# Vanishing gradient problem

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## 노트[편집]

### 위키데이터[편집]

- ID : Q18358230

### 말뭉치[편집]

- Indeed, if the terms get large enough - greater than 1 - then we will no longer have a vanishing gradient problem.
^{[1]} - Instead of a vanishing gradient problem, we'll have an exploding gradient problem.
^{[1]} - The middle Tanh waveform provides ReLTanh with the ability of nonlinear fitting, and the linear parts contribute to the relief of vanishing gradient problem.
^{[2]} - Theoretical proofs by mathematical derivations demonstrate that ReLTanh is available to diminish vanishing gradient problem and feasible to train thresholds.
^{[2]} - The exploding and vanishing gradient problem has been the major conceptual principle behind most architecture and training improvements in recurrent neural networks (RNNs) during the last decade.
^{[3]} - {The exploding and vanishing gradient problem has been the major conceptual principle behind most architecture and training improvements in recurrent neural networks (RNNs) during the last decade.
^{[3]} - To model an algorithm that is good at capturing long-term dependencies, we need to focus on handling the vanishing gradient problem, as we will do in the upcoming sections of this blog post.
^{[4]} - Now, we will see how during back propagation vanishing gradient problem occurs.
^{[5]} - Therefore, it will also lead to vanishing gradient problem.
^{[5]} - And even if this weight initialization technique is not employed, the vanishing gradient problem will most likely still occur.
^{[6]} - Thus, with deep neural nets, the vanishing gradient problem becomes a major concern.
^{[6]} - ReLUs still face the vanishing gradient problem, it’s just that they often face it to a lesser degree.
^{[6]} - This is the problem of unstable gradients and is most popularly referred to as the vanishing gradient problem.
^{[7]} - In general, the vanishing gradient problem is a problem that causes major difficulty when training a neural network.
^{[7]} - One of the issues that had to be overcome in making them more useful and transitioning to modern deep learning networks was the vanishing gradient problem.
^{[8]} - This post will examine the vanishing gradient problem, and demonstrate an improvement to the problem through the use of the rectified linear unit activation function, or ReLUs.
^{[8]} - The vanishing gradient problem comes about in deep neural networks when the f' terms are all outputting values << 1.
^{[8]} - However, even if the weights are initialized nicely, and the derivatives are sitting around the maximum i.e. ~0.2, with many layers there will still be a vanishing gradient problem.
^{[8]} - The vanishing gradient problem requires us to use small learning rates with gradient descent which then needs many small steps to converge.
^{[9]} - There are several ways to tackle the vanishing gradient problem.
^{[9]} - Thus the choice of the sigmoid function contributed to the Vanishing Gradient problem that plagued the first DLN systems.
^{[10]} - Why does the vanishing gradient problem occur?
^{[11]} - This is the exploding gradient problem, and it's not much better news than the vanishing gradient problem.
^{[11]} - To get insight into why the vanishing gradient problem occurs, let's consider the simplest deep neural network: one with just a single neuron in each layer.
^{[11]} - You're welcome to take this expression for granted, and skip to the discussion of how it relates to the vanishing gradient problem.
^{[11]} - The vanishing gradient problem (VGP) is an important issue at training time on multilayer neural networks using the backpropagation algorithm.
^{[12]} - Now let's explore the vanishing gradient problem in detail.
^{[13]} - As its name implies, the vanishing gradient problem is related to deep learning gradient descent algorithms.
^{[13]} - The vanishing gradient problem occurs when the backpropagation algorithm moves back through all of the neurons of the neural net to update their weights.
^{[13]} - To summarize, the vanishing gradient problem is caused by the multiplicative nature of the backpropagation algorithm.
^{[13]} - The vanishing gradient problem is an issue that sometimes arises when training machine learning algorithms through gradient descent.
^{[14]} - In machine learning, the vanishing gradient problem is encountered when training artificial neural networks with gradient-based learning methods and backpropagation.
^{[15]} - In Machine Learning, the Vanishing Gradient Problem is encountered while training Neural Networks with gradient-based methods (example, Back Propagation).
^{[16]} - One of the newest and most effective ways to resolve the vanishing gradient problem is with residual neural networks, or ResNets (not to be confused with recurrent neural networks).
^{[16]} - The ResNet architecture, shown below, should now make perfect sense as to how it would not allow the vanishing gradient problem to occur.
^{[16]} - This post has shown you how the vanishing gradient problem comes about when using the old canonical sigmoid activation function.
^{[16]}

### 소스[편집]

- ↑
^{1.0}^{1.1}Vanishing Gradient Problem - ↑
^{2.0}^{2.1}ReLTanh: An activation function with vanishing gradient resistance for SAE-based DNNs and its application to rotating machinery fault diagnosis - ↑
^{3.0}^{3.1}Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness - ↑ # 005 RNN – Tackling Vanishing Gradients with GRU and LSTM
- ↑
^{5.0}^{5.1}What is Vanishing Gradient Problem ? - ↑
^{6.0}^{6.1}^{6.2}Rohan #4: The vanishing gradient problem - ↑
^{7.0}^{7.1}Vanishing & Exploding Gradient explained | A problem resulting from backpropagation - ↑
^{8.0}^{8.1}^{8.2}^{8.3}The vanishing gradient problem and ReLUs - ↑
^{9.0}^{9.1}How do CNN's avoid the vanishing gradient problem - ↑ Deep Learning
- ↑
^{11.0}^{11.1}^{11.2}^{11.3}Neural networks and deep learning - ↑ A new approach for the vanishing gradient problem on sigmoid activation
- ↑
^{13.0}^{13.1}^{13.2}^{13.3}The Vanishing Gradient Problem in Recurrent Neural Networks - ↑ Vanishing Gradient Problem
- ↑ Vanishing gradient problem
- ↑
^{16.0}^{16.1}^{16.2}^{16.3}What is Vanishing Gradient Problem?

## 메타데이터[편집]

### 위키데이터[편집]

- ID : Q18358230