Tensorflow swish activation12/20/2023 For the most part, activation functions from the sigmoid activation function family are used for classifying objects, where the output is constrained to the range. Subsequently, the first neural network 13, 14 used the sigmoid activation function for modeling biological neuron firing. Other activation functions in the family include the step function, the clipped tanh function, and the clipped sigmoid function. Richards 11 developed the sigmoid activation function family that spans the S-shaped curves like the tanh 12 function and the sigmoid function. However, many activation functions have been proposed over the history of machine learning and this makes the selection difficult. One of the core tasks for automated machine learning is to find an optimal activation function for a specific model. Subsequently, the best neural network is selected from a pre-built model zoo and it is retrained to get the best results. For example, the problem may be classified as a video quantification problem or a text classification problem or a reinforcement learning problem. Moreover, it predicts the optimal parameters of the neural network such as the size of the CNN filters, the number of CNN channels, and the types of activation functions.Ĭlustering algorithms can be used to find the type of problem based on the information from the dataset. The RNN controller searches through the vast array of possible neural networks and it labels each network with a probability of being the optimal network. 10 designed an RNN controller for neural architecture search, which is trained using reinforcement learning. Probabilistic methods could be used in-conjunction with neural network approaches to create new neural network architectures. On the other hand, the Auto-DeepLab paper 9 proposes a method to search architectures at a cell level and at the network level. The RNN controller is trained using the policy gradient method. Similar to the paper above, Efficient Neural Architecture Search via Parameter Sharing (ENAS) 8 uses a recurrent neural network (RNN) controller to place and route cell blocks in order to find the optimal architecture. The architecture begins as a collection of a few cells and more cells are added by the predictor until the lowest loss is achieved. A neural network predictor is trained to place and route cells together. Moreover, the cells come with fixed activation functions that format the outputs. CNNs are constructed from cells, where each cell does a specific operation such as convolution, concatenation, and pooling. 7 propose a new method for creating convolutional neural networks (CNNs) from scratch. Moreover, Deep HyperNEAT 6 is another version of HyperNEAT that allows the design of larger and deeper neural networks.Īside from the genetic algorithms, neural network structures can also be optimized by other neural networks. The single function is then bred and mutated in order to find the best function that encodes the optimal neural architecture. Instead of finding the architecture directly, HyperNEAT finds a single function that encodes the entire network. The values of the neuron weights, the types of activation functions, and the number of neurons can be optimized by breeding and mutating different species of neural networks. Neuroevolution of augmenting topologies (NEAT) 4 uses genetic algorithms to optimize the structure of neural networks. For example, they can be used to optimize the number of neurons in each layer or the depth of the neural network. Genetic algorithms excel at optimizing discrete variables. The field of automated machine learning 1, 2, 3 solves the problem by automatically finding machine learning models using genetic algorithms, neural networks and its combination with probabilistic and clustering algorithms. However, finding the optimal model by hand is a daunting task due to the virtually infinite number of possibilities on model and the corresponding parameter selection. The goal of most machine learning algorithms is to find the optimal model for a specific problem. For the CIFAR-10 classification using the VGG-8 neural network, the UAF converges to the Mish like activation function, which has near optimal performance \(F_\) epochs with a brand new activation function, which gives the fastest convergence rate among the activation functions. For any given problem, the gradient descent algorithms are able to evolve the UAF to a suitable activation function by tuning the UAF’s parameters. This article proposes a universal activation function (UAF) that achieves near optimal performance in quantification, classification, and reinforcement learning (RL) problems.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |