Neural Networks：Momentum

一、目的

加快参数的收敛速度。

二、做法

另第t次的权重更新对第t+1次的权重更新造成影响。

从上式可看出，加入momentum后能够保持权重的更新方向，同时加快收敛。通常alpha的取值为[0.7, 0.95]

时间： 2024-08-14 11:32:06

Neural Networks：Momentum的相关文章

论文翻译：XNOR-Net: ImageNet Classification Using BinaryConvolutional Neural Networks

目录 Abstract 1 Introduction 2 Related Work 3 Binary Convolutional Neural Network 3.1 Binary-Weight-Networks 3.2 XNOR-Networks 4 Experiments 4.1 Efficiency Analysis 4.2 Image Classification 4.3 Ablation Studies 5 Conclusion 参考资料论文地址:http://ai2-website

论文笔记之：Learning Multi-Domain Convolutional Neural Networks for Visual Tracking

Learning Multi-Domain Convolutional Neural Networks for Visual Tracking CVPR 2016 本文提出了一种新的CNN 框架来处理跟踪问题.众所周知,CNN在很多视觉领域都是如鱼得水,唯独目标跟踪显得有点“慢热”,这主要是因为CNN的训练需要海量数据,纵然是在ImageNet 数据集上微调后的model 仍然不足以很好的表达要跟踪地物体,因为Tracking问题的特殊性,至于怎么特殊的,且听细细道来. 目标跟踪之所以很少被 C

《转》循环神经网络(RNN, Recurrent Neural Networks)学习笔记：基础理论

转自 http://blog.csdn.net/xingzhedai/article/details/53144126 更多参考:http://blog.csdn.net/mafeiyu80/article/details/51446558 http://blog.csdn.net/caimouse/article/details/70225998 http://kubicode.me/2017/05/15/Deep%20Learning/Understanding-about-RNN/ RNN

Deep Learning 23：dropout理解_之读论文“Improving neural networks by preventing co-adaptation of feature detectors”

理论知识:Deep learning:四十一(Dropout简单理解).深度学习(二十二)Dropout浅层理解与实现.“Improving neural networks by preventing co-adaptation of feature detectors” 感觉没什么好说的了,该说的在引用的这两篇博客里已经说得很清楚了,直接做试验吧注意: 1.在模型的测试阶段,使用”mean network(均值网络)”来得到隐含层的输出,其实就是在网络前向传播到输出层前时隐含层节点的输出值都

转：RNN(Recurrent Neural Networks)

RNN(Recurrent Neural Networks)公式推导和实现 http://x-algo.cn/index.php/2016/04/25/rnn-recurrent-neural-networks-derivation-and-implementation/ 2016-04-25 分类:Deep Learning / NLP / RNN 阅读(6997) 评论(7) 本文主要参考wildml的博客所写,所有的代码都是python实现.没有使用任何深度学习的工具,公式推导虽然枯燥,但

深度学习论文翻译解析（五）：Siamese Neural Networks for One-shot Image Recognition

论文标题:Siamese Neural Networks for One-shot Image Recognition 论文作者: Gregory Koch Richard Zemel Ruslan Salakhutdinov 论文地址:https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf 声明:小编翻译论文仅为学习,如有侵权请联系小编删除博文,谢谢! 小编是一个机器学习初学者,打算认真研究论文,但是英文水平有限,所以论文翻译中用到了Goo

吴恩达《深度学习》-课后测验-第五门课序列模型(Sequence Models)-Week 1: Recurrent Neural Networks(第一周测验：循环神经网络)

Week 1 Quiz: Recurrent Neural Networks(第一周测验:循环神经网络) \1. Suppose your training examples are sentences (sequences of words). Which of the following refers to the jth word in the ith training example?( 假设你的训练样本是句子(单词序列),下面哪个选项指的是第??个训练样本中的第??个词?) [ ]

Hacker's guide to Neural Networks

Hacker's guide to Neural Networks Andrej Karpathy blog About Hacker's guide to Neural Networks Hacker's guide to Neural Networks Hi there, I'm a CS PhD student at Stanford. I've worked on Deep Learning for a few years as part of my research and among

Reducing the Dimensionality of data with neural networks / A fast learing algorithm for deep belief net

Deeplearning原文作者Hinton代码注解 1 Matlab示例代码为两部分,分别对应不同的论文: 2 3 1. Reducing the Dimensionality of data with neural networks 4 5 ministdeepauto.m backprop.m rbmhidlinear.m 6 7 2. A fast learing algorithm for deep belief net 8 9 mnistclassify.m backpropclas