{"id":4530,"date":"2023-05-07T14:03:15","date_gmt":"2023-05-07T11:03:15","guid":{"rendered":"https:\/\/datakapital.com\/blog\/?p=4530"},"modified":"2025-07-20T17:22:33","modified_gmt":"2025-07-20T14:22:33","slug":"tensorboard-ve-keras-kutuphaneleri","status":"publish","type":"post","link":"https:\/\/datakapital.com\/blog\/tensorboard-ve-keras-kutuphaneleri\/","title":{"rendered":"Tensorboard ve Keras K\u00fct\u00fcphaneleri"},"content":{"rendered":"<p>TensorBoard, TensorFlow k\u00fct\u00fcphanesi ile birlikte kullan\u0131lan bir g\u00f6rselle\u015ftirme arac\u0131d\u0131r. Makine \u00f6\u011frenimi modellerinin performans\u0131n\u0131 izlemek, hata ay\u0131klamak ve optimize etmek i\u00e7in kullan\u0131l\u0131r. Keras ise, a\u00e7\u0131k kaynakl\u0131 bir <a href=\"https:\/\/datakapital.com\/blog\/yapay-sinir-aglari-nedir-ve-nasil-calisir\/\">yapay sinir a\u011f\u0131<\/a> k\u00fct\u00fcphanesidir ve \u00f6zellikle yapay sinir a\u011f\u0131 konusunda deneyimi olmayan geli\u015ftiriciler i\u00e7in ideal bir se\u00e7imdir. Keras, TensorBoard ile birlikte kullan\u0131labilir ve model e\u011fitimi s\u0131ras\u0131nda TensorBoard&#8217;a veri g\u00f6nderebilir. Bu sayede, modelin e\u011fitim performans\u0131 izlenebilir, model yap\u0131lar\u0131 g\u00f6rselle\u015ftirilebilir ve hata ay\u0131klama yap\u0131labilir. TensorBoard ve Keras k\u00fct\u00fcphaneleri birlikte kullan\u0131ld\u0131\u011f\u0131nda, yapay sinir a\u011f\u0131 projeleri daha verimli bir \u015fekilde geli\u015ftirilebilir.<\/p>\n<h4><span lang=\"tr\">Tensorboard K\u00fct\u00fcphanesi<\/span><\/h4>\n<p>TensorBoard, a\u00e7\u0131k kaynakl\u0131 bir makine \u00f6\u011frenimi k\u00fct\u00fcphanesi olan <a href=\"https:\/\/datakapital.com\/blog\/tensorflow-ile-yapay-sinir-aglari\/\">TensorFlow<\/a> taraf\u0131ndan geli\u015ftirilen bir g\u00f6rselle\u015ftirme arac\u0131d\u0131r. TensorBoard, yapay sinir a\u011flar\u0131 ve makine \u00f6\u011frenimi modellerinin performans\u0131n\u0131 izlemek, hata ay\u0131klamak ve optimize etmek i\u00e7in kullan\u0131l\u0131r.<\/p>\n<p>TensorBoard, bir\u00e7ok farkl\u0131 \u00f6zellik sunar. Bu \u00f6zellikler aras\u0131nda model performans\u0131n\u0131 izleme, model yap\u0131lar\u0131n\u0131 g\u00f6rselle\u015ftirme, \u00f6zet istatistiklerini g\u00f6r\u00fcnt\u00fcleme, model parametrelerini ayarlama ve hata ay\u0131klama gibi i\u015flemler yer al\u0131r. Bu \u00f6zellikler sayesinde, karma\u015f\u0131k ve b\u00fcy\u00fck veri setleri \u00fczerinde \u00e7al\u0131\u015fan <a href=\"https:\/\/datakapital.com\/\">makine \u00f6\u011frenimi projelerinde<\/a> verimli bir \u015fekilde kullan\u0131labilir.<\/p>\n<p>TensorBoard&#8217;un g\u00f6rselle\u015ftirme \u00f6zellikleri, model yap\u0131s\u0131n\u0131 ve performans\u0131n\u0131 anlamak i\u00e7in olduk\u00e7a \u00f6nemlidir. \u00d6rne\u011fin, TensorBoard&#8217;un g\u00f6sterdi\u011fi grafikler, a\u011f\u0131n katmanlar\u0131n\u0131n nas\u0131l ba\u011fland\u0131\u011f\u0131n\u0131 ve bilgi i\u015fleme yolunu g\u00f6sterir. Ayr\u0131ca, TensorBoard&#8217;un g\u00f6rselle\u015ftirme \u00f6zellikleri, modelin e\u011fitim ve do\u011frulama performans\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in kullan\u0131labilir.<\/p>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-4531\" src=\"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme.png\" alt=\"Tensorflow ve Tensorboard K\u00fct\u00fcphaneleri\" width=\"602\" height=\"219\" title=\"\" srcset=\"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme.png 602w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme-300x109.png 300w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme-150x55.png 150w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme-450x164.png 450w\" sizes=\"(max-width: 602px) 100vw, 602px\" \/><\/p>\n<h4><span lang=\"tr\">Tensorboard&#8217;un \u00d6zellikleri Nelerdir?