{"id":4461,"date":"2023-04-30T13:11:43","date_gmt":"2023-04-30T10:11:43","guid":{"rendered":"https:\/\/datakapital.com\/blog\/?p=4461"},"modified":"2025-07-20T17:32:58","modified_gmt":"2025-07-20T14:32:58","slug":"tensorflow-ile-yapay-sinir-aglari","status":"publish","type":"post","link":"https:\/\/datakapital.com\/blog\/tensorflow-ile-yapay-sinir-aglari\/","title":{"rendered":"Tensorflow ile Yapay Sinir A\u011flar\u0131"},"content":{"rendered":"

TensorFlow<\/strong>, Google Brain ekibi taraf\u0131ndan geli\u015ftirilen a\u00e7\u0131k kaynak kodlu bir makine \u00f6\u011frenimi k\u00fct\u00fcphanesidir. Makine \u00f6\u011frenimi alan\u0131nda b\u00fcy\u00fck \u00f6l\u00e7ekli say\u0131sal hesaplama, yapay sinir a\u011flar\u0131<\/a> ve makine \u00f6\u011frenimi modellerinin<\/a> olu\u015fturulmas\u0131 ve e\u011fitilmesi i\u00e7in kullan\u0131l\u0131r. TensorFlow, y\u00fcksek performansl\u0131 hesaplama yetenekleri sunarak, karma\u015f\u0131k matematiksel i\u015flemleri yapmaya olanak sa\u011flar ve kullan\u0131c\u0131lar\u0131n verileri, modelleri ve sonu\u00e7lar\u0131 g\u00f6rselle\u015ftirmesine olanak tan\u0131r. TensorFlow, birden \u00e7ok programlama dilinde kullan\u0131labilen bir API sa\u011flar, bu nedenle farkl\u0131 dillerdeki yaz\u0131l\u0131mc\u0131lar taraf\u0131ndan kullan\u0131labilir. TensorFlow, bir\u00e7ok end\u00fcstriyel uygulama, akademik ara\u015ft\u0131rma ve geli\u015ftirme projelerinde yayg\u0131n olarak kullan\u0131lmaktad\u0131r.<\/p>\n

TensorFlow, yapay sinir a\u011flar\u0131<\/a> gibi makine \u00f6\u011frenimi algoritmalar\u0131n\u0131 uygulamak i\u00e7in kullan\u0131lan a\u00e7\u0131k kaynakl\u0131 bir yaz\u0131l\u0131m k\u00fct\u00fcphanesidir. Bu k\u00fct\u00fcphane, hesaplama grafiklerinin olu\u015fturulmas\u0131, e\u011fitilmesi ve da\u011f\u0131t\u0131lmas\u0131 i\u00e7in g\u00fc\u00e7l\u00fc bir ara\u00e7t\u0131r. Ayr\u0131ca, yapay sinir a\u011flar\u0131 yan\u0131 s\u0131ra do\u011frusal regresyon, s\u0131n\u0131fland\u0131rma, k\u00fcmeleme ve di\u011fer makine \u00f6\u011frenimi algoritmalar\u0131n\u0131 da uygulamak i\u00e7in kullan\u0131labilir.<\/p>\n

Yapay sinir a\u011flar\u0131, biyolojik sinir sistemlerinden esinlenerek tasarlanm\u0131\u015ft\u0131r ve bir\u00e7ok makine \u00f6\u011frenimi problemi i\u00e7in kullan\u0131labilir. Bu a\u011f yap\u0131s\u0131, bir\u00e7ok katmandan olu\u015fur ve her katman, girdileri i\u015flerken \u00f6nceki katmanlar\u0131n \u00e7\u0131kt\u0131lar\u0131na dayan\u0131r.<\/p>\n

TensorFlow, yapay sinir a\u011f\u0131 modellerinin tasarlanmas\u0131, e\u011fitilmesi ve de\u011ferlendirilmesi i\u00e7in bir dizi API sa\u011flar. Bu API’ler, y\u00fcksek seviyeli bir API olan Keras’\u0131n yan\u0131 s\u0131ra d\u00fc\u015f\u00fck seviyeli bir API olan TensorFlow API’si ile birlikte kullan\u0131labilir.<\/p>\n

