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)
梯度消失(vanishing gradient)与梯度爆炸(exploding gradient)问题 ,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。 (3)梯度爆炸(explodinggradientproblem): 当权值过大,前面层比后面层梯度变化更快,会引起梯度...,前面层中的梯度或会消失,或会爆炸。原因:前面层上的梯度是来自于后面...
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 ...
This makes it possible to avoid both the vanishing and exploding gradient problem using this orthogonal initialization of weights. This method [9] however is not used in isolation and is often combined with other more advanced architectures like LSTMs to achieve optimal results. Conclusions In this...
(3)梯度爆炸(exploding gradient problem): 当权值过大,前面层比后面层梯度变化更快,会引起梯度爆炸问题。 (4)sigmoid时,消失和爆炸哪个更易发生? 量化分析梯度爆炸出现时a的树枝范围:因为sigmoid导数最大为1/4,故只有当abs(w)>4时才可能出现 由此计算出a的数值变化范围很小,仅仅在此窄范围内会出现梯度爆炸问...
Hello Stardust! Today we’ll see mathematical reason behind exploding and vanishing gradient problem but first let’s understand the problem in a nutshell.
Self-loops and gating units LSTM [3] GRU[4] The gates allow information to flow from inputs at any previous time steps to the end of the sequence more easily, partially addressing the vanishing gradient problem. 特殊的网络结构: special neural architectures, such as hierarchical RNNs (El ...
梯度消失和梯度爆炸问题 (exploding and vanishing gradient problem, EVGP) ,最早是由 Sepp Hochreiter 在1991年提出[2],这里就不再进行过多的介绍,知乎上有很多文章都有详细的解释。 1.1 实验改进 简单来说,神经网络由如下部分组成: 网络的参数(尤其是初始化); ...
(3)梯度爆炸(exploding gradient problem): 当权值过大,前面层比后面层梯度变化更快,会引起梯度爆炸问题。 (4)sigmoid时,消失和爆炸哪个更易发生? 量化分析梯度爆炸出现时a的树枝范围:因为sigmoid导数最大为1/4,故只有当abs(w)>4时才可能出现 由此计算出a的数值变化范围很小,仅仅在此窄范围内会出现梯度爆炸问...
Vanishing and Exploding Gradients - Deep Learning Dictionary The vanishing gradient problem is a problem that occurs during neural network training regarding unstable gradients and is a result of the backpropagation algorithm used to calculate the gradients. During training, the gradient descent optimiz...