<\/span><\/h4>\n<p>TensorBoard, TensorFlow k\u00fct\u00fcphanesi ile birlikte kullan\u0131lan bir g\u00f6rselle\u015ftirme arac\u0131d\u0131r ve bir\u00e7ok farkl\u0131 \u00f6zellik sunar. Bu \u00f6zellikler aras\u0131nda;<\/p>\n<ul>\n<li>Model performans\u0131n\u0131 izleme: TensorBoard, modelin performans\u0131n\u0131 do\u011frulama verileri \u00fczerinde ve e\u011fitim verileri \u00fczerinde g\u00f6stererek, modelin do\u011frulu\u011funu, kayb\u0131n\u0131 ve di\u011fer metrikleri izlemenizi sa\u011flar.<\/li>\n<li>Hata ay\u0131klama: TensorBoard, modelinizi e\u011fitirken ve test ederken, hata ay\u0131klama i\u015flemlerinizde size yard\u0131mc\u0131 olacak \u00e7e\u015fitli ara\u00e7lar sunar. \u00d6rne\u011fin, modelinizin e\u011fitim s\u00fcrecini s\u0131f\u0131rdan ba\u015flatmak veya modelinizi farkl\u0131 hiper parametrelerle yeniden e\u011fitmek gibi i\u015flemler yapabilirsiniz.<\/li>\n<li>Model yap\u0131s\u0131n\u0131 g\u00f6rselle\u015ftirme: TensorBoard, modelinizi interaktif bir grafik olarak g\u00f6rselle\u015ftirerek, her bir katmandaki n\u00f6ron say\u0131s\u0131, ba\u011flant\u0131lar ve di\u011fer yap\u0131sal \u00f6zellikler hakk\u0131nda bilgi sa\u011flar.<\/li>\n<li>\u00d6zet istatistiklerini g\u00f6r\u00fcnt\u00fcleme: TensorBoard, e\u011fitim s\u0131ras\u0131nda modelinizin \u00f6zet istatistiklerini (\u00f6rne\u011fin, hesaplanan ortalama veya standart sapma) g\u00f6rselle\u015ftirerek, modelinizin performans\u0131n\u0131 daha ayr\u0131nt\u0131l\u0131 bir \u015fekilde izlemenize yard\u0131mc\u0131 olur.<\/li>\n<li>Model parametrelerini ayarlama: TensorBoard, modelinize ait parametrelerin, \u00f6rne\u011fin a\u011f\u0131rl\u0131klar\u0131n veya e\u011fimlerin da\u011f\u0131l\u0131m\u0131n\u0131 g\u00f6rselle\u015ftirerek, modelinizi daha iyi anlaman\u0131za ve ayarlaman\u0131za yard\u0131mc\u0131 olur.<\/li>\n<li>TensorBoard API: TensorBoard, Python programlama dili ile uyumlu olan bir API sunar. Bu API, TensorFlow k\u00fct\u00fcphanesi ile birlikte kullan\u0131labilecek ve modellerin performans\u0131n\u0131 ve yap\u0131lar\u0131n\u0131 TensorBoard \u00fczerinde g\u00f6rselle\u015ftirmek i\u00e7in kullan\u0131labilir.<\/li>\n<\/ul>\n<p>TensorBoard&#8217;un sundu\u011fu bu \u00f6zellikler, \u00f6zellikle karma\u015f\u0131k ve b\u00fcy\u00fck veri setlerinin i\u015flendi\u011fi makine \u00f6\u011frenimi projelerinde \u00e7ok yararl\u0131d\u0131r.<\/p>\n<h4><span lang=\"tr\">Tensorboard&#8217;un Avantajlar\u0131 Nelerdir?<\/span><\/h4>\n<p>TensorBoard, yapay sinir a\u011f\u0131 ve makine \u00f6\u011frenimi projelerinde kullan\u0131lan bir g\u00f6rselle\u015ftirme arac\u0131d\u0131r. Bu ara\u00e7 sayesinde, modellerin performans\u0131 izlenebilir, hata ay\u0131klama yap\u0131labilir ve model yap\u0131lar\u0131 g\u00f6rselle\u015ftirilebilir. TensorBoard&#8217;un bir\u00e7ok avantaj\u0131 vard\u0131r;<\/p>\n<ul>\n<li>Esneklik: TensorBoard, TensorFlow k\u00fct\u00fcphanesi ile birlikte kullan\u0131labilir. Bu nedenle, TensorFlow kullanan herhangi bir makine \u00f6\u011frenimi projesinde kullan\u0131labilir.<\/li>\n<li>G\u00f6rselle\u015ftirme: TensorBoard, model yap\u0131lar\u0131n\u0131, e\u011fitim ve do\u011frulama performanslar\u0131n\u0131, \u00f6zet istatistiklerini ve daha fazlas\u0131n\u0131 g\u00f6rselle\u015ftirmek i\u00e7in kullan\u0131l\u0131r. Bu sayede, modellerin anla\u015f\u0131lmas\u0131 daha kolay hale gelir.<\/li>\n<li>Kolayl\u0131k: TensorBoard&#8217;un kullan\u0131m\u0131 olduk\u00e7a kolayd\u0131r. Kullan\u0131c\u0131 dostu aray\u00fcz\u00fc ve bir\u00e7ok \u00f6zellikleri, projelerin daha kolay bir \u015fekilde y\u00f6netilmesini sa\u011flar.<\/li>\n<li>Hata ay\u0131klama: TensorBoard, hata ay\u0131klama yapmak i\u00e7in kullan\u0131labilir. \u00d6rne\u011fin, modelin a\u011f\u0131rl\u0131klar\u0131n\u0131 veya gradyanlar\u0131 g\u00f6rselle\u015ftirmek, beklenmeyen sonu\u00e7lar\u0131 tespit etmek i\u00e7in yararl\u0131 olabilir.<\/li>\n<li>Performans izleme: TensorBoard, model performans\u0131n\u0131 izlemek i\u00e7in kullan\u0131l\u0131r. Modelin do\u011frulu\u011fu, kayb\u0131 ve di\u011fer performans \u00f6l\u00e7\u00fctleri gibi \u00f6zellikleri takip etmek m\u00fcmk\u00fcnd\u00fcr.