{"id":13913,"date":"2024-01-03T12:20:08","date_gmt":"2024-01-03T08:50:08","guid":{"rendered":"https:\/\/rasanegar.com\/blog\/%d8%af%d8%b1%da%a9-tensorflows-tf-function-decorator\/"},"modified":"2024-01-03T12:20:08","modified_gmt":"2024-01-03T08:50:08","slug":"%d8%af%d8%b1%da%a9-tensorflows-tf-function-decorator","status":"publish","type":"post","link":"https:\/\/rasanegaar.com\/blog\/%d8%af%d8%b1%da%a9-tensorflows-tf-function-decorator\/","title":{"rendered":"\u062f\u0631\u06a9 TensorFlow&#8217;s @tf.function Decorator"},"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\/%d8%af%d8%b1%da%a9-tensorflows-tf-function-decorator\/#%d9%85%d8%b9%d8%b1%d9%81%db%8c\" >\u0645\u0639\u0631\u0641\u06cc<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%af%d8%b1%da%a9-tensorflows-tf-function-decorator\/#%d8%af%da%a9%d9%88%d8%b1%d8%a7%d8%aa%d9%88%d8%b1%d9%87%d8%a7%db%8c_%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86_%d9%88_tffunction\" >\u062f\u06a9\u0648\u0631\u0627\u062a\u0648\u0631\u0647\u0627\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0648 tf.function()<\/a><\/li><\/ul><\/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\/%d8%af%d8%b1%da%a9-tensorflows-tf-function-decorator\/#%da%86%da%af%d9%88%d9%86%d9%87_tffunction_%da%a9%d8%a7%d8%b1%d8%9f\" >\u0686\u06af\u0648\u0646\u0647 tf.function() \u06a9\u0627\u0631\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\/%d8%af%d8%b1%da%a9-tensorflows-tf-function-decorator\/#%d8%a8%d9%87%d8%aa%d8%b1%db%8c%d9%86_%d8%aa%d9%85%d8%b1%db%8c%d9%86_%d9%87%d8%a7_%d8%a8%d8%a7_tffunction\" >\u0628\u0647\u062a\u0631\u06cc\u0646 \u062a\u0645\u0631\u06cc\u0646 \u0647\u0627 \u0628\u0627 @tf.function<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%af%d8%b1%da%a9-tensorflows-tf-function-decorator\/#%d9%86%d9%88%d8%b4%d8%aa%d9%86_%da%a9%d8%af_%d8%a8%d8%a7_%d8%b9%d9%85%d9%84%db%8c%d8%a7%d8%aa_tensorflow\" >\u0646\u0648\u0634\u062a\u0646 \u06a9\u062f \u0628\u0627 \u0639\u0645\u0644\u06cc\u0627\u062a TensorFlow<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%af%d8%b1%da%a9-tensorflows-tf-function-decorator\/#%d8%a7%d8%b2_%d8%a7%d8%b1%d8%ac%d8%a7%d8%b9_%d8%a8%d9%87_%d9%85%d8%aa%d8%ba%db%8c%d8%b1%d9%87%d8%a7%db%8c_%d8%ac%d9%87%d8%a7%d9%86%db%8c_%d8%ae%d9%88%d8%af%d8%af%d8%a7%d8%b1%db%8c_%da%a9%d9%86%db%8c%d8%af\" >\u0627\u0632 \u0627\u0631\u062c\u0627\u0639 \u0628\u0647 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u062c\u0647\u0627\u0646\u06cc \u062e\u0648\u062f\u062f\u0627\u0631\u06cc \u06a9\u0646\u06cc\u062f<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%af%d8%b1%da%a9-tensorflows-tf-function-decorator\/#%d8%a7%d8%b4%da%a9%d8%a7%d9%84_%d8%b2%d8%af%d8%a7%db%8c%db%8c_%d8%a7%db%8c%d9%85%db%8c%d9%84_%d9%85%d8%ad%d8%a7%d9%81%d8%b8%d8%aa_%d8%b4%d8%af%d9%87_s\" >\u0627\u0634\u06a9\u0627\u0644 \u0632\u062f\u0627\u06cc\u06cc (\u0627\u06cc\u0645\u06cc\u0644 \u0645\u062d\u0627\u0641\u0638\u062a \u0634\u062f\u0647)_s<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%af%d8%b1%da%a9-tensorflows-tf-function-decorator\/#%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\"> 5<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span><p> <br \/>\n<\/p>\n<div><noscript><\/noscript><\/p>\n<h2 id=\"introduction\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%b9%d8%b1%d9%81%db%8c\"><\/span>\u0645\u0639\u0631\u0641\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0628\u0647\u0628\u0648\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u06cc\u06a9 \u062d\u0644\u0642\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0633\u0627\u0639\u062a \u0647\u0627 \u062f\u0631 \u0632\u0645\u0627\u0646 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0647\u0646\u06af\u0627\u0645 \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0635\u0631\u0641\u0647 \u062c\u0648\u06cc\u06cc \u06a9\u0646\u062f.  \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0627\u0647 \u0647\u0627\u06cc \u0628\u0647\u0628\u0648\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u06a9\u062f TensorFlow \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 <code>tf.function()<\/code> \u062f\u06a9\u0648\u0631\u0627\u062a\u0648\u0631 &#8211; \u06cc\u06a9 \u062a\u063a\u06cc\u06cc\u0631 \u0633\u0627\u062f\u0647 \u0648 \u062a\u06a9 \u062e\u0637\u06cc \u06a9\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0639\u0645\u0644\u06a9\u0631\u062f\u0647\u0627\u06cc \u0634\u0645\u0627 \u0631\u0627 \u0628\u0647 \u0645\u06cc\u0632\u0627\u0646 \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u0633\u0631\u06cc\u0639\u062a\u0631 \u06a9\u0646\u062f.<\/p>\n<blockquote>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0631\u0627\u0647\u0646\u0645\u0627\u06cc \u06a9\u0648\u062a\u0627\u0647\u060c \u0631\u0648\u0634 \u0627\u0646\u062c\u0627\u0645 \u0622\u0646 \u0631\u0627 \u062a\u0648\u0636\u06cc\u062d \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u062f <code>tf.function()<\/code> \u0639\u0645\u0644\u06a9\u0631\u062f \u0631\u0627 \u0628\u0647\u0628\u0648\u062f \u0645\u06cc \u0628\u062e\u0634\u062f \u0648 \u0628\u0647 \u0628\u0631\u062e\u06cc \u0627\u0632 \u0628\u0647\u062a\u0631\u06cc\u0646 \u0634\u06cc\u0648\u0647 \u0647\u0627 \u0646\u06af\u0627\u0647\u06cc \u0628\u06cc\u0646\u062f\u0627\u0632\u06cc\u062f.<\/p>\n<\/blockquote>\n<h3 id=\"pythondecoratorsandtffunction\"><span class=\"ez-toc-section\" id=\"%d8%af%da%a9%d9%88%d8%b1%d8%a7%d8%aa%d9%88%d8%b1%d9%87%d8%a7%db%8c_%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86_%d9%88_tffunction\"><\/span>\u062f\u06a9\u0648\u0631\u0627\u062a\u0648\u0631\u0647\u0627\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0648 <em>tf.function()<\/em><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646\u060c \u062f\u06a9\u0648\u0631\u0627\u062a\u0648\u0631 \u062a\u0627\u0628\u0639\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0631\u0641\u062a\u0627\u0631 \u0633\u0627\u06cc\u0631 \u062a\u0648\u0627\u0628\u0639 \u0631\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u0645\u06cc \u062f\u0647\u062f.  \u0628\u0631\u0627\u06cc \u0645\u062b\u0627\u0644\u060c \u0641\u0631\u0636 \u06a9\u0646\u06cc\u062f \u062a\u0627\u0628\u0639 \u0632\u06cc\u0631 \u0631\u0627 \u062f\u0631 a \u0641\u0631\u0627\u062e\u0648\u0627\u0646\u06cc \u06a9\u0646\u06cc\u062f notebook \u0633\u0644\u0648\u0644:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> tensorflow <span class=\"hljs-keyword\">as<\/span> tf\n\nx = tf.random.uniform(shape=(<span class=\"hljs-number\">100<\/span>, <span class=\"hljs-number\">100<\/span>), minval=-<span class=\"hljs-number\">1<\/span>, maxval=<span class=\"hljs-number\">1<\/span>, dtype=tf.dtypes.float32)\n\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">some_costly_computation<\/span>(<span class=\"hljs-params\">x<\/span>):<\/span>\n    aux = tf.eye(<span class=\"hljs-number\">100<\/span>, dtype=tf.dtypes.float32)\n    result = tf.zeros(<span class=\"hljs-number\">100<\/span>, dtype = tf.dtypes.float32)\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\">1<\/span>,<span class=\"hljs-number\">100<\/span>):\n        aux = tf.matmul(x,aux)\/i\n        result = result + aux\n    <span class=\"hljs-keyword\">return<\/span> result\n\n%timeit some_costly_computation(x)\n<\/code><\/pre>\n<pre><code class=\"hljs\">16.2 ms \u00b1 103 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 100 loops each)\n<\/code><\/pre>\n<p>\u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u0627\u06af\u0631 \u062a\u0627\u0628\u0639 \u067e\u0631\u0647\u0632\u06cc\u0646\u0647 \u0631\u0627 \u0628\u0647 a \u0645\u0646\u062a\u0642\u0644 \u06a9\u0646\u06cc\u0645 <code>tf.function()<\/code>:<\/p>\n<pre><code class=\"hljs\">quicker_computation = tf.function(some_costly_computation)\n%timeit quicker_computation(x)\n<\/code><\/pre>\n<p>\u0645\u0627 \u06af\u0631\u0641\u062a\u06cc\u0645 <code>quicker_computation()<\/code> &#8211; \u06cc\u06a9 \u0639\u0645\u0644\u06a9\u0631\u062f \u062c\u062f\u06cc\u062f \u06a9\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0633\u0631\u06cc\u0639\u062a\u0631 \u0627\u0632 \u0639\u0645\u0644\u06a9\u0631\u062f \u0642\u0628\u0644\u06cc \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc \u0634\u0648\u062f:<\/p>\n<pre><code class=\"hljs\">4.99 ms \u00b1 139 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1 loop each)\n<\/code><\/pre>\n<p>\u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646\u060c <code>tf.function()<\/code> <em>\u0627\u0635\u0644\u0627\u062d \u0645\u06cc \u06a9\u0646\u062f<\/em> <code>some_costly_computation()<\/code>  \u0648 \u062e\u0631\u0648\u062c\u06cc \u0645\u06cc \u062f\u0647\u062f <code>quicker_computation()<\/code> \u062a\u0627\u0628\u0639.  \u062f\u06a9\u0648\u0631\u0627\u062a\u0648\u0631\u0647\u0627 \u0646\u06cc\u0632 \u0639\u0645\u0644\u06a9\u0631\u062f\u0647\u0627 \u0631\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u0645\u06cc \u062f\u0647\u0646\u062f\u060c \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646 \u0633\u0627\u062e\u062a\u0646 \u0622\u0646 \u0637\u0628\u06cc\u0639\u06cc \u0628\u0648\u062f <code>tf.function()<\/code> \u06cc\u06a9 \u062f\u06a9\u0648\u0631\u0627\u062a\u0648\u0631 \u0646\u06cc\u0632<\/p>\n<p>\u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0646\u0645\u0627\u062f \u062f\u06a9\u0648\u0631\u0627\u062a\u0648\u0631 \u0645\u0627\u0646\u0646\u062f \u0641\u0631\u0627\u062e\u0648\u0627\u0646\u06cc \u0627\u0633\u062a <code>tf.function(function)<\/code>:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-meta\">@tf.function<\/span>\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">quick_computation<\/span>(<span class=\"hljs-params\">x<\/span>):<\/span>\n  aux = tf.eye(<span class=\"hljs-number\">100<\/span>, dtype=tf.dtypes.float32)\n  result = tf.zeros(<span class=\"hljs-number\">100<\/span>, dtype = tf.dtypes.float32)\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\">1<\/span>,<span class=\"hljs-number\">100<\/span>):\n    aux = tf.matmul(x,aux)\/i\n    result = result + aux\n  <span