![]() This kind of backend agnostic framework is great for developers. Although one of my favorite libraries PlaidML have built their own support for Keras. Using Keras you can swap out the “backend” between many frameworks in eluding TensorFlow, Theano, or CNTK officially. Keras is called a “front-end” api for machine learning. TensorFlow is even replacing their high level API with Keras come TensorFlow version 2. Keras is a favorite tool among many in Machine Learning. arXiv preprint arXiv:1608.Implementing Swish Activation Function in Keras (2016) Lets keep it simple, using simple architectures to outperform deeper and more complex architectures. In: Proceedings of the IEEE conference on computer vision and pattern recognition. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Zagoruyko S, Komodakis N (2016) Wide residual networks. In: Advances in neural information processing systems. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. arXiv preprint arXiv:1710.05941Īlcaide E (2018) E-Swish: adjusting activations to different network depths. Ramachandran P, Zoph B, Le QV (2017) Searching for activation functions. Jin X et al (2015) Deep learning with s-shaped rectified linear activation units. Hendrycks D, Gimpel K (2016) Bridging nonlinearities and stochastic regularizers with gaussian error linear units In: 2015 7th international joint conference on knowledge discovery, knowledge engineering and knowledge management (IC3K), vol 1. Godfrey LB, Gashler MS (2015) A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks. Klambauer G, Unterthiner T, Mayr A, Hochreiter S (2017) Self-normalizing neural networks. In: ICMLĬlevert DA, Unterthiner T, Hochreiter S (2015) Fast and accurate deep network learning by exponential linear units (elus). Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034 He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. Xu B et al (2015) Empirical evaluation of rectified activations in convolutional network. Hahnloser RHR, Sarpeshkar R, Mahowald MA, Douglas RJ, Seung HS (2000) Erratum: digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. Minai AA, Williams RD (1993) On the derivatives of the sigmoid. In: Proceedings of the 30th Chinese control and decision conference, CCDC 2018. J Teknol Siri 50(D):73–91ĭing B, Qian H, Zhou J (2018) Activation functions and their characteristics in deep neural networks. Maarof M, Selamat A, Shamsuddin SM (2009) Text content analysis for illicit web pages by using neural networks. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. In: Proceedings of 2018 6th international renewable and sustainable energy conference, IRSEC 2018 Salam A, El Hibaoui A (2018) Comparison of machine learning algorithms for the power consumption prediction-case study of Tetouan city. Schmidhuber J (2015) Deep learning in neural networks: an overview. Also on Cifar100 dataset for WRN 16–4, E_Swish Beta provides 1.77, 0.69, and 0.27% relative to ReLU, Swish, and adjusted Swish. It performs 0.53, 0.61, and 0.42% improvement relative to ReLU, Swish, and E_Swish, respectively, on Cifar10 dataset for WRN 16–4 model. We show that E_Swish Beta enhances the result better than others do. We examine E_Swish Beta, E_Swish, Swish, and ReLU in different datasets and models. This paper presents a new activation function similar to Swish and adjusted Swish, f( x) = βx*Sigmoid ( βx), which we name it E_Swish Beta. In addition, trying to enhance the Swish function introduces adjusted Swish, f( x) = βx*Sigmoid( x). This function provides good results and outperforms ReLU. Google brain invented an activation function called Swish and defined as f( x) = x*Sigmoid ( βx). Nowadays, there are many activation functions, but the well-known is the rectified linear unit (ReLU). Activation function is the heart of the neural network and its impact is different from one to another. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |