How you should change your weights or learning rates of your neural network to reduce the losses is defined by the optimizers you use. Optimization algorithms are responsible for reducing the losses and to provide the most accurate results possible. Various optimizers are researched within the last ...
The heuristic optimization algorithms implemented in the form of computer libraries are developed. The problems of neural network algorithm implementation using high-level hybrid computing are considered. The effect of the application of the algorithms proposed in the paper is to reduce the time and ...
beta = 0.9,2beta1 = 0.9, beta2 = 0.999, epsilon = 1e-8, num_epochs = 10000, print_cost =True):3"""43-layer neural network model which can be run in different optimizer modes.56Arguments:7X -- input data, of shape (2, number of examples)8Y -- true "label" vector (1...
Fig. 8. Cost function minimization plot for three optimization algorithms in neural network design. Table 5. Performance of neural network optimum model by different methods. ParameterCOA–LMICA–LMPSO–LM AAD 0.099 0.104 0.119 NMSE 0. 012 0.017 0.020 R 0.978 0.961 0.943 R2 0.957 0.924 0.889 7...
in the solution space and search for the optimal solution during the iterative process, in order to achieve the goal of improving efficiency and reducing costs. Intelligent optimization algorithms have strong robustness and global search ability, and can be applied in various fields, such as ...
The MEA has a faster training speed than genetic algorithms, which significantly reduces the training time of neural networks and is thus more practical. First, the individuals in the subgroups are optimized by the convergence operation, and then the mature subgroups compete globally through the alie...
The results are superior to the existing optimization algorithms. Introduction In recent years, Deep Neural Network (DNN) has greatly changed the landscape of the information science [1]. Breakthroughs have been made in numerous research fields due to this technique. Among these research topics, ...
简介:【深度学习系列】(二)--An overview of gradient descent optimization algorithms 一、摘要 梯度下降优化算法虽然越来越流行,但经常被用作黑盒优化器,因为很难找到对其优缺点的实际解释。本文旨在为读者提供有关不同算法行为的直观信息,使他们能够使用这些算法。在本概述过程中,我们将介绍梯度下降的不同变体,总结...
Abstract Feed-forward neural networks are commonly used for pattern classification. The classification accuracy of feed-forward neural networks depends on the configuration selected and the training process. Once the architecture of the network is decided, training algorithms, usually gradient descent techni...
In Proc. 33rd AAAI Conference on Artificial Intelligence 3558–3565 (AAAI, 2019). Angelini, M. C. & Ricci-Tersenghi, F. Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set. Nature Mach. Intell. 5, ...