627 \u0646\u0633\u0628\u062a \u0628\u0647 \u0627\u062e\u062a\u0644\u0627\u0644\u0627\u062a \u062e\u0627\u0635 \u062f\u0631 \u0647\u0646\u06af\u0627\u0645 \u062a\u0635\u0645\u06cc\u0645 \u06af\u06cc\u0631\u06cc \u062e\u0644\u0627\u0635\u0647 \u0645\u06cc \u0634\u0648\u062f.  \u0627\u06af\u0631 \u0622\u0646 \u0631\u0627 \u0622\u06cc\u0646\u0647 \u06a9\u0646\u06cc\u062f \u06cc\u0627 \u0628\u0686\u0631\u062e\u0627\u0646\u06cc\u062f\u060c \u062a\u0635\u0648\u06cc\u0631 \u06af\u0631\u0628\u0647 \u0647\u0645\u0686\u0646\u0627\u0646 \u062a\u0635\u0648\u06cc\u0631\u06cc \u0627\u0632 \u06af\u0631\u0628\u0647 \u0627\u0633\u062a.<\/p>\n<\/p><\/div><\/div><\/div>\n<p>\u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u0627\u0641\u0632\u0627\u06cc\u0634 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0647 \u0634\u06a9\u0644\u06cc \u06a9\u0647 \u0627\u0632 \u0622\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0631\u062f\u06cc\u0645\u060c a \u0631\u0627 \u0631\u0645\u0632\u06af\u0630\u0627\u0631\u06cc \u0645\u06cc \u06a9\u0646\u062f <em>\u0639\u062f\u0645<\/em> \u062f\u0627\u0646\u0634 \u062f\u0631 \u0645\u0648\u0631\u062f \u0648\u0627\u0631\u06cc\u0627\u0646\u0633 \u062a\u0631\u062c\u0645\u0647\u060c \u06a9\u0647 \u0628\u0631\u0627\u06cc \u062a\u0634\u062e\u06cc\u0635 \u0634\u06cc\u060c \u062a\u0642\u0633\u06cc\u0645 \u0628\u0646\u062f\u06cc \u0645\u0639\u0646\u0627\u06cc\u06cc \u0648 \u0646\u0645\u0648\u0646\u0647 \u0648 \u063a\u06cc\u0631\u0647 \u0645\u0647\u0645 \u0627\u0633\u062a &#8211; <em>\u062a\u063a\u06cc\u06cc\u0631 \u0646\u0627\u067e\u0630\u06cc\u0631\u06cc<\/em> \u0627\u063a\u0644\u0628 \u0627\u0648\u0642\u0627\u062a \u0628\u0631\u0627\u06cc \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc \u0645\u0637\u0644\u0648\u0628 \u0627\u0633\u062a\u060c \u0648 \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646\u060c \u062a\u0642\u0648\u06cc\u062a \u0628\u0647 \u0637\u0648\u0631 \u0645\u0639\u0645\u0648\u0644 \u0648 \u062a\u0647\u0627\u062c\u0645\u06cc\u200c\u062a\u0631 \u0628\u0631\u0627\u06cc \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc \u0627\u0639\u0645\u0627\u0644 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/p>\n<h2 id=\"typesofaugmentation\"><span class=\"ez-toc-section\" id=\"%d8%a7%d9%86%d9%88%d8%a7%d8%b9_%d8%a7%d9%81%d8%b2%d8%a7%db%8c%d8%b4\"><\/span>\u0627\u0646\u0648\u0627\u0639 \u0627\u0641\u0632\u0627\u06cc\u0634<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u0641\u0632\u0627\u06cc\u0634 \u0628\u0633\u06cc\u0627\u0631 \u0633\u0627\u062f\u0647 \u0634\u0631\u0648\u0639 \u0634\u062f &#8211; \u0686\u0631\u062e\u0634 \u0647\u0627\u06cc \u06a9\u0648\u0686\u06a9\u060c \u0686\u0631\u062e\u0634 \u0647\u0627\u06cc \u0627\u0641\u0642\u06cc \u0648 \u0639\u0645\u0648\u062f\u06cc\u060c \u06a9\u0646\u062a\u0631\u0627\u0633\u062a \u06cc\u0627 \u0646\u0648\u0633\u0627\u0646\u0627\u062a \u0631\u0648\u0634\u0646\u0627\u06cc\u06cc \u0648 \u063a\u06cc\u0631\u0647. \u062f\u0631 \u0633\u0627\u0644 \u0647\u0627\u06cc \u0627\u062e\u06cc\u0631\u060c \u0631\u0648\u0634 \u0647\u0627\u06cc \u067e\u06cc\u0686\u06cc\u062f\u0647 \u062a\u0631\u06cc \u0627\u0628\u062f\u0627\u0639 \u0634\u062f\u0647 \u0627\u0633\u062a\u060c \u0627\u0632 \u062c\u0645\u0644\u0647 <em>CutOut<\/em> (\u0627\u0641\u062a\u0627\u062f\u06af\u06cc \u0641\u0636\u0627\u06cc\u06cc \u0628\u0627 \u0645\u0639\u0631\u0641\u06cc \u0645\u0631\u0628\u0639 \u0647\u0627\u06cc \u0633\u06cc\u0627\u0647 \u0628\u0647 \u0635\u0648\u0631\u062a \u062a\u0635\u0627\u062f\u0641\u06cc \u062f\u0631 \u062a\u0635\u0627\u0648\u06cc\u0631 \u0648\u0631\u0648\u062f\u06cc) \u0648 <em>\u0645\u062e\u0644\u0648\u0637 \u06a9\u0631\u062f\u0646<\/em> (\u062a\u0631\u06a9\u06cc\u0628 \u0628\u062e\u0634\u06cc \u0627\u0632 \u062a\u0635\u0627\u0648\u06cc\u0631 \u0648 \u0628\u0647 \u0631\u0648\u0632 \u0631\u0633\u0627\u0646\u06cc \u0646\u0633\u0628\u062a \u0628\u0631\u0686\u0633\u0628)\u060c \u0648 \u062a\u0631\u06a9\u06cc\u0628 \u0622\u0646\u0647\u0627 &#8211; <em>\u06a9\u0627\u062a \u0645\u06cc\u06a9\u0633<\/em>.  \u0631\u0648\u0634\u200c\u0647\u0627\u06cc \u062c\u062f\u06cc\u062f\u062a\u0631 \u0627\u0641\u0632\u0627\u06cc\u0634 \u062f\u0631 \u0648\u0627\u0642\u0639 \u0628\u0631\u0686\u0633\u0628\u200c\u0647\u0627 \u0631\u0627 \u062f\u0631 \u0646\u0638\u0631 \u0645\u06cc\u200c\u06af\u06cc\u0631\u0646\u062f\u060c \u0648 \u0631\u0648\u0634\u200c\u0647\u0627\u06cc\u06cc \u0645\u0627\u0646\u0646\u062f CutMix \u0646\u0633\u0628\u062a \u0628\u0631\u0686\u0633\u0628\u200c\u0647\u0627 \u0631\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u0645\u06cc\u200c\u062f\u0647\u0646\u062f \u062a\u0627 \u0628\u0631\u0627\u0628\u0631 \u0628\u0627 \u0646\u0633\u0628\u062a \u062a\u0635\u0648\u06cc\u0631 \u06af\u0631\u0641\u062a\u0647 \u0634\u062f\u0647 \u062a\u0648\u0633\u0637 \u0628\u062e\u0634\u200c\u0647\u0627\u06cc\u06cc \u0627\u0632 \u0647\u0631 \u06a9\u0644\u0627\u0633 \u062f\u0631 \u062d\u0627\u0644 \u0645\u062e\u0644\u0648\u0637 \u0634\u062f\u0646 \u0628\u0627\u0634\u062f.