\"Keras<\/p>\n

Yapay sinir a\u011flar\u0131, bir\u00e7ok farkl\u0131 makine \u00f6\u011frenimi problemini \u00e7\u00f6zmek i\u00e7in kullan\u0131labilir. \u00d6rne\u011fin, g\u00f6r\u00fcnt\u00fc tan\u0131ma, do\u011fal dil i\u015fleme, ses tan\u0131ma ve daha pek \u00e7ok alanda kullan\u0131labilir. TensorFlow’un yapay sinir a\u011flar\u0131 i\u00e7in kapsaml\u0131 bir k\u00fct\u00fcphane sunmas\u0131, bu alanlarda \u00e7al\u0131\u015fan ara\u015ft\u0131rmac\u0131lar\u0131n ve geli\u015ftiricilerin i\u015flerini kolayla\u015ft\u0131rmaktad\u0131r.<\/p>\n

Tensorflow 2.1.12<\/h2>\n

TensorFlow 2.12.0, TensorFlow 2.x serisinin son s\u00fcr\u00fcm\u00fcd\u00fcr ve \u00f6nceki TensorFlow s\u00fcr\u00fcmlerine g\u00f6re bir\u00e7ok avantaj sunar. TensorFlow 1.x serisi ile kar\u015f\u0131la\u015ft\u0131r\u0131ld\u0131\u011f\u0131nda, TensorFlow 2.x serisi daha y\u00fcksek d\u00fczeyde API’ler sunar ve kullan\u0131m\u0131 daha kolayd\u0131r. \u00d6zellikle, TensorFlow 2.x, Keras API’si gibi y\u00fcksek d\u00fczeyli API’ler sa\u011flar ve bu API’ler sayesinde model olu\u015fturma ve e\u011fitme s\u00fcreci daha h\u0131zl\u0131 ve kolay hale gelir.<\/p>\n

TensorFlow 2.12.0, TensorFlow 2.x serisinin di\u011fer s\u00fcr\u00fcmlerine k\u0131yasla bir\u00e7ok iyile\u015ftirme i\u00e7erir. \u00d6rne\u011fin, TensorFlow Lite model d\u00f6n\u00fc\u015ft\u00fcr\u00fcc\u00fcs\u00fc ve TFLite GPU deste\u011fi gibi \u00f6nemli geli\u015ftirmeler yap\u0131lm\u0131\u015ft\u0131r. Ayr\u0131ca, GPU’lar\u0131n kullan\u0131m\u0131 i\u00e7in daha iyi destek sa\u011flanm\u0131\u015ft\u0131r ve TensorFlow Eager Execution modu da iyile\u015ftirilmi\u015ftir.<\/p>\n

\"TensorFlow<\/p>\n

\u00d6zetle, TensorFlow 2.12.0, \u00f6nceki TensorFlow s\u00fcr\u00fcmlerine g\u00f6re daha geli\u015fmi\u015f bir ara\u00e7t\u0131r ve makine \u00f6\u011frenimi ve derin \u00f6\u011frenme alan\u0131nda \u00e7al\u0131\u015fan geli\u015ftiriciler i\u00e7in \u00f6nemli bir kaynakt\u0131r. TensorFlow 2.x serisi, daha y\u00fcksek d\u00fczeyde API’ler ve kullan\u0131m kolayl\u0131\u011f\u0131 sa\u011flayarak model olu\u015fturma ve e\u011fitme s\u00fcrecini daha h\u0131zl\u0131 ve verimli hale getirir.<\/p>\n

Basit Linear Model (MNIST)<\/h2>\n

MNIST veri k\u00fcmesi, makine \u00f6\u011frenimi alan\u0131nda en s\u0131k kullan\u0131lan veri setlerinden biridir. Bu veri k\u00fcmesi, 60.000 e\u011fitim \u00f6rne\u011fi ve 10.000 test \u00f6rne\u011fi i\u00e7eren 28×28 piksel boyutlar\u0131nda gri tonlama rakam g\u00f6r\u00fcnt\u00fclerinden olu\u015fur. Bu veri k\u00fcmesi, el yaz\u0131s\u0131 rakamlar\u0131n tan\u0131nmas\u0131 i\u00e7in s\u0131kl\u0131kla kullan\u0131l\u0131r.<\/p>\n