<\/li>\n<li>Verimlilik: TensorBoard, b\u00fcy\u00fck ve karma\u015f\u0131k veri setleri ile \u00e7al\u0131\u015fan projelerde verimlili\u011fi art\u0131rabilir. \u00d6zellikle, modellerin performans\u0131 izlemek ve hata ay\u0131klamak i\u00e7in kullan\u0131ld\u0131\u011f\u0131nda \u00e7ok yararl\u0131d\u0131r.<\/li>\n<\/ul>\n<p>Bu avantajlar, TensorBoard&#8217;un yapay sinir a\u011f\u0131 ve makine \u00f6\u011frenimi projelerinde \u00f6nemli bir ara\u00e7 olmas\u0131n\u0131 sa\u011flar.<\/p>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-4532\" src=\"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorflow-sembolu.png\" alt=\"Tensorflow g\u00f6rsel\" width=\"480\" height=\"170\" title=\"\" srcset=\"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorflow-sembolu.png 480w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorflow-sembolu-300x106.png 300w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorflow-sembolu-150x53.png 150w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorflow-sembolu-450x159.png 450w\" sizes=\"(max-width: 480px) 100vw, 480px\" \/><\/p>\n<h4><span lang=\"tr\">Tensorboard ve TensorFlow Kullan\u0131m\u0131<\/span><\/h4>\n<p>TensorFlow, yapay sinir a\u011flar\u0131 ve makine \u00f6\u011frenimi modelleri olu\u015fturmak i\u00e7in kullan\u0131lan a\u00e7\u0131k kaynakl\u0131 bir k\u00fct\u00fcphanedir. TensorFlow, Google taraf\u0131ndan geli\u015ftirilmi\u015f ve bir\u00e7ok ara\u015ft\u0131rmac\u0131 ve geli\u015ftirici taraf\u0131ndan kullan\u0131lmaktad\u0131r. TensorFlow&#8217;un g\u00fc\u00e7l\u00fc yanlar\u0131 aras\u0131nda y\u00fcksek performansl\u0131 hesaplama, esneklik ve \u00f6l\u00e7eklenebilirlik yer al\u0131r.<\/p>\n<p>TensorBoard, TensorFlow k\u00fct\u00fcphanesi ile birlikte kullan\u0131lan bir g\u00f6rselle\u015ftirme arac\u0131d\u0131r. TensorBoard, modellerin performans\u0131n\u0131 izlemek, hata ay\u0131klamak ve optimize etmek i\u00e7in kullan\u0131labilir. Bu nedenle, TensorBoard&#8217;un kullan\u0131m\u0131, TensorFlow ile yapay sinir a\u011f\u0131 projeleri geli\u015ftirirken b\u00fcy\u00fck bir avantaj sa\u011flar.<\/p>\n<p>TensorBoard&#8217;un kullan\u0131m\u0131 olduk\u00e7a kolayd\u0131r. \u0130lk olarak, bir TensorFlow modeli olu\u015fturulur ve ard\u0131ndan TensorBoard ile ileti\u015fim kurmak i\u00e7in bir &#8220;SummaryWriter&#8221; nesnesi olu\u015fturulur. &#8220;SummaryWriter&#8221; nesnesi, modelin e\u011fitimi s\u0131ras\u0131nda TensorBoard&#8217;a veri g\u00f6nderir.<\/p>\n<p>TensorBoard&#8217;un bir\u00e7ok \u00f6zelli\u011fi vard\u0131r. Bunlar aras\u0131nda model performans\u0131n\u0131 izleme, model yap\u0131lar\u0131n\u0131 g\u00f6rselle\u015ftirme, \u00f6zet istatistiklerini g\u00f6r\u00fcnt\u00fcleme, model parametrelerini ayarlama ve hata ay\u0131klama gibi i\u015flemler yer al\u0131r. Bu \u00f6zellikler, karma\u015f\u0131k ve b\u00fcy\u00fck veri setlerinin i\u015flendi\u011fi makine \u00f6\u011frenimi projelerinde \u00e7ok yararl\u0131d\u0131r.<\/p>\n<p>TensorBoard, model performans\u0131n\u0131n izlenmesi i\u00e7in bir\u00e7ok grafik ve grafiksel ara\u00e7lar sunar. Bu ara\u00e7lar, modelin do\u011frulu\u011funu, kay\u0131p fonksiyonlar\u0131n\u0131, do\u011fruluk oranlar\u0131n\u0131 ve di\u011fer performans metriklerini g\u00f6sterir. Ayr\u0131ca, modelin e\u011fitimi s\u0131ras\u0131nda TensorBoard, modelin girdilerini, \u00e7\u0131kt\u0131lar\u0131n\u0131 ve ara katmanlar\u0131n\u0131 da g\u00f6rselle\u015ftirebilir.<\/p>\n<p>TensorBoard&#8217;un hata ay\u0131klama \u00f6zellikleri de olduk\u00e7a g\u00fc\u00e7l\u00fcd\u00fcr. TensorBoard, modelin a\u011f\u0131rl\u0131klar\u0131n\u0131, gradyanlar\u0131n\u0131 ve di\u011fer parametrelerini de g\u00f6rselle\u015ftirebilir. B\u00f6ylece, modelin davran\u0131\u015f\u0131n\u0131 analiz etmek ve hatalar\u0131 tespit etmek daha kolay hale gelir.