<\/p>\n<p>\u0628\u0627 \u0641\u0647\u0631\u0633\u062a \u0631\u0648 \u0628\u0647 \u0631\u0634\u062f\u06cc \u0627\u0632 \u0627\u0641\u0632\u0627\u06cc\u0634\u200c\u0647\u0627\u06cc \u0627\u062d\u062a\u0645\u0627\u0644\u06cc\u060c \u0628\u0631\u062e\u06cc \u0634\u0631\u0648\u0639 \u0628\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0622\u0646\u200c\u0647\u0627 \u0628\u0647\u200c\u0637\u0648\u0631 \u062a\u0635\u0627\u062f\u0641\u06cc (\u06cc\u0627 \u062d\u062f\u0627\u0642\u0644 \u0628\u0631\u062e\u06cc \u0627\u0632 \u0632\u06cc\u0631\u0645\u062c\u0645\u0648\u0639\u0647\u200c\u0647\u0627\u06cc \u0622\u0646\u200c\u0647\u0627) \u06a9\u0631\u062f\u0647\u200c\u0627\u0646\u062f\u060c \u0628\u0627 \u0627\u06cc\u0646 \u0627\u06cc\u062f\u0647 \u06a9\u0647 \u0645\u062c\u0645\u0648\u0639\u0647\u200c\u0627\u06cc \u062a\u0635\u0627\u062f\u0641\u06cc \u0627\u0632 \u062a\u0642\u0648\u06cc\u062a\u200c\u0647\u0627\u060c \u0627\u0633\u062a\u062d\u06a9\u0627\u0645 \u0645\u062f\u0644\u200c\u0647\u0627 \u0631\u0627 \u062a\u0642\u0648\u06cc\u062a \u0645\u06cc\u200c\u06a9\u0646\u062f \u0648 \u0645\u062c\u0645\u0648\u0639\u0647 \u0627\u0635\u0644\u06cc \u0631\u0627 \u0628\u0627 \u0645\u062c\u0645\u0648\u0639\u0647\u200c\u0627\u06cc \u0628\u0633\u06cc\u0627\u0631 \u0628\u0632\u0631\u06af\u200c\u062a\u0631 \u062c\u0627\u06cc\u06af\u0632\u06cc\u0646 \u0645\u06cc\u200c\u06a9\u0646\u062f. \u0641\u0636\u0627\u06cc \u062a\u0635\u0627\u0648\u06cc\u0631 \u0648\u0631\u0648\u062f\u06cc  \u0627\u06cc\u0646\u062c\u0627\u0633\u062a \u06a9\u0647 <code>RandAugment<\/code> \u0644\u06af\u062f \u0648\u0627\u0631\u062f \u0645\u06cc \u06a9\u0646\u062f!<\/p>\n<h2 id=\"kerascvandrandaugment\"><span class=\"ez-toc-section\" id=\"kerascv_%d9%88_randaugment\"><\/span>KerasCV \u0648 RandAugment<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><a target=\"_blank\" rel=\"nofollow noopener noreferrer\" href=\"https:\/\/github.com\/keras-team\/keras-cv\">KerasCV<\/a> \u06cc\u06a9 \u0628\u0633\u062a\u0647 \u062c\u062f\u0627\u06af\u0627\u0646\u0647 \u0627\u0633\u062a\u060c \u0627\u0645\u0627 \u0647\u0645\u0686\u0646\u0627\u0646 \u06cc\u06a9 \u0627\u0636\u0627\u0641\u0647 \u0631\u0633\u0645\u06cc \u0628\u0647 Keras \u0627\u0633\u062a \u06a9\u0647 \u062a\u0648\u0633\u0637 \u062a\u06cc\u0645 Keras \u062a\u0648\u0633\u0639\u0647 \u06cc\u0627\u0641\u062a\u0647 \u0627\u0633\u062a.  \u0627\u06cc\u0646 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0647\u0645\u0627\u0646 \u0645\u0642\u062f\u0627\u0631 \u062c\u0644\u0627 \u0648 \u0648\u0636\u0648\u062d \u0628\u0633\u062a\u0647 \u0627\u0635\u0644\u06cc \u0631\u0627 \u062f\u0627\u0631\u062f\u060c \u0627\u0645\u0627 \u0628\u0627 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0645\u0639\u0645\u0648\u0644\u06cc Keras \u0648 \u0644\u0627\u06cc\u0647\u200c\u0647\u0627\u06cc \u0622\u0646\u200c\u0647\u0627 \u0646\u06cc\u0632 \u0628\u06cc\u200c\u0646\u0638\u06cc\u0631 \u0627\u062f\u063a\u0627\u0645 \u0645\u06cc\u200c\u0634\u0648\u062f.  \u062a\u0646\u0647\u0627 \u062a\u0641\u0627\u0648\u062a\u06cc \u06a9\u0647 \u062a\u0627 \u0628\u0647 \u062d\u0627\u0644 \u0645\u062a\u0648\u062c\u0647 \u062e\u0648\u0627\u0647\u06cc\u062f \u0634\u062f \u062a\u0645\u0627\u0633 \u06af\u0631\u0641\u062a\u0646 \u0627\u0633\u062a <code>keras_cv.layers...<\/code> \u0628\u062c\u0627\u06cc <code>keras.layers...<\/code>.<\/p>\n<p>KerasCV \u0647\u0646\u0648\u0632 \u062f\u0631 \u062d\u0627\u0644 \u062a\u0648\u0633\u0639\u0647 \u0627\u0633\u062a \u0648 \u062f\u0631 \u062d\u0627\u0644 \u062d\u0627\u0636\u0631 \u0634\u0627\u0645\u0644 27 \u0644\u0627\u06cc\u0647 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u062c\u062f\u06cc\u062f \u0627\u0633\u062a. <code>RandAugment<\/code>\u060c <code>CutMix<\/code>\u060c \u0648 <code>MixUp<\/code> \u0628\u0631\u062e\u06cc \u0627\u0632 \u0622\u0646\u0647\u0627 \u0628\u0648\u062f\u0646  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 \u0622\u0646\u0686\u0647 \u0628\u0647 \u0646\u0638\u0631 \u0645\u06cc \u0631\u0633\u062f \u0627\u0639\u0645\u0627\u0644 \u0645\u06cc \u0634\u0648\u062f <code>RandAugment<\/code> \u0628\u0647 \u062a\u0635\u0627\u0648\u06cc\u0631\u060c \u0648 \u0627\u06cc\u0646\u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u06cc\u06a9 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u06a9\u0646\u0646\u062f\u0647 \u0631\u0627 \u0628\u0627 \u0648 \u0628\u062f\u0648\u0646 \u0627\u0641\u0632\u0627\u06cc\u0634 \u062a\u0635\u0627\u062f\u0641\u06cc \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645.<\/p>\n<p>\u0627\u0628\u062a\u062f\u0627 \u0646\u0635\u0628 \u06a9\u0646\u06cc\u062f <code>keras_cv<\/code>:<\/p>\n<pre><code class=\"hljs\">$ pip install keras_cv\n<\/code><\/pre>\n<div class=\"alert alert-note\">\n<div class=\"flex\">\n<div class=\"flex-shrink-0 mr-3\"><\/div>\n<div class=\"w-full\">\n<p><strong>\u062a\u0648\u062c\u0647 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f:<\/strong> KerasCV \u0628\u0631\u0627\u06cc \u06a9\u0627\u0631 \u06a9\u0631\u062f\u0646 \u0628\u0647 TensorFlow 2.9 \u0646\u06cc\u0627\u0632 \u062f\u0627\u0631\u062f.  \u0627\u06af\u0631 \u0642\u0628\u0644\u0627\u064b \u0622\u0646 \u0631\u0627 \u0646\u062f\u0627\u0631\u06cc\u062f\u060c \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f <code>$ pip install -U tensorflow<\/code> \u0627\u0648\u0644\u06cc\u0646.