Basit Linear Model, MNIST veri k\u00fcmesinde kullan\u0131lan bir s\u0131n\u0131fland\u0131rma modelidir. Bu model, girdi olarak 28×28 piksel boyutlar\u0131nda bir g\u00f6r\u00fcnt\u00fc al\u0131r ve her pikseli tek bir say\u0131 ile temsil eder. Bu say\u0131lar, 784 boyutlu bir vekt\u00f6rde birle\u015ftirilir. Model, bu vekt\u00f6r\u00fc al\u0131r ve \u00e7\u0131kt\u0131 olarak 0-9 aras\u0131ndaki olas\u0131 s\u0131n\u0131flar i\u00e7in bir olas\u0131l\u0131k da\u011f\u0131l\u0131m\u0131 verir.<\/p>\n

Modelin yap\u0131s\u0131 olduk\u00e7a basittir. \u0130lk katman, girdi vekt\u00f6r\u00fcn\u00fc al\u0131r ve do\u011frudan \u00e7\u0131kt\u0131 katman\u0131na ba\u011flan\u0131r. \u00c7\u0131kt\u0131 katman\u0131, 10 s\u0131n\u0131f i\u00e7in softmax aktivasyon fonksiyonunu kullanarak olas\u0131 s\u0131n\u0131flar i\u00e7in bir olas\u0131l\u0131k da\u011f\u0131l\u0131m\u0131 \u00fcretir. Model, adam optimize ediciyi ve sparse_categorical_crossentropy kay\u0131p fonksiyonunu kullanarak derlenir. Bu kay\u0131p fonksiyonu, s\u0131n\u0131fland\u0131rma modellerinde yayg\u0131n olarak kullan\u0131l\u0131r.<\/p>\n

\"MNIST<\/p>\n

Modelin e\u011fitimi, MNIST veri k\u00fcmesindeki \u00f6rnekler \u00fczerinde ger\u00e7ekle\u015ftirilir. Veriler \u00f6ncelikle normalize edilir ve ard\u0131ndan e\u011fitim \u00f6rnekleri i\u00e7in model uygun bir \u015fekilde derlenir. Model, fit() y\u00f6ntemi kullan\u0131larak e\u011fitilir ve do\u011frulama veri k\u00fcmesinde de\u011ferlendirilir. E\u011fitim s\u00fcreci boyunca, model, kay\u0131p fonksiyonunu minimize etmek i\u00e7in geriye do\u011fru yay\u0131l\u0131m (backpropagation) algoritmas\u0131 kullanarak a\u011f\u0131rl\u0131klar\u0131 g\u00fcnceller. Model, e\u011fitim s\u00fcreci sonunda test \u00f6rnekleri \u00fczerinde de\u011ferlendirilir ve do\u011fruluk oran\u0131 hesaplan\u0131r.<\/p>\n

Basit Linear Model, MNIST veri k\u00fcmesindeki s\u0131n\u0131fland\u0131rma problemini \u00e7\u00f6zmek i\u00e7in yeterli bir modeldir. Ancak, daha karma\u015f\u0131k yapay sinir a\u011f\u0131 yap\u0131lar\u0131 kullan\u0131larak daha y\u00fcksek do\u011fruluk oranlar\u0131 elde edilebilir.<\/p>\n

Basit bir lineer modeli MNIST veri k\u00fcmesiyle e\u011fitmek i\u00e7in \u00f6rnek bir kod par\u00e7as\u0131 a\u015fa\u011f\u0131daki gibi olabilir:<\/p>\n

import tensorflow as tf<\/strong><\/em><\/p>\n

from tensorflow.keras.datasets import mnist<\/strong><\/em><\/p>\n

# Veri k\u00fcmesini y\u00fckle<\/strong><\/em><\/p>\n

(x_train, y_train), (x_test, y_test) = mnist.load_data()<\/strong><\/em><\/p>\n

# Verileri normalize et<\/strong><\/em><\/p>\n

x_train = x_train \/ 255.0<\/strong><\/em><\/p>\n

x_test = x_test \/ 255.0<\/strong><\/em><\/p>\n

# Lineer modeli tan\u0131mla<\/strong><\/em><\/p>\n

model = tf.keras.models.Sequential([<\/strong><\/em><\/p>\n

\u00a0\u00a0\u00a0 tf.keras.layers.Flatten(input_shape=(28, 28)),<\/strong><\/em><\/p>\n

\u00a0\u00a0\u00a0 tf.keras.layers.Dense(10, activation=’softmax’)<\/strong><\/em><\/p>\n