class=\"hljs-keyword\">return<\/span> result\n\n%timeit quick_computation(x)\n<\/code><\/pre>\n<pre><code class=\"hljs\">5.09 ms \u00b1 283 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1 loop each)\n<\/code><\/pre>\n<h2 id=\"howdoestffunctionwork\"><span class=\"ez-toc-section\" id=\"%da%86%da%af%d9%88%d9%86%d9%87_tffunction_%da%a9%d8%a7%d8%b1%d8%9f\"><\/span>\u0686\u06af\u0648\u0646\u0647 <code>tf.function()<\/code> \u06a9\u0627\u0631\u061f<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<blockquote>\n<p>\u0686\u06af\u0648\u0646\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0639\u0645\u0644\u06a9\u0631\u062f\u0647\u0627\u06cc \u062e\u0627\u0635\u06cc \u0631\u0627 \u06f2 \u062a\u0627 \u06f3 \u0628\u0631\u0627\u0628\u0631 \u0633\u0631\u06cc\u0639\u200c\u062a\u0631 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u0645\u061f<\/p>\n<\/blockquote>\n<p>\u06a9\u062f TensorFlow \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u062f\u0631 \u062f\u0648 \u062d\u0627\u0644\u062a \u0627\u062c\u0631\u0627 \u06a9\u0631\u062f: <em>\u062d\u0627\u0644\u062a \u0645\u0634\u062a\u0627\u0642<\/em> \u0648 <em>\u062d\u0627\u0644\u062a \u0646\u0645\u0648\u062f\u0627\u0631<\/em>.  \u062d\u0627\u0644\u062a \u0627\u0634\u062a\u06cc\u0627\u0642 \u0631\u0648\u0634 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u0648 \u062a\u0639\u0627\u0645\u0644\u06cc \u0628\u0631\u0627\u06cc \u0627\u062c\u0631\u0627\u06cc \u06a9\u062f \u0627\u0633\u062a: <em>\u0647\u0631 \u0628\u0627\u0631 \u06a9\u0647 \u06cc\u06a9 \u062a\u0627\u0628\u0639 \u0631\u0627 \u0641\u0631\u0627\u062e\u0648\u0627\u0646\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u062f\u060c \u0627\u062c\u0631\u0627 \u0645\u06cc \u0634\u0648\u062f<\/em>.<\/p>\n<p>\u062d\u0627\u0644\u062a \u0646\u0645\u0648\u062f\u0627\u0631\u060c \u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u06a9\u0645\u06cc \u0645\u062a\u0641\u0627\u0648\u062a \u0627\u0633\u062a.  \u062f\u0631 \u062d\u0627\u0644\u062a \u06af\u0631\u0627\u0641\u060c \u0642\u0628\u0644 \u0627\u0632 \u0627\u062c\u0631\u0627\u06cc \u062a\u0627\u0628\u0639\u060c TensorFlow \u06cc\u06a9 \u0646\u0645\u0648\u062f\u0627\u0631 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u062f \u06a9\u0647 \u06cc\u06a9 \u0633\u0627\u062e\u062a\u0627\u0631 \u062f\u0627\u062f\u0647 \u062d\u0627\u0648\u06cc \u0639\u0645\u0644\u06cc\u0627\u062a \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632 \u0628\u0631\u0627\u06cc \u0627\u062c\u0631\u0627\u06cc \u062a\u0627\u0628\u0639 \u0627\u0633\u062a.  \u0646\u0645\u0648\u062f\u0627\u0631 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0628\u0647 TensorFlow \u0627\u062c\u0627\u0632\u0647 \u0645\u06cc \u062f\u0647\u062f \u062a\u0627 \u0645\u062d\u0627\u0633\u0628\u0627\u062a \u0631\u0627 \u0633\u0627\u062f\u0647 \u06a9\u0646\u062f \u0648 \u0641\u0631\u0635\u062a \u0647\u0627\u06cc\u06cc \u0628\u0631\u0627\u06cc \u0645\u0648\u0627\u0632\u06cc \u0633\u0627\u0632\u06cc \u067e\u06cc\u062f\u0627 \u06a9\u0646\u062f.  \u0627\u06cc\u0646 \u0646\u0645\u0648\u062f\u0627\u0631 \u0647\u0645\u0686\u0646\u06cc\u0646 \u062a\u0627\u0628\u0639 \u0631\u0627 \u0627\u0632 \u06a9\u062f \u067e\u0627\u06cc\u062a\u0648\u0646 \u067e\u0648\u0634\u0627\u0646\u0646\u062f\u0647 \u062c\u062f\u0627 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u0628\u0647 \u0622\u0646 \u0627\u062c\u0627\u0632\u0647 \u0645\u06cc \u062f\u0647\u062f \u062a\u0627 \u0628\u0647 \u0637\u0648\u0631 \u0645\u0648\u062b\u0631 \u0627\u062c\u0631\u0627 \u0634\u0648\u062f \u0631\u0648\u06cc \u0628\u0633\u06cc\u0627\u0631\u06cc \u0627\u0632 \u062f\u0633\u062a\u06af\u0627\u0647 \u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641<\/p>\n<p>\u06cc\u06a9 \u062a\u0627\u0628\u0639 \u062a\u0632\u0626\u06cc\u0646 \u0634\u062f\u0647 \u0628\u0627 <code>@tf.function<\/code> \u062f\u0631 \u062f\u0648 \u0645\u0631\u062d\u0644\u0647 \u0627\u062c\u0631\u0627 \u0645\u06cc \u0634\u0648\u062f:<\/p>\n<ol>\n<li>\u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0627\u0648\u0644\u060c TensorFlow \u06a9\u062f \u067e\u0627\u06cc\u062a\u0648\u0646 \u0631\u0627 \u0628\u0631\u0627\u06cc \u062a\u0627\u0628\u0639 \u0627\u062c\u0631\u0627 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u06cc\u06a9 \u0646\u0645\u0648\u062f\u0627\u0631 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0631\u0627 \u06a9\u0627\u0645\u067e\u0627\u06cc\u0644 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u0627\u062c\u0631\u0627\u06cc \u0647\u0631 \u0639\u0645\u0644\u06cc\u0627\u062a TensorFlow \u0631\u0627 \u0628\u0647 \u062a\u0627\u062e\u06cc\u0631 \u0645\u06cc \u0627\u0646\u062f\u0627\u0632\u062f.<\/li>\n<li>\u0633\u067e\u0633 \u0646\u0645\u0648\u062f\u0627\u0631 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0627\u062c\u0631\u0627 \u0645\u06cc \u0634\u0648\u062f.<\/li>\n<\/ol>\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> \u0645\u0631\u062d\u0644\u0647 \u0627\u0648\u0644 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0634\u0646\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a <strong><em>&#8220;\u0631\u062f\u06cc\u0627\u0628\u06cc&#8221;<\/em><\/strong>.<\/p>\n<\/p><\/div><\/div><\/div>\n<p>\u0627\u06af\u0631 \u0646\u06cc\u0627\u0632\u06cc \u0628\u0647 \u0627\u06cc\u062c\u0627\u062f \u06cc\u06a9 \u0646\u0645\u0648\u062f\u0627\u0631 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u062c\u062f\u06cc\u062f \u0646\u0628\u0627\u0634\u062f\u060c \u0645\u0631\u062d\u0644\u0647 \u0627\u0648\u0644 \u0646\u0627\u062f\u06cc\u062f\u0647 \u06af\u0631\u0641\u062a\u0647 \u0645\u06cc \u0634\u0648\u062f.  \u0627\u06cc\u0646 \u0639\u0645\u0644\u06a9\u0631\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u0631\u0627 \u0628\u0647\u0628\u0648\u062f \u0645\u06cc \u0628\u062e\u0634\u062f\u060c \u0627\u0645\u0627 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u062a\u0627\u0628\u0639 \u0645\u0627\u0646\u0646\u062f \u06a9\u062f \u0645\u0639\u0645\u0648\u0644\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 (\u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0647\u0631 \u062e\u0637 \u0627\u062c\u0631\u0627\u06cc\u06cc \u0627\u062c\u0631\u0627 \u0645\u06cc \u0634\u0648\u062f) \u0627\u062c\u0631\u0627 \u0646\u0645\u06cc \u0634\u0648\u062f.  \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u0642\u0628\u0644\u06cc \u062e\u0648\u062f \u0631\u0627 \u0627\u0635\u0644\u0627\u062d \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-meta\">@tf.function<\/span>\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">quick_computation<\/span>(<span class=\"hljs-params\">x<\/span>):<\/span>\n  <span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">'Only prints the first time!'<\/span>)\n  aux = tf.eye(<span class=\"hljs-number\">100<\/span>, dtype=tf.dtypes.float32)\n  result = tf.zeros(<span class=\"hljs-number\">100<\/span>, dtype = tf.dtypes.float32)\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\">1<\/span>,<span class=\"hljs-number\">100<\/span>):\n    aux = tf.matmul(x,aux)\/i\n    result = result + aux\n  <span class=\"hljs-keyword\">return<\/span> result\n\nquick_computation(x)\nquick_computation(x)\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0646\u062a\u06cc\u062c\u0647 \u062f\u0631:<\/p>\n<pre><code class=\"hljs\">Only prints the first time!\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 <code>print()<\/code> \u0641\u0642\u0637 \u06cc\u06a9 \u0628\u0627\u0631 \u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0631\u062f\u06cc\u0627\u0628\u06cc \u0627\u062c\u0631\u0627 \u0645\u06cc \u0634\u0648\u062f\u060c \u06cc\u0639\u0646\u06cc \u0632\u0645\u0627\u0646\u06cc \u06a9\u0647 \u06a9\u062f \u0645\u0639\u0645\u0648\u0644\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0627\u062c\u0631\u0627 \u0645\u06cc \u0634\u0648\u062f.  \u0641\u0631\u0627\u062e\u0648\u0627\u0646\u06cc \u0647\u0627\u06cc \u0628\u0639\u062f\u06cc \u062a\u0627\u0628\u0639 \u0641\u0642\u0637 \u0639\u0645\u0644\u06cc\u0627\u062a TenforFlow \u0631\u0627 \u0627\u0632 \u0646\u0645\u0648\u062f\u0627\u0631 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc (\u0639\u0645\u0644\u06cc\u0627\u062a TensorFlow) \u0627\u062c\u0631\u0627 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u0627\u06af\u0631 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645 <code>tf.print()<\/code> \u0628\u062c\u0627\u06cc:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-meta\">@tf.function<\/span>\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">quick_computation_with_print<\/span>(<span class=\"hljs-params\">x<\/span>):<\/span>\n  tf.<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Prints every time!\"<\/span>)\n  aux = tf.eye(<span class=\"hljs-number\">100<\/span>, dtype=tf.dtypes.float32)\n  result = tf.zeros(<span class=\"hljs-number\">100<\/span>, dtype = tf.dtypes.float32)\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\">1<\/span>,<span class=\"hljs-number\">100<\/span>):\n    aux = tf.matmul(x,aux)\/i\n    result = result + aux\n  <span class=\"hljs-keyword\">return<\/span> result\n\nquick_computation_with_print(x)\nquick_computation_with_print(x)\n\n<\/code><\/pre>\n<pre><code class=\"hljs\">Prints every time!\nPrints every time!\n<\/code><\/pre>\n<p>TensorFlow \u0634\u0627\u0645\u0644 <code>tf.print()<\/code> \u062f\u0631 \u0646\u0645\u0648\u062f\u0627\u0631 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0622\u0646 \u0686\u0648\u0646 \u06cc\u06a9 \u0639\u0645\u0644\u06cc\u0627\u062a TensorFlow \u0627\u0633\u062a &#8211; \u0646\u0647 \u06cc\u06a9 \u062a\u0627\u0628\u0639 \u0645\u0639\u0645\u0648\u0644\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646.<\/p>\n<div class=\"alert alert-warn\">\n<div class=\"flex\">\n<div class=\"flex-shrink-0 mr-3\"><\/div>\n<div class=\"w-full\">\n<p><strong>\u0647\u0634\u062f\u0627\u0631:<\/strong> \u0647\u0645\u0647 \u06a9\u062f\u0647\u0627\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u062f\u0631 \u0647\u0631 \u0641\u0631\u0627\u062e\u0648\u0627\u0646\u06cc \u0628\u0647 \u062a\u0627\u0628\u0639\u06cc \u06a9\u0647 \u0628\u0627 \u0622\u0646 \u062a\u0632\u0626\u06cc\u0646 \u0634\u062f\u0647 \u0627\u0633\u062a \u0627\u062c\u0631\u0627 \u0646\u0645\u06cc \u0634\u0648\u062f <code>@tf.function<\/code>.  \u067e\u0633 \u0627\u0632 \u0631\u062f\u06cc\u0627\u0628\u06cc\u060c \u0641\u0642\u0637 \u0639\u0645\u0644\u06cc\u0627\u062a \u0627\u0632 \u0646\u0645\u0648\u062f\u0627\u0631 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0627\u062c\u0631\u0627 \u0645\u06cc \u0634\u0648\u062f\u060c \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u06a9\u0647 \u0628\u0627\u06cc\u062f \u062f\u0631 \u06a9\u062f \u0645\u0627 \u06a9\u0645\u06cc \u062f\u0642\u062a \u0634\u0648\u062f.<\/p>\n<\/p><\/div><\/div><\/div>\n<h2 id=\"bestpracticeswithtffunction\"><span class=\"ez-toc-section\" id=\"%d8%a8%d9%87%d8%aa%d8%b1%db%8c%d9%86_%d8%aa%d9%85%d8%b1%db%8c%d9%86_%d9%87%d8%a7_%d8%a8%d8%a7_tffunction\"><\/span>\u0628\u0647\u062a\u0631\u06cc\u0646 \u062a\u0645\u0631\u06cc\u0646 \u0647\u0627 \u0628\u0627 <code>@tf.function<\/code><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3 id=\"writingcodewithtensorflowoperations\"><span class=\"ez-toc-section\" id=\"%d9%86%d9%88%d8%b4%d8%aa%d9%86_%da%a9%d8%af_%d8%a8%d8%a7_%d8%b9%d9%85%d9%84%db%8c%d8%a7%d8%aa_tensorflow\"><\/span>\u0646\u0648\u0634\u062a\u0646 \u06a9\u062f \u0628\u0627 \u0639\u0645\u0644\u06cc\u0627\u062a TensorFlow<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u06cc\u0645\u060c \u0628\u0631\u062e\u06cc \u0627\u0632 \u0628\u062e\u0634\u200c\u0647\u0627\u06cc \u06a9\u062f \u062a\u0648\u0633\u0637 \u0646\u0645\u0648\u062f\u0627\u0631 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0646\u0627\u062f\u06cc\u062f\u0647 \u06af\u0631\u0641\u062a\u0647 \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.  \u0627\u06cc\u0646 \u0627\u0645\u0631 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u0631\u0641\u062a\u0627\u0631 \u062a\u0627\u0628\u0639 \u0631\u0627 \u062f\u0631 \u0647\u0646\u06af\u0627\u0645 \u06a9\u062f\u0646\u0648\u06cc\u0633\u06cc \u0628\u0627 \u06a9\u062f \u067e\u0627\u06cc\u062a\u0648\u0646 &#8220;\u0639\u0627\u062f\u06cc&#8221; \u062f\u0634\u0648\u0627\u0631 \u0645\u06cc\u200c\u06a9\u0646\u062f\u060c \u0647\u0645\u0627\u0646\u0637\u0648\u0631 \u06a9\u0647 \u0642\u0628\u0644\u0627\u064b \u0645\u0634\u0627\u0647\u062f\u0647 \u06a9\u0631\u062f\u06cc\u0645. <code>print()<\/code>.  \u0628\u0631\u0627\u06cc \u062c\u0644\u0648\u06af\u06cc\u0631\u06cc \u0627\u0632 \u0631\u0641\u062a\u0627\u0631 \u063a\u06cc\u0631\u0645\u0646\u062a\u0638\u0631\u0647\u060c \u0628\u0647\u062a\u0631 \u0627\u0633\u062a \u0639\u0645\u0644\u06a9\u0631\u062f \u062e\u0648\u062f \u0631\u0627 \u062f\u0631 \u0635\u0648\u0631\u062a \u0627\u0645\u06a9\u0627\u0646 \u0628\u0627 \u0639\u0645\u0644\u06cc\u0627\u062a TensorFlow \u06a9\u062f\u0646\u0648\u06cc\u0633\u06cc \u06a9\u0646\u06cc\u062f.<\/p>\n<p>\u0628\u0631\u0627\u06cc \u0645\u062b\u0627\u0644\u060c <code>for<\/code> \u0648 <code>while<\/code> \u062d\u0644\u0642\u0647 \u0647\u0627 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0628\u0647 \u062d\u0644\u0642\u0647 TensorFlow \u0645\u0639\u0627\u062f\u0644 \u062a\u0628\u062f\u06cc\u0644 \u0634\u0648\u0646\u062f \u06cc\u0627 \u0646\u0647.  \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646\u060c \u0628\u0647\u062a\u0631 \u0627\u0633\u062a \u062f\u0631 \u0635\u0648\u0631\u062a \u0627\u0645\u06a9\u0627\u0646 \u062d\u0644\u0642\u0647 &#8220;for&#8221; \u062e\u0648\u062f \u0631\u0627 \u0628\u0647 \u0635\u0648\u0631\u062a \u0639\u0645\u0644\u06cc\u0627\u062a \u0628\u0631\u062f\u0627\u0631\u06cc \u0634\u062f\u0647 \u0628\u0646\u0648\u06cc\u0633\u06cc\u062f.  \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0639\u0645\u0644\u06a9\u0631\u062f \u06a9\u062f \u0634\u0645\u0627 \u0631\u0627 \u0628\u0647\u0628\u0648\u062f \u0645\u06cc \u0628\u062e\u0634\u062f \u0648 \u062a\u0636\u0645\u06cc\u0646 \u0645\u06cc \u06a9\u0646\u062f \u06a9\u0647 \u0639\u0645\u0644\u06a9\u0631\u062f \u0634\u0645\u0627 \u0628\u0647 \u062f\u0631\u0633\u062a\u06cc \u0631\u062f\u06cc\u0627\u0628\u06cc \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0646\u0645\u0648\u0646\u0647 \u0645\u0648\u0627\u0631\u062f \u0632\u06cc\u0631 \u0631\u0627 \u062f\u0631 \u0646\u0638\u0631 \u0628\u06af\u06cc\u0631\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">x = tf.random.uniform(shape=(<span class=\"hljs-number\">100<\/span>, <span class=\"hljs-number\">100<\/span>), minval=-<span class=\"hljs-number\">1<\/span>, maxval=<span class=\"hljs-number\">1<\/span>, dtype=tf.dtypes.float32)\n\n<span class=\"hljs-meta\">@tf.function<\/span>\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">function_with_for<\/span>(<span class=\"hljs-params\">x<\/span>):<\/span>\n    summ = <span class=\"hljs-built_in\">float<\/span>(<span class=\"hljs-number\">0<\/span>)\n    <span class=\"hljs-keyword\">for<\/span> row <span class=\"hljs-keyword\">in<\/span> x:\n      