"Hyperbolic tangent"의 두 판 사이의 차이
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− | == 메타데이터 == | + | ==메타데이터== |
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===위키데이터=== | ===위키데이터=== | ||
* ID : [https://www.wikidata.org/wiki/Q1274703 Q1274703] | * ID : [https://www.wikidata.org/wiki/Q1274703 Q1274703] | ||
+ | ===Spacy 패턴 목록=== | ||
+ | * [{'LOWER': 'hyperbolic'}, {'LEMMA': 'tangent'}] | ||
+ | * [{'LEMMA': 'tanh'}] |
2021년 2월 17일 (수) 00:34 기준 최신판
노트
위키데이터
- ID : Q1274703
말뭉치
- One point to mention is that the gradient is stronger for tanh than sigmoid ( derivatives are steeper).[1]
- Deciding between the sigmoid or tanh will depend on your requirement of gradient strength.[1]
- Using a sigmoid or tanh will cause almost all neurons to fire in an analog way ( remember? ).[1]
- ReLu is less computationally expensive than tanh and sigmoid because it involves simpler mathematical operations.[1]
- This can be addressed by scaling the sigmoid function which is exactly what happens in the tanh function.[2]
- The gradient of the tanh function is steeper as compared to the sigmoid function.[2]
- Since only a certain number of neurons are activated, the ReLU function is far more computationally efficient when compared to the sigmoid and tanh function.[2]
- The hyperbolic tangent function, or tanh for short, is a similar shaped nonlinear activation function that outputs values between -1.0 and 1.0.[3]
- A general problem with both the sigmoid and tanh functions is that they saturate.[3]
- This means that large values snap to 1.0 and small values snap to -1 or 0 for tanh and sigmoid respectively.[3]
- Traditionally, LSTMs use the tanh activation function for the activation of the cell state and the sigmoid activation function for the node output.[3]
- Tanh function also knows as Tangent Hyperbolic function.[4]
- The tanh non-linearity is shown on the image above on the right.[5]
- Therefore, in practice the tanh non-linearity is always preferred to the sigmoid nonlinearity.[5]
- the convergence of stochastic gradient descent compared to the sigmoid/tanh functions.[5]
- The demo program illustrates three common neural network activation functions: logistic sigmoid, hyperbolic tangent and softmax.[6]
- The same inputs, weights and bias values yield outputs of 0.5006 and 0.5772 when the hyperbolic tangent activation function is used.[6]
- WriteLine("Computing outputs using Hyperbolic Tangent activation"); dnn.[6]
- The hyperbolic tangent function is often abbreviated as tanh.[6]
- It lags behind the Sigmoid and Tanh for some of the use cases.[7]
- Cons Tanh also has the vanishing gradient problem.[7]
- : (a) Activation functions compared the rectified linear units (ReLU), Sigmoid (“sigm”) and Tanh (“tanh”), Fig.[8]
- The only perk we will get using Tanh function is that the slope of function does not decrease quickly like sigmoid function.[9]
- Sigmoid activation function and Tanh activation function works terribly for the hidden layer.[9]
- Tanh: Hyperbolic tangent is an activation function similar to sigmoid but the output values range between -1 to 1.[10]
- Unlike sigmoid the output of Tanh function is zero centred, therefore Tanh is preferred more than sigmoid.[10]
- Arctangent: This activation function is similar to sigmoid and Tanh, it maps the inputs to outputs which range between (-2,2).[10]
- It somehow lags the sigmoid and Tanh for a few cases.[10]
- ReLU, Sigmoid and Tanh are today’s most widely used activation functions.[11]
- The results suggest that Tanh performs worse than ReLU and Sigmoid.[11]
- In my master’s thesis, I found that in some cases Tanh works better than ReLU.[11]
소스
- ↑ 1.0 1.1 1.2 1.3 Understanding Activation Functions in Neural Networks
- ↑ 2.0 2.1 2.2 Fundamentals Of Deep Learning
- ↑ 3.0 3.1 3.2 3.3 A Gentle Introduction to the Rectified Linear Unit (ReLU)
- ↑ Activation functions in Neural Networks
- ↑ 5.0 5.1 5.2 CS231n Convolutional Neural Networks for Visual Recognition
- ↑ 6.0 6.1 6.2 6.3 Neural Network Activation Functions in C# -- Visual Studio Magazine
- ↑ 7.0 7.1 Activation Functions — ML Glossary documentation
- ↑ Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
- ↑ 9.0 9.1 Mastering Activation Functions in Neural Networks
- ↑ 10.0 10.1 10.2 10.3 Neural Network Activation Functions - 360DigiTMG
- ↑ 11.0 11.1 11.2 Implementing ReLU, Sigmoid and Tanh in Keras – MachineCurve
메타데이터
위키데이터
- ID : Q1274703
Spacy 패턴 목록
- [{'LOWER': 'hyperbolic'}, {'LEMMA': 'tangent'}]
- [{'LEMMA': 'tanh'}]