<\/p>\n<\/p><\/div><\/div><\/div>\n<p>\u062d\u0627\u0644\u0627 \u0648\u0642\u062a\u0647 import TensorFlow\u060c Keras \u0648 KerasCV\u060c \u062f\u0631 \u06a9\u0646\u0627\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc TensorFlow \u0628\u0631\u0627\u06cc \u062f\u0633\u062a\u0631\u0633\u06cc \u0622\u0633\u0627\u0646 \u0628\u0647 Imagenette:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> tensorflow <span class=\"hljs-keyword\">as<\/span> tf\n<span class=\"hljs-keyword\">from<\/span> tensorflow <span class=\"hljs-keyword\">import<\/span> keras\n<span class=\"hljs-keyword\">import<\/span> keras_cv\n<span class=\"hljs-keyword\">import<\/span> tensorflow_datasets <span class=\"hljs-keyword\">as<\/span> tfds\n<\/code><\/pre>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u06cc\u06a9 \u062a\u0635\u0648\u06cc\u0631 \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u0622\u0646 \u0631\u0627 \u0628\u0647 \u0634\u06a9\u0644 \u0627\u0635\u0644\u06cc \u0646\u0645\u0627\u06cc\u0634 \u062f\u0647\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n<span class=\"hljs-keyword\">import<\/span> cv2\n\ncat_img = cv2.cvtColor(cv2.imread(<span class=\"hljs-string\">'cat.jpg'<\/span>), cv2.COLOR_BGR2RGB)\ncat_img = cv2.resize(cat_img, (<span class=\"hljs-number\">224<\/span>, <span class=\"hljs-number\">224<\/span>))\nplt.imshow(cat_img)\n<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/randaugment-for-image-classification-with-keras-and-tensorflow-1.png\" alt=\"\" title=\"\"><\/p>\n<p>\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u062f\u0631\u062e\u0648\u0627\u0633\u062a \u06a9\u0646\u06cc\u0645 <code>RandAugment<\/code> \u0628\u0647 \u0622\u0646\u060c \u0686\u0646\u062f\u06cc\u0646 \u0628\u0627\u0631 \u0648 \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 \u0646\u062a\u0627\u06cc\u062c:<\/p>\n<pre><code class=\"hljs\">fig = plt.figure(figsize=(<span class=\"hljs-number\">10<\/span>,<span class=\"hljs-number\">10<\/span>))\n<span class=\"hljs-keyword\">for<\/span> i <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(<span class=\"hljs-number\">16<\/span>):\n    ax = fig.add_subplot(<span class=\"hljs-number\">4<\/span>,<span class=\"hljs-number\">4<\/span>,i+<span class=\"hljs-number\">1<\/span>)\n    aug_img = keras_cv.layers.RandAugment(value_range=(<span class=\"hljs-number\">0<\/span>, <span class=\"hljs-number\">255<\/span>))(cat_img)\n    \n    ax.imshow(aug_img.numpy().astype(<span class=\"hljs-string\">'int'<\/span>))\n<\/code><\/pre>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/randaugment-for-image-classification-with-keras-and-tensorflow-2.png\" alt=\"\" title=\"\"><\/p>\n<p>\u0644\u0627\u06cc\u0647 \u062f\u0627\u0631\u0627\u06cc \u06cc\u06a9 <code>magnitude<\/code> \u0622\u0631\u06af\u0648\u0645\u0627\u0646\u060c \u06a9\u0647 \u0628\u0647 \u0637\u0648\u0631 \u067e\u06cc\u0634 \u0641\u0631\u0636 \u0628\u0647 <code>0.5<\/code> \u0648 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0622\u0646 \u0631\u0627 \u0628\u0631\u0627\u06cc \u0627\u0641\u0632\u0627\u06cc\u0634 \u06cc\u0627 \u06a9\u0627\u0647\u0634 \u0627\u062b\u0631 \u0627\u0641\u0632\u0627\u06cc\u0634 \u062a\u063a\u06cc\u06cc\u0631 \u062f\u0627\u062f:<\/p>\n<pre><code class=\"hljs\">fig = plt.figure(figsize=(<span class=\"hljs-number\">10<\/span>,<span class=\"hljs-number\">10<\/span>))\n<span class=\"hljs-keyword\">for<\/span> i <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">range<\/span>(<span class=\"hljs-number\">16<\/span>):\n    ax = fig.add_subplot(<span class=\"hljs-number\">4<\/span>,<span class=\"hljs-number\">4<\/span>,i+<span class=\"hljs-number\">1<\/span>)\n    aug_img = keras_cv.layers.RandAugment(value_range=(<span class=\"hljs-number\">0<\/span>, <span class=\"hljs-number\">255<\/span>), magnitude=<span class=\"hljs-number\">0.1<\/span>)(cat_img)\n    ax.imshow(aug_img.numpy().astype(<span class=\"hljs-string\">'int'<\/span>))\n<\/code><\/pre>\n<p>\u0648\u0642\u062a\u06cc \u0631\u0648\u06cc \u0645\u0642\u062f\u0627\u0631 \u06a9\u0645 \u062a\u0646\u0638\u06cc\u0645 \u0645\u06cc \u0634\u0648\u062f \u0645\u0627\u0646\u0646\u062f <code>0.1<\/code> &#8211; \u0627\u0641\u0632\u0627\u06cc\u0634 \u0628\u0633\u06cc\u0627\u0631 \u062a\u0647\u0627\u062c\u0645\u06cc \u06a9\u0645\u062a\u0631\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/randaugment-for-image-classification-with-keras-and-tensorflow-3.png\" alt=\"\" title=\"\"><\/p>\n<p>\u0644\u0627\u06cc\u0647 \u0628\u0648\u062f\u0646 &#8211; \u0645\u06cc \u062a\u0648\u0627\u0646 \u0627\u0632 \u0622\u0646 \u062f\u0631 \u0645\u062f\u0644 \u0647\u0627 \u06cc\u0627 \u062f\u0631 \u062f\u0627\u062e\u0644 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f <code>tf.data<\/code> \u062e\u0637\u0648\u0637 \u0644\u0648\u0644\u0647 \u0647\u0646\u06af\u0627\u0645 \u0627\u06cc\u062c\u0627\u062f \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627  \u0627\u06cc\u0646 \u0628\u0627\u0639\u062b \u0645\u06cc \u0634\u0648\u062f <code>RandAugment<\/code> \u0628\u0633\u06cc\u0627\u0631 \u0627\u0646\u0639\u0637\u0627\u0641 \u067e\u0630\u06cc\u0631!  \u0627\u0633\u062a\u062f\u0644\u0627\u0644 \u0647\u0627\u06cc \u0627\u0636\u0627\u0641\u06cc \u0647\u0633\u062a\u0646\u062f <code>augmentations_per_image<\/code> \u0648 <code>rate<\/code> \u0627\u0633\u062a\u062f\u0644\u0627\u0644 \u0647\u0627\u06cc\u06cc \u06a9\u0647 \u0628\u0627 \u0647\u0645 \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u0646\u062f.<\/p>\n<p>\u0628\u0631\u0627\u06cc <code>0...augmentations_per_image<\/code>\u060c \u0644\u0627\u06cc\u0647 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u062a\u0635\u0627\u062f\u0641\u06cc \u0628\u0647 \u062e\u0637 \u0644\u0648\u0644\u0647 \u0627\u0636\u0627\u0641\u0647 \u0645\u06cc \u06a9\u0646\u062f \u062a\u0627 \u0631\u0648\u06cc \u06cc\u06a9 \u062a\u0635\u0648\u06cc\u0631 \u0627\u0639\u0645\u0627\u0644 \u0634\u0648\u062f.  \u062f\u0631 \u0645\u0648\u0631\u062f \u067e\u06cc\u0634 \u0641\u0631\u0636 <code>3<\/code> &#8211; \u0633\u0647 \u0639\u0645\u0644\u06cc\u0627\u062a \u0645\u062e\u062a\u0644\u0641 \u0628\u0647 \u062e\u0637 \u0644\u0648\u0644\u0647 \u0627\u0636\u0627\u0641\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a.  \u0633\u067e\u0633\u060c \u06cc\u06a9 \u0639\u062f\u062f \u062a\u0635\u0627\u062f\u0641\u06cc \u0628\u0631\u0627\u06cc \u0647\u0631 \u0627\u0641\u0632\u0627\u06cc\u0634 \u062f\u0631 \u062e\u0637 \u0644\u0648\u0644\u0647 \u0646\u0645\u0648\u0646\u0647 \u0628\u0631\u062f\u0627\u0631\u06cc \u0645\u06cc \u0634\u0648\u062f &#8211; \u0648 \u0627\u06af\u0631 \u06a9\u0645\u062a\u0631 \u0627\u0632 <code>rate<\/code> (\u067e\u06cc\u0634 \u0641\u0631\u0636 \u0628\u0647 \u0627\u0637\u0631\u0627\u0641 \u0627\u0633\u062a <code>0.9<\/code>) &#8211; \u0627\u0641\u0632\u0627\u06cc\u0634 \u0627\u0639\u0645\u0627\u0644 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<blockquote>\n<p>\u062f\u0631 \u0627\u0635\u0644 &#8211; \u0628\u0647 \u0627\u062d\u062a\u0645\u0627\u0644 90\u066a \u0647\u0631 \u0627\u0641\u0632\u0627\u06cc\u0634 (\u062a\u0635\u0627\u062f\u0641\u06cc) \u062f\u0631 \u062e\u0637 \u0644\u0648\u0644\u0647 \u0631\u0648\u06cc \u062a\u0635\u0648\u06cc\u0631 \u0627\u0639\u0645\u0627\u0644 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<\/blockquote>\n<p>\u0627\u06cc\u0646 \u0628\u0647 \u0637\u0648\u0631 \u0637\u0628\u06cc\u0639\u06cc \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0647\u0645\u0647 \u062a\u0642\u0648\u06cc\u062a\u200c\u0647\u0627 \u0646\u0628\u0627\u06cc\u062f \u0627\u0639\u0645\u0627\u0644 \u0634\u0648\u0646\u062f\u060c \u0628\u0647 \u062e\u0635\u0648\u0635 \u0627\u06af\u0631 \u0645\u06cc\u0632\u0627\u0646 \u0631\u0627 \u067e\u0627\u06cc\u06cc\u0646 \u0628\u06cc\u0627\u0648\u0631\u06cc\u062f <code>rate<\/code>.  \u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u062f \u0639\u0645\u0644\u06cc\u0627\u062a \u0645\u062c\u0627\u0632 \u0631\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 a \u0633\u0641\u0627\u0631\u0634\u06cc \u06a9\u0646\u06cc\u062f <code>RandomAugmentationPipeline<\/code> \u0644\u0627\u06cc\u0647\u060c \u06a9\u0647 <code>RandAugment<\/code> \u0645\u0648\u0631\u062f \u062e\u0627\u0635 \u0627\u0633\u062a.  \u0631\u0627\u0647\u0646\u0645\u0627\u06cc \u062c\u062f\u0627\u06af\u0627\u0646\u0647 \u0631\u0648\u06cc <code>RandomAugmentationPipeline<\/code>  \u0628\u0647 \u0632\u0648\u062f\u06cc \u0645\u0646\u062a\u0634\u0631 \u062e\u0648\u0627\u0647\u062f \u0634\u062f.<\/p>\n<h2 id=\"trainingaclassifierwithandwithoutrandaugment\"><span class=\"ez-toc-section\" id=\"%d8%a2%d9%85%d9%88%d8%b2%d8%b4_classifier_%d8%a8%d8%a7_%d9%88_%d8%a8%d8%af%d9%88%d9%86_randaugment\"><\/span>\u0622\u0645\u0648\u0632\u0634 Classifier \u0628\u0627 \u0648 \u0628\u062f\u0648\u0646 RandAugment<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0628\u0631\u0627\u06cc \u0633\u0627\u062f\u0647 \u0633\u0627\u0632\u06cc \u062c\u0646\u0628\u0647 \u0622\u0645\u0627\u062f\u0647 \u0633\u0627\u0632\u06cc\/\u0628\u0627\u0631\u06af\u06cc\u0631\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u0648 \u062a\u0645\u0631\u06a9\u0632 \u0631\u0648\u06cc <code>RandAugment<\/code>\u060c \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645 <code>tfds<\/code> \u0628\u0631\u0627\u06cc \u0628\u0627\u0631\u06af\u06cc\u0631\u06cc \u062f\u0631 \u0628\u062e\u0634\u06cc \u0627\u0632 Imagenette:<\/p>\n<pre><code class=\"hljs\">(train, valid_set, test_set), info = tfds.load(<span class=\"hljs-string\">\"imagenette\"<\/span>, \n                                           split=(<span class=\"hljs-string\">\"train(:70%)\"<\/span>, <span class=\"hljs-string\">\"validation\"<\/span>, <span class=\"hljs-string\">\"train(70%:)\"<\/span>),\n                                           as_supervised=<span class=\"hljs-literal\">True<\/span>, with_info=<span class=\"hljs-literal\">True<\/span>)\n\nclass_names = info.features(<span class=\"hljs-string\">\"label\"<\/span>).names\nn_classes = info.features(<span class=\"hljs-string\">\"label\"<\/span>).num_classes\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">f'Class names: <span class=\"hljs-subst\">{class_names}<\/span>'<\/span>) \n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">'Num of classes:'<\/span>, n_classes) \n\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Train set size:\"<\/span>, <span class=\"hljs-built_in\">len<\/span>(train)) \n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Test set size:\"<\/span>, <span class=\"hljs-built_in\">len<\/span>(test_set)) \n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Valid set size:\"<\/span>, <span class=\"hljs-built_in\">len<\/span>(valid_set)) \n<\/code><\/pre>\n<p>\u0645\u0627 \u0641\u0642\u0637 \u0628\u062e\u0634\u06cc \u0627\u0632 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0627 \u062f\u0631 \u0622\u0646 \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u06a9\u0631\u062f\u0647\u200c\u0627\u06cc\u0645 \u062a\u0627 \u062f\u0631 \u062f\u0648\u0631\u0647\u200c\u0647\u0627\u06cc \u06a9\u0645\u062a\u0631\u06cc \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647\u200c\u0647\u0627 \u0627\u0636\u0627\u0641\u0647 \u0634\u0648\u062f (\u062f\u0631 \u0648\u0627\u0642\u0639 \u0622\u0632\u0645\u0627\u06cc\u0634 \u0645\u0627 \u0633\u0631\u06cc\u0639\u200c\u062a\u0631 \u0627\u062c\u0631\u0627 \u0634\u0648\u062f).  \u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u062a\u0635\u0627\u0648\u06cc\u0631 \u062f\u0631 Imagenette \u0627\u0646\u062f\u0627\u0632\u0647 \u0647\u0627\u06cc \u0645\u062a\u0641\u0627\u0648\u062a\u06cc \u062f\u0627\u0631\u0646\u062f\u060c \u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f a \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645 <code>preprocess()<\/code> \u062a\u0627\u0628\u0639\u06cc \u06a9\u0647 \u0627\u0646\u062f\u0627\u0632\u0647 \u0622\u0646\u0647\u0627 \u0631\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u0645\u06cc \u062f\u0647\u062f \u062a\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u0628\u0627 \u0622\u0646 \u062a\u0631\u0633\u06cc\u0645 \u06a9\u0646\u062f\u060c \u0648 \u0647\u0645\u0686\u0646\u06cc\u0646 \u06cc\u06a9 <code>augment()<\/code> \u0639\u0645\u0644\u06a9\u0631\u062f\u06cc \u06a9\u0647 \u062a\u0635\u0627\u0648\u06cc\u0631 \u0631\u0627 \u062f\u0631 a \u062a\u0642\u0648\u06cc\u062a \u0645\u06cc \u06a9\u0646\u062f <code>tf.data.Dataset<\/code>:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">preprocess<\/span>(<span class=\"hljs-params\">images, labels<\/span>):<\/span>\n  <span class=\"hljs-keyword\">return<\/span> tf.image.resize(images, (<span class=\"hljs-number\">224<\/span>, <span class=\"hljs-number\">224<\/span>)), tf.one_hot(labels, <span class=\"hljs-number\">10<\/span>)\n  \n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">augment<\/span>(<span class=\"hljs-params\">images, labels<\/span>):<\/span>\n  inputs = {<span class=\"hljs-string\">\"images\"<\/span>: images, <span class=\"hljs-string\">\"labels\"<\/span>: labels}\n  outputs = keras_cv.layers.RandAugment(value_range=(<span class=\"hljs-number\">0<\/span>, <span class=\"hljs-number\">255<\/span>))(inputs)\n  <span class=\"hljs-keyword\">return<\/span> outputs(<span class=\"hljs-string\">'images'<\/span>), outputs(<span class=\"hljs-string\">'labels'<\/span>)\n<\/code><\/pre>\n<p>\u0627\u06a9\u0646\u0648\u0646 &#8211; \u0645\u0627 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627 \u0631\u0627 \u06cc\u06a9\u0628\u0627\u0631\u0647 \u06a9\u062f\u06af\u0630\u0627\u0631\u06cc \u06a9\u0631\u062f\u06cc\u0645.  \u0645\u0627 \u0644\u0632\u0648\u0645\u0627\u064b \u0645\u062c\u0628\u0648\u0631 \u0646\u0628\u0648\u062f\u06cc\u0645\u060c \u0627\u0645\u0627 \u0628\u0631\u0627\u06cc \u0627\u0641\u0632\u0627\u06cc\u0634 \u0645\u0627\u0646\u0646\u062f <code>CutMix<\/code> \u0628\u0627\u06cc\u062f \u0628\u0631\u0686\u0633\u0628 \u0647\u0627 \u0648 \u0646\u0633\u0628\u062a \u0622\u0646\u0647\u0627 \u0631\u0627 \u062f\u0633\u062a\u06a9\u0627\u0631\u06cc \u06a9\u0646\u06cc\u062f.  \u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0628\u062e\u0648\u0627\u0647\u06cc\u062f \u0622\u0646\u0647\u0627 \u0631\u0627 \u0646\u06cc\u0632 \u0627\u0639\u0645\u0627\u0644 \u06a9\u0646\u06cc\u062f <code>RandAugment<\/code> \u0628\u0631\u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc\u200c\u06a9\u0646\u0646\u062f\u0647\u200c\u0647\u0627\u06cc \u0642\u0648\u06cc \u0628\u0627 \u0622\u0646\u0647\u0627 \u0628\u0633\u06cc\u0627\u0631 \u062e\u0648\u0628 \u06a9\u0627\u0631 \u0645\u06cc\u200c\u06a9\u0646\u062f &#8211; \u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f \u0631\u0645\u0632\u06af\u0630\u0627\u0631\u06cc \u06cc\u06a9\u200c\u0637\u0631\u0641\u0647 \u0631\u0627 \u0648\u0627\u0631\u062f \u06a9\u0646\u06cc\u0645. <code>RandAugment<\/code> \u06cc\u06a9 \u0641\u0631\u0647\u0646\u06af \u0644\u063a\u062a \u0628\u0627 \u062a\u0635\u0627\u0648\u06cc\u0631 \u0648 \u0628\u0631\u0686\u0633\u0628\u200c\u0647\u0627 \u062f\u0642\u06cc\u0642\u0627\u064b \u0628\u0647 \u0647\u0645\u06cc\u0646 \u062f\u0644\u06cc\u0644 \u0627\u0633\u062a &#8211; \u0628\u0631\u062e\u06cc \u0627\u0632 \u0627\u0641\u0632\u0648\u062f\u0646\u06cc\u200c\u0647\u0627\u06cc\u06cc \u06a9\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0636\u0627\u0641\u0647 \u06a9\u0646\u06cc\u062f \u062f\u0631 \u0648\u0627\u0642\u0639 \u0628\u0631\u0686\u0633\u0628\u200c\u0647\u0627 \u0631\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u0645\u06cc\u200c\u062f\u0647\u0646\u062f\u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0627\u062c\u0628\u0627\u0631\u06cc \u0647\u0633\u062a\u0646\u062f.  \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u062a\u0635\u0627\u0648\u06cc\u0631 \u0648 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627\u06cc \u0627\u0641\u0632\u0648\u062f\u0647 \u0634\u062f\u0647 \u0631\u0627 \u0627\u0632 \u0622\u0646 \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u06a9\u0646\u06cc\u062f <code>outputs<\/code> \u0641\u0631\u0647\u0646\u06af \u0644\u063a\u062a \u0628\u0647 \u0631\u0627\u062d\u062a\u06cc\u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0627\u06cc\u0646 \u06cc\u06a9 \u0645\u0631\u062d\u0644\u0647 \u0627\u0636\u0627\u0641\u06cc \u0648 \u062f\u0631 \u0639\u06cc\u0646 \u062d\u0627\u0644 \u0633\u0627\u062f\u0647 \u0627\u0633\u062a \u06a9\u0647 \u062f\u0631 \u062d\u06cc\u0646 \u062a\u0642\u0648\u06cc\u062a \u0628\u0627\u06cc\u062f \u0628\u0631\u062f\u0627\u0634\u062a\u0647 \u0634\u0648\u062f.<\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u0648\u062c\u0648\u062f \u0628\u0631\u06af\u0631\u062f\u0627\u0646\u062f\u0647 \u0634\u062f\u0647 \u0627\u0632 \u0631\u0627 \u0646\u0642\u0634\u0647 \u0628\u0631\u062f\u0627\u0631\u06cc \u06a9\u0646\u06cc\u0645 <code>tfds<\/code> \u0628\u0627 <code>preprocess()<\/code> \u0639\u0645\u0644\u06a9\u0631\u062f\u060c \u0622\u0646\u0647\u0627 \u0631\u0627 \u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u06a9\u0646\u06cc\u062f \u0648 \u0641\u0642\u0637 \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0627 \u062a\u0642\u0648\u06cc\u062a \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">valid_set = valid_set.