{"id":14063,"date":"2024-01-04T00:14:09","date_gmt":"2024-01-03T20:44:09","guid":{"rendered":"https:\/\/rasanegar.com\/blog\/from_logitstrue-%d8%af%d8%b1-%d8%aa%d9%88%d8%a7%d8%a8%d8%b9-%d8%a7%d8%b2-%d8%af%d8%b3%d8%aa-%d8%af%d8%a7%d8%af%d9%86-keras-tensorflow-%da%86%db%8c%d8%b3%d8%aa%d8%9f\/"},"modified":"2024-01-04T00:14:09","modified_gmt":"2024-01-03T20:44:09","slug":"from_logitstrue-%d8%af%d8%b1-%d8%aa%d9%88%d8%a7%d8%a8%d8%b9-%d8%a7%d8%b2-%d8%af%d8%b3%d8%aa-%d8%af%d8%a7%d8%af%d9%86-keras-tensorflow-%da%86%db%8c%d8%b3%d8%aa%d8%9f","status":"publish","type":"post","link":"https:\/\/rasanegaar.com\/blog\/from_logitstrue-%d8%af%d8%b1-%d8%aa%d9%88%d8%a7%d8%a8%d8%b9-%d8%a7%d8%b2-%d8%af%d8%b3%d8%aa-%d8%af%d8%a7%d8%af%d9%86-keras-tensorflow-%da%86%db%8c%d8%b3%d8%aa%d8%9f\/","title":{"rendered":"\u00abfrom_logits=True\u00bb \u062f\u0631 \u062a\u0648\u0627\u0628\u0639 \u0627\u0632 \u062f\u0633\u062a \u062f\u0627\u062f\u0646 Keras\/TensorFlow \u0686\u06cc\u0633\u062a\u061f"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\"><p class=\"ez-toc-title\" style=\"cursor:inherit\">\u0633\u0631\u0641\u0635\u0644\u0647\u0627\u06cc \u0645\u0637\u0644\u0628<\/p>\n<\/div><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/rasanegaar.com\/blog\/from_logitstrue-%d8%af%d8%b1-%d8%aa%d9%88%d8%a7%d8%a8%d8%b9-%d8%a7%d8%b2-%d8%af%d8%b3%d8%aa-%d8%af%d8%a7%d8%af%d9%86-keras-tensorflow-%da%86%db%8c%d8%b3%d8%aa%d8%9f\/#logits_%d9%88_softmax_probabilities\" >Logits \u0648 SoftMax Probabilities<\/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\/from_logitstrue-%d8%af%d8%b1-%d8%aa%d9%88%d8%a7%d8%a8%d8%b9-%d8%a7%d8%b2-%d8%af%d8%b3%d8%aa-%d8%af%d8%a7%d8%af%d9%86-keras-tensorflow-%da%86%db%8c%d8%b3%d8%aa%d8%9f\/#%d9%88%d9%82%d8%aa%db%8c_%d8%a8%d8%a7%db%8c%d8%af_from_logits%d8%af%d8%b1%d8%b3%d8%aa_%d8%a7%d8%b3%d8%aa\" >\u0648\u0642\u062a\u06cc \u0628\u0627\u06cc\u062f from_logits=\u062f\u0631\u0633\u062a \u0627\u0633\u062a?<\/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\/from_logitstrue-%d8%af%d8%b1-%d8%aa%d9%88%d8%a7%d8%a8%d8%b9-%d8%a7%d8%b2-%d8%af%d8%b3%d8%aa-%d8%af%d8%a7%d8%af%d9%86-keras-tensorflow-%da%86%db%8c%d8%b3%d8%aa%d8%9f\/#%d8%b2%d9%85%d8%a7%d9%86_%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87_%d8%a7%d8%b2_softmax_%d8%b1%d9%88%db%8c_%d8%ae%d8%b1%d9%88%d8%ac%db%8c%d8%9f\" >\u0632\u0645\u0627\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 SoftMax \u0631\u0648\u06cc \u062e\u0631\u0648\u062c\u06cc\u061f<\/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\/from_logitstrue-%d8%af%d8%b1-%d8%aa%d9%88%d8%a7%d8%a8%d8%b9-%d8%a7%d8%b2-%d8%af%d8%b3%d8%aa-%d8%af%d8%a7%d8%af%d9%86-keras-tensorflow-%da%86%db%8c%d8%b3%d8%aa%d8%9f\/#%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\"> 3<\/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>\u0686\u0627\u0631\u0686\u0648\u0628\u200c\u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0645\u0627\u0646\u0646\u062f Keras \u0645\u0648\u0627\u0646\u0639 \u0648\u0631\u0648\u062f \u062a\u0648\u062f\u0647\u200c\u0647\u0627 \u0631\u0627 \u06a9\u0627\u0647\u0634 \u0645\u06cc\u200c\u062f\u0647\u0646\u062f \u0648 \u062a\u0648\u0633\u0639\u0647 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc DL \u0631\u0627 \u0628\u0631\u0627\u06cc \u0627\u0641\u0631\u0627\u062f \u0628\u06cc\u200c\u062a\u062c\u0631\u0628\u0647 \u062f\u0645\u0648\u06a9\u0631\u0627\u062a\u06cc\u06a9 \u0645\u06cc\u200c\u06a9\u0646\u0646\u062f \u06a9\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u0646\u062f \u0628\u0647 \u0622\u0646 \u0627\u0639\u062a\u0645\u0627\u062f \u06a9\u0646\u0646\u062f. \u0631\u0648\u06cc \u067e\u06cc\u0634\u200c\u0641\u0631\u0636\u200c\u0647\u0627\u06cc \u0645\u0639\u0642\u0648\u0644 \u0648 API\u0647\u0627\u06cc \u0633\u0627\u062f\u0647\u200c\u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u062a\u062d\u0645\u0644 \u0628\u0627\u0631 \u0633\u0646\u06af\u06cc\u0646 \u0633\u0646\u06af\u06cc\u0646\u200c\u06a9\u0631\u062f\u0646 \u0648 \u062a\u0648\u0644\u06cc\u062f \u0646\u062a\u0627\u06cc\u062c \u0645\u0646\u0627\u0633\u0628.<\/p>\n<p>\u0647\u0646\u06af\u0627\u0645 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0648\u0627\u0628\u0639 \u0627\u0632 \u062f\u0633\u062a \u062f\u0627\u062f\u0646 Keras \u0628\u0631\u0627\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc\u060c \u06cc\u06a9 \u0633\u0631\u062f\u0631\u06af\u0645\u06cc \u0631\u0627\u06cc\u062c \u0628\u06cc\u0646 \u062a\u0645\u0631\u06cc\u0646 \u06a9\u0646\u0646\u062f\u06af\u0627\u0646 \u062c\u062f\u06cc\u062f\u062a\u0631 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u0634\u0648\u062f\u060c \u0645\u0627\u0646\u0646\u062f <code>CategoricalCrossentropy<\/code> \u0648 <code>SparseCategoricalCrossentropy<\/code>:<\/p>\n<pre><code class=\"hljs\">loss = keras.losses.SparseCategoricalCrossentropy(from_logits=<span class=\"hljs-literal\">True<\/span>)\n\nloss = keras.losses.SparseCategoricalCrossentropy(from_logits=<span class=\"hljs-literal\">False<\/span>)\n<\/code><\/pre>\n<blockquote>\n<p>\u0686\u0647 \u0645\u06cc \u06a9\u0646\u062f <code>from_logits<\/code> \u067e\u0631\u0686\u0645 \u0627\u0634\u0627\u0631\u0647 \u0628\u0647\u061f<\/p>\n<\/blockquote>\n<p>\u067e\u0627\u0633\u062e \u0646\u0633\u0628\u062a\u0627\u064b \u0633\u0627\u062f\u0647 \u0627\u0633\u062a\u060c \u0627\u0645\u0627 \u0646\u06cc\u0627\u0632 \u0628\u0647 \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 \u062e\u0631\u0648\u062c\u06cc \u0634\u0628\u06a9\u0647 \u0627\u06cc \u062f\u0627\u0631\u062f \u06a9\u0647 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0627\u0628\u0639 \u0636\u0631\u0631 \u062f\u0631\u062c\u0647 \u0628\u0646\u062f\u06cc \u06a9\u0646\u06cc\u0645.<\/p>\n<h2 id=\"logitsandsoftmaxprobabilities\"><span class=\"ez-toc-section\" id=\"logits_%d9%88_softmax_probabilities\"><\/span>Logits \u0648 SoftMax Probabilities<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0628\u0637\u0648\u0631 \u062e\u0644\u0627\u0635\u0647:<\/p>\n<blockquote>\n<p>\u0627\u062d\u062a\u0645\u0627\u0644\u0627\u062a \u0639\u0627\u062f\u06cc \u0645\u06cc \u0634\u0648\u0646\u062f &#8211; \u06cc\u0639\u0646\u06cc \u0645\u062d\u062f\u0648\u062f\u0647 \u0627\u06cc \u0628\u06cc\u0646 \u0622\u0646\u0647\u0627 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f <code>(0..1)<\/code>.  \u0644\u0627\u062c\u06cc\u062a \u0647\u0627 \u0639\u0627\u062f\u06cc \u0646\u06cc\u0633\u062a\u0646\u062f \u0648 \u0645\u06cc \u062a\u0648\u0627\u0646\u0646\u062f \u0645\u062d\u062f\u0648\u062f\u0647 \u0627\u06cc \u0628\u06cc\u0646 \u0622\u0646\u0647\u0627 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u0646\u062f <code>(-inf...+inf)<\/code>.<\/p>\n<\/blockquote>\n<p>\u0628\u0633\u062a\u0647 \u0628\u0647 \u0631\u0648\u06cc \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0634\u0628\u06a9\u0647 \u0634\u0645\u0627:<\/p>\n<pre><code class=\"hljs\">output = keras.layers.Dense(n, activation=<span class=\"hljs-string\">'softmax'<\/span>)(x)\n\noutput = keras.layers.Dense(n)(x)\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0627\u0632 <code>Dense<\/code> \u0644\u0627\u06cc\u0647 \u062e\u0648\u0627\u0647\u062f <em>\u06cc\u0627<\/em> \u0628\u0631\u06af\u0634\u062a:<\/p>\n<ul>\n<li><strong><em>\u0627\u062d\u062a\u0645\u0627\u0644\u0627\u062a<\/em><\/strong>: \u062e\u0631\u0648\u062c\u06cc \u0627\u0632 \u0637\u0631\u06cc\u0642 \u06cc\u06a9 \u062a\u0627\u0628\u0639 SoftMax \u0627\u0631\u0633\u0627\u0644 \u0645\u06cc \u0634\u0648\u062f \u06a9\u0647 \u062e\u0631\u0648\u062c\u06cc \u0631\u0627 \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0627\u06cc \u0627\u0632 \u0627\u062d\u062a\u0645\u0627\u0644\u0627\u062a \u0639\u0627\u062f\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0645\u06cc \u06a9\u0646\u062f. <code>n<\/code>\u060c \u06a9\u0647 \u0647\u0645\u0647 \u062c\u0645\u0639 \u0645\u06cc \u0634\u0648\u0646\u062f <code>1<\/code>.<\/li>\n<li><strong><em>logits<\/em><\/strong>: <code>n<\/code> \u0641\u0639\u0627\u0644 \u0633\u0627\u0632\u06cc \u0647\u0627<\/li>\n<\/ul>\n<p>\u0627\u06cc\u0646 \u062a\u0635\u0648\u0631 \u0627\u0634\u062a\u0628\u0627\u0647 \u0627\u062d\u062a\u0645\u0627\u0644\u0627\u064b \u0627\u0632 \u0646\u062d\u0648 \u06a9\u0648\u062a\u0627\u0647\u06cc \u0646\u0627\u0634\u06cc \u0645\u06cc \u0634\u0648\u062f \u06a9\u0647 \u0628\u0647 \u0634\u0645\u0627 \u0627\u0645\u06a9\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u06cc\u06a9 \u0641\u0639\u0627\u0644 \u0633\u0627\u0632\u06cc \u0631\u0627 \u0628\u0647 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u0627\u0636\u0627\u0641\u0647 \u06a9\u0646\u06cc\u062f. <em>\u0638\u0627\u0647\u0631\u0627<\/em> \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u06cc\u06a9 \u0644\u0627\u06cc\u0647\u060c \u062d\u062a\u06cc \u0627\u06af\u0631 \u0641\u0642\u0637 \u0645\u062e\u062a\u0635\u0631 \u0628\u0627\u0634\u062f:<\/p>\n<pre><code class=\"hljs\">output = keras.layers.Dense(n, activation=<span class=\"hljs-string\">'softmax'<\/span>)(x)\n\ndense = keras.layers.Dense(n)(x)\noutput = keras.layers.Activation(<span class=\"hljs-string\">'softmax'<\/span>)(dense)\n<\/code><\/pre>\n<p>\u062a\u0627\u0628\u0639 \u0636\u0631\u0631 \u0634\u0645\u0627 \u0628\u0627\u06cc\u062f \u062f\u0631 \u0645\u0648\u0631\u062f \u0627\u06cc\u0646\u06a9\u0647 \u0622\u06cc\u0627 \u0628\u0627\u06cc\u062f \u0627\u0646\u062a\u0638\u0627\u0631 \u062a\u0648\u0632\u06cc\u0639 \u0646\u0631\u0645\u0627\u0644 \u0634\u062f\u0647 (\u062e\u0631\u0648\u062c\u06cc \u0639\u0628\u0648\u0631 \u0627\u0632 \u06cc\u06a9 \u062a\u0627\u0628\u0639 SoftMax) \u0631\u0627 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f \u06cc\u0627 logit \u0647\u0627 \u0631\u0627 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f.  \u0627\u0632 \u0627\u06cc\u0646 \u0631\u0648 <code>from_logits<\/code> \u067e\u0631\u0686\u0645!<\/p>\n<h2 id=\"whenshouldfrom_logitstrue\"><span class=\"ez-toc-section\" id=\"%d9%88%d9%82%d8%aa%db%8c_%d8%a8%d8%a7%db%8c%d8%af_from_logits%d8%af%d8%b1%d8%b3%d8%aa_%d8%a7%d8%b3%d8%aa\"><\/span>\u0648\u0642\u062a\u06cc \u0628\u0627\u06cc\u062f <em>from_logits=\u062f\u0631\u0633\u062a \u0627\u0633\u062a<\/em>?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<blockquote>\n<p>\u0627\u06af\u0631 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0634\u0645\u0627 \u062f\u0627\u0631\u0627\u06cc \u06cc\u06a9 <code>'softmax'<\/code> \u0641\u0639\u0627\u0644 \u0633\u0627\u0632\u06cc\u060c <code>from_logits<\/code> \u0628\u0627\u06cc\u062f \u0628\u0627\u0634\u062f <code>False<\/code>.  \u0627\u06af\u0631 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0634\u0645\u0627 a \u0646\u062f\u0627\u0631\u062f <code>'softmax'<\/code> \u0641\u0639\u0627\u0644 \u0633\u0627\u0632\u06cc\u060c <code>from_logits<\/code> \u0628\u0627\u06cc\u062f \u0628\u0627\u0634\u062f <code>True<\/code>.<\/p>\n<\/blockquote>\n<p>\u0627\u06af\u0631 \u0634\u0628\u06a9\u0647 \u0634\u0645\u0627 \u0627\u062d\u062a\u0645\u0627\u0644\u0627\u062a \u062e\u0631\u0648\u062c\u06cc \u0631\u0627 \u0639\u0627\u062f\u06cc \u0645\u06cc \u06a9\u0646\u062f\u060c \u062a\u0627\u0628\u0639 \u0636\u0631\u0631 \u0634\u0645\u0627 \u0628\u0627\u06cc\u062f \u062a\u0646\u0638\u06cc\u0645 \u0634\u0648\u062f <code>from_logits<\/code> \u0628\u0647 <code>False<\/code>\u060c \u0632\u06cc\u0631\u0627 \u0644\u0627\u062c\u06cc\u062a \u0631\u0627 \u0646\u0645\u06cc \u067e\u0630\u06cc\u0631\u062f.  \u0627\u06cc\u0646 \u0646\u06cc\u0632 \u0645\u0642\u062f\u0627\u0631 \u067e\u06cc\u0634\u200c\u0641\u0631\u0636 \u062a\u0645\u0627\u0645 \u06a9\u0644\u0627\u0633\u200c\u0647\u0627\u06cc \u0636\u0631\u0631\u06cc \u0627\u0633\u062a \u06a9\u0647 \u067e\u0631\u0686\u0645 \u0631\u0627 \u0645\u06cc\u200c\u067e\u0630\u06cc\u0631\u0646\u062f\u060c \u0632\u06cc\u0631\u0627 \u0627\u06a9\u062b\u0631 \u0627\u0641\u0631\u0627\u062f \u06cc\u06a9 \u0639\u0644\u0627\u0645\u062a \u0631\u0627 \u0627\u0636\u0627\u0641\u0647 \u0645\u06cc\u200c\u06a9\u0646\u0646\u062f <code>activation='softmax'<\/code> \u0628\u0647 \u0644\u0627\u06cc\u0647 \u0647\u0627\u06cc \u062e\u0631\u0648\u062c\u06cc \u0622\u0646\u0647\u0627:<\/p>\n<pre><code class=\"hljs\">model = keras.Sequential((\n    keras.layers.Input(shape=(<span class=\"hljs-number\">10<\/span>, <span