<\/p>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-4533\" src=\"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Keras-Kutuphanesi-Sembolu.png\" alt=\"Keras Nas\u0131l \u00c7al\u0131\u015f\u0131r\" width=\"528\" height=\"244\" title=\"\" srcset=\"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Keras-Kutuphanesi-Sembolu.png 528w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Keras-Kutuphanesi-Sembolu-300x139.png 300w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Keras-Kutuphanesi-Sembolu-150x69.png 150w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Keras-Kutuphanesi-Sembolu-450x208.png 450w\" sizes=\"(max-width: 528px) 100vw, 528px\" \/><\/p>\n<h4><span lang=\"tr\">Keras K\u00fct\u00fcphanesi<\/span><\/h4>\n<p><a href=\"https:\/\/datakapital.com\/blog\/konvolusyonel-sinir-aglari-nedir\/\">Keras, Python programlama dili i\u00e7in a\u00e7\u0131k kaynakl\u0131 bir yapay sinir a\u011f\u0131 k\u00fct\u00fcphanesidir<\/a>. Keras, yapay sinir a\u011f\u0131 modellerinin olu\u015fturulmas\u0131, e\u011fitilmesi ve de\u011ferlendirilmesi i\u00e7in kullan\u0131l\u0131r. Yap\u0131sal olarak, Keras, TensorFlow, Theano ve CNTK gibi di\u011fer yapay sinir a\u011f\u0131 k\u00fct\u00fcphaneleri ile birlikte kullan\u0131labilir. Keras, \u00f6zellikle geli\u015ftiricilerin yapay sinir a\u011f\u0131 modelleri olu\u015ftururken daha az kod yazmalar\u0131n\u0131 sa\u011flayarak, modellerin h\u0131zl\u0131 bir \u015fekilde prototip edilmesine olanak tan\u0131r.<\/p>\n<p>Keras, y\u00fcksek seviye bir API olarak tasarlanm\u0131\u015ft\u0131r ve kullan\u0131m\u0131 kolay bir aray\u00fcz sa\u011flar. Bu nedenle, \u00f6zellikle yapay sinir a\u011f\u0131 konusunda deneyimi olmayan geli\u015ftiriciler i\u00e7in ideal bir se\u00e7imdir. Keras, bir\u00e7ok \u00f6nceden e\u011fitilmi\u015f model sunar ve bu modeller, geli\u015ftiricilerin ihtiya\u00e7lar\u0131na g\u00f6re \u00f6zelle\u015ftirilebilir.<\/p>\n<p>Keras&#8217;\u0131n di\u011fer bir avantaj\u0131, bir\u00e7ok farkl\u0131 sinir a\u011f\u0131 katman\u0131 ve aktivasyon fonksiyonlar\u0131 gibi \u00e7e\u015fitli mod\u00fcl se\u00e7eneklerine sahip olmas\u0131d\u0131r. Bu mod\u00fcller, geli\u015ftiricilerin yapay sinir a\u011f\u0131 modellerini \u00f6zelle\u015ftirmelerine ve istedikleri sonu\u00e7lar\u0131 elde etmelerine olanak tan\u0131r. Keras, ayr\u0131ca sinir a\u011f\u0131 modellerini e\u011fitmek i\u00e7in farkl\u0131 optimizasyon algoritmalar\u0131 ve kay\u0131p fonksiyonlar\u0131 sunar.<\/p>\n<h4><span lang=\"tr\">Keras&#8217;\u0131n \u00d6zellikleri Nelerdir?<\/span><\/h4>\n<p>Keras, Python programlama dili i\u00e7in a\u00e7\u0131k kaynakl\u0131 bir yapay sinir a\u011f\u0131 k\u00fct\u00fcphanesidir. Kullan\u0131m\u0131 kolay bir aray\u00fcz ve bir\u00e7ok \u00f6nceden e\u011fitilmi\u015f model sunar. Keras&#8217;\u0131n \u00f6zellikleri \u015funlard\u0131r;<\/p>\n<ul>\n<li>Mod\u00fcler Yap\u0131: Keras, mod\u00fcler bir yap\u0131ya sahiptir. Bu nedenle, yapay sinir a\u011f\u0131 modellerini olu\u015fturmak i\u00e7in farkl\u0131 mod\u00fcller kullan\u0131labilir. Keras ayr\u0131ca, farkl\u0131 mod\u00fcllerin birle\u015ftirilerek daha karma\u015f\u0131k modeller olu\u015fturulmas\u0131na olanak tan\u0131r.<\/li>\n<li>Uyumlu Backend: Keras, farkl\u0131 yapay sinir a\u011f\u0131 backend&#8217;leri ile uyumlu \u00e7al\u0131\u015fabilir. Bunlar aras\u0131nda TensorFlow, Theano ve CNTK gibi pop\u00fcler backend&#8217;ler yer al\u0131r. Bu nedenle, geli\u015ftiriciler, Keras&#8217;\u0131 backend olarak tercih ettikleri herhangi bir k\u00fct\u00fcphane ile kullanabilirler.<\/li>\n<li>\u00d6nceden E\u011fitilmi\u015f Modeller: Keras, \u00f6nceden e\u011fitilmi\u015f bir\u00e7ok model sunar. Bu modeller, resim s\u0131n\u0131fland\u0131rma, dil i\u015fleme, duyarl\u0131l\u0131k analizi ve di\u011fer bir\u00e7ok yapay zeka uygulamas\u0131 i\u00e7in kullan\u0131labilir. Bu modeller, geli\u015ftiricilerin kendi verilerini e\u011fitmeden \u00f6nce bir\u00e7ok temel i\u015flemi yapmalar\u0131na olanak tan\u0131r.