The Effect of Activation Functions on Performance in Image Classification with Transfer Deep Learning Techniques

Yıl 2024, Cilt: 24 Sayı: 2, 294 - 307, 29.04.2024

Gül Cihan Habek , Sakir Tasdemır , Fatih Basciftci , Ahmet Yılmaz

https://doi.org/10.35414/akufemubid.1334098

Öz

Convolutional neural networks (CNN), a feedforward neural network model, are frequently used in image classification problems. New architectures have been developed by making some additions and changes in order to improve the performance on the CNN model, which generally consists of convolution layer, pooling layer and fully connected layer. By adding different numbers of hidden layers with sigmoid, tanh and ReLu activation functions to the CNN-based VGG16 and ResNet50 architectures developed in this study, images were classified by deep transfer learning technique and the performances were compared. The dataset used for classification is a multi-class dataset related to fruits, and a 10-neuron softmax classifier was used in the last layer of the established models. Entering the epoch as 10, results were obtained for four different metrics: accuracy, precision, recall, and f1-score. When the results are compared, it has been observed that the model created by adding a total of three layers, including two hidden layers with 256 and 128 neurons, and a softmax classifier layer with 10 neurons, to the ResNet50 architecture with sigmoid activation function, gives the best result with a classification accuracy value of %97.5. Finally, the results obtained for the four metrics were subjected to the Friedman and Nemenyi post-hoc tests, a statistical analysis was made and the relationship between the models was tested. It was concluded that the models created as a result of the test were related to each other.

Anahtar Kelimeler

Deep Learning, ResNet50 Architecture, Transfer Deep Learning, VGG16 Architecture, Image Classification

Transfer Derin Öğrenme Teknikleri ile Görüntü Sınıflandırmada Aktivasyon Fonksiyonlarının Performans Üzerindeki Etkisi

Öz

İleri beslemeli yapay sinir ağı modeli olan konvolüsyonel sinir ağları (CNN) görüntülerin sınıflandırılması problemlerinde sıklıkla kullanılmaktadır. Genel olarak konvolüsyon katmanı, havuzlama katmanı ve tam bağlı katmandan oluşan CNN modeli üzerinde performansı iyileştirmek amacı ile birtakım eklemeler ve değişiklikler yapılarak yeni mimariler geliştirilmiştir. Bu çalışmada geliştirilen CNN tabanlı VGG16 ve ResNet50 mimarilerine sigmoid, tanh ve ReLu aktivasyon fonksiyonlu farklı sayıda gizli katman eklenerek derin transfer öğrenme tekniği ile görüntüler sınıflandırılmış ve performansları karşılaştırılmıştır. Sınıflandırma için kullanılan veri seti meyveler ile ilgili çoklu sınıflı bir veri seti olup kurulan modellerin son katmanında 10 nöronlu softmax sınıflandırıcı kullanılmıştır. Devir sayısı 10 girilerek sınıflandırma doğruluğu (accuracy), duyarlılık (precision), geri çağırma (recall) ve f1-ölçütü olmak üzere dört farklı metrik için sonuçlar alınmıştır. Alınan sonuçlar kıyaslandığında modeller arasında ResNet50 mimarisine sigmoid aktivasyon fonksiyonlu, 256 ve 128 nöronlu iki gizli katman ve 10 nöronlu bir softmax sınıflandırıcı katmanı olmak üzere toplam üç katman eklenerek oluşturulan modelin %97.5 sınıflandırma doğruluğu değeri ile en iyi sonucu verdiği gözlemlenmiştir. Son olarak dört metrik için alınan sonuçlar Friedman ve Nemenyi post-hoc testlerine tabi tutularak istatistiksel bir analiz yapılmış, modeller arasındaki ilişki test edilmiştir. Test sonucunda oluşturulan modellerin birbirleri ile ilişkili olduğu sonucuna varılmıştır.

Anahtar Kelimeler

Görüntü Sınıflandırma, Derin Öğrenme, ResNet50 Mimarisi, Transfer Derin Öğrenme, VGG16 Mimarisi

Kaynakça

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