class=\"hljs-number\">1<\/span>)),\n    \n    keras.layers.Dense(<span class=\"hljs-number\">10<\/span>, activation=<span class=\"hljs-string\">'softmax'<\/span>) \n))\n\ninput_data = tf.random.uniform(shape=(<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>))\noutput = model(input_data)\n<span class=\"hljs-built_in\">print<\/span>(output)\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0646\u062a\u06cc\u062c\u0647 \u062f\u0631:<\/p>\n<pre><code class=\"hljs\">tf.Tensor(\n(((0.12467965 0.10423233 0.10054766 0.09162105 0.09144577 0.07093797\n   0.12523937 0.11292477 0.06583504 0.11253635))), shape=(1, 1, 10), dtype=float32)\n<\/code><\/pre>\n<p>\u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0627\u06cc\u0646 \u0634\u0628\u06a9\u0647 \u0645\u0646\u062c\u0631 \u0628\u0647 \u062a\u0648\u0632\u06cc\u0639 \u0646\u0631\u0645\u0627\u0644 \u0645\u06cc \u0634\u0648\u062f &#8211; \u0647\u0646\u06af\u0627\u0645 \u0645\u0642\u0627\u06cc\u0633\u0647 \u062e\u0631\u0648\u062c\u06cc \u0647\u0627 \u0628\u0627 \u062e\u0631\u0648\u062c\u06cc \u0647\u0627\u06cc \u0647\u062f\u0641\u060c \u0648 \u062f\u0631\u062c\u0647 \u0628\u0646\u062f\u06cc \u0622\u0646\u0647\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u06cc\u06a9 \u062a\u0627\u0628\u0639 \u0636\u0631\u0631 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc (\u0628\u0631\u0627\u06cc \u06a9\u0627\u0631 \u0645\u0646\u0627\u0633\u0628) &#8211; <strong>\u0634\u0645\u0627 \u0628\u0627\u06cc\u062f \u062a\u0646\u0638\u06cc\u0645 \u06a9\u0646\u06cc\u062f <code>from_logits<\/code> \u0628\u0647 <code>False<\/code><\/strong>\u060c \u06cc\u0627 \u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f \u0645\u0642\u062f\u0627\u0631 \u067e\u06cc\u0634 \u0641\u0631\u0636 \u0628\u0627\u0642\u06cc \u0628\u0645\u0627\u0646\u062f.<\/p>\n<p>\u0627\u0632 \u0637\u0631\u0641 \u062f\u06cc\u06af\u0631\u060c \u0627\u06af\u0631 \u0634\u0628\u06a9\u0647 \u0634\u0645\u0627 SoftMax \u0631\u0627 \u0627\u0639\u0645\u0627\u0644 \u0646\u0645\u06cc \u06a9\u0646\u062f \u0631\u0648\u06cc \u062e\u0631\u0648\u062c\u06cc:<\/p>\n<pre><code class=\"hljs\">model = keras.Sequential((\n    keras.layers.Input(shape=(<span class=\"hljs-number\">10<\/span>, <span class=\"hljs-number\">1<\/span>)),\n    \n    keras.layers.Dense(<span class=\"hljs-number\">10<\/span>)\n))\n\ninput_data = tf.random.uniform(shape=(<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">1<\/span>))\noutput = model(input_data)\n<span class=\"hljs-built_in\">print<\/span>(output)\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0646\u062a\u06cc\u062c\u0647 \u062f\u0631:<\/p>\n<pre><code class=\"hljs\">tf.Tensor(\n(((-0.06081138  0.04154852  0.00153442  0.0705068  -0.01139916\n    0.08506121  0.1211026  -0.10112958 -0.03410497  0.08653068))), shape=(1, 1, 10), dtype=float32)\n<\/code><\/pre>\n<p>\u0634\u0645\u0627 \u0628\u0627\u06cc\u062f \u062a\u0646\u0638\u06cc\u0645 \u06a9\u0646\u06cc\u062f <strong><code>from_logits<\/code>  \u0628\u0647 <code>True<\/code><\/strong>  \u0628\u0631\u0627\u06cc \u0627\u06cc\u0646\u06a9\u0647 \u0639\u0645\u0644\u06a9\u0631\u062f \u062a\u0644\u0641\u0627\u062a \u0628\u0647 \u062f\u0631\u0633\u062a\u06cc \u0628\u0627 \u062e\u0631\u0648\u062c\u06cc \u0647\u0627 \u0631\u0641\u062a\u0627\u0631 \u06a9\u0646\u062f.