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\u0646\u0633\u0628 \u06c1\u06d2\u06d4 20 \u0648\u06cc\u06ba \u0635\u062f\u06cc \u06a9\u06d2 AI \u0645\u0648\u0633\u0645 \u0633\u0631\u0645\u0627 \u06a9\u06d2 \u062f\u0648\u0631\u0627\u0646\u060c \u062c\u0628 \u06c1\u0645 \u0633\u0648\u0686\u062a\u06d2 \u062a\u06be\u06d2 \u06a9\u06c1 \u0627\u0639\u0635\u0627\u0628\u06cc \u0646\u06cc\u0679 \u0648\u0631\u06a9 \u062e\u062a\u0645 \u06c1\u0648 \u0686\u06a9\u06d2 \u06c1\u06cc\u06ba\u060c \u0627\u0633 \u0627\u0633\u062a\u0627\u062f \u0627\u0648\u0631 \u0637\u0627\u0644\u0628 \u0639\u0644\u0645 \u06a9\u06d2 \u0633\u0644\u0633\u0644\u06d2 \u0646\u06d2 \u062e\u0648\u0627\u0628 \u06a9\u0648 \u0632\u0646\u062f\u06c1 \u0631\u06a9\u06be\u0627\u06d4<\/p>\n<p>\u062c\u06cc\u0641\u0631\u06cc \u06c1\u0646\u0679\u0646\u060c \u062c\u0633\u06d2 \u0627\u06d2 \u0622\u0626\u06cc \u06a9\u0627 \u0628\u0627\u067e \u0633\u0645\u062c\u06be\u0627 \u062c\u0627\u062a\u0627 \u062a\u06be\u0627\u060c \u0627\u06cc\u06a9 \u062a\u062c\u0631\u0628\u06c1 \u06af\u0627\u06c1 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\u0627\u0633 \u067e\u0631 \u0639\u0645\u0644 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u0633\u06d2 \u06a9\u0644\u0648\u0646 \u06a9\u0631\u0646\u0627 \u06c1\u0648\u06af\u0627\u06d4<\/p>\n<\/li>\n<li>\n<p>\u0627\u0632\u06af\u0631 \u06a9\u0627 \u0628\u0646\u06cc\u0627\u062f\u06cc \u0639\u0644\u0645 \u0627\u0648\u0631 \u0622\u0628\u062c\u06cc\u06a9\u0679 \u0627\u0648\u0631\u06cc\u0646\u0679\u0688 \u067e\u0631\u0648\u06af\u0631\u0627\u0645\u0646\u06af \u06a9\u06d2 \u062a\u0635\u0648\u0631\u0627\u062a (\u062c\u06cc\u0633\u06d2 \u06a9\u0644\u0627\u0633\u0632 \u06a9\u06d2 \u0633\u0627\u062a\u06be \u06a9\u0627\u0645 \u06a9\u0631\u0646\u0627)\u06d4<\/p>\n<\/li>\n<li>\n<p>\u06af\u06c1\u0631\u06cc \u0633\u06cc\u06a9\u06be\u0646\u06d2 \u06a9\u06d2 \u0628\u0646\u06cc\u0627\u062f\u06cc \u0627\u0635\u0648\u0644\u0648\u06ba \u06a9\u0627 \u0627\u06cc\u06a9 \u062c\u0627\u0626\u0632\u06c1\u06d4 \u0622\u067e \u06a9\u0648 \u062a\u0635\u0648\u0631\u0627\u062a \u06a9\u0648 \u0633\u0645\u062c\u06be\u0646\u06d2 \u06a9\u06cc \u0636\u0631\u0648\u0631\u062a \u06c1\u06d2 \u062c\u06cc\u0633\u06d2 \u0639\u06c1\u062f\u060c \u0641\u0627\u0631\u0648\u0631\u0688 \u067e\u0631\u0648\u067e\u06cc\u06af\u06cc\u0634\u0646\u060c \u0646\u0642\u0635\u0627\u0646 \u06a9\u06d2 \u0627\u0641\u0639\u0627\u0644 \u0648\u063a\u06cc\u0631\u06c1\u06d4<\/p>\n<\/li>\n<li>\n<p>PyTorch \u06a9\u06cc \u0628\u0646\u06cc\u0627\u062f\u06cc \u0628\u0627\u062a\u06cc\u06ba: PyTorch \u0679\u06cc\u0646\u0633\u0631 \u0627\u0648\u0631 \u0679\u0631\u06cc\u0646\u0646\u06af \u0644\u0648\u067e\u0633 \u06a9\u0627 \u0627\u06cc\u06a9 \u0628\u0646\u06cc\u0627\u062f\u06cc \u0639\u0644\u0645 \u0645\u062f\u062f\u06af\u0627\u0631 \u062b\u0627\u0628\u062a \u06c1\u0648\u06af\u0627 \u062c\u0628 \u06c1\u0645 \u06a9\u0648\u0688 \u06a9\u06d2 \u06c1\u0631 \u062d\u0635\u06d2 \u06a9\u06cc \u0648\u0636\u0627\u062d\u062a \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n<\/li>\n<li>\n<p>\u062a\u0631\u0642\u06cc\u0627\u062a\u06cc \u0645\u0627\u062d\u0648\u0644: \u0622\u067e \u06a9\u06d2 \u06a9\u0645\u067e\u06cc\u0648\u0679\u0631 \u067e\u0631 \u0627\u0632\u06af\u0631 \u0627\u0646\u0633\u0679\u0627\u0644 \u06c1\u0648\u0646\u0627 \u0636\u0631\u0648\u0631\u06cc \u06c1\u06d2\u06d4 \u0645\u06cc\u06ba \u0627\u067e\u0646\u06d2 \u0627\u06cc\u0688\u06cc\u0679\u0631 \u06a9\u06d2 \u0628\u0637\u0648\u0631 Visual Studio Code \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u0648\u06ba \u06af\u0627\u060c \u0644\u06cc\u06a9\u0646 \u0622\u067e \u0627\u067e\u0646\u06cc \u067e\u0633\u0646\u062f \u06a9\u0627 \u06a9\u0648\u0626\u06cc \u0628\u06be\u06cc \u0627\u06cc\u0688\u06cc\u0679\u0631 \u06cc\u0627 IDE \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631 \u0633\u06a9\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n<\/li>\n<li>\n<p>\u06c1\u0627\u0631\u0688 \u0648\u06cc\u0626\u0631: \u06cc\u06c1 \u067e\u0631\u0648\u062c\u06cc\u06a9\u0679 \u0646\u0633\u0628\u062a\u0627\u064b \u0686\u06be\u0648\u0679\u0627 \u0688\u06cc\u0679\u0627 \u0633\u06cc\u0679 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u060c \u0644\u06c1\u0630\u0627 \u0627\u06cc\u06a9 \u0645\u0639\u06cc\u0627\u0631\u06cc CPU \u0627\u0686\u06be\u06cc \u0637\u0631\u062d \u06a9\u0627\u0645 \u06a9\u0631\u06d2 \u06af\u0627\u06d4<\/p>\n<\/li>\n<\/ul>\n<h2 id=\"heading-what-is-model-distillation\">\u0645\u0627\u0688\u0644 \u0688\u0633\u0679\u0644\u06cc\u0634\u0646 \u06a9\u06cc\u0627 \u06c1\u06d2\u061f<\/h2>\n<p>\u0645\u0627\u0688\u0644 \u0688\u0633\u0679\u0644\u06cc\u0634\u0646 (\u06cc\u0627 \u0646\u0627\u0644\u062c \u0688\u0633\u0679\u0644\u06cc\u0634\u0646) \u0627\u06cc\u06a9 \u0627\u06cc\u0633\u0627 \u0637\u0631\u06cc\u0642\u06c1 \u06c1\u06d2 \u062c\u0633 \u06a9\u0627 \u0645\u0642\u0635\u062f \u0627\u06cc\u06a9 \u0628\u0691\u06d2\u060c \u0627\u0646\u062a\u06c1\u0627\u0626\u06cc \u062f\u0631\u0633\u062a \u0645\u0627\u0688\u0644 \u06a9\u06d2 \u0633\u06cc\u06a9\u06be\u06d2 \u06c1\u0648\u0626\u06d2 \u0631\u0648\u06cc\u06d2 \u06a9\u0648 \u0627\u06cc\u06a9 \u0686\u06be\u0648\u0679\u06d2\u060c \u06a9\u0645\u067e\u06cc\u0648\u0679\u06cc\u0634\u0646\u0644 \u0637\u0648\u0631 \u067e\u0631 \u0645\u0648\u062b\u0631 \u0645\u0627\u0688\u0644 \u0645\u06cc\u06ba \u0645\u0646\u062a\u0642\u0644 \u06a9\u0631\u0646\u0627 \u06c1\u06d2 \u062c\u0648 \u062d\u0642\u06cc\u0642\u06cc \u062f\u0646\u06cc\u0627 \u06a9\u06cc \u062a\u0639\u06cc\u0646\u0627\u062a\u06cc\u0648\u06ba \u06a9\u06d2 \u0644\u06cc\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u0645\u0648\u0632\u0648\u06ba \u06c1\u06d2\u06d4<\/p>\n<p>\u0688\u0633\u0679\u0644\u06cc\u0634\u0646 \u0627\u06cc\u06a9 \u06a9\u0645\u067e\u0631\u06cc\u0634\u0646 \u062a\u06a9\u0646\u06cc\u06a9 \u06c1\u06d2 \u062c\u0633 \u0645\u06cc\u06ba \u0627\u06cc\u06a9 \u0686\u06be\u0648\u0679\u0627 \u0645\u0627\u0688\u0644 (\u062c\u0633\u06d2 \u0637\u0627\u0644\u0628 \u0639\u0644\u0645 \u06a9\u06c1\u0627 \u062c\u0627\u062a\u0627 \u06c1\u06d2) \u0627\u06cc\u06a9 \u0628\u0691\u06d2\u060c \u0632\u06cc\u0627\u062f\u06c1 \u067e\u06cc\u0686\u06cc\u062f\u06c1 \u0645\u0627\u0688\u0644 (\u0627\u0633\u062a\u0627\u062f) \u06a9\u06d2 \u0622\u0624\u0679 \u067e\u0679 \u06a9\u06cc \u0646\u0642\u0644 \u06a9\u0631\u0646\u0627 \u0633\u06cc\u06a9\u06be\u062a\u0627 \u06c1\u06d2\u06d4 \u0637\u0627\u0644\u0628 \u0639\u0644\u0645 \u06a9\u06d2 \u0645\u0627\u0688\u0644 \u06a9\u06d2 \u0644\u06cc\u06d2 \u06c1\u062f\u0641 \u06cc\u06c1 \u06c1\u06d2 \u06a9\u06c1 \u0648\u06c1 \u0627\u0633\u062a\u0627\u062f \u06a9\u06d2 \u0642\u0631\u06cc\u0628 \u06a9\u0627\u0631\u06a9\u0631\u062f\u06af\u06cc \u06a9\u06cc \u0633\u0637\u062d \u06a9\u0648 \u062d\u0627\u0635\u0644 \u06a9\u0631\u06d2\u060c \u062c\u0628\u06a9\u06c1 \u0646\u0645\u0627\u06cc\u0627\u06ba \u0637\u0648\u0631 \u067e\u0631 \u06a9\u0645 \u067e\u06cc\u0631\u0627\u0645\u06cc\u0679\u0631\u0632 \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u0648\u0626\u06d2\u060c \u062a\u0639\u06cc\u0646\u0627\u062a\u06cc \u06a9\u0648 \u0632\u06cc\u0627\u062f\u06c1 \u0645\u0648\u062b\u0631 \u0628\u0646\u0627\u06cc\u0627 \u062c\u0627\u0626\u06d2\u06d4 (\u0630\u0631\u06cc\u0639\u06c1)<\/p>\n<h3 id=\"heading-why-is-model-distillation-necessary\">\u06c1\u0645\u06cc\u06ba \u0645\u0627\u0688\u0644 \u06a9\u0634\u06cc\u062f \u06a9\u06cc \u0636\u0631\u0648\u0631\u062a \u06a9\u06cc\u0648\u06ba \u06c1\u06d2\u061f<\/h3>\n<p>\u0627\u0633 \u062a\u062d\u0631\u06cc\u0631 \u06a9\u06d2 \u0648\u0642\u062a\u060c \u0628\u0646\u062f \u0627\u0648\u0631 \u0627\u0648\u067e\u0646 \u0633\u0648\u0631\u0633 \u062f\u0648\u0646\u0648\u06ba \u0645\u0627\u0688\u0644\u0632 \u06a9\u0648 \u06a9\u06be\u0631\u0628\u0648\u06ba \u067e\u06cc\u0631\u0627\u0645\u06cc\u0679\u0631\u0632 \u067e\u0631 \u062a\u0631\u0628\u06cc\u062a \u062f\u06cc \u06af\u0626\u06cc \u06c1\u06d2\u06d4 \u06cc\u06c1 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id=\"heading-model-distillation-techniques\">\u0645\u0627\u0688\u0644 \u06a9\u0634\u06cc\u062f \u0679\u06cc\u06a9\u0646\u0627\u0644\u0648\u062c\u06cc<\/h2>\n<p>\u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u0634\u062f\u06c1 \u06a9\u0634\u06cc\u062f \u062a\u06a9\u0646\u06cc\u06a9 \u06a9\u0627 \u0627\u0646\u062d\u0635\u0627\u0631 \u0641\u0631\u0627\u06c1\u0645 \u06a9\u0631\u062f\u06c1 \u0639\u0644\u0645 \u067e\u0631 \u06c1\u0648\u062a\u0627 \u06c1\u06d2\u06d4 \u06a9\u0686\u06be \u0637\u0631\u06cc\u0642\u06d2 \u0635\u0631\u0641 \u062d\u062a\u0645\u06cc \u067e\u06cc\u0634\u06cc\u0646 \u06af\u0648\u0626\u06cc \u0628\u06be\u06cc\u062c\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u062f\u0648\u0633\u0631\u06d2 \u062f\u0627\u062e\u0644\u06cc \u0646\u0645\u0627\u0626\u0646\u062f\u06af\u06cc\u060c \u062a\u0648\u062c\u06c1 \u06a9\u06d2 \u0646\u0642\u0634\u06d2\u060c \u062a\u062e\u0645\u06cc\u0646\u06c1 \u06a9\u06d2 \u0645\u0631\u0627\u062d\u0644\u060c \u06cc\u0627 \u06cc\u06c1\u0627\u06ba \u062a\u06a9 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\u0627\u0633 \u0646\u062a\u06cc\u062c\u06d2 \u067e\u0631 \u06a9\u06cc\u0633\u06d2 \u067e\u06c1\u0646\u0686\u0627 \u06cc\u0627 \u06cc\u06c1 \u06a9\u06cc\u0633\u06d2 \u06a9\u0627\u0645 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n<h3 id=\"heading-feature-based-distillation\">\u0641\u0646\u06a9\u0634\u0646 \u067e\u0631 \u0645\u0628\u0646\u06cc \u06a9\u0634\u06cc\u062f<\/h3>\n<p>\u0635\u0631\u0641 \u062d\u062a\u0645\u06cc \u0622\u0624\u0679 \u067e\u0679 \u06a9\u0648 \u0645\u0646\u062a\u0642\u0644 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0628\u062c\u0627\u0626\u06d2\u060c \u06cc\u06c1 \u062a\u06a9\u0646\u06cc\u06a9 \u0628\u0691\u06d2 \u0645\u0627\u0688\u0644 \u06a9\u06cc \u0627\u0646\u062f\u0631\u0648\u0646\u06cc \u062e\u0635\u0648\u0635\u06cc\u062a \u06a9\u06cc \u0646\u0645\u0627\u0626\u0646\u062f\u06af\u06cc \u06a9\u0648 \u0645\u0646\u062a\u0642\u0644 \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u06d4<\/p>\n<p>\u0627\u0633 \u06a9\u06d2 \u0628\u0627\u0631\u06d2 \u0645\u06cc\u06ba \u0633\u0648\u0686\u0646\u06d2 \u06a9\u0627 \u0627\u06cc\u06a9 \u0622\u0633\u0627\u0646 \u0637\u0631\u06cc\u0642\u06c1 \u06cc\u06c1 \u06c1\u06d2 \u06a9\u06c1 \u0622\u067e \u06cc\u06c1 \u062a\u0635\u0648\u0631 \u06a9\u0631\u06cc\u06ba \u06a9\u06c1 \u0622\u067e \u0627\u06cc\u06a9 \u0639\u0638\u06cc\u0645 \u0631\u06cc\u0627\u0636\u06cc \u06a9\u06d2 \u0627\u0633\u062a\u0627\u062f \u06a9\u06d2 \u0637\u0627\u0644\u0628 \u0639\u0644\u0645 \u06c1\u06cc\u06ba\u06d4 \u0627\u0633\u062a\u0627\u062f \u0631\u06cc\u0627\u0636\u06cc \u06a9\u0627 \u0645\u0633\u0626\u0644\u06c1 \u067e\u06cc\u0634 \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u0627\u0648\u0631 \u062d\u062a\u0645\u06cc \u0646\u062a\u06cc\u062c\u06c1 \u062f\u06a9\u06be\u0627\u062a\u0627 \u06c1\u06d2\u06d4 \u0622\u067e \u0628\u0627\u0644\u0622\u062e\u0631 \u0635\u062d\u06cc\u062d \u0641\u0627\u0631\u0645\u0648\u0644\u06d2 \u06a9\u0627 \u0627\u0646\u062f\u0627\u0632\u06c1 \u0644\u06af\u0627 \u0633\u06a9\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u0644\u06cc\u06a9\u0646 \u0627\u0633 \u0645\u06cc\u06ba \u0633\u06d2 \u0632\u06cc\u0627\u062f\u06c1 \u062a\u0631 \u0635\u0631\u0641 \u0646\u062a\u0627\u0626\u062c \u06a9\u0648 \u06cc\u0627\u062f \u0631\u06a9\u06be\u0646\u06d2 \u06a9\u0627 \u0637\u0631\u06cc\u0642\u06c1 \u0633\u06cc\u06a9\u06be \u0631\u06c1\u0627 \u06c1\u06d2\u06d4<\/p>\n<p>\u0644\u06cc\u06a9\u0646 \u0627\u06af\u0631 \u06a9\u0648\u0626\u06cc \u0627\u0633\u062a\u0627\u062f \u0622\u067e \u0633\u06d2 \u0627\u0646 \u06a9\u06cc \u0646\u0648\u0679 \u0628\u06a9 \u06a9\u0648 \u062f\u06cc\u06a9\u06be\u0646\u06d2 \u0627\u0648\u0631 \u0627\u0646 \u06a9\u06d2 \u06a9\u0627\u0645 \u06a9\u0648 \u062f\u06cc\u06a9\u06be\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u06a9\u06c1\u06d2\u060c \u062a\u0648 \u0622\u067e \u0627\u0633\u062a\u0627\u062f \u06a9\u06d2 \u0635\u062d\u06cc\u062d \u0633\u0648\u0686\u0646\u06d2 \u06a9\u06d2 \u0639\u0645\u0644 \u06a9\u0648 \u0633\u06cc\u06a9\u06be \u0631\u06c1\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n<p>\u0641\u06cc\u0686\u0631 \u067e\u0631 \u0645\u0628\u0646\u06cc \u0688\u0633\u0679\u0644\u06cc\u0634\u0646 \u0645\u06cc\u06ba\u060c \u0679\u0631\u06cc\u0646\u0646\u06af \u06a9\u06d2 \u062f\u0648\u0631\u0627\u0646\u060c \u0637\u0627\u0644\u0628 \u0639\u0644\u0645 \u06a9\u06d2 \u0645\u0627\u0688\u0644 \u06a9\u0648 \u0644\u0627\u0632\u0645\u06cc \u0637\u0648\u0631 \u067e\u0631 \u0686\u06be\u067e\u06cc \u06c1\u0648\u0626\u06cc \u067e\u0631\u062a \u0627\u06cc\u06a9\u0679\u06cc\u0648\u06cc\u0634\u0646\u0632 \u067e\u06cc\u062f\u0627 \u06a9\u0631\u0646\u0627 \u06c1\u0648\u06ba \u06af\u06cc \u062c\u0648 \u0645\u0637\u0644\u0628 \u0645\u0631\u0628\u0639 \u0627\u06cc\u0631\u0631 (MSE) \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u0648\u0626\u06d2 \u0627\u0633\u062a\u0627\u062f \u0633\u06d2 \u0645\u0645\u0627\u062b\u0644 \u06c1\u0648\u06ba\u06d4 \u0627\u0648\u0633\u0637 \u0645\u0631\u0628\u0639 \u063a\u0644\u0637\u06cc \u06a9\u06d2 \u0644\u06cc\u06d2\u060c \u0637\u0627\u0644\u0628 \u0639\u0644\u0645 \u06a9\u06d2 \u0645\u0627\u0688\u0644 \u06a9\u0648 \u0646\u06c1 \u0635\u0631\u0641 \u062d\u062a\u0645\u06cc \u0622\u0624\u0679 \u067e\u0679 \u06a9\u0627 \u0635\u062d\u06cc\u062d \u0627\u0646\u062f\u0627\u0632\u06c1 \u0644\u06af\u0627\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u0646\u0639\u0627\u0645 \u062f\u06cc\u0627 \u062c\u0627\u062a\u0627 \u06c1\u06d2\u06d4 \u0627\u06af\u0631 \u0627\u0646 \u06a9\u0627 \u0627\u0646\u062f\u0631\u0648\u0646\u06cc \u0641\u0646\u06a9\u0634\u0646\u0644 \u0633\u0633\u0679\u0645 \u0679\u06cc\u0686\u0631 \u06a9\u06d2 \u0645\u0627\u0688\u0644 \u06a9\u06d2 \u0641\u0646\u06a9\u0634\u0646\u0644 \u0633\u0633\u0679\u0645 \u0633\u06d2 \u0645\u06cc\u0644 \u0646\u06c1\u06cc\u06ba \u06a9\u06be\u0627\u062a\u0627 \u06c1\u06d2 \u062a\u0648 \u0627\u0646 \u067e\u0631 \u0628\u06be\u06cc \u062c\u0631\u0645\u0627\u0646\u06c1 \u0639\u0627\u0626\u062f \u06a9\u06cc\u0627 \u062c\u0627\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n<p>\u06cc\u06c1 \u0637\u0631\u06cc\u0642\u06c1 \u0628\u0691\u06d2 \u067e\u06cc\u0645\u0627\u0646\u06d2 \u067e\u0631 convolutional \u0639\u0635\u0628\u06cc \u0646\u06cc\u0679 \u0648\u0631\u06a9\u0633\u060c \u0648\u0698\u0646 \u0679\u0631\u0627\u0646\u0633\u0641\u0627\u0631\u0645\u0631\u0632\u060c \u0622\u0628\u062c\u06cc\u06a9\u0679 \u06a9\u0627 \u067e\u062a\u06c1 \u0644\u06af\u0627\u0646\u06d2 \u0648\u063a\u06cc\u0631\u06c1 \u0645\u06cc\u06ba \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06c1\u0648\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n<p>\u0627\u0633 \u0637\u0631\u06cc\u0642\u06c1 \u06a9\u0627\u0631 \u06a9\u0627 \u0645\u0646\u0641\u06cc \u067e\u06c1\u0644\u0648 \u06cc\u06c1 \u06c1\u06d2 \u06a9\u06c1 \u0645\u06cc\u0645\u0648\u0631\u06cc \u0627\u0648\u0631 \u0628\u062c\u0644\u06cc \u06a9\u06cc \u06a9\u06be\u067e\u062a \u0645\u06cc\u06ba \u062a\u06cc\u0632\u06cc \u0633\u06d2 \u0627\u0636\u0627\u0641\u06c1 \u06c1\u0648 \u0633\u06a9\u062a\u0627 \u06c1\u06d2 \u06a9\u06cc\u0648\u0646\u06a9\u06c1 \u0637\u0627\u0644\u0628 \u0639\u0644\u0645 \u06a9\u0648 \u06c1\u0631 \u062a\u0631\u0628\u06cc\u062a\u06cc \u062a\u06a9\u0631\u0627\u0631 \u067e\u0631 \u0679\u06cc\u0686\u0631 \u0645\u0627\u0688\u0644 \u06a9\u06d2 \u0627\u0646\u062f\u0631\u0648\u0646\u06cc \u0641\u06cc\u0686\u0631 \u0646\u0642\u0634\u06d2 \u06a9\u0648 \u0646\u06a9\u0627\u0644\u0646\u0627 \u0627\u0648\u0631 \u0630\u062e\u06cc\u0631\u06c1 \u06a9\u0631\u0646\u0627 \u0686\u0627\u06c1\u06cc\u06d2\u06d4 \u0627\u0633 \u0633\u06d2 \u0627\u0633\u06d2 \u0686\u0644\u0627\u0646\u0627 \u0628\u06c1\u062a \u0645\u06c1\u0646\u06af\u0627 \u067e\u0691\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n<h3 id=\"heading-attention-distillation\">\u06a9\u0634\u06cc\u062f<\/h3>\n<p>\u0627\u0633 \u0646\u0642\u0637\u06c1 \u0646\u0638\u0631 \u0645\u06cc\u06ba\u060c \u0637\u0627\u0644\u0628 \u0639\u0644\u0645 \u06a9\u06d2 \u0645\u0627\u0688\u0644 \u06a9\u0648 \u06c1\u0631 \u0646\u06cc\u0648\u0631\u0627\u0646 \u06a9\u06cc \u0646\u0642\u0644 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0628\u062c\u0627\u0626\u06d2\u060c \u0628\u0691\u0627 \u0645\u0627\u0688\u0644 \u0645\u0634\u0627\u06c1\u062f\u06c1 \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u06a9\u06c1 \u0627\u0633 \u06a9\u06cc \u062a\u0648\u062c\u06c1 \u06a9\u06c1\u0627\u06ba \u0645\u0631\u06a9\u0648\u0632 \u06c1\u06d2 \u0627\u0648\u0631 \u0627\u0646 \u0646\u06cc\u0648\u0631\u0627\u0646 \u06a9\u0648 \u06a9\u0627\u067e\u06cc \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n<p>\u0679\u0631\u0627\u0646\u0633\u0688\u06cc\u0648\u0633\u0631 \u0645\u0627\u0688\u0644 \u0645\u06cc\u06ba\u060c \u0627\u06cc\u06a9 \u0645\u0627\u0688\u0644 \u062c\u0648 \u0627\u0646\u0641\u0631\u0627\u062f\u06cc \u0627\u0644\u0641\u0627\u0638 \u067e\u0631 \u062a\u0648\u062c\u06c1 \u062f\u06cc\u0646\u06d2 \u06a9\u06d2 \u0628\u062c\u0627\u0626\u06d2 \u062c\u0645\u0644\u0648\u06ba \u06a9\u0648 \u0633\u0645\u062c\u06be\u0646\u06d2 \u06a9\u06cc \u06a9\u0648\u0634\u0634 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u060c \u062a\u0648\u062c\u06c1 \u06a9\u0627 \u0646\u0642\u0634\u06c1 \u0627\u0646 \u067e\u0679 \u0679\u0648\u06a9\u0646\u0632 \u06cc\u0627 \u062e\u0637\u0648\u06ba \u06a9\u06cc \u0646\u0645\u0627\u0626\u0646\u062f\u06af\u06cc \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u062c\u0646\u06c1\u06cc\u06ba \u0645\u0627\u0688\u0644 \u067e\u06cc\u0634\u06cc\u0646 \u06af\u0648\u0626\u06cc \u06a9\u0631\u062a\u06d2 \u0648\u0642\u062a \u0633\u0628 \u0633\u06d2 \u0627\u06c1\u0645 \u0633\u0645\u062c\u06be\u062a\u0627 \u06c1\u06d2\u06d4 \u0637\u0644\u0628\u0627\u0621 \u0627\u0646 \u0646\u0642\u0634\u0648\u06ba \u06cc\u0627 \u0627\u0633\u0627\u062a\u0630\u06c1 \u06a9\u06d2 \u0645\u0627\u0688\u0644\u0632 \u06a9\u06cc \u062a\u0642\u0633\u06cc\u0645 \u06a9\u0648 \u062f\u0648\u0628\u0627\u0631\u06c1 \u062a\u06cc\u0627\u0631 \u06a9\u0631\u0646\u06d2 \u06a9\u06cc \u06a9\u0648\u0634\u0634 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u06cc\u06c1 \u0637\u0644\u0628\u0627\u0621 \u06a9\u0648 \u062f\u06a9\u06be\u0627\u062a\u0627 \u06c1\u06d2 \u06a9\u06c1 \u0645\u0633\u0626\u0644\u06c1 \u06a9\u0648 \u062d\u0644 \u06a9\u0631\u0646\u06d2 \u06a9\u06cc \u06a9\u0648\u0634\u0634 \u06a9\u0631\u062a\u06d2 \u0648\u0642\u062a &quot;\u062f\u06cc\u06a9\u06be\u0646\u0627&#8221; \u06a9\u06c1\u0627\u06ba \u06c1\u06d2\u06d4<\/p>\n<p>\u0627\u0679\u06cc\u0646\u0634\u0646 \u0688\u0633\u0679\u0644\u06cc\u0634\u0646 \u06a9\u0627 \u0645\u0646\u0641\u06cc \u067e\u06c1\u0644\u0648 \u06cc\u06c1 \u06c1\u06d2 \u06a9\u06c1 \u0627\u0633 \u0645\u06cc\u06ba \u0679\u0631\u06cc\u0646\u0646\u06af \u0679\u0627\u0626\u0645 \u0627\u0648\u0631 \u06c1\u0627\u0631\u0688 \u0648\u06cc\u0626\u0631 \u06a9\u06d2 \u0648\u0633\u0627\u0626\u0644 \u062f\u0648\u0646\u0648\u06ba \u0645\u06cc\u06ba \u0646\u0645\u0627\u06cc\u0627\u06ba \u0627\u0648\u0648\u0631 \u06c1\u06cc\u0688 \u06c1\u06d2\u06d4 \u0627\u0633 \u06a9\u06cc \u0648\u062c\u06c1 \u06cc\u06c1 \u06c1\u06d2 \u06a9\u06c1 Mean Squared Error (MSE) \u0627\u0648\u0631 Kullback-Leibler (KL) \u0688\u0627\u0626\u06cc\u0648\u0631\u062c\u0646 \u062c\u06cc\u0633\u06cc \u0645\u06cc\u0679\u0631\u06a9\u0633 \u06a9\u0627 \u062d\u0633\u0627\u0628 \u0644\u06af\u0627\u0646\u06d2 \u06a9\u06cc \u0636\u0631\u0648\u0631\u062a \u06c1\u06d2\u06d4 \u06cc\u06c1 \u0645\u06cc\u0679\u0631\u06a9\u0633 \u0646\u0642\u0635\u0627\u0646 \u06a9\u06d2 \u0641\u0646\u06a9\u0634\u0646 \u06a9\u06d2 \u0637\u0648\u0631 \u067e\u0631 \u06a9\u0627\u0645 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba \u062c\u0648 \u0645\u0627\u0688\u0644 \u06a9\u0648 \u0628\u0627\u0644\u06a9\u0644 \u0628\u062a\u0627\u062a\u0627 \u06c1\u06d2 \u06a9\u06c1 \u0627\u0633 \u06a9\u06cc \u067e\u06cc\u0634\u06cc\u0646 \u06af\u0648\u0626\u06cc\u0627\u06ba \u06cc\u0627 \u0627\u0646\u062f\u0631\u0648\u0646\u06cc \u0646\u0645\u0627\u0626\u0646\u062f\u06af\u06cc \u0627\u0633\u0627\u062a\u0630\u06c1 \u06a9\u06d2 \u0645\u0627\u0688\u0644 \u0633\u06d2 \u06a9\u062a\u0646\u06cc \u0645\u062e\u062a\u0644\u0641 \u06c1\u06cc\u06ba\u06d4 \u0628\u0691\u06d2 \u062a\u0648\u062c\u06c1 \u0648\u0627\u0644\u06d2 \u0679\u06cc\u0646\u0633\u0631 \u067e\u0631 \u0627\u0633 \u062d\u0633\u0627\u0628 \u06a9\u0648 \u0627\u0646\u062c\u0627\u0645 \u062f\u06cc\u0646\u06d2 \u0633\u06d2 \u0648\u0633\u0627\u0626\u0644 \u06a9\u06cc \u06a9\u06be\u067e\u062a \u0645\u06cc\u06ba \u0646\u0645\u0627\u06cc\u0627\u06ba \u062d\u0635\u06c1 \u06c1\u0648\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n<h3 id=\"heading-self-distillation\">\u06af\u06be\u0631 \u0622\u0633\u0648\u0646<\/h3>\n<p>\u0645\u0627\u0688\u0644 \u062e\u0648\u062f \u0628\u06be\u06cc \u0633\u06cc\u06a9\u06be \u0633\u06a9\u062a\u0627 \u06c1\u06d2\u06d4 \u0627\u0633 \u0645\u06cc\u06ba \u067e\u0686\u06be\u0644\u06d2 \u0648\u0631\u0698\u0646 \u0633\u06d2 \u0628\u0639\u062f \u06a9\u06d2 \u0648\u0631\u0698\u0646 \u0645\u06cc\u06ba \u0639\u0644\u0645 \u06a9\u06cc \u0645\u0646\u062a\u0642\u0644\u06cc \u0634\u0627\u0645\u0644 \u06c1\u06d2\u06d4 \u0627\u0633 \u0645\u06cc\u06ba \u0646\u06cc\u0679 \u0648\u0631\u06a9 \u06a9\u06cc \u0645\u062e\u062a\u0644\u0641 \u067e\u0631\u062a\u0648\u06ba \u06a9\u06d2 \u062f\u0631\u0645\u06cc\u0627\u0646 \u0639\u0644\u0645 \u06a9\u06cc \u0645\u0646\u062a\u0642\u0644\u06cc \u0628\u06be\u06cc \u0634\u0627\u0645\u0644 \u06c1\u0648 \u0633\u06a9\u062a\u06cc \u06c1\u06d2\u06d4<\/p>\n<p>\u0627\u0633 \u06a9\u0627 \u0641\u0627\u0626\u062f\u06c1 \u06cc\u06c1 \u06c1\u06d2 \u06a9\u06c1 \u0645\u0627\u0688\u0644 \u0627\u067e\u0646\u06d2 \u0633\u0627\u0626\u0632 \u0645\u06cc\u06ba \u0627\u0636\u0627\u0641\u06c1 \u06a9\u06cc\u06d2 \u0628\u063a\u06cc\u0631 \u0628\u06c1\u062a\u0631 \u06c1\u0648 \u0633\u06a9\u062a\u0627 \u06c1\u06d2\u060c \u0628\u0691\u06d2 \u0645\u0627\u0688\u0644\u0632 \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u0648\u0626\u06d2 \u0637\u0644\u0628\u0627\u0621 \u06a9\u06d2 \u0645\u0627\u0688\u0644\u0632 \u06a9\u06cc \u062a\u0631\u0628\u06cc\u062a \u06a9\u06cc \u0644\u0627\u06af\u062a \u06a9\u0648 \u06a9\u0645 \u06a9\u0631 \u0633\u06a9\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n<h2 id=\"heading-how-to-implement-knowledge-distillation-in-pytorch\">PyTorch \u0645\u06cc\u06ba \u0639\u0644\u0645 \u06a9\u0634\u06cc\u062f \u06a9\u0648 \u06a9\u06cc\u0633\u06d2 \u0646\u0627\u0641\u0630 \u06a9\u06cc\u0627 \u062c\u0627\u0626\u06d2\u06d4<\/h2>\n<p>\u06cc\u06c1 \u0638\u0627\u06c1\u0631 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u06a9\u06c1 \u0645\u0627\u0688\u0644 \u0688\u0633\u0679\u0644\u06cc\u0634\u0646 \u0631\u06cc\u0626\u0644 \u0679\u0627\u0626\u0645 \u0645\u06cc\u06ba \u06a9\u06cc\u0633\u06d2 \u06a9\u0627\u0645 \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u060c \u06c1\u0645 MNIST \u0688\u06cc\u0679\u0627\u0633\u06cc\u0679 \u0627\u0648\u0631 \u06a9\u0644\u0627\u0633\u06cc\u0641\u0627\u0626\u0631 \u06a9\u0648 PyTorch \u06a9\u06d2 \u0633\u0627\u062a\u06be \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u0648\u0626\u06d2 \u0627\u0633\u062a\u0627\u062f \u0627\u0648\u0631 \u0637\u0627\u0644\u0628 \u0639\u0644\u0645 \u06a9\u06d2 \u0645\u0627\u0688\u0644\u0632 \u06a9\u0648 \u062a\u0631\u0628\u06cc\u062a \u062f\u06cc\u06ba \u06af\u06d2\u06d4<\/p>\n<p>\u0688\u06cc\u0679\u0627\u0633\u06cc\u0679 \u0645\u06cc\u06ba \u06c1\u0627\u062a\u06be \u0633\u06d2 \u0644\u06a9\u06be\u06d2 \u06c1\u0648\u0626\u06d2 \u06c1\u0646\u062f\u0633\u0648\u06ba \u06a9\u06cc \u062a\u0642\u0631\u06cc\u0628\u0627\u064b 70,000 \u06af\u0631\u06d2 \u0627\u0633\u06a9\u06cc\u0644 \u062a\u0635\u0627\u0648\u06cc\u0631 \u06c1\u06cc\u06ba\u06d4 \u0627\u0633\u062a\u0627\u062f \u06a9\u0648 \u062a\u0631\u0628\u06cc\u062a \u062f\u06cc\u06ba \u0627\u0648\u0631 \u067e\u06be\u0631 \u0627\u0633\u06d2 \u0645\u0646\u062c\u0645\u062f \u06a9\u0631\u06cc\u06ba \u062c\u0628 \u0622\u067e \u0627\u0633\u0627\u062a\u0630\u06c1 \u06a9\u06d2 \u0633\u062e\u062a \u0644\u06cc\u0628\u0644\u0632 \u0627\u0648\u0631 \u0646\u0631\u0645 \u0627\u06c1\u062f\u0627\u0641 \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u0648\u0626\u06d2 \u0637\u0644\u0628\u0627\u0621 \u06a9\u0648 \u062a\u0631\u0628\u06cc\u062a \u062f\u06cc\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n<p>MNIST \u0688\u06cc\u0679\u0627\u0633\u06cc\u0679 \u0646\u0633\u0628\u062a\u0627\u064b \u0686\u06be\u0648\u0679\u0627 \u0688\u06cc\u0679\u0627\u0633\u06cc\u0679 \u06c1\u06d2\u060c \u0627\u0633 \u0644\u06cc\u06d2 \u0688\u0633\u0679\u0644\u06cc\u0634\u0646 \u06a9\u06d2 \u0641\u0648\u0627\u0626\u062f \u0627\u06c1\u0645 \u0646\u06c1\u06cc\u06ba \u06c1\u0648 \u0633\u06a9\u062a\u06d2\u06d4 \u062a\u0627\u06c1\u0645\u060c \u06cc\u06c1 \u067e\u0631\u0648\u062c\u06cc\u06a9\u0679 \u0688\u0633\u0679\u0644\u06cc\u0634\u0646 \u0648\u0631\u06a9 \u0641\u0644\u0648 \u06a9\u0648 \u0638\u0627\u06c1\u0631 \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u0627\u0648\u0631 \u0628\u0691\u06d2 \u0688\u06cc\u0679\u0627 \u0633\u06cc\u0679\u0633 \u06a9\u0648 \u0686\u0644\u0627\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u06cc\u06a9 \u0627\u0686\u06be\u0627 \u067e\u0633 \u0645\u0646\u0638\u0631 \u0641\u0631\u0627\u06c1\u0645 \u06a9\u0631\u06d2 \u06af\u0627\u06d4<\/p>\n<p>\u0627\u0633 \u067e\u0631\u0648\u062c\u06cc\u06a9\u0679 \u06a9\u0648 \u0686\u0644\u0627\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0622\u067e \u06a9\u0648 \u0645\u0646\u062f\u0631\u062c\u06c1 \u0630\u06cc\u0644 \u0627\u0632\u06af\u0631 \u067e\u06cc\u06a9\u062c\u0632 \u06a9\u06cc \u0636\u0631\u0648\u0631\u062a \u06c1\u06d2\u06d4<\/p>\n<table>\n<thead>\n<tr>\n<th>\u067e\u06cc\u06a9\u062c<\/th>\n<th>\u0645\u0642\u0635\u062f<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><code>torch<\/code><\/td>\n<td>\u0646\u06cc\u0648\u0631\u0644 \u0646\u06cc\u0679 \u0648\u0631\u06a9\u0633 \u06a9\u06cc \u062a\u0639\u0645\u06cc\u0631 \u0627\u0648\u0631 \u062a\u0631\u0628\u06cc\u062a \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u06cc\u06a9 \u0628\u0646\u06cc\u0627\u062f\u06cc \u06af\u06c1\u0631\u06cc \u0633\u06cc\u06a9\u06be\u0646\u06d2 \u06a9\u0627 \u0641\u0631\u06cc\u0645 \u0648\u0631\u06a9\u06d4<\/td>\n<\/tr>\n<tr>\n<td><code>torchvision<\/code><\/td>\n<td>\u0688\u06cc\u0679\u0627 \u0633\u06cc\u0679\u0633 \u0641\u0631\u0627\u06c1\u0645 \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u062c\u06cc\u0633\u06d2 MNIST \u0627\u0648\u0631 \u062a\u0635\u0648\u06cc\u0631\u06cc \u0627\u0641\u0627\u062f\u06cc\u062a\u06d4<\/td>\n<\/tr>\n<tr>\n<td><code>numpy<\/code><\/td>\n<td>\u0639\u062f\u062f\u06cc \u062d\u0633\u0627\u0628 \u0627\u0648\u0631 \u0635\u0641 \u06a9\u06d2 \u0622\u067e\u0631\u06cc\u0634\u0646\u06d4<\/td>\n<\/tr>\n<tr>\n<td><code>matplotlib<\/code><\/td>\n<td>\u06af\u0631\u0627\u0641 \u0628\u0646\u0627\u0626\u06cc\u06ba \u0627\u0648\u0631 \u0628\u0635\u0631\u06cc \u062a\u0635\u0627\u0648\u06cc\u0631\/\u0646\u062a\u0627\u0626\u062c \u06a9\u0648 \u062f\u06cc\u06a9\u06be\u06cc\u06ba\u06d4<\/td>\n<\/tr>\n<tr>\n<td><code>tqdm<\/code><\/td>\n<td>\u062a\u0631\u0628\u06cc\u062a \u06a9\u06d2 \u062f\u0648\u0631\u0627\u0646 \u0627\u06cc\u06a9 \u067e\u0631\u0648\u06af\u0631\u06cc\u0633 \u0628\u0627\u0631 \u062f\u06a9\u06be\u0627\u062a\u0627 \u06c1\u06d2\u06d4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u06c1\u0645 \u0627\u0633 \u067e\u0631\u0648\u062c\u06cc\u06a9\u0679 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0628\u0635\u0631\u06cc \u0627\u0633\u0679\u0648\u0688\u06cc\u0648 \u06a9\u0648\u0688 \u0627\u06cc\u0688\u06cc\u0679\u0631 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u06cc\u06ba \u06af\u06d2\u060c \u0644\u06cc\u06a9\u0646 \u0622\u067e \u0627\u067e\u0646\u06cc \u067e\u0633\u0646\u062f \u06a9\u0627 \u06a9\u0648\u0626\u06cc \u062f\u0648\u0633\u0631\u0627 \u06a9\u0648\u0688 \u0627\u06cc\u0688\u06cc\u0679\u0631 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u0633\u06a9\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n<p>\u06cc\u06c1 \u06c1\u0645\u0627\u0631\u0627 \u067e\u0631\u0648\u062c\u06cc\u06a9\u0679 \u0688\u06be\u0627\u0646\u0686\u06c1 \u06c1\u06d2\u06d4 \u0645\u06cc\u06ba \u0627\u067e\u0646\u06d2 \u067e\u0631\u0648\u062c\u06cc\u06a9\u0679 \u06a9\u06d2 \u06a9\u0648\u0688 \u06a9\u06d2 \u0633\u0627\u062a\u06be \u0646\u06cc\u0686\u06d2 \u0641\u0648\u0644\u0688\u0631\u0632 \u0627\u0648\u0631 \u0641\u0627\u0626\u0644\u0648\u06ba \u06a9\u06cc \u0648\u0636\u0627\u062d\u062a \u06a9\u0631\u0648\u06ba \u06af\u0627\u06d4<\/p>\n<pre><code class=\"language-plaintext\">knowledge-distillation\/\n\u2502\n\u251c\u2500\u2500 data\/\n\u2502\n\u251c\u2500\u2500 models.py\n\u2502\n\u251c\u2500\u2500 engine.py\n\u2502\n\u251c\u2500\u2500 losses.py\n\u2502\n\u251c\u2500\u2500 train_teacher.py\n\u2502\n\u251c\u2500\u2500 train_student.py\n\u2502\n\u251c\u2500\u2500 train_distilled_student.py\n\u2502\n\u251c\u2500\u2500 evaluate.py\n\u2502\n\u251c\u2500\u2500 visualize.py\n\u2502\n\u251c\u2500\u2500 utils.py\n\u2502\n\u251c\u2500\u2500 plots.py\n\u2502\n\u251c\u2500\u2500 requirements.txt\n\u2502\n\u2514\u2500\u2500 README.md\n<\/code><\/pre>\n<p>\u0641\u0648\u0644\u0688\u0631 \u06a9\u06cc \u0633\u0627\u062e\u062a \u0645\u06cc\u06ba \u067e\u06c1\u0644\u06cc \u0686\u06cc\u0632 \u06c1\u06d2 <code>data<\/code> \u0641\u0648\u0644\u0688\u0631 \u06cc\u06c1 MNIST \u0688\u06cc\u0679\u0627\u0633\u06cc\u0679 \u067e\u0631 \u0645\u0634\u062a\u0645\u0644 \u06c1\u06d2\u06d4<\/p>\n<p>\u06c1\u0645\u0627\u0631\u06d2 <code>models.py<\/code> \u0641\u0627\u0626\u0644 \u067e\u0631\u0648\u062c\u06cc\u06a9\u0679 \u0645\u06cc\u06ba \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06c1\u0648\u0646\u06d2 \u0648\u0627\u0644\u06d2 \u062a\u0645\u0627\u0645 \u0646\u06cc\u0648\u0631\u0644 \u0646\u06cc\u0679 \u0648\u0631\u06a9\u0633 \u06a9\u06d2 \u0641\u0646 \u062a\u0639\u0645\u06cc\u0631 \u06a9\u06cc \u0648\u0636\u0627\u062d\u062a \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u06d4 \u0627\u0646 \u0645\u06cc\u06ba \u0679\u06cc\u0686\u0631 \u0646\u06cc\u0679 \u0648\u0631\u06a9 \u06a9\u0644\u0627\u0633\u0632 \u0627\u0648\u0631 \u0627\u0633\u0679\u0648\u0688\u0646\u0679 \u0646\u06cc\u0679 \u0648\u0631\u06a9 \u06a9\u0644\u0627\u0633\u0632 \u0634\u0627\u0645\u0644 \u06c1\u06cc\u06ba\u06d4 \u06c1\u0631 \u06a9\u0644\u0627\u0633 \u062f\u0631\u062c\u06c1 \u0628\u0646\u062f\u06cc\u060c \u0622\u06af\u06d2 \u0628\u0691\u06be\u0646\u06d2\u060c \u0627\u0648\u0631 \u0646\u06cc\u0679 \u0648\u0631\u06a9 \u06a9\u06d2 \u0630\u0631\u06cc\u0639\u06d2 \u0688\u06cc\u0679\u0627 \u06a9\u06d2 \u0628\u06c1\u0627\u0624 \u06a9\u06cc \u0648\u0636\u0627\u062d\u062a \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n<pre><code class=\"language-python\">import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass TeacherNet(nn.Module):\n\n    def __init__(self):\n        super().__init__()\n\n        self.flatten = nn.Flatten()\n\n        self.fc1 = nn.Linear(28 * 28, 1024)\n        self.fc2 = nn.Linear(1024, 512)\n        self.fc3 = nn.Linear(512, 256)\n        self.fc4 = nn.Linear(256, 128)\n        self.fc5 = nn.Linear(128, 10)\n\n    def forward(self, x):\n\n        x = self.flatten(x)\n\n        x = F.relu(self.fc1(x))\n        x = F.relu(self.fc2(x))\n        x = F.relu(self.fc3(x))\n        x = F.relu(self.fc4(x))\n\n        logits = self.fc5(x)\n\n        return logits\n\n\nclass StudentNet(nn.Module):\n    \"\"\"\n    Smaller neural network (Student)\n    \"\"\"\n\n    def __init__(self):\n        super().__init__()\n\n        self.flatten = nn.Flatten()\n\n        self.fc1 = nn.Linear(28 * 28, 128)\n        self.fc2 = nn.Linear(128, 10)\n\n    def forward(self, x):\n\n        x = self.flatten(x)\n\n        x = F.relu(self.fc1(x))\n\n        logits = self.fc2(x)\n\n        return logits\n<\/code><\/pre>\n<p>\u06a9\u06c1 <code>utils.py<\/code> \u0627\u06cc\u06a9 \u0641\u0627\u0626\u0644 \u0688\u06cc\u0679\u0627 \u0633\u06cc\u0679 \u062a\u06cc\u0627\u0631 \u06a9\u0631\u062a\u06cc \u06c1\u06d2 \u0627\u0648\u0631 \u0627\u0633 \u0633\u0648\u0627\u0644 \u06a9\u0627 \u062c\u0648\u0627\u0628 \u062f\u06cc\u062a\u06cc \u06c1\u06d2\u060c &quot;\u0688\u06cc\u0679\u0627 \u0633\u06cc\u0679 \u06a9\u06c1\u0627\u06ba \u0633\u06d2 \u0622\u062a\u0627 \u06c1\u06d2\u061f&#8221; \u06cc\u06c1\u0627\u06ba \u06c1\u0645 MNIST \u0688\u06cc\u0679\u0627\u0633\u06cc\u0679 \u0688\u0627\u0624\u0646 \u0644\u0648\u0688 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u062a\u0635\u0627\u0648\u06cc\u0631 \u06a9\u0648 \u0645\u0639\u0645\u0648\u0644 \u067e\u0631 \u0644\u0627\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u0627\u0648\u0631 \u0679\u0631\u06cc\u0646\u0646\u06af \u0627\u0648\u0631 \u0679\u06cc\u0633\u0679 \u0644\u0648\u0688\u0631\u0632 \u0628\u0646\u0627\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n<pre><code class=\"language-python\">from torchvision import datasets, transforms\nfrom torch.utils.data import DataLoader\n\n\ndef get_dataloaders(batch_size=64):\n    \"\"\"\n    Downloads MNIST (if needed) and returns\n    training and testing DataLoaders.\n    \"\"\"\n\n    transform = transforms.ToTensor()\n\n    train_dataset = datasets.MNIST(\n        root=\"data\",\n        train=True,\n        download=True,\n        transform=transform\n    )\n\n    test_dataset = datasets.MNIST(\n        root=\"data\",\n        train=False,\n        download=True,\n        transform=transform\n    )\n\n    train_loader = DataLoader(\n        train_dataset,\n        batch_size=batch_size,\n        shuffle=True\n    )\n\n    test_loader = DataLoader(\n        test_dataset,\n        batch_size=batch_size,\n        shuffle=False\n    )\n\n    return train_loader, test_loader\n<\/code><\/pre>\n<p>\u06a9\u06c1 <code>engine.py<\/code> \u0641\u0627\u0626\u0644\u0648\u06ba \u0645\u06cc\u06ba \u062f\u0648\u0628\u0627\u0631\u06c1 \u0642\u0627\u0628\u0644 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u0633\u06cc\u06a9\u06be\u0646\u06d2 \u06a9\u06d2 \u0627\u0644\u06af\u0648\u0631\u062a\u06be\u0645 \u06c1\u0648\u062a\u06d2 \u06c1\u06cc\u06ba \u0627\u0648\u0631 \u062a\u0631\u0628\u06cc\u062a \u06a9\u06cc \u062a\u0631\u0642\u06cc \u06a9\u06d2 \u0637\u0631\u06cc\u0642\u06c1 \u06a9\u0627\u0631 \u06a9\u06cc \u0631\u06c1\u0646\u0645\u0627\u0626\u06cc \u0645\u06cc\u06ba \u0645\u062f\u062f \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 model.py \u06a9\u0648\u0688 \u062d\u0627\u0635\u0644 \u06a9\u0631\u06cc\u06ba \u0627\u0648\u0631 \u0627\u0633\u06d2 \u062a\u0631\u0628\u06cc\u062a \u062f\u06cc\u06ba\u06d4<\/p>\n<pre><code class=\"language-python\">import torch\nfrom tqdm import tqdm\n\n\ndef train_one_epoch(\n    model,\n    dataloader,\n    criterion,\n    optimizer,\n    device,\n):\n    model.train()\n\n    running_loss = 0\n\n    progress = tqdm(dataloader)\n\n    for images, labels in progress:\n\n        images = images.to(device)\n        labels = labels.to(device)\n\n        optimizer.zero_grad()\n\n        outputs = model(images)\n\n        loss = criterion(outputs, labels)\n\n        loss.backward()\n\n        optimizer.step()\n\n        running_loss += loss.item()\n\n        progress.set_description(\n            f\"Loss {loss.item():.4f}\"\n        )\n\n    return running_loss \/ len(dataloader)\n\n\ndef evaluate(model, dataloader, device):\n\n    model.eval()\n\n    correct = 0\n    total = 0\n\n    with torch.no_grad():\n\n        for images, labels in dataloader:\n\n            images = images.to(device)\n            labels = labels.to(device)\n\n            outputs = model(images)\n\n            _, predicted = torch.max(outputs, dim=1)\n\n            total += labels.size(0)\n\n            correct += (predicted == labels).sum().item()\n\n    accuracy = 100 * correct \/ total\n\n    return accuracy\n\n\ndef predict(model, images, device):\n\n    model.eval()\n\n    with torch.no_grad():\n\n        images = images.to(device)\n\n        outputs = model(images)\n\n        _, predictions = torch.max(outputs, dim=1)\n\n    return predictions\n\ndef train_distillation_epoch(\n    teacher,\n    student,\n    dataloader,\n    optimizer,\n    device,\n    loss_fn,\n    temperature=4.0,\n    alpha=0.5,\n):\n    \"\"\"\n    Train the student using a frozen teacher.