])<\/strong><\/em><\/p>\n

# Modeli derle<\/strong><\/em><\/p>\n

model.compile(optimizer=’adam’,<\/strong><\/em><\/p>\n

\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0loss=’sparse_categorical_crossentropy’,<\/strong><\/em><\/p>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 metrics=[‘accuracy’])<\/strong><\/em><\/p>\n

# Modeli e\u011fit<\/strong><\/em><\/p>\n

model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))<\/strong><\/em><\/p>\n

# Modeli de\u011ferlendir<\/strong><\/em><\/p>\n

test_loss, test_acc = model.evaluate(x_test, y_test)<\/strong><\/em><\/p>\n

print(‘Test accuracy:’, test_acc)<\/strong><\/em><\/p>\n

Bu kod blo\u011fu, MNIST veri setini y\u00fckler ve ard\u0131ndan bir lineer model tan\u0131mlayarak, e\u011fitir ve do\u011frulama veri setinde de\u011ferlendirir. Model, Sequential API’sini kullanarak olu\u015fturulur.<\/p>\n

\u0130lk katman, girdi boyutunu (28×28 piksel) d\u00fczle\u015ftirir, ikinci katman ise \u00e7\u0131kt\u0131 katman\u0131d\u0131r ve 10 s\u0131n\u0131f i\u00e7in softmax aktivasyon fonksiyonunu kullan\u0131r. Model, adam optimize ediciyi ve sparse_categorical_crossentropy kay\u0131p fonksiyonunu kullanarak derlenir.<\/p>\n

Son olarak, model, fit() y\u00f6ntemi kullan\u0131larak e\u011fitilir ve do\u011frulama veri k\u00fcmesinde de\u011ferlendirilir.<\/p>\n

Bu \u00f6rnek, yapay sinir a\u011flar\u0131 alan\u0131ndaki temel konular\u0131 kapsar ve basit bir modeli e\u011fiterek MNIST veri setinde do\u011fruluk elde etmektedir. Ancak, daha karma\u015f\u0131k modeller kullanarak daha y\u00fcksek do\u011fruluk elde etmek m\u00fcmk\u00fcnd\u00fcr.<\/p>\n

\"MNIST<\/p>\n

\u00c7ok Layerl\u0131 Sinir A\u011f\u0131<\/h2>\n

\u00c7ok katmanl\u0131 sinir a\u011f\u0131, bir yapay sinir a\u011f\u0131 t\u00fcr\u00fcd\u00fcr ve derin \u00f6\u011frenmenin temelini olu\u015fturur. Bu a\u011f yap\u0131s\u0131, verileri i\u015flemek ve \u00e7\u0131kt\u0131 \u00fcretmek i\u00e7in birbirine ba\u011fl\u0131 bir\u00e7ok yapay sinir h\u00fccresi ya da n\u00f6ronlar i\u00e7eren birden fazla katmandan olu\u015fur. Bu katmanlar, bir \u00f6nceki katman\u0131n \u00e7\u0131kt\u0131lar\u0131n\u0131 alarak verileri daha y\u00fcksek seviyelerde temsil edebilir.<\/p>\n

\u00c7ok katmanl\u0131 sinir a\u011flar\u0131, genellikle y\u00fcksek boyutlu ve karma\u015f\u0131k verileri i\u015flemek i\u00e7in kullan\u0131l\u0131r. Bu veriler, g\u00f6r\u00fcnt\u00fcler, ses dosyalar\u0131, do\u011fal dil metinleri veya sens\u00f6rlerden gelen veriler gibi farkl\u0131 t\u00fcrlerde olabilir. Bu a\u011flar, \u00f6zellikle derin \u00f6\u011frenme ve yapay zeka alanlar\u0131nda \u00f6nemli bir rol oynar.<\/p>\n

\u00c7ok katmanl\u0131 sinir a\u011flar\u0131n\u0131n en basit hali, bir giri\u015f katman\u0131, bir \u00e7\u0131kt\u0131 katman\u0131 ve bir ya da daha fazla ara katmandan olu\u015fur. Bu ara katmanlar, girdi verilerinin i\u015flenmesi i\u00e7in \u00e7e\u015fitli matematiksel i\u015flemler yaparlar ve ard\u0131ndan \u00e7\u0131kt\u0131 katman\u0131na y\u00f6nlendirirler.<\/p>\n

\u00c7\u0131kt\u0131 katman\u0131, son tahmin veya s\u0131n\u0131fland\u0131rma sonu\u00e7lar\u0131n\u0131 \u00fcretir.<\/p>\n