<\/p>\n<h2 id=\"whentousesoftmaxontheoutput\"><span class=\"ez-toc-section\" id=\"%d8%b2%d9%85%d8%a7%d9%86_%d8%a7%d8%b3%d8%aa%d9%81%d8%a7%d8%af%d9%87_%d8%a7%d8%b2_softmax_%d8%b1%d9%88%db%8c_%d8%ae%d8%b1%d9%88%d8%ac%db%8c%d8%9f\"><\/span>\u0632\u0645\u0627\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 SoftMax \u0631\u0648\u06cc \u062e\u0631\u0648\u062c\u06cc\u061f<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u06a9\u062b\u0631 \u067e\u0632\u0634\u06a9\u0627\u0646 SoftMax \u0631\u0627 \u0627\u0639\u0645\u0627\u0644 \u0645\u06cc \u06a9\u0646\u0646\u062f \u0631\u0648\u06cc \u062e\u0631\u0648\u062c\u06cc \u0628\u0631\u0627\u06cc \u0627\u0631\u0627\u0626\u0647 \u06cc\u06a9 \u062a\u0648\u0632\u06cc\u0639 \u0627\u062d\u062a\u0645\u0627\u0644 \u0646\u0631\u0645\u0627\u0644 \u0634\u062f\u0647\u060c \u0632\u06cc\u0631\u0627 \u062f\u0631 \u0628\u0633\u06cc\u0627\u0631\u06cc \u0627\u0632 \u0645\u0648\u0627\u0631\u062f \u0627\u06cc\u0646 \u0686\u06cc\u0632\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0634\u0645\u0627 \u0627\u0632 \u06cc\u06a9 \u0634\u0628\u06a9\u0647 \u0628\u0631\u0627\u06cc \u0622\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u062f \u06a9\u0631\u062f &#8211; \u0628\u0647 \u0648\u06cc\u0698\u0647 \u062f\u0631 \u0645\u0637\u0627\u0644\u0628 \u0622\u0645\u0648\u0632\u0634\u06cc \u0633\u0627\u062f\u0647 \u0634\u062f\u0647.  \u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u062f\u0631 \u0628\u0631\u062e\u06cc \u0645\u0648\u0627\u0631\u062f\u060c \u0634\u0645\u0627 <em>\u0646\u06a9\u0646<\/em> \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u062a\u0627\u0628\u0639 \u0631\u0627 \u062f\u0631 \u062e\u0631\u0648\u062c\u06cc\u060c \u0628\u0647 \u0627\u0639\u0645\u0627\u0644 \u06a9\u0646\u06cc\u062f process \u0642\u0628\u0644 \u0627\u0632 \u0627\u0639\u0645\u0627\u0644 SoftMax \u06cc\u0627 \u062a\u0627\u0628\u0639 \u062f\u06cc\u06af\u0631\u06cc\u060c \u0622\u0646 \u0631\u0627 \u0628\u0647 \u0631\u0648\u0634\u06cc \u0645\u062a\u0641\u0627\u0648\u062a \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u062f.<\/p>\n<p>\u06cc\u06a9 \u0645\u062b\u0627\u0644 \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647 \u0627\u0632 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc NLP \u0645\u06cc\u200c\u0622\u06cc\u062f\u060c \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u06cc\u06a9 \u0627\u062d\u062a\u0645\u0627\u0644 \u0648\u0627\u0642\u0639\u06cc \u0628\u06cc\u0634 \u0627\u0632 \u06cc\u06a9 \u0648\u0627\u0698\u06af\u0627\u0646 \u0628\u0632\u0631\u06af \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u062f\u0631 \u062a\u0627\u0646\u0633\u0648\u0631 \u062e\u0631\u0648\u062c\u06cc \u0648\u062c\u0648\u062f \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f.  \u0627\u0639\u0645\u0627\u0644 SoftMax \u0631\u0648\u06cc <em>\u0647\u0645\u0647 \u0622\u0646\u0647\u0627<\/em> \u0648 \u062d\u0631\u06cc\u0635\u0627\u0646\u0647 \u06af\u0631\u0641\u062a\u0646 <code>argmax<\/code> \u0645\u0639\u0645\u0648\u0644\u0627\u064b \u0646\u062a\u0627\u06cc\u062c \u062e\u06cc\u0644\u06cc \u062e\u0648\u0628\u06cc \u0627\u06cc\u062c\u0627\u062f \u0646\u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u0627\u06af\u0631 logit \u0647\u0627 \u0631\u0627 \u0645\u0634\u0627\u0647\u062f\u0647 \u06a9\u0631\u062f\u06cc\u062f\u060c Top-K \u0631\u0627 \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u06a9\u0646\u06cc\u062f (\u06a9\u0647 \u062f\u0631 \u0622\u0646 K \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0647\u0631 \u0639\u062f\u062f\u06cc \u0628\u0627\u0634\u062f \u0627\u0645\u0627 \u0645\u0639\u0645\u0648\u0644\u0627\u064b \u062c\u0627\u06cc\u06cc \u0628\u06cc\u0646 <code>(0...10)<\/code>\u060c \u0648 \u062a\u0646\u0647\u0627 \u067e\u0633 \u0627\u0632 \u0627\u0639\u0645\u0627\u0644 SoftMax \u0628\u0647 <em>top-k<\/em> \u0646\u0634\u0627\u0646\u0647 \u0647\u0627\u06cc \u0645\u0645\u06a9\u0646 \u062f\u0631 \u0648\u0627\u0698\u06af\u0627\u0646 \u062a\u0648\u0632\u06cc\u0639 \u0631\u0627 \u0628\u0647 \u0637\u0648\u0631 \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u062a\u063a\u06cc\u06cc\u0631 \u0645\u06cc \u062f\u0647\u0646\u062f \u0648 \u0645\u0639\u0645\u0648\u0644\u0627 \u0646\u062a\u0627\u06cc\u062c \u0648\u0627\u0642\u0639\u06cc \u062a\u0631\u06cc \u062a\u0648\u0644\u06cc\u062f \u0645\u06cc \u06a9\u0646\u0646\u062f.<\/p>\n<p>\u0627\u06cc\u0646 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0646\u0645\u0648\u0646\u0647 \u0628\u0631\u062f\u0627\u0631\u06cc Top-K \u0634\u0646\u0627\u062e\u062a\u0647 \u0645\u06cc \u0634\u0648\u062f\u060c \u0648 \u0627\u06af\u0631\u0686\u0647 \u0627\u0633\u062a\u0631\u0627\u062a\u0698\u06cc \u0627\u06cc\u062f\u0647 \u0622\u0644 \u0646\u06cc\u0633\u062a\u060c \u0645\u0639\u0645\u0648\u0644\u0627\u064b \u0628\u0647 \u0637\u0648\u0631 \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u0627\u0632 \u0646\u0645\u0648\u0646\u0647 \u0628\u0631\u062f\u0627\u0631\u06cc \u062d\u0631\u06cc\u0635\u0627\u0646\u0647 \u0628\u0647\u062a\u0631 \u0639\u0645\u0644 \u0645\u06cc \u06a9\u0646\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>\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 \u0622\u0646 \u0627\u0646\u062f\u0627\u062e\u062a\u0647\u200c\u0627\u06cc\u0645 <code>from_logits<\/code> \u0627\u0633\u062a\u062f\u0644\u0627\u0644 \u0628\u0631\u0627\u06cc \u06a9\u0644\u0627\u0633 \u0647\u0627\u06cc \u0636\u0631\u0631 Keras\u060c \u06a9\u0647 \u0627\u063a\u0644\u0628 \u0633\u0648\u0627\u0644\u0627\u062a\u06cc \u0631\u0627 \u0628\u0627 \u062a\u0645\u0631\u06cc\u0646 \u06a9\u0646\u0646\u062f\u06af\u0627\u0646 \u062c\u062f\u06cc\u062f\u062a\u0631 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u0627\u06cc\u0646 \u0633\u0631\u062f\u0631\u06af\u0645\u06cc \u0627\u062d\u062a\u0645\u0627\u0644\u0627\u064b \u0627\u0632 \u0646\u062d\u0648 \u06a9\u0648\u062a\u0627\u0647\u06cc \u0646\u0627\u0634\u06cc \u0645\u06cc\u200c\u0634\u0648\u062f \u06a9\u0647 \u0627\u062c\u0627\u0632\u0647 \u0645\u06cc\u200c\u062f\u0647\u062f \u0644\u0627\u06cc\u0647\u200c\u0647\u0627\u06cc \u0641\u0639\u0627\u0644\u200c\u0633\u0627\u0632\u06cc \u0627\u0636\u0627\u0641\u0647 \u0634\u0648\u062f. \u0631\u0648\u06cc \u0628\u0627\u0644\u0627\u06cc \u0644\u0627\u06cc\u0647 \u0647\u0627\u06cc \u062f\u06cc\u06af\u0631\u060c \u062f\u0631 \u062a\u0639\u0631\u06cc\u0641 \u062e\u0648\u062f \u06cc\u06a9 \u0644\u0627\u06cc\u0647.  \u0645\u0627 \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a \u0646\u06af\u0627\u0647\u06cc \u0628\u0647 \u0632\u0645\u0627\u0646 \u062a\u0646\u0638\u06cc\u0645 \u0622\u0631\u06af\u0648\u0645\u0627\u0646 \u0627\u0646\u062f\u0627\u062e\u062a\u06cc\u0645 <code>True<\/code> \u06cc\u0627 <code>False<\/code>\u060c \u0648 \u0632\u0645\u0627\u0646\u06cc \u06a9\u0647 \u06cc\u06a9 \u062e\u0631\u0648\u062c\u06cc \u0628\u0627\u06cc\u062f \u0628\u0647 \u0635\u0648\u0631\u062a logit \u0628\u0627\u0642\u06cc \u0628\u0645\u0627\u0646\u062f \u06cc\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u06cc\u06a9 \u062a\u0627\u0628\u0639 \u0641\u0639\u0627\u0644 \u0633\u0627\u0632\u06cc \u0645\u0627\u0646\u0646\u062f SoftMax \u0645\u0646\u062a\u0642\u0644 \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 1403-01-04 00:14:04<br \/>\n<\/p>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-center kksr-valign-bottom\"\n    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Keras \u0645\u0648\u0627\u0646\u0639 \u0648\u0631\u0648\u062f \u062a\u0648\u062f\u0647\u200c\u0647\u0627 \u0631\u0627 \u06a9\u0627\u0647\u0634 \u0645\u06cc\u200c\u062f\u0647\u0646\u062f \u0648 \u062a\u0648\u0633\u0639\u0647 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc DL \u0631\u0627 \u0628\u0631\u0627\u06cc \u0627\u0641\u0631\u0627\u062f \u0628\u06cc\u200c\u062a\u062c\u0631\u0628\u0647 \u062f\u0645\u0648\u06a9\u0631\u0627\u062a\u06cc\u06a9 \u0645\u06cc\u200c\u06a9\u0646\u0646\u062f \u06a9\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u0646\u062f \u0628\u0647 \u0622\u0646 \u0627\u0639\u062a\u0645\u0627\u062f \u06a9\u0646\u0646\u062f. \u0631\u0648\u06cc \u067e\u06cc\u0634\u200c\u0641\u0631\u0636\u200c\u0647\u0627\u06cc \u0645\u0639\u0642\u0648\u0644 \u0648 API\u0647\u0627\u06cc \u0633\u0627\u062f\u0647\u200c\u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u062a\u062d\u0645\u0644 \u0628\u0627\u0631 \u0633\u0646\u06af\u06cc\u0646 \u0633\u0646\u06af\u06cc\u0646\u200c\u06a9\u0631\u062f\u0646 \u0648 \u062a\u0648\u0644\u06cc\u062f \u0646\u062a\u0627\u06cc\u062c \u0645\u0646\u0627\u0633\u0628. \u0647\u0646\u06af\u0627\u0645 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0648\u0627\u0628\u0639 \u0627\u0632 \u062f\u0633\u062a \u062f\u0627\u062f\u0646 Keras \u0628\u0631\u0627\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc\u060c \u06cc\u06a9 \u0633\u0631\u062f\u0631\u06af\u0645\u06cc 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