\n    \"\"\"\n\n    teacher.eval()\n    student.train()\n\n    running_loss = 0\n\n    progress = tqdm(dataloader)\n\n    for images, labels in progress:\n\n        images = images.to(device)\n        labels = labels.to(device)\n\n        optimizer.zero_grad()\n\n        # Teacher predictions (NO gradients)\n        with torch.no_grad():\n            teacher_logits = teacher(images)\n\n        # Student predictions\n        student_logits = student(images)\n\n        # Combined loss\n        loss = loss_fn(\n            student_logits=student_logits,\n            teacher_logits=teacher_logits,\n            labels=labels,\n            temperature=temperature,\n            alpha=alpha,\n        )\n\n        loss.backward()\n\n        optimizer.step()\n\n        running_loss += loss.item()\n\n        progress.set_description(\n            f\"Distill Loss {loss.item():.4f}\"\n        )\n\n    return running_loss \/ len(dataloader)\n<\/code><\/pre>\n<p>\u06a9\u06c1 <code>losses.py<\/code> \u0641\u0627\u0626\u0644 \u0627\u0633 \u0628\u0627\u062a \u06a9\u06cc \u0648\u0636\u0627\u062d\u062a \u06a9\u0631\u062a\u06cc \u06c1\u06d2 \u06a9\u06c1 \u0645\u0627\u0688\u0644 \u062d\u0642\u06cc\u0642\u06cc \u0646\u0645\u0628\u0631\u0648\u06ba \u06a9\u06cc \u067e\u06cc\u0645\u0627\u0626\u0634 \u06a9\u06cc\u0633\u06d2 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4 \u0639\u0635\u0628\u06cc \u0646\u06cc\u0679 \u0648\u0631\u06a9 \u0646\u0642\u0635\u0627\u0646 \u06a9\u0648 \u06a9\u0645 \u0633\u06d2 \u06a9\u0645 \u06a9\u0631\u06a9\u06d2 \u0633\u06cc\u06a9\u06be\u062a\u06d2 \u06c1\u06cc\u06ba \u0627\u0648\u0631 \u0627\u0633 \u0648\u062c\u06c1 \u0633\u06d2 \u0628\u06cc\u0633 \u0644\u0627\u0626\u0646 \u0648\u06cc\u0698\u0648\u0644\u0627\u0626\u0632\u06cc\u0634\u0646 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06c1\u0648\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 <code>CrossEntropyLoss<\/code>\u0627\u0648\u0631 \u0639\u0644\u0645 \u06a9\u0634\u06cc\u062f \u06a9\u06d2 \u0644\u06cc\u06d2 \u06c1\u0645 <code>distillation_loss()<\/code>.<\/p>\n<p>\u0627\u0633 \u0641\u0627\u0626\u0644 \u06a9\u06d2 \u0627\u0646\u062f\u0631\u060c \u06c1\u0645 \u0646\u06d2 \u06a9\u0631\u0627\u0633 \u0627\u06cc\u0646\u0679\u0631\u0648\u067e\u06cc \u0627\u0648\u0631 k1 \u0688\u0627\u0626\u06cc\u0648\u0631\u062c\u06cc\u0646\u0633 \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u0648\u0626\u06d2 \u0633\u062e\u062a \u0644\u06cc\u0628\u0644\u0632 \u0627\u0648\u0631 \u0646\u0631\u0645 \u0627\u06c1\u062f\u0627\u0641 \u06a9\u0648 \u0645\u0644\u0627\u06cc\u0627\u06d4 \u06cc\u06c1 \u0648\u06c1 \u062c\u06af\u06c1 \u06c1\u06d2 \u062c\u06c1\u0627\u06ba \u062c\u06cc\u0641\u0631\u06cc \u06c1\u0646\u0679\u0646 \u06a9\u0627 \u0627\u0644\u06af\u0648\u0631\u062a\u06be\u0645 \u06a9\u0627\u0645 \u0645\u06cc\u06ba \u0622\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n<pre><code class=\"language-python\">import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\ncross_entropy = nn.CrossEntropyLoss()\n\n\ndef distillation_loss(\n    student_logits,\n    teacher_logits,\n    labels,\n    temperature=4.0,\n    alpha=0.5,\n):\n    \"\"\"\n    Geoffrey Hinton's Knowledge Distillation Loss\n\n    Parameters\n    ----------\n    student_logits : output from student model\n    teacher_logits : output from teacher model\n    labels         : ground-truth labels\n    temperature    : controls softness of probability distribution\n    alpha          : weight between hard and soft losses\n    \"\"\"\n\n    # Hard Label Loss\n    hard_loss = cross_entropy(student_logits, labels)\n\n    # Teacher Soft Targets\n    teacher_probs = F.softmax(\n        teacher_logits \/ temperature,\n        dim=1\n    )\n\n    # Student Soft Predictions\n    student_log_probs = F.log_softmax(\n        student_logits \/ temperature,\n        dim=1\n    )\n\n    # KL Divergence\n    soft_loss = F.kl_div(\n        student_log_probs,\n        teacher_probs,\n        reduction=\"batchmean\"\n    )\n\n    # Final Distillation Loss\n    loss = (\n        alpha * hard_loss\n        +\n        (1 - alpha)\n        * (temperature ** 2)\n        * soft_loss\n    )\n\n    return loss\n<\/code><\/pre>\n<p>\u06a9\u06c1 <code>train_teacher.py<\/code> \u0641\u0627\u0626\u0644 \u0627\u0633\u0627\u062a\u0630\u06c1 \u06a9\u06d2 \u0646\u06cc\u0679 \u0648\u0631\u06a9 \u06a9\u0648 \u062a\u0631\u0628\u06cc\u062a \u062f\u06cc\u0646\u0627 \u06c1\u06d2\u06d4 \u06c1\u0645 \u0627\u0633 \u067e\u06c1\u0644\u06d2 \u0646\u0641\u0627\u0630 \u06a9\u06d2 \u0628\u0639\u062f \u0627\u0633\u0627\u062a\u0630\u06c1 \u06a9\u0648 \u062f\u0648\u0628\u0627\u0631\u06c1 \u062a\u0631\u0628\u06cc\u062a \u0646\u06c1\u06cc\u06ba \u062f\u06cc\u06ba \u06af\u06d2\u06d4<\/p>\n<pre><code class=\"language-python\">import torch\nimport torch.optim as optim\n\nfrom models import TeacherNet\nfrom utils import get_dataloaders\nfrom engine import train_one_epoch, evaluate\nfrom losses import cross_entropy\n\ndevice = torch.device(\n    \"cuda\" if torch.cuda.is_available() else \"cpu\"\n)\n\ntrain_loader, test_loader = get_dataloaders()\n\nteacher = TeacherNet().to(device)\n\noptimizer = optim.Adam(\n    teacher.parameters(),\n    lr=0.001,\n)\n\nepochs = 10\n\nfor epoch in range(epochs):\n\n    loss = train_one_epoch(\n        teacher,\n        train_loader,\n        cross_entropy,\n        optimizer,\n        device,\n    )\n\n    accuracy = evaluate(\n        teacher,\n        test_loader,\n        device,\n    )\n\n    print(\n        f\"Epoch {epoch+1}\/{epochs}\"\n        f\" | Loss: {loss:.4f}\"\n        f\" | Accuracy: {accuracy:.2f}%\"\n    )\n\ntorch.save(\n    teacher.state_dict(),\n    \"teacher.pth\",\n)\n\nprint(\"nTeacher saved successfully.\")\n<\/code><\/pre>\n<p>\u06a9\u06c1 <code>train_student.p<\/code>y \u0641\u0627\u0626\u0644 \u0648\u06c1 \u062c\u06af\u06c1 \u06c1\u06d2 \u062c\u06c1\u0627\u06ba \u06c1\u0645 \u0627\u067e\u0646\u06d2 \u0637\u0644\u0628\u0627\u0621 \u06a9\u0648 \u062a\u0631\u0628\u06cc\u062a \u062f\u06cc\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4 \u0679\u0631\u06cc\u0646_\u0679\u06cc\u0686\u0631 \u06a9\u06cc \u0637\u0631\u062d\u060c \u0633\u0648\u0627\u0626\u06d2 \u0627\u0633 \u06a9\u06d2 \u06a9\u06c1 \u0646\u06cc\u0679 \u0648\u0631\u06a9 \u0686\u06be\u0648\u0679\u0627 \u06c1\u06d2 \u0627\u0648\u0631 \u0627\u0633\u0627\u062a\u0630\u06c1 \u06a9\u06cc \u0631\u06c1\u0646\u0645\u0627\u0626\u06cc \u06cc\u06c1\u0627\u06ba \u0646\u0627\u0641\u0630 \u0646\u06c1\u06cc\u06ba \u06a9\u06cc \u06af\u0626\u06cc \u06c1\u06d2\u06d4 \u06cc\u06c1 \u0627\u06cc\u06a9 \u0628\u0646\u06cc\u0627\u062f\u06cc \u062f\u0631\u0633\u062a\u06af\u06cc \u0641\u0631\u0627\u06c1\u0645 \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u06a9\u06c1 \u0637\u0627\u0644\u0628 \u0639\u0644\u0645 \u0627\u0633\u062a\u0627\u062f \u06a9\u06d2 \u0628\u063a\u06cc\u0631 \u06a9\u06cc\u0633\u06d2 \u06a9\u0627\u0631\u06a9\u0631\u062f\u06af\u06cc \u06a9\u0627 \u0645\u0638\u0627\u06c1\u0631\u06c1 \u06a9\u0631\u06d2 \u06af\u0627\u06d4<\/p>\n<pre><code class=\"language-python\">import torch\nimport torch.optim as optim\n\nfrom models import StudentNet\nfrom utils import get_dataloaders\nfrom engine import train_one_epoch, evaluate\nfrom losses import cross_entropy\n\ndevice = torch.device(\n    \"cuda\" if torch.cuda.is_available() else \"cpu\"\n)\n\ntrain_loader, test_loader = get_dataloaders()\n\nstudent = StudentNet().to(device)\n\noptimizer = optim.Adam(\n    student.parameters(),\n    lr=0.001,\n)\n\nepochs = 10\n\nfor epoch in range(epochs):\n\n    loss = train_one_epoch(\n        student,\n        train_loader,\n        cross_entropy,\n        optimizer,\n        device,\n    )\n\n    accuracy = evaluate(\n        student,\n        test_loader,\n        device,\n    )\n\n    print(\n        f\"Epoch {epoch+1}\/{epochs}\"\n        f\" | Loss: {loss:.4f}\"\n        f\" | Accuracy: {accuracy:.2f}%\"\n    )\n\ntorch.save(\n    student.state_dict(),\n    \"student_baseline.pth\",\n)\n\nprint(\"nBaseline student saved.