<\/li>\n<li>Kolay Yap\u0131land\u0131rma: Keras, birka\u00e7 sat\u0131r kod ile bir yapay sinir a\u011f\u0131 modeli olu\u015fturulmas\u0131n\u0131 sa\u011flar. Keras&#8217;\u0131n basit aray\u00fcz\u00fc, geli\u015ftiricilerin model parametrelerini ayarlamas\u0131n\u0131 ve h\u0131zl\u0131 bir \u015fekilde sonu\u00e7lar elde etmesini kolayla\u015ft\u0131r\u0131r.<\/li>\n<li>H\u0131zl\u0131 \u00d6\u011frenme: Keras, h\u0131zl\u0131 bir \u015fekilde \u00f6\u011frenmek i\u00e7in uygun bir aray\u00fcze sahiptir. Geli\u015ftiriciler, Keras&#8217;\u0131n belgelerini okuyarak ve \u00f6rneklerini inceleyerek h\u0131zl\u0131 bir \u015fekilde \u00f6\u011frenebilirler.<\/li>\n<li>T\u00fcmle\u015fik Veri \u0130\u015fleme: Keras, girdi verilerini i\u015flemek i\u00e7in t\u00fcmle\u015fik bir API sunar. Bu API, verileri y\u00fckleme, \u00f6ni\u015fleme ve e\u011fitim s\u0131ras\u0131nda verileri art\u0131rma i\u015flemlerini kolayla\u015ft\u0131r\u0131r.<\/li>\n<li>Geni\u015fletilebilir: Keras, farkl\u0131 ihtiya\u00e7lara g\u00f6re geni\u015fletilebilir bir yap\u0131ya sahiptir. Geli\u015ftiriciler, kendi mod\u00fcllerini olu\u015fturarak veya Keras&#8217;\u0131n mevcut mod\u00fcllerini de\u011fi\u015ftirerek, ihtiya\u00e7lar\u0131na uygun bir yapay sinir a\u011f\u0131 modeli olu\u015fturabilirler.<\/li>\n<\/ul>\n<h4>Keras&#8217;\u0131n Avantajlar\u0131 Nelerdir?<\/h4>\n<p>Keras, yapay sinir a\u011f\u0131 modellerinin olu\u015fturulmas\u0131 ve e\u011fitimi i\u00e7in kullan\u0131lan bir k\u00fct\u00fcphanedir. Keras&#8217;\u0131n baz\u0131 avantajlar\u0131 \u015funlard\u0131r;<\/p>\n<ul>\n<li>Kolay Kullan\u0131m: Keras, kullan\u0131m\u0131 kolay bir aray\u00fcz sunar ve do\u011fru bir \u015fekilde yap\u0131land\u0131r\u0131ld\u0131\u011f\u0131ndan, kullan\u0131c\u0131lar\u0131n daha az kod yazarak daha h\u0131zl\u0131 sonu\u00e7lar elde etmelerini sa\u011flar.<\/li>\n<li>Mod\u00fcler Yap\u0131: Keras, mod\u00fcler bir yap\u0131ya sahiptir ve katmanlar, kay\u0131p fonksiyonlar\u0131 ve optimizasyon algoritmalar\u0131 gibi \u00e7e\u015fitli bile\u015fenleri bir araya getirerek \u00f6zelle\u015ftirilmi\u015f yapay sinir a\u011f\u0131 modellerinin kolayca olu\u015fturulmas\u0131n\u0131 sa\u011flar.<\/li>\n<li>\u00c7oklu Arka U\u00e7 Deste\u011fi: Keras, TensorFlow, Theano ve CNTK gibi farkl\u0131 yapay sinir a\u011f\u0131 k\u00fct\u00fcphaneleriyle birlikte kullan\u0131labilir. Bu sayede, kullan\u0131c\u0131lar hangi arka u\u00e7un en iyi performans\u0131 sa\u011flad\u0131\u011f\u0131na ba\u011fl\u0131 olarak se\u00e7im yapabilirler.<\/li>\n<li>\u00d6nceden E\u011fitilmi\u015f Modeller: Keras, \u00f6nceden e\u011fitilmi\u015f modeller sunar ve bu modellerin y\u00fcklenmesi ve \u00f6zelle\u015ftirilmesi kolayd\u0131r. Bu \u00f6nceden e\u011fitilmi\u015f modeller, daha az veriye sahip projelerde kullan\u0131labilir ve daha h\u0131zl\u0131 sonu\u00e7lar elde edilmesini sa\u011flar.<\/li>\n<\/ul>\n<p>Topluluk Deste\u011fi: Keras, geni\u015f bir kullan\u0131c\u0131 toplulu\u011funa sahiptir ve bu topluluk, kullan\u0131c\u0131lar\u0131n sorular\u0131n\u0131 yan\u0131tlamak ve sorunlar\u0131n\u0131 \u00e7\u00f6zmek i\u00e7in forumlar, bloglar ve di\u011fer kaynaklar sa\u011flar.<\/p>\n<h4><span lang=\"tr\">Keras ve TensorFlow Kullan\u0131m\u0131<\/span><\/h4>\n<p>Keras ve TensorFlow, yapay sinir a\u011f\u0131 ve makine \u00f6\u011frenimi projelerinde birlikte kullan\u0131lan \u00f6nemli ara\u00e7lard\u0131r. Keras, bir\u00e7ok yapay sinir a\u011f\u0131 k\u00fct\u00fcphanesiyle birlikte kullan\u0131labilen, Python tabanl\u0131 bir a\u00e7\u0131k kaynakl\u0131 yapay sinir a\u011f\u0131 k\u00fct\u00fcphanesidir. TensorFlow ise, Google taraf\u0131ndan geli\u015ftirilen a\u00e7\u0131k kaynak kodlu bir makine \u00f6\u011frenimi k\u00fct\u00fcphanesidir ve daha d\u00fc\u015f\u00fck seviyeli i\u015flemler i\u00e7in optimize edilmi\u015ftir.