Bu sinir a\u011f\u0131 t\u00fcr\u00fc, \u00f6\u011frenme s\u00fcreci s\u0131ras\u0131nda geriye do\u011fru yay\u0131l\u0131m (backpropagation) ad\u0131 verilen bir algoritma kullan\u0131r. Bu algoritma, verilen bir girdiye kar\u015f\u0131l\u0131k beklenen \u00e7\u0131kt\u0131ya yakla\u015fmak i\u00e7in a\u011fdaki a\u011f\u0131rl\u0131klar\u0131n g\u00fcncellenmesini sa\u011flar. Bu s\u00fcre\u00e7, veri k\u00fcmesindeki \u00f6rneklerin tekrar tekrar a\u011fa beslenerek, a\u011f\u0131rl\u0131klar\u0131n optimize edilmesi ile ger\u00e7ekle\u015ftirilir.<\/p>\n

\u00c7ok katmanl\u0131 sinir a\u011flar\u0131, derin \u00f6\u011frenme alan\u0131ndaki ba\u015far\u0131lar\u0131yla \u00f6ne \u00e7\u0131kar. Bu a\u011f yap\u0131s\u0131, \u00e7e\u015fitli g\u00f6r\u00fcnt\u00fc, ses ve do\u011fal dil i\u015fleme g\u00f6revlerinde \u00f6zellikle y\u00fcksek do\u011fruluk oranlar\u0131 elde eder. \u00d6rne\u011fin, g\u00f6r\u00fcnt\u00fc tan\u0131ma, nesne tespiti, y\u00fcz tan\u0131ma ve otomatik s\u00fcr\u00fc\u015f teknolojilerinde kullan\u0131l\u0131r. Ayr\u0131ca, do\u011fal dil i\u015fleme uygulamalar\u0131nda, metin s\u0131n\u0131fland\u0131rma, dil modelleme ve makine \u00e7evirisi gibi g\u00f6revlerde de kullan\u0131l\u0131r.<\/p>\n

Loss Grafi\u011fi<\/h2>\n

Loss grafi\u011fi, makine \u00f6\u011frenimi modellerinin e\u011fitimi s\u0131ras\u0131nda kullan\u0131lan \u00f6nemli bir ara\u00e7t\u0131r. Bu grafik, modelin kayb\u0131n\u0131 (loss) e\u011fitim s\u00fcreci boyunca takip etmek i\u00e7in kullan\u0131l\u0131r. Kay\u0131p, modelin tahminlerinin ger\u00e7ek de\u011ferlerden ne kadar uzak oldu\u011funu \u00f6l\u00e7en bir metrik olarak kullan\u0131l\u0131r ve modelin amac\u0131 kayb\u0131 m\u00fcmk\u00fcn oldu\u011funca d\u00fc\u015f\u00fck tutarak daha do\u011fru tahminler yapmakt\u0131r.<\/p>\n

Loss grafi\u011fi, genellikle e\u011fitim ve do\u011frulama kay\u0131plar\u0131n\u0131 ayn\u0131 grafikte g\u00f6sterir.<\/p>\n

E\u011fitim kayb\u0131, modelin e\u011fitim verileri \u00fczerindeki performans\u0131n\u0131 yans\u0131t\u0131rken, do\u011frulama kayb\u0131, modelin do\u011frulama verileri \u00fczerindeki performans\u0131n\u0131 yans\u0131t\u0131r. Bu grafi\u011fin amac\u0131, modelin e\u011fitim s\u00fcrecinde a\u015f\u0131r\u0131 uyuma (overfitting) yapmad\u0131\u011f\u0131n\u0131 ve do\u011frulama verileri \u00fczerinde de iyi bir performans sergiledi\u011fini kontrol etmektir.<\/p>\n

\"Loss<\/p>\n

Loss grafi\u011fi genellikle e\u011fitim d\u00f6ng\u00fcs\u00fc (epoch) say\u0131s\u0131 kar\u015f\u0131s\u0131nda \u00e7izilir. E\u011fitim d\u00f6ng\u00fcs\u00fc say\u0131s\u0131 artt\u0131k\u00e7a, kay\u0131p genellikle azal\u0131r \u00e7\u00fcnk\u00fc model daha iyi hale gelir ve daha do\u011fru tahminler yapabilir. Ancak, a\u015f\u0131r\u0131 uyum gibi problemler nedeniyle kay\u0131p, e\u011fitim d\u00f6ng\u00fcs\u00fc say\u0131s\u0131 artt\u0131k\u00e7a artabilir.<\/p>\n

