![]() ![]() "Incorporating second-order functional knowledge for better option pricing." Advances in neural information processing systems 13 (2000). Source Paper : Dugas, Charles, Yoshua Bengio, François Bélisle, Claude Nadeau, and René Garcia."Unitary evolution recurrent neural networks." In International conference on machine learning, pp. Source Paper : Arjovsky, Martin, Amar Shah, and Yoshua Bengio."Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint arXiv:1704.04861 (2017). Source Paper : Howard, Andrew G., Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam."Empirical evaluation of rectified activations in convolutional network." arXiv preprint arXiv:1505.00853 (2015). Source Paper : Xu, Bing, Naiyan Wang, Tianqi Chen, and Mu Li."Delving deep into rectifiers: Surpassing human-level performance on imagenet classification." In Proceedings of the IEEE international conference on computer vision, pp. Source Paper : He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun."Searching for Efficient Transformers for Language Modeling." Advances in Neural Information Processing Systems 34 (2021): 6010-6022. Source Paper : So, David, Wojciech Mańke, Hanxiao Liu, Zihang Dai, Noam Shazeer, and Quoc V."TanhExp: A smooth activation function with high convergence speed for lightweight neural networks." IET Computer Vision 15, no. Source Paper : Liu, Xinyu, and Xiaoguang Di."Efficient backprop." In Neural networks: Tricks of the trade, pp. Source Paper : LeCun, Yann A., Léon Bottou, Genevieve B. ![]() "Sigmoid transfer functions in backpropagation neural networks." Analytical Chemistry 65, no. Source Paper : Harrington, Peter de B."Activation function comparison in neural-symbolic integration." In AIP Conference Proceedings, vol. Source Paper : Mansor, Mohd Asyraf, and Saratha Sathasivam."Complementary log-log and probit: activation functions implemented in artificial neural networks." In 2008 Eighth International Conference on Hybrid Intelligent Systems, pp. Anything else is linearly-interpolated between. Everything less than than this range will be 0, and everything greater than this range will be 1. "Sigmoid-weighted linear units for neural network function approximation in reinforcement learning." Neural Networks 107 (2018): 3-11.Ĭhoose some xmin and xmax, which is our "range". Source Paper : Elfwing, Stefan, Eiji Uchibe, and Kenji Doya."Binaryconnect: Training deep neural networks with binary weights during propagations." Advances in neural information processing systems 28 (2015). Source Paper : Courbariaux, Matthieu, Yoshua Bengio, and Jean-Pierre David."The influence of the sigmoid function parameters on the speed of backpropagation learning." In International workshop on artificial neural networks, pp. Source Paper : Han, Jun, and Claudio Moraga."Rectified linear units improve restricted boltzmann machines." In Icml. Source Paper : Nair, Vinod, and Geoffrey E.SeGLU is an activation function which is a variant of GLU. SwiGLU is an activation function which is a variant of GLU. GeGLU is an activation function which is a variant of GLU. Source Paper : GLU Variants Improve Transformer.ReGLU is an activation function which is a variant of GLU. Source Paper : Parameter Efficient Deep Neural Networks with Bilinear Projections.Source Paper : Language Modeling with Gated Convolutional Networks.from ActTensor_tf import reluĬlasses and Functions are available in ActTensor_tf Activation Name The main function of the activation layers are also availabe but it maybe defined as different name. The source code is currently hosted on GitHub at:īinary installers for the latest released version are available at the Pythonįrom ActTensor_tf import ReLU # name of the layerįunctional api inputs = tf. ( shape =( 28,28 )) x = tf. ()(inputs ) x = tf. ( 128 )(x ) # wanted class name x = ReLU ()(x ) output = tf. ( 10,activation = 'softmax' )(x ) model = tf. ( inputs = inputs,outputs =output ) Wrting another one requires time and energy however, this package has most of the widely-used, and even state-of-the-art activation functions that are ready to use in your models. Why not using tf.keras.activations?Īs you may know, TensorFlow only has a few defined activation functions and most importantly it does not include newly-introduced activation functions. ActTensor: Activation Functions for TensorFlowĪctTensor is a Python package that provides state-of-the-art activation functions which facilitate using them in Deep Learning projects in an easy and fast manner. ![]()
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