<span class=\"hljs-built_in\">map<\/span>(preprocess).batch(<span class=\"hljs-number\">32<\/span>).prefetch(tf.data.AUTOTUNE)\ntrain_set = train.<span class=\"hljs-built_in\">map<\/span>(preprocess).batch(<span class=\"hljs-number\">32<\/span>).prefetch(tf.data.AUTOTUNE)\ntrain_set_aug = train.<span class=\"hljs-built_in\">map<\/span>(preprocess).<span class=\"hljs-built_in\">map<\/span>(augment_data, \n                                          num_parallel_calls=tf.data.AUTOTUNE).batch(<span class=\"hljs-number\">32<\/span>).prefetch(tf.data.AUTOTUNE)\n<\/code><\/pre>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645! <code>keras_cv.models<\/code> \u062f\u0627\u0631\u0627\u06cc \u0628\u0631\u062e\u06cc \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u062f\u0627\u062e\u0644\u06cc\u060c \u0645\u0634\u0627\u0628\u0647 <code>keras.applications<\/code>.  \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u0644\u06cc\u0633\u062a \u0647\u0646\u0648\u0632 \u06a9\u0648\u062a\u0627\u0647 \u0627\u0633\u062a &#8211; \u062f\u0631 \u0637\u0648\u0644 \u0632\u0645\u0627\u0646 \u06af\u0633\u062a\u0631\u0634 \u0645\u06cc \u06cc\u0627\u0628\u062f \u0648 \u0647\u0645\u0647 \u0686\u06cc\u0632 \u0631\u0627 \u0628\u0647 \u062f\u0633\u062a \u0645\u06cc \u06af\u06cc\u0631\u062f <code>keras.applications<\/code>.  API \u0628\u0633\u06cc\u0627\u0631 \u0634\u0628\u06cc\u0647 \u0627\u0633\u062a\u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0627\u0646\u062a\u0642\u0627\u0644 \u06a9\u062f \u0628\u0631\u0627\u06cc \u0627\u06a9\u062b\u0631 \u067e\u0632\u0634\u06a9\u0627\u0646 \u0646\u0633\u0628\u062a\u0627\u064b \u0622\u0633\u0627\u0646 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f:<\/p>\n<pre><code class=\"hljs\">\neffnet = keras_cv.models.EfficientNetV2B0(include_rescaling=<span class=\"hljs-literal\">True<\/span>, include_top=<span class=\"hljs-literal\">True<\/span>, classes=<span class=\"hljs-number\">10<\/span>)\n          \neffnet.<span class=\"hljs-built_in\">compile<\/span>(\n    loss=<span class=\"hljs-string\">'categorical_crossentropy'<\/span>,\n    optimizer=<span class=\"hljs-string\">'adam'<\/span>,\n    metrics=(<span class=\"hljs-string\">'accuracy'<\/span>)\n)\n\nhistory = effnet.fit(train_set, epochs=<span class=\"hljs-number\">25<\/span>, validation_data = valid_set)\n<\/code><\/pre>\n<p>\u0628\u0647 \u0637\u0648\u0631 \u0645\u062a\u0646\u0627\u0648\u0628\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0632 \u062c\u0631\u06cc\u0627\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f <code>keras.applications<\/code>:<\/p>\n<pre><code class=\"hljs\">effnet = keras.applications.EfficientNetV2B0(weights=<span class=\"hljs-literal\">None<\/span>, classes=<span class=\"hljs-number\">10<\/span>)\n\neffnet.<span class=\"hljs-built_in\">compile<\/span>(\n  loss=<span class=\"hljs-string\">'categorical_crossentropy'<\/span>,\n  optimizer=<span class=\"hljs-string\">'adam'<\/span>,\n  metrics=(<span class=\"hljs-string\">'accuracy'<\/span>)\n)\n\nhistory1 = effnet.fit(train_set, epochs=<span class=\"hljs-number\">50<\/span>, validation_data=valid_set)\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0645\u0646\u062c\u0631 \u0628\u0647 \u0645\u062f\u0644\u06cc \u0645\u06cc \u0634\u0648\u062f \u06a9\u0647 \u0648\u0627\u0642\u0639\u0627\u064b \u062e\u0648\u0628 \u0639\u0645\u0644 \u0646\u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">Epoch 1\/50\n208\/208 (==============================) - 60s 238ms\/step - loss: 2.7742 - accuracy: 0.2313 - val_loss: 3.2200 - val_accuracy: 0.3085\n...\nEpoch 50\/50\n208\/208 (==============================) - 48s 229ms\/step - loss: 0.0272 - accuracy: 0.9925 - val_loss: 2.0638 - val_accuracy: 0.6887\n<\/code><\/pre>\n<p>\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0647\u0645\u0627\u0646 \u062a\u0646\u0638\u06cc\u0645\u0627\u062a \u0634\u0628\u06a9\u0647 \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645 \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0627\u0641\u0632\u0648\u062f\u0647 \u0634\u062f\u0647  \u0647\u0631 \u062f\u0633\u062a\u0647 \u0628\u0647\u200c\u0635\u0648\u0631\u062a \u062c\u062f\u0627\u06af\u0627\u0646\u0647 \u0627\u0641\u0632\u0648\u062f\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f\u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0647\u0631 \u0632\u0645\u0627\u0646 \u06a9\u0647 \u062f\u0633\u062a\u0647\u200c\u0627\u06cc \u0627\u0632 \u062a\u0635\u0627\u0648\u06cc\u0631 (\u062f\u0631 \u062f\u0648\u0631\u0647 \u0628\u0639\u062f\u06cc) \u0628\u0647\u200c\u0648\u062c\u0648\u062f \u0622\u0645\u062f &#8211; \u062a\u0642\u0648\u06cc\u062a\u200c\u0647\u0627\u06cc \u0645\u062a\u0641\u0627\u0648\u062a\u06cc \u062e\u0648\u0627\u0647\u0646\u062f \u062f\u0627\u0634\u062a:<\/p>\n<pre><code class=\"hljs\">effnet = keras.applications.EfficientNetV2B0(weights=<span class=\"hljs-literal\">None<\/span>, classes=<span class=\"hljs-number\">10<\/span>)\neffnet.