summ = summ + tf.reduce_mean(row)\n    <span class=\"hljs-keyword\">return<\/span> summ\n\n<span class=\"hljs-meta\">@tf.function<\/span>\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">vectorized_function<\/span>(<span class=\"hljs-params\">x<\/span>):<\/span>\n  result = tf.reduce_mean(x, axis=<span class=\"hljs-number\">0<\/span>)\n  <span class=\"hljs-keyword\">return<\/span> tf.reduce_sum(result)\n\n\n<span class=\"hljs-built_in\">print<\/span>(function_with_for(x))\n<span class=\"hljs-built_in\">print<\/span>(vectorized_function(x))\n\n%timeit function_with_for(x)\n%timeit vectorized_function(x)\n<\/code><\/pre>\n<pre><code class=\"hljs\">tf.Tensor(0.672811, shape=(), dtype=float32)\ntf.Tensor(0.67281103, shape=(), dtype=float32)\n1.58 ms \u00b1 177 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\n440 \u00b5s \u00b1 8.34 \u00b5s per loop (mean \u00b1 std. dev. of 7 runs, 1000 loops each)\n<\/code><\/pre>\n<p>\u06a9\u062f \u0628\u0627 \u0639\u0645\u0644\u06cc\u0627\u062a TensorFlow \u0628\u0633\u06cc\u0627\u0631 \u0633\u0631\u06cc\u0639\u062a\u0631 \u0627\u0633\u062a.<\/p>\n<h3 id=\"avoidreferencestoglobalvariables\"><span class=\"ez-toc-section\" id=\"%d8%a7%d8%b2_%d8%a7%d8%b1%d8%ac%d8%a7%d8%b9_%d8%a8%d9%87_%d9%85%d8%aa%d8%ba%db%8c%d8%b1%d9%87%d8%a7%db%8c_%d8%ac%d9%87%d8%a7%d9%86%db%8c_%d8%ae%d9%88%d8%af%d8%af%d8%a7%d8%b1%db%8c_%da%a9%d9%86%db%8c%d8%af\"><\/span>\u0627\u0632 \u0627\u0631\u062c\u0627\u0639 \u0628\u0647 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u062c\u0647\u0627\u0646\u06cc \u062e\u0648\u062f\u062f\u0627\u0631\u06cc \u06a9\u0646\u06cc\u062f<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u06a9\u062f \u0632\u06cc\u0631 \u0631\u0627 \u062f\u0631 \u0646\u0638\u0631 \u0628\u06af\u06cc\u0631\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">x = tf.Variable(<span class=\"hljs-number\">2<\/span>, dtype=tf.dtypes.float32)\ny = <span class=\"hljs-number\">2<\/span>\n\n<span class=\"hljs-meta\">@tf.function<\/span>\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">power<\/span>(<span class=\"hljs-params\">x<\/span>):<\/span>\n  <span class=\"hljs-keyword\">return<\/span> tf.<span class=\"hljs-built_in\">pow<\/span>(x,y)\n\n<span class=\"hljs-built_in\">print<\/span>(power(x))\n\ny = <span class=\"hljs-number\">3<\/span>\n\n<span class=\"hljs-built_in\">print<\/span>(power(x))\n<\/code><\/pre>\n<pre><code class=\"hljs\">tf.Tensor(4.0, shape=(), dtype=float32)\ntf.Tensor(4.0, shape=(), dtype=float32)\n<\/code><\/pre>\n<p>\u0627\u0648\u0644\u06cc\u0646 \u0628\u0627\u0631 \u0639\u0645\u0644\u06a9\u0631\u062f \u062a\u0632\u0626\u06cc\u0646 \u0634\u062f\u0647 \u0627\u0633\u062a <code>power()<\/code> \u0641\u0631\u0627\u062e\u0648\u0627\u0646\u06cc \u0634\u062f\u060c \u0645\u0642\u062f\u0627\u0631 \u062e\u0631\u0648\u062c\u06cc 4 \u0645\u0648\u0631\u062f \u0627\u0646\u062a\u0638\u0627\u0631 \u0628\u0648\u062f. \u0627\u0645\u0627\u060c \u0628\u0627\u0631 \u062f\u0648\u0645\u060c \u062a\u0627\u0628\u0639 \u0646\u0627\u062f\u06cc\u062f\u0647 \u06af\u0631\u0641\u062a \u06a9\u0647 \u0645\u0642\u062f\u0627\u0631 <code>y<\/code> \u062a\u063a\u06cc\u06cc\u0631 \u06a9\u0631\u062f.  \u0627\u06cc\u0646 \u0628\u0647 \u0627\u06cc\u0646 \u062f\u0644\u06cc\u0644 \u0627\u0633\u062a \u06a9\u0647 \u0645\u0642\u062f\u0627\u0631 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u062c\u0647\u0627\u0646\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc \u062a\u0627\u0628\u0639 \u067e\u0633 \u0627\u0632 \u0631\u062f\u06cc\u0627\u0628\u06cc \u062b\u0627\u0628\u062a \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u06cc\u06a9 \u0631\u0627\u0647 \u0628\u0647\u062a\u0631 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0622\u0646 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f <code>tf.Variable()<\/code> \u0628\u0631\u0627\u06cc \u0647\u0645\u0647 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u062e\u0648\u062f \u0648 \u0647\u0631 \u062f\u0648 \u0631\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0622\u0631\u06af\u0648\u0645\u0627\u0646 \u0628\u0647 \u062a\u0627\u0628\u0639 \u062e\u0648\u062f \u0627\u0631\u0633\u0627\u0644 \u06a9\u0646\u06cc\u062f.<\/p>\n<pre><code class=\"hljs\">x = tf.Variable(<span class=\"hljs-number\">2<\/span>, dtype=tf.dtypes.float32)\ny = tf.Variable(<span class=\"hljs-number\">2<\/span>, dtype = tf.dtypes.float32)\n\n<span class=\"hljs-meta\">@tf.function<\/span>\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">power<\/span>(<span class=\"hljs-params\">x,y<\/span>):<\/span>\n  <span class=\"hljs-keyword\">return<\/span> tf.