<\/p>\n<p>Keras, kullan\u0131m\u0131 kolay bir aray\u00fcz sa\u011flar ve \u00f6nceden e\u011fitilmi\u015f modelleme algoritmalar\u0131 sunar. Bu nedenle, yapay sinir a\u011f\u0131 konusunda deneyimi olmayan geli\u015ftiriciler i\u00e7in ideal bir se\u00e7imdir. Keras, bir\u00e7ok yayg\u0131n kullan\u0131lan veri bilimi ve yapay sinir a\u011f\u0131 modeli kullan\u0131larak \u00f6nceden e\u011fitilmi\u015f modeller sa\u011flar. Ayr\u0131ca, \u00f6zelle\u015ftirilmi\u015f modeller olu\u015fturmak i\u00e7in kullan\u0131labilen esnek bir API sunar.<\/p>\n<p>TensorFlow ise, daha d\u00fc\u015f\u00fck seviyeli i\u015flemler i\u00e7in optimize edilmi\u015f bir k\u00fct\u00fcphanedir. TensorFlow, d\u00fc\u015f\u00fck seviyeli i\u015flemler i\u00e7in kullan\u0131lan veri ak\u0131\u015f\u0131 grafi\u011fi yap\u0131s\u0131n\u0131 kullan\u0131r. Bu yap\u0131s\u0131 sayesinde, birden fazla i\u015flemi paralel olarak \u00e7al\u0131\u015ft\u0131rabilir ve bu i\u015flemler aras\u0131nda veri ak\u0131\u015f\u0131n\u0131 kontrol edebilirsiniz. Ayr\u0131ca, TensorFlow, veri paralelizasyonu ve GPU h\u0131zland\u0131rmas\u0131 gibi geli\u015fmi\u015f \u00f6zellikler sunar.<\/p>\n<p>Keras, TensorFlow ile birlikte kullan\u0131labilir ve TensorFlow&#8217;un t\u00fcm \u00f6zelliklerini kullanabilir. Keras, TensorFlow&#8217;un y\u00fcksek seviyeli API&#8217;sini kullanarak, model olu\u015fturma, e\u011fitim ve de\u011ferlendirme i\u015flemlerini kolayla\u015ft\u0131r\u0131r. Keras, TensorFlow ile birlikte kullan\u0131ld\u0131\u011f\u0131nda, yapay sinir a\u011f\u0131 projeleri daha verimli bir \u015fekilde geli\u015ftirilebilir.<\/p>\n<p>Keras ve TensorFlow birbirlerini tamamlayan iki \u00f6nemli ara\u00e7t\u0131r. Keras, kullan\u0131m\u0131 kolay bir aray\u00fcz sa\u011flar ve \u00f6nceden e\u011fitilmi\u015f modeller sunar. TensorFlow ise, daha d\u00fc\u015f\u00fck seviyeli i\u015flemler i\u00e7in optimize edilmi\u015f bir k\u00fct\u00fcphanedir ve veri paralelizasyonu ve GPU h\u0131zland\u0131rmas\u0131 gibi geli\u015fmi\u015f \u00f6zellikler sunar. Bu iki k\u00fct\u00fcphane birlikte kullan\u0131ld\u0131\u011f\u0131nda, yapay sinir a\u011f\u0131 projeleri daha verimli bir \u015fekilde geli\u015ftirilebilir.<\/p>\n<h4><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-4534\" src=\"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Keras-ve-Tensorflow-Birlikte-Kullanimi.png\" alt=\"Keras ve Tensorflow K\u00fct\u00fcphaneleri Kullan\u0131m\u0131 \" width=\"546\" height=\"294\" title=\"\" srcset=\"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Keras-ve-Tensorflow-Birlikte-Kullanimi.png 546w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Keras-ve-Tensorflow-Birlikte-Kullanimi-300x162.png 300w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Keras-ve-Tensorflow-Birlikte-Kullanimi-150x81.png 150w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Keras-ve-Tensorflow-Birlikte-Kullanimi-450x242.png 450w\" sizes=\"(max-width: 546px) 100vw, 546px\" \/>Tensorboard ve Keras Birlikte Kullan\u0131m\u0131<\/h4>\n<p>Keras, Python&#8217;da kullan\u0131lan bir yapay sinir a\u011f\u0131 k\u00fct\u00fcphanesidir ve derin \u00f6\u011frenme k\u00fct\u00fcphaneleri olan TensorFlow, Theano ve CNTK gibi arka u\u00e7lara sahiptir. Keras, yapay sinir a\u011f\u0131 modelleri olu\u015fturmak, e\u011fitmek ve de\u011ferlendirmek i\u00e7in kullan\u0131lan bir ara\u00e7t\u0131r. Keras&#8217;\u0131n \u00f6zellikleri aras\u0131nda basit bir aray\u00fcz, mod\u00fcler tasar\u0131m, \u00f6nceden e\u011fitilmi\u015f modeller ve \u00e7e\u015fitli katman tipleri gibi \u00f6zellikler yer al\u0131r.<\/p>\n<p>TensorBoard ise TensorFlow k\u00fct\u00fcphanesi ile birlikte kullan\u0131lan bir g\u00f6rselle\u015ftirme arac\u0131d\u0131r. Model e\u011fitimi s\u0131ras\u0131nda verileri g\u00f6rselle\u015ftirmek, model performans\u0131n\u0131 izlemek, hata ay\u0131klama yapmak ve model yap\u0131lar\u0131n\u0131 g\u00f6rselle\u015ftirmek i\u00e7in kullan\u0131l\u0131r. TensorBoard, Keras ile birlikte kullan\u0131labildi\u011finde, model e\u011fitimi s\u0131ras\u0131nda ger\u00e7ek zamanl\u0131 olarak model performans\u0131n\u0131 ve \u00f6\u011frenme e\u011frilerini takip etmek i\u00e7in kullan\u0131labilir.