<span class=\"hljs-built_in\">compile<\/span>(\n  loss=<span class=\"hljs-string\">'categorical_crossentropy'<\/span>,\n  optimizer=<span class=\"hljs-string\">'adam'<\/span>,\n  metrics=(<span class=\"hljs-string\">'accuracy'<\/span>)\n)\n\nhistory2 = effnet.fit(train_set_aug, epochs=<span class=\"hljs-number\">50<\/span>, validation_data = valid_set)\n<\/code><\/pre>\n<pre><code class=\"hljs\">Epoch 1\/50\n208\/208 (==============================) - 141s 630ms\/step - loss: 2.9966 - accuracy: 0.1314 - val_loss: 2.7398 - val_accuracy: 0.2395\n...\nEpoch 50\/50\n208\/208 (==============================) - 125s 603ms\/step - loss: 0.7313 - accuracy: 0.7583 - val_loss: 0.6101 - val_accuracy: 0.8143\n<\/code><\/pre>\n<p>\u062e\u06cc\u0644\u06cc \u0628\u0647\u062a\u0631!  \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u0634\u0645\u0627 \u0647\u0646\u0648\u0632 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u0627\u0632 \u0627\u0641\u0632\u0627\u06cc\u0634 \u0647\u0627\u06cc \u062f\u06cc\u06af\u0631 \u0645\u0627\u0646\u0646\u062f <code>CutMix<\/code> \u0648 <code>MixUp<\/code>\u060c \u062f\u0631 \u06a9\u0646\u0627\u0631 \u0633\u0627\u06cc\u0631 \u062a\u06a9\u0646\u06cc\u06a9 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0628\u0631\u0627\u06cc \u0628\u0647 \u062d\u062f\u0627\u06a9\u062b\u0631 \u0631\u0633\u0627\u0646\u062f\u0646 \u062f\u0642\u062a \u0634\u0628\u06a9\u0647 &#8211; \u0641\u0642\u0637 <code>RandAugment<\/code> \u0628\u0647 \u0637\u0648\u0631 \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u06a9\u0645\u06a9 \u06a9\u0631\u062f \u0648 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u0627 \u06cc\u06a9 \u062e\u0637 \u0644\u0648\u0644\u0647 \u062a\u0642\u0648\u06cc\u062a \u0637\u0648\u0644\u0627\u0646\u06cc \u062a\u0631 \u0642\u0627\u0628\u0644 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0628\u0627\u0634\u062f.<\/p>\n<p>\u0627\u06af\u0631 \u0645\u0646\u062d\u0646\u06cc \u0647\u0627\u06cc \u062a\u0645\u0631\u06cc\u0646 \u0631\u0627 \u0627\u0632 \u062c\u0645\u0644\u0647 \u0622\u0645\u0648\u0632\u0634 \u0645\u0642\u0627\u06cc\u0633\u0647 \u06a9\u0646\u06cc\u062f <em>\u0648<\/em> \u0645\u0646\u062d\u0646\u06cc \u0647\u0627\u06cc \u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc &#8211; \u0641\u0642\u0637 \u0645\u0634\u062e\u0635 \u0645\u06cc \u0634\u0648\u062f \u06a9\u0647 \u0686\u0642\u062f\u0631 \u0627\u0633\u062a <code>RandAugment<\/code> \u06a9\u0645\u06a9 \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/randaugment-for-image-classification-with-keras-and-tensorflow-4.png\" alt=\"\" title=\"\"><\/p>\n<p>\u062f\u0631 \u062e\u0637 \u0644\u0648\u0644\u0647 \u063a\u06cc\u0631\u0627\u0641\u0632\u0648\u062f\u0647\u060c \u0634\u0628\u06a9\u0647 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0645\u0646\u0627\u0633\u0628 \u0645\u06cc \u0634\u0648\u062f (\u062f\u0642\u062a \u0622\u0645\u0648\u0632\u0634 \u0628\u0647 \u0633\u0642\u0641 \u0645\u06cc \u0631\u0633\u062f) \u0648 \u062f\u0642\u062a \u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc \u067e\u0627\u06cc\u06cc\u0646 \u0645\u06cc \u0645\u0627\u0646\u062f.  \u062f\u0631 \u062e\u0637 \u0644\u0648\u0644\u0647 \u062a\u0642\u0648\u06cc\u062a \u0634\u062f\u0647\u060c <em>\u062f\u0642\u062a \u0622\u0645\u0648\u0632\u0634 \u0627\u0632 \u0627\u0628\u062a\u062f\u0627 \u062a\u0627 \u0627\u0646\u062a\u0647\u0627 \u06a9\u0645\u062a\u0631 \u0627\u0632 \u062f\u0642\u062a \u0627\u0639\u062a\u0628\u0627\u0631 \u0633\u0646\u062c\u06cc \u0628\u0627\u0642\u06cc \u0645\u06cc \u0645\u0627\u0646\u062f<\/em>.<\/p>\n<p>\u0628\u0627 \u062a\u0644\u0641\u0627\u062a \u0622\u0645\u0648\u0632\u0634\u06cc \u0628\u06cc\u0634\u062a\u0631\u060c \u0634\u0628\u06a9\u0647 \u0627\u0632 \u0627\u0634\u062a\u0628\u0627\u0647\u0627\u062a\u06cc \u06a9\u0647 \u0647\u0646\u0648\u0632 \u0645\u0631\u062a\u06a9\u0628 \u0645\u06cc\u200c\u0634\u0648\u062f \u0628\u0633\u06cc\u0627\u0631 \u0622\u06af\u0627\u0647\u200c\u062a\u0631 \u0627\u0633\u062a \u0648 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646 \u0628\u0647\u200c\u0631\u0648\u0632\u0631\u0633\u0627\u0646\u06cc\u200c\u0647\u0627\u06cc \u0628\u06cc\u0634\u062a\u0631\u06cc \u0627\u0646\u062c\u0627\u0645 \u062f\u0627\u062f \u062a\u0627 \u0622\u0646 \u0631\u0627 \u0646\u0633\u0628\u062a \u0628\u0647 \u062a\u062d\u0648\u0644\u0627\u062a \u062a\u063a\u06cc\u06cc\u0631 \u0646\u062f\u0647\u062f.  \u0627\u0648\u0644\u06cc \u0646\u06cc\u0627\u0632\u06cc \u0628\u0647 \u0628\u0647 \u0631\u0648\u0632 \u0631\u0633\u0627\u0646\u06cc \u0646\u0645\u06cc \u0628\u06cc\u0646\u062f\u060c \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u062f\u0648\u0645\u06cc \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc \u062f\u0647\u062f \u0648 \u0633\u0642\u0641 \u067e\u062a\u0627\u0646\u0633\u06cc\u0644 \u0631\u0627 \u0628\u0627\u0644\u0627 \u0645\u06cc \u0628\u0631\u062f.<\/p>\n<h2 id=\"conclusion\"><span class=\"ez-toc-section\" id=\"%d9%86%d8%aa%db%8c%d8%ac%d9%87\"><\/span>\u0646\u062a\u06cc\u062c\u0647<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><a target=\"_blank\" rel=\"nofollow noopener noreferrer\" href=\"https:\/\/github.com\/keras-team\/keras-cv\">KerasCV<\/a> \u06cc\u06a9 \u0628\u0633\u062a\u0647 \u062c\u062f\u0627\u06af\u0627\u0646\u0647 \u0627\u0633\u062a\u060c \u0627\u0645\u0627 \u0647\u0645\u0686\u0646\u0627\u0646 \u06cc\u06a9 \u0627\u0641\u0632\u0648\u062f\u0646\u06cc \u0631\u0633\u0645\u06cc \u0628\u0647 Keras \u0627\u0633\u062a \u06a9\u0647 \u062a\u0648\u0633\u0637 \u062a\u06cc\u0645 Keras \u062a\u0648\u0633\u0639\u0647 \u06cc\u0627\u0641\u062a\u0647 \u0627\u0633\u062a\u060c \u0628\u0627 \u0647\u062f\u0641 \u0627\u0631\u0627\u0626\u0647 CV \u0628\u0627 \u0642\u062f\u0631\u062a \u0635\u0646\u0639\u062a\u06cc \u0628\u0647 \u067e\u0631\u0648\u0698\u0647 \u0647\u0627\u06cc Keras \u0634\u0645\u0627.  