<span class=\"hljs-built_in\">pow<\/span>(x,y)\n\n<span class=\"hljs-built_in\">print<\/span>(power(x,y))\n\ny.assign(<span class=\"hljs-number\">3<\/span>)\n\n<span class=\"hljs-built_in\">print<\/span>(power(x,y))\n<\/code><\/pre>\n<pre><code class=\"hljs\">tf.Tensor(4.0, shape=(), dtype=float32)\ntf.Tensor(8.0, shape=(), dtype=float32)\n<\/code><\/pre>\n<h3 id=\"debugging_tffunction_s\"><span class=\"ez-toc-section\" id=\"%d8%a7%d8%b4%da%a9%d8%a7%d9%84_%d8%b2%d8%af%d8%a7%db%8c%db%8c_%d8%a7%db%8c%d9%85%db%8c%d9%84_%d9%85%d8%ad%d8%a7%d9%81%d8%b8%d8%aa_%d8%b4%d8%af%d9%87_s\"><\/span>\u0627\u0634\u06a9\u0627\u0644 \u0632\u062f\u0627\u06cc\u06cc (\u0627\u06cc\u0645\u06cc\u0644 \u0645\u062d\u0627\u0641\u0638\u062a \u0634\u062f\u0647)_s<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u0628\u0647 \u0637\u0648\u0631 \u06a9\u0644\u06cc\u060c \u0634\u0645\u0627 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u062e\u0648\u062f \u0631\u0627 \u062f\u0631 \u062d\u0627\u0644\u062a \u0645\u0634\u062a\u0627\u0642 \u0627\u0634\u06a9\u0627\u0644 \u0632\u062f\u0627\u06cc\u06cc \u06a9\u0646\u06cc\u062f \u0648 \u0633\u067e\u0633 \u0622\u0646\u0647\u0627 \u0631\u0627 \u0628\u0627 \u0622\u0646 \u062a\u0632\u0626\u06cc\u0646 \u06a9\u0646\u06cc\u062f <code>@tf.function<\/code> \u067e\u0633 \u0627\u0632 \u0627\u06cc\u0646\u06a9\u0647 \u06a9\u062f \u0634\u0645\u0627 \u0628\u0647 \u062f\u0631\u0633\u062a\u06cc \u0627\u062c\u0631\u0627 \u0634\u062f \u0632\u06cc\u0631\u0627 \u067e\u06cc\u0627\u0645 \u0647\u0627\u06cc \u062e\u0637\u0627 \u062f\u0631 \u062d\u0627\u0644\u062a \u0645\u0634\u062a\u0627\u0642 \u0622\u0645\u0648\u0632\u0646\u062f\u0647 \u062a\u0631 \u0647\u0633\u062a\u0646\u062f.<\/p>\n<p>\u0628\u0631\u062e\u06cc \u0627\u0632 \u0645\u0634\u06a9\u0644\u0627\u062a \u0631\u0627\u06cc\u062c \u0639\u0628\u0627\u0631\u062a\u0646\u062f \u0627\u0632 \u062e\u0637\u0627\u0647\u0627\u06cc \u0646\u0648\u0639 \u0648 \u0627\u0634\u06a9\u0627\u0644.  \u062e\u0637\u0627\u0647\u0627\u06cc \u0646\u0648\u0639 \u0632\u0645\u0627\u0646\u06cc \u0627\u062a\u0641\u0627\u0642 \u0645\u06cc \u0627\u0641\u062a\u062f \u06a9\u0647 \u062f\u0631 \u0646\u0648\u0639 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc \u062f\u0631\u06af\u06cc\u0631 \u062f\u0631 \u06cc\u06a9 \u0639\u0645\u0644\u06cc\u0627\u062a \u0639\u062f\u0645 \u062a\u0637\u0627\u0628\u0642 \u0648\u062c\u0648\u062f \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f:<\/p>\n<pre><code class=\"hljs\">x = tf.Variable(<span class=\"hljs-number\">1<\/span>, dtype = tf.dtypes.float32)\ny = tf.Variable(<span class=\"hljs-number\">1<\/span>, dtype = tf.dtypes.int32)\n\nz = tf.add(x,y)\n<\/code><\/pre>\n<pre><code class=\"hljs\">InvalidArgumentError: cannot compute AddV2 as input #1(zero-based) was expected to be a float tensor but is a int32 tensor (Op:AddV2)\n<\/code><\/pre>\n<p>\u062e\u0637\u0627\u0647\u0627\u06cc \u0646\u0648\u0639 \u0628\u0647 \u0631\u0627\u062d\u062a\u06cc \u0648\u0627\u0631\u062f \u0645\u06cc \u0634\u0648\u0646\u062f \u0648 \u0628\u0647 \u0631\u0627\u062d\u062a\u06cc \u0645\u06cc \u062a\u0648\u0627\u0646 \u0628\u0627 \u0641\u0631\u0633\u062a\u0627\u062f\u0646 \u06cc\u06a9 \u0645\u062a\u063a\u06cc\u0631 \u0628\u0647 \u0646\u0648\u0639 \u062f\u06cc\u06af\u0631\u06cc \u0622\u0646 \u0631\u0627 \u0628\u0631\u0637\u0631\u0641 \u06a9\u0631\u062f:<\/p>\n<pre><code class=\"hljs\">y = tf.cast(y, tf.dtypes.float32)\nz = tf.add(x, y) \ntf.<span class=\"hljs-built_in\">print<\/span>(z) \n<\/code><\/pre>\n<p>\u062e\u0637\u0627\u0647\u0627\u06cc \u0634\u06a9\u0644 \u0632\u0645\u0627\u0646\u06cc \u0627\u062a\u0641\u0627\u0642 \u0645\u06cc\u200c\u0627\u0641\u062a\u0646\u062f \u06a9\u0647 \u062a\u0627\u0646\u0633\u0648\u0631\u0647\u0627\u06cc \u0634\u0645\u0627 \u0634\u06a9\u0644 \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632 \u0639\u0645\u0644\u06cc\u0627\u062a \u0634\u0645\u0627 \u0631\u0627 \u0646\u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">x = tf.random.uniform(shape=(<span class=\"hljs-number\">100<\/span>, <span class=\"hljs-number\">100<\/span>), minval=-<span class=\"hljs-number\">1<\/span>, maxval=<span class=\"hljs-number\">1<\/span>, dtype=tf.dtypes.float32)\ny = tf.random.uniform(shape=(<span class=\"hljs-number\">1<\/span>, <span class=\"hljs-number\">100<\/span>), minval=-<span class=\"hljs-number\">1<\/span>, maxval=<span class=\"hljs-number\">1<\/span>, dtype=tf.dtypes.float32)\n\nz = tf.matmul(x,y)\n<\/code><\/pre>\n<pre><code class=\"hljs\">InvalidArgumentError: Matrix size-incompatible: In(0): (100,100), In(1): (1,100) (Op:MatMul)\n<\/code><\/pre>\n<p>\u06cc\u06a9\u06cc \u0627\u0632 \u0627\u0628\u0632\u0627\u0631\u0647\u0627\u06cc \u0645\u0646\u0627\u0633\u0628 \u0628\u0631\u0627\u06cc \u0631\u0641\u0639 \u0647\u0631 \u062f\u0648 \u0646\u0648\u0639 \u062e\u0637\u0627\u060c \u062f\u06cc\u0628\u0627\u06af\u0631 \u062a\u0639\u0627\u0645\u0644\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0647 \u0637\u0648\u0631 \u062e\u0648\u062f\u06a9\u0627\u0631 \u0622\u0646 \u0631\u0627 \u0641\u0631\u0627\u062e\u0648\u0627\u0646\u06cc \u06a9\u0646\u06cc\u062f. Jupyter \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0646\u0648\u062a \u0628\u0648\u06a9 <code>%pdb<\/code>.  \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0622\u0646\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u062a\u0627\u0628\u0639 \u062e\u0648\u062f \u0631\u0627 \u06a9\u062f\u0646\u0648\u06cc\u0633\u06cc \u06a9\u0631\u062f\u0647 \u0648 \u0622\u0646 \u0631\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0628\u0631\u062e\u06cc \u0645\u0648\u0627\u0631\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0631\u0627\u06cc\u062c \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f.  \u0627\u06af\u0631 \u062e\u0637\u0627\u06cc\u06cc \u0648\u062c\u0648\u062f \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f\u060c \u06cc\u06a9 \u0627\u0639\u0644\u0627\u0646 \u062a\u0639\u0627\u0645\u0644\u06cc \u0628\u0627\u0632 \u0645\u06cc \u0634\u0648\u062f.  \u0627\u06cc\u0646 \u0627\u0639\u0644\u0627\u0646 \u0628\u0647 \u0634\u0645\u0627 \u0627\u062c\u0627\u0632\u0647 \u0645\u06cc \u062f\u0647\u062f \u062a\u0627 \u0644\u0627\u06cc\u0647 \u0647\u0627\u06cc \u0627\u0646\u062a\u0632\u0627\u0639\u06cc \u0631\u0627 \u062f\u0631 \u06a9\u062f \u062e\u0648\u062f \u0628\u0627\u0644\u0627 \u0648 \u067e\u0627\u06cc\u06cc\u0646 \u06a9\u0646\u06cc\u062f \u0648 \u0645\u0642\u0627\u062f\u06cc\u0631\u060c \u0627\u0646\u0648\u0627\u0639 \u0648 \u0627\u0634\u06a9\u0627\u0644 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627\u06cc TensorFlow \u062e\u0648\u062f \u0631\u0627 \u0628\u0631\u0631\u0633\u06cc \u06a9\u0646\u06cc\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>\u0645\u0627 \u062f\u06cc\u062f\u06cc\u0645 \u06a9\u0647 \u062a\u0646\u0633\u0648\u0631\u0641\u0644\u0648 \u0686\u06af\u0648\u0646\u0647 \u0627\u0633\u062a <code>tf.function()<\/code> \u0639\u0645\u0644\u06a9\u0631\u062f \u0634\u0645\u0627 \u0631\u0627 \u06a9\u0627\u0631\u0622\u0645\u062f\u062a\u0631 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u0686\u06af\u0648\u0646\u0647 <code>@tf.function<\/code> \u062f\u06a9\u0648\u0631\u0627\u062a\u0648\u0631 \u0639\u0645\u0644\u06a9\u0631\u062f \u0631\u0627 \u0628\u0647 \u062e\u0648\u062f\u062a\u0627\u0646 \u0627\u0639\u0645\u0627\u0644 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u0627\u06cc\u0646 \u0627\u0641\u0632\u0627\u06cc\u0634 \u0633\u0631\u0639\u062a \u062f\u0631 \u0639\u0645\u0644\u06a9\u0631\u062f\u0647\u0627\u06cc\u06cc \u06a9\u0647 \u0628\u0627\u0631\u0647\u0627 \u0641\u0631\u0627\u062e\u0648\u0627\u0646\u06cc \u0645\u06cc \u0634\u0648\u0646\u062f\u060c \u0645\u0627\u0646\u0646\u062f \u0645\u0631\u0627\u062d\u0644 \u0622\u0645\u0648\u0632\u0634 \u0633\u0641\u0627\u0631\u0634\u06cc \u0628\u0631\u0627\u06cc \u0645\u062f\u0644 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0645\u0641\u06cc\u062f \u0627\u0633\u062a.<\/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 12:20:03<br \/>\n<\/p>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-center kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;center&quot;,&quot;id&quot;:&quot;13913&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;\u062f\u0631\u06a9 TensorFlow\\u0026#039;s @tf.function Decorator&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\"> 5<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span>\u0645\u0639\u0631\u0641\u06cc \u0628\u0647\u0628\u0648\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u06cc\u06a9 \u062d\u0644\u0642\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0633\u0627\u0639\u062a \u0647\u0627 \u062f\u0631 \u0632\u0645\u0627\u0646 \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0647\u0646\u06af\u0627\u0645 \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u0635\u0631\u0641\u0647 \u062c\u0648\u06cc\u06cc \u06a9\u0646\u062f. \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0627\u0647 \u0647\u0627\u06cc \u0628\u0647\u0628\u0648\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u06a9\u062f TensorFlow \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 tf.function() \u062f\u06a9\u0648\u0631\u0627\u062a\u0648\u0631 &#8211; \u06cc\u06a9 \u062a\u063a\u06cc\u06cc\u0631 \u0633\u0627\u062f\u0647 \u0648 \u062a\u06a9 \u062e\u0637\u06cc \u06a9\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0639\u0645\u0644\u06a9\u0631\u062f\u0647\u0627\u06cc \u0634\u0645\u0627 \u0631\u0627 \u0628\u0647 \u0645\u06cc\u0632\u0627\u0646 \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u0633\u0631\u06cc\u0639\u062a\u0631 \u06a9\u0646\u062f. \u062f\u0631 \u0627\u06cc\u0646 [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":9398,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1743,620],"tags":[],"class_list":["post-13913","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\/13913","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=13913"}],"version-history":[{"count":0,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/13913\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media\/9398"}],"wp:attachment":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media?parent=13913"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/categories?post=13913"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/tags?post=13913"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}