<\/p>\n<p>Keras&#8217;\u0131n TensorBoard ile birlikte kullan\u0131m\u0131, model e\u011fitimi s\u0131ras\u0131nda do\u011frudan TensorBoard&#8217;da grafiksel olarak g\u00f6rselle\u015ftirilen e\u011fitim verileri toplamas\u0131na izin verir. Bu sayede, modelin e\u011fitim performans\u0131n\u0131 izlemek, model yap\u0131lar\u0131n\u0131 g\u00f6rselle\u015ftirmek ve hata ay\u0131klama yapmak m\u00fcmk\u00fcn olur. Keras, TensorBoard&#8217;un &#8220;callback&#8221; API&#8217;sini kullanarak, model e\u011fitimi s\u0131ras\u0131nda TensorBoard&#8217;a veri g\u00f6nderebilir. Bu sayede, modelin performans\u0131n\u0131n izlenmesi ve geli\u015ftirilmesi kolayla\u015f\u0131r.<\/p>\n<p>Keras, \u00f6nceden e\u011fitilmi\u015f bir\u00e7ok model sunar. Bu modeller, g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rmas\u0131, do\u011fal dil i\u015fleme, ses tan\u0131ma gibi bir\u00e7ok makine \u00f6\u011frenimi uygulamas\u0131nda kullan\u0131labilir. Keras ve TensorBoard&#8217;un birlikte kullan\u0131lmas\u0131, makine \u00f6\u011frenimi projelerinin geli\u015ftirilmesini ve optimize edilmesini kolayla\u015ft\u0131r\u0131r. Model e\u011fitimini izlemek ve hata ay\u0131klama yapmak i\u00e7in TensorBoard kullan\u0131l\u0131rken, model olu\u015fturma, derleme ve e\u011fitim i\u00e7in Keras kullan\u0131l\u0131r.<\/p>\n<h4><span lang=\"tr\">Tensorboard Callback API&#8217;si<\/span><\/h4>\n<p><a href=\"https:\/\/github.com\/tensorflow\/tensorboard\" target=\"_blank\" rel=\"noopener\">TensorBoard,<\/a> TensorFlow k\u00fct\u00fcphanesi ile birlikte kullan\u0131lan bir g\u00f6rselle\u015ftirme arac\u0131d\u0131r. Model e\u011fitimi s\u0131ras\u0131nda verileri g\u00f6rselle\u015ftirmek, model performans\u0131n\u0131 izlemek, hata ay\u0131klama yapmak ve model yap\u0131lar\u0131n\u0131 g\u00f6rselle\u015ftirmek i\u00e7in kullan\u0131l\u0131r. TensorBoard&#8217;un &#8220;callback&#8221; API&#8217;si, TensorFlow ve Keras modellerinin e\u011fitimi s\u0131ras\u0131nda TensorBoard&#8217;a veri g\u00f6ndermek i\u00e7in kullan\u0131lan bir aray\u00fczd\u00fcr<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-4535 size-full\" src=\"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Callback-APIsi-Nasil-Calisir.png\" alt=\"Tensorboard Callback API Kodlar\u0131\" width=\"602\" height=\"304\" title=\"\" srcset=\"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Callback-APIsi-Nasil-Calisir.png 602w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Callback-APIsi-Nasil-Calisir-300x151.png 300w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Callback-APIsi-Nasil-Calisir-150x76.png 150w, https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Callback-APIsi-Nasil-Calisir-450x227.png 450w\" sizes=\"(max-width: 602px) 100vw, 602px\" \/><\/p>\n<p>Keras, TensorBoard&#8217;un &#8220;callback&#8221; API&#8217;sini kullanarak, model e\u011fitimi s\u0131ras\u0131nda TensorBoard&#8217;a veri g\u00f6nderebilir. Keras modelleri, model e\u011fitiminde TensorBoard&#8217;u kullanmak i\u00e7in, \u00f6ncelikle TensorBoard geri \u00e7a\u011fr\u0131 nesnelerini i\u00e7eren bir listeyi model.fit() fonksiyonuna iletmelidir. Bu geri \u00e7a\u011fr\u0131lar, model e\u011fitimi s\u0131ras\u0131nda TensorBoard&#8217;a hangi verilerin g\u00f6nderilece\u011fini belirler.<\/p>\n<p>\u00d6rne\u011fin, bir Keras modelinin e\u011fitimi s\u0131ras\u0131nda TensorBoard kullanmak i\u00e7in, a\u015fa\u011f\u0131daki gibi bir callback nesnesi olu\u015fturulabilir;<\/p>\n<p><em><strong>from tensorflow.keras.callbacks import TensorBoard<\/strong><\/em><\/p>\n<p><em><strong>tensorboard_callback = TensorBoard(log_dir=&#8221;.\/logs&#8221;)<\/strong><\/em><\/p>\n<p>Bu callback nesnesi, e\u011fitim s\u0131ras\u0131nda TensorBoard&#8217;a verileri g\u00f6nderir. log_dir parametresi, TensorBoard taraf\u0131ndan olu\u015fturulan verilerin kaydedilece\u011fi dizini belirtir.