\")\n<\/code><\/pre>\n<p>\u06a9\u06c1 <code>train_distilled_student.py<\/code> \u06cc\u06c1 \u06a9\u0648\u0688 \u06a9\u0627 \u0628\u0646\u06cc\u0627\u062f\u06cc \u062d\u0635\u06c1 \u06c1\u06d2\u06d4 \u06cc\u06c1 \u0641\u0627\u0626\u0644 \u0637\u0644\u0628\u0627\u0621 \u06a9\u0648 \u067e\u0691\u06be\u0627\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0644\u06cc\u0628\u0644\u0632 \u0627\u0648\u0631 \u0627\u0633\u062a\u0627\u062f \u06a9\u06d2 \u0639\u0644\u0645 \u062f\u0648\u0646\u0648\u06ba \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06cc \u06c1\u06d2\u06d4 \u06cc\u06c1\u0627\u06ba \u06c1\u0645 \u0631\u062f\u0639\u0645\u0644 \u067e\u0631 \u0645\u0628\u0646\u06cc \u0639\u0644\u0645 \u06a9\u0634\u06cc\u062f \u06a9\u0648 \u0646\u0627\u0641\u0630 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n<pre><code class=\"language-python\">import torch\nimport torch.optim as optim\n\nfrom models import TeacherNet, StudentNet\nfrom utils import get_dataloaders\nfrom engine import (\n    train_distillation_epoch,\n    evaluate,\n)\nfrom losses import distillation_loss\n\ndevice = torch.device(\n    \"cuda\" if torch.cuda.is_available() else \"cpu\"\n)\n\ntrain_loader, test_loader = get_dataloaders()\n\n# Load Teacher\nteacher = TeacherNet().to(device)\nteacher.load_state_dict(\n    torch.load(\n        \"teacher.pth\",\n        map_location=device,\n    )\n)\n\nteacher.eval()\n\n# Student\nstudent = StudentNet().to(device)\n\noptimizer = optim.Adam(\n    student.parameters(),\n    lr=0.001,\n)\n\nepochs = 10\n\nfor epoch in range(epochs):\n\n    loss = train_distillation_epoch(\n        teacher,\n        student,\n        train_loader,\n        optimizer,\n        device,\n        distillation_loss,\n        temperature=3,\n        alpha=0.7,\n    )\n\n    accuracy = evaluate(\n        student,\n        test_loader,\n        device,\n    )\n\n    print(\n        f\"Epoch {epoch+1}\/{epochs}\"\n        f\" | Loss: {loss:.4f}\"\n        f\" | Accuracy: {accuracy:.2f}%\"\n    )\n\ntorch.save(\n    student.state_dict(),\n    \"student_distilled.pth\",\n)\n\nprint(\"nDistilled student saved.\")\n<\/code><\/pre>\n<p>\u06a9\u06c1 <code>evaluate.py<\/code> \u067e\u06cc\u0645\u0627\u0626\u0634 \u06a9\u0631\u062a\u0627 \u06c1\u06d2 \u06a9\u06c1 \u062a\u0631\u0628\u06cc\u062a \u06cc\u0627\u0641\u062a\u06c1 \u0645\u0627\u0688\u0644 \u06a9\u062a\u0646\u06cc \u0627\u0686\u06be\u06cc \u06a9\u0627\u0631\u06a9\u0631\u062f\u06af\u06cc \u06a9\u0627 \u0645\u0638\u0627\u06c1\u0631\u06c1 \u06a9\u0631\u062a\u0627 \u06c1\u06d2\u06d4 \u06cc\u06c1\u0627\u06ba \u06c1\u0645 \u0635\u0631\u0641 \u0645\u0627\u0688\u0644 \u0644\u0648\u0688 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u0679\u06cc\u0633\u0679 \u0688\u06cc\u0679\u0627 \u0644\u0648\u0688 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u067e\u06cc\u0634\u06cc\u0646 \u06af\u0648\u0626\u06cc\u0627\u06ba \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u060c \u0627\u0648\u0631 \u062f\u0631\u0633\u062a\u06af\u06cc \u06a9\u0627 \u062d\u0633\u0627\u0628 \u0644\u06af\u0627\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n<pre><code class=\"language-python\">import torch\n\nfrom models import TeacherNet\nfrom utils import get_dataloaders\nfrom engine import evaluate\n\ndevice = torch.device(\n    \"cuda\" if torch.cuda.is_available() else \"cpu\"\n)\n\n_, test_loader = get_dataloaders()\n\nteacher = TeacherNet().to(device)\n\nteacher.load_state_dict(\n    torch.load(\n        \"teacher.pth\",\n        map_location=device,\n    )\n)\n\naccuracy = evaluate(\n    teacher,\n    test_loader,\n    device,\n)\n\nprint(\n    f\"Teacher Accuracy: {accuracy:.2f}%\"\n)\n<\/code><\/pre>\n<p>\u06a9\u06c1 <code>visualize.py<\/code> \u0641\u0627\u0626\u0644\u06cc\u06ba \u06c1\u0645\u0627\u0631\u06d2 \u0646\u062a\u0627\u0626\u062c \u06a9\u06cc \u0628\u0635\u0631\u06cc \u0646\u0645\u0627\u0626\u0646\u062f\u06af\u06cc \u06c1\u06cc\u06ba\u06d4 \u06c1\u0645 \u0679\u06cc\u0686\u0631\u060c \u0628\u06cc\u0633 \u0627\u0633\u0679\u0648\u0688\u0646\u0679\u060c \u0627\u0648\u0631 \u0688\u0633\u0679\u0644\u06cc\u0634\u0646 \u0627\u0633\u0679\u0648\u0688\u0646\u0679 \u06a9\u06d2 \u0646\u062a\u0627\u0626\u062c \u06a9\u0648 \u062f\u06cc\u06a9\u06be\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 Python \u067e\u06cc\u06a9\u062c matplotlib \u06a9\u0627 \u0627\u0633\u062a\u0639\u0645\u0627\u0644 \u06a9\u0631\u062a\u06d2 \u06c1\u06cc\u06ba\u06d4<\/p>\n<pre><code class=\"language-python\">import matplotlib.pyplot as plt\n\nmodels = [\n    \"Teacher\",\n    \"BaselinenStudent\",\n    \"DistillednStudent\"\n]\n\naccuracy = [98.20, 97.86, 97.91]\n\nplt.figure(figsize=(7,5))\nplt.bar(models, accuracy)\n\nplt.title(\"Knowledge Distillation Performance Comparison\")\nplt.ylabel(\"Accuracy (%)\")\n\nfor i, v in enumerate(accuracy):\n    plt.text(i, v + 0.02, f\"{v:.2f}%\", ha=\"center\")\n\nplt.ylim(97.5, 98.5)\n\nplt.tight_layout()\nplt.savefig(\"accuracy_comparison.png\", dpi=300)\nplt.show()\n<\/code><\/pre>\n<p>\u0630\u06cc\u0644 \u0645\u06cc\u06ba \u0622\u067e \u067e\u0631\u0648\u06af\u0631\u0627\u0645 \u06a9\u0627 \u0645\u06a9\u0645\u0644 \u0648\u0631\u06a9 \u0641\u0644\u0648 \u062f\u06cc\u06a9\u06be \u0633\u06a9\u062a\u06d2 \u06c1\u06cc\u06ba \u062a\u0627\u06a9\u06c1 \u0622\u067e \u0633\u0645\u062c\u06be \u0633\u06a9\u06cc\u06ba \u06a9\u06c1 \u06cc\u06c1 \u0633\u0628 \u06a9\u06cc\u0633\u06d2 \u0627\u06a9\u0679\u06be\u0627 \u06c1\u0648\u062a\u0627 \u06c1\u06d2\u06d4<\/p>\n<pre><code class=\"language-plaintext\">                       utils.py\n                          \u2502\n                 Load MNIST Dataset\n                          \u2502\n                          \u25bc\n                     models.py\n             TeacherNet \/ StudentNet\n                          \u2502\n                          \u25bc\n                     losses.py\n  Cross Entropy + Distillation Loss Function\n                          \u2502\n                          \u25bc\n                     engine.py\n    Generic Training and Evaluation Functions\n             \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u253c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n             \u2502            \u2502            \u2502\n             \u25bc            \u25bc            \u25bc\n  train_teacher.py  train_student.py  distilled_student.py\n             \u2502            \u2502            \u2502\n             \u25bc            \u25bc            \u25bc\n   teacher.pth  student_baseline.pth  distilled.pth\n                          \u2502\n                          \u25bc\n                     evaluate.py\n              Compute Final Accuracy\n                          \u2502\n                          \u25bc\n                    visualize.py\n     Generate Charts and Experimental Figures\n<\/code><\/pre>\n<h2 id=\"heading-how-to-evaluate-the-results\">\u0627\u067e\u0646\u06d2 \u0646\u062a\u0627\u0626\u062c \u06a9\u0627 \u0627\u0646\u062f\u0627\u0632\u06c1 \u06a9\u06cc\u0633\u06d2 \u0644\u06af\u0627\u0626\u06cc\u06ba\u06d4<\/h2>\n<p>\u0627\u0628 \u0622\u0626\u06cc\u06d2 \u0686\u06cc\u06a9 \u06a9\u0631\u06cc\u06ba \u0627\u0648\u0631 \u0646\u062a\u0627\u0626\u062c \u06a9\u0627 \u0627\u0646\u062f\u0627\u0632\u06c1 \u06a9\u0631\u06cc\u06ba\u06d4 \u06cc\u06c1 \u06c1\u0631 \u0646\u06cc\u0679 \u0648\u0631\u06a9 \u06a9\u06d2 \u0644\u06cc\u06d2 10 \u062f\u0648\u0631 \u0686\u0644\u0627\u0646\u06d2 \u06a9\u0627 \u0627\u0633\u06a9\u0631\u06cc\u0646 \u0634\u0627\u0679 \u06c1\u06d2: \u0627\u0633\u062a\u0627\u062f\u060c \u0637\u0627\u0644\u0628 \u0639\u0644\u0645\u060c \u0627\u0648\u0631 \u0688\u0633\u0679\u0644\u0688 \u0627\u0633\u0679\u0648\u0688\u0646\u0679 \u0646\u06cc\u0679 \u0648\u0631\u06a9\u06d4<\/p>\n<p>\u0679\u06cc\u0686\u0631 \u0646\u06cc\u0679 \u0648\u0631\u06a9: 10 \u0645\u06a9\u0645\u0644 \u0686\u06a9\u0631\u0648\u06ba \u06a9\u06d2 \u0628\u0639\u062f\u060c \u06c1\u0645 \u0646\u06d2 98.20% \u06a9\u06cc \u062f\u0631\u0633\u062a\u06af\u06cc \u062d\u0627\u0635\u0644 \u06a9\u06cc\u06d4<\/p>\n<p>\u0637\u0644\u0628\u0627\u0621 \u06a9\u0627 \u0646\u06cc\u0679 \u0648\u0631\u06a9: 10 \u0645\u06a9\u0645\u0644 \u0686\u06a9\u0631\u0648\u06ba \u06a9\u06d2 \u0628\u0639\u062f 97.74% \u062f\u0631\u0633\u062a\u06af\u06cc \u062d\u0627\u0635\u0644 \u06a9\u06cc\u06d4 \u0679\u06cc\u0686\u0631 \u0645\u0627\u0688\u0644 \u06a9\u06d2 \u0628\u063a\u06cc\u0631 \u0686\u06be\u0648\u0679\u06d2 \u0646\u06cc\u0679 \u0648\u0631\u06a9\u0633 \u06a9\u06d2 \u0644\u06cc\u06d2 \u06cc\u06c1 \u06a9\u0627\u0641\u06cc \u0627\u0686\u06be\u06cc \u06a9\u0627\u0631\u06a9\u0631\u062f\u06af\u06cc \u06c1\u06d2\u06d4<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/umang.pk\/wp-content\/uploads\/2026\/10\/1792633663_56_\u0645\u0627\u0688\u0644-\u0688\u0633\u0679\u0644\u06cc\u0634\u0646-\u06a9\u06cc\u0627-\u06c1\u06d2\u061f-\u0688\u0648\u06cc\u0644\u067e\u0631\u0632-\u06a9\u06d2-\u0644\u06cc\u06d2-\u0627\u06cc\u06a9-\u06af\u0627\u0626\u06cc\u0688.png\" alt=\"10-\u062c\u0646\u0631\u06cc\u0634\u0646 \u0628\u06cc\u0633 \u0644\u0627\u0626\u0646 \u0627\u0633\u0679\u0648\u0688\u0646\u0679 \u0645\u0627\u0688\u0644 \u0679\u0631\u06cc\u0646\u0646\u06af \u06a9\u06d2 \u0645\u0631\u062d\u0644\u06d2 \u0645\u06cc\u06ba 97.74% \u06a9\u06cc \u062f\u0631\u0633\u062a\u06af\u06cc \u062d\u0627\u0635\u0644 \u06a9\u06cc \u06af\u0626\u06cc\u06d4\" style=\"display: block;\" width=\"600\" height=\"400\" loading=\"lazy\" title=\"\"><\/p>\n<p>\u0688\u0633\u0679\u0644\u0688 \u0627\u0633\u0679\u0648\u0688\u0646\u0679 \u0646\u06cc\u0679 \u0648\u0631\u06a9: 10 \u0645\u06a9\u0645\u0644 \u0686\u06a9\u0631\u0648\u06ba \u06a9\u06d2 \u0628\u0639\u062f 98.20% \u062f\u0631\u0633\u062a\u06af\u06cc \u062d\u0627\u0635\u0644 \u06a9\u06cc\u06d4 \u0645\u0627\u0688\u0644 \u06a9\u06d2 \u0628\u0627\u0631\u06d2 \u0645\u06cc\u06ba \u0627\u0633\u062a\u0627\u062f \u06a9\u06d2 \u0639\u0644\u0645 \u06a9\u06d2 \u0628\u063a\u06cc\u0631 97.74% \u06a9\u06cc \u0628\u06c1\u062a\u0631\u06cc \u062d\u0627\u0635\u0644 \u06a9\u06cc \u06af\u0626\u06cc\u06d4<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/umang.pk\/wp-content\/uploads\/2026\/10\/1792633663_539_\u0645\u0627\u0688\u0644-\u0688\u0633\u0679\u0644\u06cc\u0634\u0646-\u06a9\u06cc\u0627-\u06c1\u06d2\u061f-\u0688\u0648\u06cc\u0644\u067e\u0631\u0632-\u06a9\u06d2-\u0644\u06cc\u06d2-\u0627\u06cc\u06a9-\u06af\u0627\u0626\u06cc\u0688.png\" alt=\"10 \u062f\u0648\u0631\u0648\u06ba \u06a9\u06d2 \u0688\u0633\u0679\u0644\u0688 \u0627\u0633\u0679\u0648\u0688\u0646\u0679 \u0645\u0627\u0688\u0644 \u0679\u0631\u06cc\u0646\u0646\u06af \u0645\u0631\u062d\u0644\u06d2 \u0645\u06cc\u06ba 97.82% \u06a9\u06cc \u062f\u0631\u0633\u062a\u06af\u06cc \u062d\u0627\u0635\u0644 \u06a9\u06cc \u06af\u0626\u06cc\u06d4\" style=\"display: block;\" width=\"600\" height=\"400\" loading=\"lazy\" title=\"\"><\/p>\n<p>\u0646\u06cc\u0686\u06d2 \u062f\u06cc\u0626\u06d2 \u06af\u0626\u06d2 \u0686\u0627\u0631\u0679 \u06a9\u0648 \u062f\u06cc\u06a9\u06be\u06cc\u06ba \u06a9\u06c1 \u06c1\u0631 \u0645\u0627\u0688\u0644 \u06a9\u06cc \u06a9\u0627\u0631\u06a9\u0631\u062f\u06af\u06cc \u06a9\u06cc\u0633\u06d2 \u062f\u06a9\u06be\u0627\u0626\u06cc \u062f\u06cc\u062a\u06cc \u06c1\u06d2\u06d4<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/umang.pk\/wp-content\/uploads\/2026\/10\/1792633663_163_\u0645\u0627\u0688\u0644-\u0688\u0633\u0679\u0644\u06cc\u0634\u0646-\u06a9\u06cc\u0627-\u06c1\u06d2\u061f-\u0688\u0648\u06cc\u0644\u067e\u0631\u0632-\u06a9\u06d2-\u0644\u06cc\u06d2-\u0627\u06cc\u06a9-\u06af\u0627\u0626\u06cc\u0688.png\" alt=\"\u06af\u0631\u0627\u0641 \u062c\u0633 \u0645\u06cc\u06ba \u0679\u06cc\u0686\u0631 \u0645\u0627\u0688\u0644 \u06a9\u0648 98.20%\u060c \u0645\u0639\u06cc\u0627\u0631\u06cc \u0637\u0627\u0644\u0628 \u0639\u0644\u0645 \u0646\u06d2 97.86%\u060c \u0627\u0648\u0631 \u0688\u0633\u0679\u0644\u0688 \u0637\u0627\u0644\u0628 \u0639\u0644\u0645 \u0646\u06d2 97.91% \u062d\u0627\u0635\u0644 \u06a9\u06cc\u0627 \u06c1\u06d2\u06d4\" style=\"display: block;\" width=\"600\" height=\"400\" loading=\"lazy\" title=\"\"><\/p>\n<h2 id=\"heading-other-approaches-for-reducing-model-size\">\u0645\u0627\u0688\u0644 \u06a9\u06d2 \u0633\u0627\u0626\u0632 \u06a9\u0648 \u06a9\u0645 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0645\u062e\u062a\u0644\u0641 \u0637\u0631\u06cc\u0642\u06d2<\/h2>\n<p>\u0627\u0628 \u06c1\u0645 \u0646\u06d2 \u062f\u06cc\u06a9\u06be\u0627 \u06c1\u06d2 \u06a9\u06c1 \u0627\u0635\u0644\u06cc \u06a9\u0648\u0688 \u06a9\u06cc \u0645\u062b\u0627\u0644 \u06a9\u06d2 \u0633\u0627\u062a\u06be \u06a9\u06cc\u0627 \u0645\u0627\u0688\u0644 \u0646\u06a9\u0627\u0644\u0646\u0627 \u06c1\u06d2\u06d4 \u0622\u0626\u06cc\u06d2 \u062f\u06cc\u06a9\u06be\u062a\u06d2 \u06c1\u06cc\u06ba \u06a9\u06c1 \u0688\u06cc\u0648\u0644\u067e\u0631\u0632 \u0644\u06cc\u0679\u06cc\u0646\u0633\u06cc\u060c \u0645\u06cc\u0645\u0648\u0631\u06cc \u0627\u0633\u067e\u06cc\u0633\u060c \u0627\u0648\u0631 \u06a9\u0645\u067e\u06cc\u0648\u0679 \u06a9\u06cc \u0644\u0627\u06af\u062a \u06a9\u0648 \u0628\u0686\u0627\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0628\u0691\u06d2 \u0644\u06cc\u0646\u06af\u0648\u06cc\u062c \u0645\u0627\u0688\u0644\u0632 \u06a9\u06d2 \u0633\u0627\u0626\u0632 \u06a9\u0648 \u06a9\u0645 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 \u0644\u06cc\u06d2 \u0645\u062e\u062a\u0644\u0641 \u0637\u0631\u06cc\u0642\u06d2 \u0627\u062e\u062a\u06cc\u0627\u0631 \u06a9\u0631 \u0631\u06c1\u06d2 \u06c1\u06cc\u06ba\u060c \u0627\u0648\u0631 \u0648\u06c1 \u0688\u0633\u0679\u0644\u06cc\u0634\u0646 \u0633\u06d2 \u06a9\u06cc\u0633\u06d2 \u0645\u062e\u062a\u0644\u0641 \u06c1\u06cc\u06ba\u06d4<\/p>\n<p>\u0627\u0633 \u06a9\u06d2 \u0644\u06cc\u06d2 \u062a\u06cc\u0646 \u0627\u06c1\u0645 \u0637\u0631\u06cc\u0642\u06d2 \u06c1\u06cc\u06ba:<\/p>\n<h3 id=\"heading-quantization\">\u06a9\u0648\u0627\u0646\u0679\u0627\u0626\u0632\u06cc\u0634\u0646<\/h3>\n<p>\u06a9\u0648\u0627\u0646\u0679\u0627\u0626\u0632\u06cc\u0634\u0646 \u0646\u06cc\u0648\u0631\u0644 \u0646\u06cc\u0679 \u0648\u0631\u06a9 \u06a9\u06d2 \u0639\u062f\u062f\u06cc \u0648\u0632\u0646 \u0627\u0648\u0631 \u0627\u06cc\u06a9\u0679\u06cc\u0648\u06cc\u0634\u0646 \u06a9\u0648 \u0630\u062e\u06cc\u0631\u06c1 \u06a9\u0631\u0646\u06d2 \u06a9\u06d2 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