KerasCV \u0647\u0646\u0648\u0632 \u062f\u0631 \u062d\u0627\u0644 \u062a\u0648\u0633\u0639\u0647 \u0627\u0633\u062a \u0648 \u062f\u0631 \u062d\u0627\u0644 \u062d\u0627\u0636\u0631 \u0634\u0627\u0645\u0644 27 \u0644\u0627\u06cc\u0647 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u062c\u062f\u06cc\u062f \u0627\u0633\u062a. <code>RandAugment<\/code>\u060c <code>CutMix<\/code>\u060c \u0648 <code>MixUp<\/code> \u0628\u0631\u062e\u06cc \u0627\u0632 \u0622\u0646\u0647\u0627 \u0628\u0648\u062f\u0646<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0631\u0627\u0647\u0646\u0645\u0627\u06cc \u06a9\u0648\u062a\u0627\u0647\u060c \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 \u0631\u0648\u0634 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0634\u0645\u0627 \u0627\u0646\u062f\u0627\u062e\u062a\u0647 \u0627\u06cc\u0645 <code>RandAugment<\/code> \u0628\u0631\u0627\u06cc \u0627\u0639\u0645\u0627\u0644 \u062a\u0639\u062f\u0627\u062f\u06cc \u062a\u0628\u062f\u06cc\u0644 \u062a\u0635\u0627\u062f\u0641\u06cc \u0627\u0632 \u06cc\u06a9 \u0644\u06cc\u0633\u062a \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u0627\u0632 \u062a\u0628\u062f\u06cc\u0644 \u0647\u0627\u06cc \u06a9\u0627\u0631\u0628\u0631\u062f\u06cc\u060c \u0648 \u0686\u0642\u062f\u0631 \u0622\u0633\u0627\u0646 \u0627\u0633\u062a \u06a9\u0647 \u062f\u0631 \u0647\u0631 \u062e\u0637 \u0644\u0648\u0644\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc Keras \u06af\u0646\u062c\u0627\u0646\u062f\u0647 \u0634\u0648\u062f.<\/p>\n<\/div>\n<p><script>\n                        !function(f,b,e,v,n,t,s)\n                        {if(f.fbq)return;n=f.fbq=function(){n.callMethod?\n                        n.callMethod.apply(n,arguments):n.queue.push(arguments)};\n                        if(!f._fbq)f._fbq=n;n.push=n;n.loaded=!0;n.version='2.0';\n                        n.queue=();t=b.createElement(e);t.async=!0;\n                        t.src=v;s=b.getElementsByTagName(e)(0);\n                        s.parentNode.insertBefore(t,s)}(window, document,'script',\n                        'https:\/\/connect.facebook.net\/en_US\/fbevents.js');\n                        fbq('init', '525232124909042');\n                        fbq('track', 'PageView');\n                    <\/script>    (\u0628\u0631\u0686\u0633\u0628\u200c\u0647\u0627 \u0628\u0647 \u062a\u0631\u062c\u0645\u0647)# python<br \/>\n<br \/><br \/>\n<br \/>\u0645\u0646\u062a\u0634\u0631 \u0634\u062f\u0647 \u062f\u0631 1403-01-03 13:30:04<br \/>\n<\/p>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-center kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;center&quot;,&quot;id&quot;:&quot;13926&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;RandAugment \u0628\u0631\u0627\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u062a\u0635\u0648\u06cc\u0631 \u0628\u0627 Keras\\\/TensorFlow&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\"> 6<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span>\u0627\u0641\u0632\u0627\u06cc\u0634 \u062f\u0627\u062f\u0647 \u0628\u0631\u0627\u06cc \u0645\u062f\u062a \u0637\u0648\u0644\u0627\u0646\u06cc \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0648\u0633\u06cc\u0644\u0647 \u0627\u06cc \u0628\u0631\u0627\u06cc \u062c\u0627\u06cc\u06af\u0632\u06cc\u0646\u06cc \u06cc\u06a9 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 &#8220;\u0627\u06cc\u0633\u062a\u0627&#8221; \u0628\u0627 \u0627\u0646\u0648\u0627\u0639 \u062a\u0628\u062f\u06cc\u0644 \u0634\u062f\u0647 \u0639\u0645\u0644 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u062a\u063a\u06cc\u06cc\u0631 \u0646\u0627\u067e\u0630\u06cc\u0631\u06cc \u0631\u0627 \u062a\u0642\u0648\u06cc\u062a \u0645\u06cc \u06a9\u0646\u062f. \u0634\u0628\u06a9\u0647 \u0647\u0627\u06cc \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646 (CNN)\u060c \u0648 \u0645\u0639\u0645\u0648\u0644\u0627\u064b \u0645\u0646\u062c\u0631 \u0628\u0647 \u0627\u0633\u062a\u062d\u06a9\u0627\u0645 \u0648\u0631\u0648\u062f\u06cc \u0645\u06cc \u0634\u0648\u062f. \u062a\u0648\u062c\u0647 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f: \u062a\u063a\u06cc\u06cc\u0631 \u0646\u0627\u067e\u0630\u06cc\u0631\u06cc \u0628\u0647 \u06a9\u0648\u0631 \u06a9\u0631\u062f\u0646 \u0645\u062f\u0644 \u0647\u0627 \u0646\u0633\u0628\u062a \u0628\u0647 \u0627\u062e\u062a\u0644\u0627\u0644\u0627\u062a [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":13927,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1743,620],"tags":[],"class_list":["post-13926","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","category-programming"],"acf":[],"_links":{"self":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/13926","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/comments?post=13926"}],"version-history":[{"count":0,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/13926\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media\/13927"}],"wp:attachment":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media?parent=13926"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/categories?post=13926"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/tags?post=13926"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}