Adam:最常用的优化器了,结合了 Momentum 和 adagrad 的思想,计算动量和累加梯度的平方和,接着就是一顿算,根据动量算方向和大小,根据累加梯度的平方和算学习率。 Yogi:改进 Adam 的问题,在累加梯度的平方时,这个梯度可能很大(blows up),使到 Adam 不能收敛(even in convex setting),结合公式看一下就知道为什么...
四、Stochastic Gradient Descent (SGD) The extreme case of this is a setting where the mini-batch contains only a single example. This process is calledStochastic Gradient Descent (SGD)(or also sometimeson-linegradient descent). This is relatively less common to see because in practice due to ...
Examples: linear regression or overparametrized neural network in the realizable case. Polyak-Lojasiewicz condition Let f be a smooth function (may not be convex) Polyak-Lojasiewicz condition: there exists some $\mu>0$ such that \begin{equation}\tag{13} \|\nabla f(x)\|_2^2\geq 2\mu [...
In fact, there are many results based on entropy error function in neural network and its applications. However, the theory of such an algorithm and its convergence have not been fully studied so far. To tackle the issue, this works proposes a novel entropy function with smoothing l_0 ...
but can vary for different applications. Mini-batch gradient descent is typically the algorithm of choice when training a neural network and the term SGD usually is employed also when mini-batches are used. Note: In modifications of SGD in the rest of this post, we leave out the parameters ...
在机器学习和深度学习的广袤领域中,优化算法不断推陈出新,为模型训练和性能提升注入强大动力。Downpour SGD算法作为一种颇具特色的随机梯度下降(SGD)变体,备受关注。下面将深入探讨其原理与应用场景。 Downpour SGD算法原理 基本架构:Downpour SGD采用参数服务器架构,整个系统由一个参数服务器和多个工作节点组成。参数服务...
(source: http://cs231n.github.io/neural-networks-3) 这一方法也称为NAG,即 Nesterov Accelerated Gradient,是在SGD、SGD-M 的基础上的进一步改进,改进点在于步骤 1。我们知道在时刻 t 的主要下降方向是由累积动量决定的,自己的梯度方向说了也不算,那与其看当前梯度方向,不如先看看如果跟着累积动量走了一步...
Note——Neural Network and Deep Learning (1)[神经网络与深度学习学习笔记(1)] 话不多说直接上参考书籍:http://neuralnetworksanddeeplearning.com/index.html。当然中文版的百度很容得到。 一、初学神经网络的体会 正如书中作者说的神经网络可以被称作最美的编程范式之一,神经网络将我们需要解决的复杂问题,比如...
in Machine Learning文章链接,文中也探讨了在自适应优化算法:AdaGrad, RMSProp, and Adam和SGD算法性能之间的比较和选择,因此在此搬一下结论和感想。 Abstract 经过本文的实验,得出最重要的结论是: We observe that the solutions foundbyadaptive methods generalize worse (often significantly worse) than SGD, even...
# Simple example using recurrent neural network to predict time series values from __future__ import division, print_function, absolute_import importtflearnfrom tflearn.layers.normalization importbatch_normalizationimport numpy as np import tensorflow as tf ...