Loss grafi\u011fi, modelin performans\u0131n\u0131 izlemenin yan\u0131 s\u0131ra, hiper parametrelerin ayarlanmas\u0131 ve farkl\u0131 modellerin performanslar\u0131n\u0131n kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131 gibi kararlar\u0131n verilmesinde de kullan\u0131l\u0131r.<\/p>\n

\u00d6rne\u011fin, \u00f6\u011frenme oran\u0131 gibi hiper parametrelerinin ayarlanmas\u0131nda loss grafi\u011fi, hangi de\u011ferin daha iyi performans g\u00f6sterdi\u011fini g\u00f6rmek i\u00e7in kullan\u0131labilir.<\/p>\n

Sonu\u00e7 olarak, loss grafi\u011fi, modelin kayb\u0131n\u0131 g\u00f6rselle\u015ftiren bir ara\u00e7t\u0131r ve modelin performans\u0131n\u0131 izlemek, hiper parametreleri ayarlamak ve farkl\u0131 modellerin performans\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131rmak gibi bir\u00e7ok ama\u00e7la kullan\u0131labilir.<\/p>\n

Dropout<\/h2>\n

Dropout, makine \u00f6\u011frenimi ve derin \u00f6\u011frenme modellerinde a\u015f\u0131r\u0131 uyum (overfitting) sorununu \u00f6nlemek i\u00e7in kullan\u0131lan bir d\u00fczenlile\u015ftirme tekni\u011fidir. Bu teknik, a\u011f\u0131rl\u0131klar\u0131n d\u00fczenlile\u015ftirilmesi ve modelin genelle\u015ftirilmesi i\u00e7in kullan\u0131l\u0131r. Dropout tekni\u011fi, modelin belirli bir b\u00f6l\u00fcm\u00fcn\u00fc (n\u00f6ronlar veya \u00f6zellikler) her e\u011fitim d\u00f6ng\u00fcs\u00fcnde rastgele olarak atarak \u00e7al\u0131\u015f\u0131r.<\/p>\n

Dropout teknikleri, \u00f6zellikle b\u00fcy\u00fck ve karma\u015f\u0131k veri setleri i\u00e7in kullan\u0131\u015fl\u0131d\u0131r. Bu teknik, a\u015f\u0131r\u0131 uyum sorununu \u00f6nlemeye yard\u0131mc\u0131 olur ve modelin daha genel bir veri k\u00fcmesi \u00fczerinde do\u011fru sonu\u00e7lar \u00fcretmesini sa\u011flar. Dropout, \u00f6zellikle y\u00fcksek boyutlu veri setleri veya \u00e7ok\u00a0 say\u0131da \u00f6zellik i\u00e7eren veri setleri gibi zorlu senaryolarda ba\u015far\u0131l\u0131d\u0131r.<\/p>\n

\"Dropoutve<\/p>\n

Dropout, a\u015fa\u011f\u0131daki \u015fekilde \u00e7al\u0131\u015f\u0131r:<\/p>\n

    \n
  • Dropout, n\u00f6ronlar\u0131n belirli bir y\u00fczdesini her e\u011fitim d\u00f6ng\u00fcs\u00fcnde rastgele olarak atar. Bu sayede, a\u011fdaki farkl\u0131 n\u00f6ronlar her e\u011fitim d\u00f6ng\u00fcs\u00fcnde farkl\u0131 bir \u015fekilde aktive olur.<\/li>\n
  • Bu rastgele atma i\u015flemi, modelin farkl\u0131 \u00f6zellikleri (n\u00f6ronlar) \u00f6\u011frenmesini sa\u011flar ve b\u00f6ylece model, daha geni\u015f bir veri k\u00fcmesi \u00fczerinde daha iyi genelle\u015ftirilebilir.<\/li>\n
  • Dropout, ayn\u0131 zamanda modelin a\u015f\u0131r\u0131 uyum yapmas\u0131n\u0131 da \u00f6nler. \u00c7\u00fcnk\u00fc at\u0131lan n\u00f6ronlar, modelin sadece bir b\u00f6l\u00fcm\u00fcn\u00fc temsil eder ve modelin genelle\u015ftirme kabiliyetini art\u0131r\u0131r.<\/li>\n
  • Dropout, n\u00f6ronlar aras\u0131ndaki ba\u011f\u0131ml\u0131l\u0131klar\u0131 azalt\u0131r ve b\u00f6ylece model, daha iyi bir \u00f6zellik se\u00e7imine yol a\u00e7ar. Bu sayede, modelin e\u011fitim s\u00fcreci daha h\u0131zl\u0131 hale gelir ve daha az haf\u0131za gerektirir.<\/li>\n<\/ul>\n