<\/p>\n<p>Keras modelleri, TensorBoard geri \u00e7a\u011fr\u0131 nesneleri kullan\u0131larak e\u011fitildi\u011finde, TensorBoard otomatik olarak e\u011fitim s\u00fcrecinde \u00e7e\u015fitli g\u00f6rsel \u00f6zetler olu\u015fturur. \u00d6zetler aras\u0131nda, e\u011fitim ve do\u011frulama kayb\u0131, do\u011fruluk, hassasiyet, geri \u00e7a\u011f\u0131rma, F1 puan\u0131, \u00f6zelle\u015ftirilmi\u015f metrikler, a\u011f\u0131rl\u0131klar\u0131n histogramlar\u0131 ve da\u011f\u0131l\u0131m\u0131, gradyanlar\u0131n histogramlar\u0131 ve da\u011f\u0131l\u0131m\u0131, \u00f6zelle\u015ftirilmi\u015f histogramlar ve di\u011fer \u00f6zetler bulunur.<\/p>\n<p>TensorBoard&#8217;un &#8220;callback&#8221; API&#8217;si, Keras modellerinin e\u011fitim s\u00fcrecinde ger\u00e7ek zamanl\u0131 veri g\u00f6rselle\u015ftirmesi yapmas\u0131na olanak sa\u011flar ve modelin performans\u0131n\u0131 izlemek, hata ay\u0131klama yapmak ve model yap\u0131lar\u0131n\u0131 g\u00f6rselle\u015ftirmek i\u00e7in kullan\u0131labilir.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>TensorBoard, TensorFlow k\u00fct\u00fcphanesi ile birlikte kullan\u0131lan bir g\u00f6rselle\u015ftirme arac\u0131d\u0131r. Makine \u00f6\u011frenimi modellerinin performans\u0131n\u0131 izlemek, hata ay\u0131klamak ve optimize etmek i\u00e7in kullan\u0131l\u0131r. Keras ise, a\u00e7\u0131k kaynakl\u0131 bir yapay sinir a\u011f\u0131 k\u00fct\u00fcphanesidir ve \u00f6zellikle yapay sinir a\u011f\u0131 konusunda deneyimi olmayan geli\u015ftiriciler i\u00e7in ideal bir se\u00e7imdir.<\/p>\n","protected":false},"author":12,"featured_media":4531,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[38],"tags":[331,398,328,316],"class_list":{"0":"post-4530","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-python-ile-veri-isleme","8":"tag-keras","9":"tag-tensorboard","10":"tag-tensorflow","11":"tag-yapay-sinir-aglari"},"better_featured_image":{"id":4531,"alt_text":"Tensorflow ve Tensorboard K\u00fct\u00fcphaneleri","caption":"","description":"","media_type":"image","media_details":{"width":602,"height":219,"file":"2023\/05\/Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme.png","filesize":50845,"sizes":{"medium":{"file":"Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme-300x109.png","width":300,"height":109,"mime-type":"image\/png","filesize":15909,"source_url":"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme-300x109.png"},"thumbnail":{"file":"Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme-150x150.png","width":150,"height":150,"mime-type":"image\/png","filesize":10611,"source_url":"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme-150x150.png"},"bunyad-small":{"file":"Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme-150x55.png","width":150,"height":55,"mime-type":"image\/png","filesize":5580,"source_url":"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme-150x55.png"},"bunyad-medium":{"file":"Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme-450x164.png","width":450,"height":164,"mime-type":"image\/png","filesize":30615,"source_url":"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme-450x164.png"}},"image_meta":{"aperture":"0","credit":"","camera":"","caption":"","created_timestamp":"0","copyright":"","focal_length":"0","iso":"0","shutter_speed":"0","title":"","orientation":"0","keywords":[]}},"post":4530,"source_url":"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/05\/Tensorboard-Kutuphanesi-ve-Veri-Gorsellestirme.png"},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/posts\/4530","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/comments?post=4530"}],"version-history":[{"count":2,"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/posts\/4530\/revisions"}],"predecessor-version":[{"id":5173,"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/posts\/4530\/revisions\/5173"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/media\/4531"}],"wp:attachment":[{"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/media?parent=4530"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/categories?post=4530"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/tags?post=4530"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}