梯度下降(Gradient descent) 梯度下降算法的定位 梯度下降算法是一种求解局部最小值的算法,在线性模型和非线性模型中都可以用。 在用某个模型对数据进行拟合时,会用损失函数(或者叫错误函数等等)去评估拟合的准确性,这个时候往往要找到损失函数的最小值,即求出达到最佳拟合效果时各参数的值。求函数的最小值时往往用到梯度下降
1、梯度消失(vanishing gradient problem)、梯度爆炸(exploding gradient problem)原因 神经网络最终的目的是希望损失函数loss取得极小值。所以最终的问题就变成了一个寻找函数最小值的问题,在数学上,很自然的就会想到使用梯度下降(求导)来解决。 梯度消失、梯度爆炸其根本原因在于反向传播训练法则(BP算法):是指在使用梯...
神经网络中梯度不稳定的根本原因:在于前层上的梯度的计算来自于后层上梯度的乘积(链式法则)。当层数很多时,就容易出现不稳定。下边3个隐含层为例: 其b1的梯度为: 推导过程(参考):https://blog.csdn.net/junjun150013652/article/details/81274958 加入激活函数为sigmoid,则其导数如下图: sigmoid导数σ'的最大值...
This paper aims to provide additional insights into the differences between RNNs and Gated Units in order to explain the superior perfomance of gated recurrent units. It is argued, that Gated Units are easier to optimize not because they solve the vanishing gradient problem, but because they ...
The first NaN shows up at the 33rd update, but the explosion started at the 31st update: The average gradient goes from tens (1e+01) to ten millions (1e+07) in one step, to (1e+21) or whatever this is called in the next, to a full-blown NaN. ...
This leads us to the factwould avoid both the vanishing and exploding gradient problems and although it is not as straightforward as it seems. This fact however has been used as the intuition behind creating most of the proposed solutions. ...
(2)梯度消失(vanishing gradient problem): 原因:例如三个隐层、单神经元网络: 则可以得到: 然而,sigmoid方程的导数曲线为: 可以看到,sigmoid导数的最大值为1/4,通常abs(w)<1,则: 前面的层比后面的层梯度变化更小,故变化更慢,从而引起了梯度消失问题。
While exploding gradient is a manifestation of the instability of the underlying dynamical system, vanishing gradient results from a lossy system, properties that have been widely studied in the dynamical system literature. 在动力系统中,如果梯度爆炸,说明系统不稳定,梯度消失源于有损系统。 系统建模:从...
这么看来,梯度消失/爆炸(Vanishing/Exploding Gradient)的根本原因就是反向传播训练法则,这是先天不足,也就是系统误差导致的,如果Hinton提出的 capsulecapsulecapsule 能彻底替代反向传播并大范围普及,那将会是一个崭新的时代,那真是一个revolution。 从激活函数来看的话,sigmoidsigmoidsigmoid 的问题就比较大了,梯度消失就...
Hello Stardust! Today we’ll see mathematical reason behind exploding and vanishing gradient problem but first let’s understand the problem in a nutshell.