    Dropout tekni\u011fi, hem geleneksel yapay sinir a\u011f\u0131 (ANN) modelleri hem de evri\u015fimli sinir a\u011f\u0131 (CNN) modelleri i\u00e7in kullan\u0131labilir. Dropout, TensorFlow, Keras ve PyTorch gibi pop\u00fcler makine \u00f6\u011frenimi \u00e7er\u00e7eveleri taraf\u0131ndan desteklenir.<\/p>\n","protected":false},"excerpt":{"rendered":"

    TensorFlow, Google Brain ekibi taraf\u0131ndan geli\u015ftirilen a\u00e7\u0131k kaynak kodlu bir makine \u00f6\u011frenimi k\u00fct\u00fcphanesidir. Makine \u00f6\u011frenimi alan\u0131nda b\u00fcy\u00fck \u00f6l\u00e7ekli say\u0131sal hesaplama, yapay sinir a\u011flar\u0131 ve makine \u00f6\u011frenimi modellerinin olu\u015fturulmas\u0131 ve e\u011fitilmesi i\u00e7in kullan\u0131l\u0131r. TensorFlow, y\u00fcksek performansl\u0131 hesaplama yetenekleri sunarak, karma\u015f\u0131k matematiksel i\u015flemleri yapmaya olanak sa\u011flar ve kullan\u0131c\u0131lar\u0131n verileri, modelleri ve sonu\u00e7lar\u0131 g\u00f6rselle\u015ftirmesine olanak tan\u0131r. TensorFlow, birden<\/p>\n","protected":false},"author":12,"featured_media":4462,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[38],"tags":[333,329,330,331,332,328,316],"class_list":{"0":"post-4461","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-python-ile-veri-isleme","8":"tag-derin-ogrenme","9":"tag-google","10":"tag-google-brain","11":"tag-keras","12":"tag-loss-fonksiyonu","13":"tag-tensorflow","14":"tag-yapay-sinir-aglari"},"better_featured_image":{"id":4462,"alt_text":"TensorFlow K\u00fct\u00fcphanesi","caption":"","description":"","media_type":"image","media_details":{"width":602,"height":249,"file":"2023\/04\/TensorFlow-ile-Yapay-Sinir-aglari.png","filesize":21217,"sizes":{"medium":{"file":"TensorFlow-ile-Yapay-Sinir-aglari-300x124.png","width":300,"height":124,"mime-type":"image\/png","filesize":10254,"source_url":"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/04\/TensorFlow-ile-Yapay-Sinir-aglari-300x124.png"},"thumbnail":{"file":"TensorFlow-ile-Yapay-Sinir-aglari-150x150.png","width":150,"height":150,"mime-type":"image\/png","filesize":6086,"source_url":"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/04\/TensorFlow-ile-Yapay-Sinir-aglari-150x150.png"},"bunyad-small":{"file":"TensorFlow-ile-Yapay-Sinir-aglari-150x62.png","width":150,"height":62,"mime-type":"image\/png","filesize":4480,"source_url":"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/04\/TensorFlow-ile-Yapay-Sinir-aglari-150x62.png"},"bunyad-medium":{"file":"TensorFlow-ile-Yapay-Sinir-aglari-450x186.png","width":450,"height":186,"mime-type":"image\/png","filesize":17343,"source_url":"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/04\/TensorFlow-ile-Yapay-Sinir-aglari-450x186.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":4461,"source_url":"https:\/\/datakapital.com\/blog\/wp-content\/uploads\/2023\/04\/TensorFlow-ile-Yapay-Sinir-aglari.png"},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/posts\/4461","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=4461"}],"version-history":[{"count":1,"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/posts\/4461\/revisions"}],"predecessor-version":[{"id":4469,"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/posts\/4461\/revisions\/4469"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/media\/4462"}],"wp:attachment":[{"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/media?parent=4461"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/categories?post=4461"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datakapital.com\/blog\/wp-json\/wp\/v2\/tags?post=4461"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}