# 활성화 함수

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## 노트

### 위키데이터

- ID : Q4677469

### 말뭉치

- An activation function is a function used in artificial neural networks which outputs a small value for small inputs, and a larger value if its inputs exceed a threshold.
^{[1]} - The activation function g could be any of the activation functions listed so far.
^{[1]} - In fact, a neural network of just two layers, provided it contains an activation function, is able to implement any possible function, not just the XOR.
^{[1]} - The first thing that comes to our minds is how about a threshold based activation function?
^{[2]} - So this makes an activation function for a neuron.
^{[2]} - Hope you got the idea behind activation function, why they are used and how do we decide which one to use.
^{[2]} - The rectified linear activation function or ReLU for short is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero.
^{[3]} - The simplest activation function is referred to as the linear activation, where no transform is applied at all.
^{[3]} - The sigmoid activation function, also called the logistic function, is traditionally a very popular activation function for neural networks.
^{[3]} - 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]} - The ReLU is the most used activation function in the world right now.
^{[4]} - Applies the sigmoid activation function.
^{[5]} - Can we do without an activation function ?
^{[6]} - Finally, the output from the activation function moves to the next hidden layer and the same process is repeated.
^{[6]} - We understand that using an activation function introduces an additional step at each layer during the forward propagation.
^{[6]} - In other words, if the input to the activation function is greater than a threshold, then the neuron is activated, else it is deactivated, i.e. its output is not considered for the next hidden layer.
^{[6]} - In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs.
^{[7]} - The seminal 2012 AlexNet computer vision architecture uses the ReLU activation function, as did the seminal 2015 computer vision architecture ResNet.
^{[7]} - The identity activation function does not satisfy this property.
^{[7]} - When multiple layers use the identity activation function, the entire network is equivalent to a single-layer model.
^{[7]} - Thus, selecting the ReLU as the activation function, one bypasses problems related to the slowing down when derivatives get small values.
^{[8]} - In The process of building a neural network, one of the choices you get to make is what activation function to use in the hidden layer as well as at the output layer of the network.
^{[9]} - Definition of activation function:- Activation function decides, whether a neuron should be activated or not by calculating weighted sum and further adding bias with it.
^{[9]} - It is the most widely used activation function.
^{[9]} - In this post, we’ll be discussing what an activation function is and how we use these functions in neural networks.
^{[10]} - We’ll also look at a couple of different activation functions, and we'll see how to specify an activation function in code with Keras.
^{[10]} - Let's give a definition for an activation function: In an artificial neural network, an activation function is a function that maps a node's inputs to its corresponding output.
^{[10]} - We took the weighted sum of each incoming connection for each node in the layer, and passed that weighted sum to an activation function.
^{[10]} - In deep learning, very complicated tasks are image classification, language transformation, object detection, etc which are needed to address with the help of neural networks and activation function.
^{[11]} - Activation function defines the output of input or set of inputs or in other terms defines node of the output of node that is given in inputs.
^{[11]} - Activation function also helps to normalize the output of any input in the range between 1 to -1.
^{[11]} - Activation function basically decides in any neural network that given input or receiving information is relevant or it is irrelevant.
^{[11]} - Using a biological analogy, the activation function determines the “firing rate” of a neuron in response to an input or stimulus.
^{[12]} - In order to solve the above problem, the influence of the activation function in the CNN model is studied in this paper.
^{[13]} - According to the design principle of the activation function in CNN model, a new piecewise activation function is proposed.
^{[13]} - Based on this rate code interpretation, we model the firing rate of the neuron with an activation function \(f\), which represents the frequency of the spikes along the axon.
^{[14]} - Every activation function (or non-linearity) takes a single number and performs a certain fixed mathematical operation on it.
^{[14]} - Rectified Linear Unit (ReLU) activation function, which is zero when x < 0 and then linear with slope 1 when x > 0.
^{[14]} - Some people report success with this form of activation function, but the results are not always consistent.
^{[14]} - The above expressions involve the derivative of the activation function , and therefore require continuous functions.
^{[15]} - Now that we've added an activation function, adding layers has more impact.
^{[16]} - In fact, any mathematical function can serve as an activation function.
^{[16]} - Suppose that \(\sigma\) represents our activation function (Relu, Sigmoid, or whatever).
^{[16]} - An activation function that transforms the output of each node in a layer.
^{[16]} - In a neural network, an activation function normalizes the input and produces an output which is then passed forward into the subsequent layer.
^{[17]} - Why do Neural Networks Need an Activation Function?
^{[18]} - However, you may have noticed that in my network diagrams, the representation of the activation function is not a unit step.
^{[19]} - If we intend to train a neural network using gradient descent, we need a differentiable activation function.
^{[19]} - The accuracy and computational time of classification model were depending on the activation function.
^{[20]} - Based on experimental results, the average accuracy can reach 80.56% on ELUs activation function and the maximum accuracy 88.73% on TanHRe.
^{[20]} - To achieve functional adaptation, an adaptive sigmoidal activation function is proposed for the hidden layers’ node.
^{[21]} - Four variants of the proposed algorithm are developed and discussed on the basis of activation function used.
^{[21]} - This input undergoes convolutions (labeled as conv), pooling (labeled as maxpool), and experimental ReLU6 operations, followed by two fully connected layers and a softmax activation function.
^{[22]} - So, an activation function is basically just a simple function that transforms its inputs into outputs that have a certain range.
^{[23]} - If the activation function is not applied, the output signal becomes a simple linear function.
^{[23]} - A neural network without activation function will act as a linear regression with limited learning power.
^{[23]} - The activations functions that were used mostly before ReLU such as sigmoid or tanh activation function saturated.
^{[23]} - The activation function is the most important factor in a neural network which decided whether or not a neuron will be activated or not and transferred to the next layer.
^{[24]} - Linear is the most basic activation function, which implies proportional to the input.
^{[24]} - Rectified Linear Unit is the most used activation function in hidden layers of a deep learning model.
^{[24]} - Demerits – ELU has the property of becoming smooth slowly and thus can blow up the activation function greatly.
^{[24]} - Rectified Linear Units is an activation function that deals with this problem and speeds up the learning process.
^{[25]} - In order to beat the performance of DNNs with ReLU, we propose a new activation function technique for DNNs that deals with the positive part of ReLU.
^{[25]} - For generalization, the mean function between the two considered functions is used as activation function for the trained DNNs.
^{[25]} - Notably, the ReLU activation function maintains a high degree of gradient propagation while presenting greater model sparsity and computational efficiency over Softplus.
^{[26]} - The activation function is the non-linear function that we apply over the output data coming out of a particular layer of neurons before it propagates as the input to the next layer.
^{[27]} - In this article, we went over two core components of a deep learning model – activation function and optimizer algorithm.
^{[27]} - The nonlinear behavior of an activation function allows our neural network to learn nonlinear relationships in the data.
^{[28]} - Recall that we included the derivative of the activation function in calculating the "error" term for each layer in the backpropagation algorithm.
^{[28]} - The way this is usually done is by applying the softmax activation function.
^{[29]} - Combining with state 0, it forms a special activation function including three states.
^{[30]} - If neural networks are used to deal with logic problems, this activation function will be helpful on some certain conditions.
^{[30]} - When DNNs are pretrained using MSAFs, they are not optimal due to the fact that the activation function of a restricted Boltzmann machine (RBM) is different from MSAFs.
^{[30]} - For instance, let the activation function be and ; then the network will classify random points shown in Figure 9.
^{[30]}

### 소스

- ↑
^{1.0}^{1.1}^{1.2}Activation Function - ↑
^{2.0}^{2.1}^{2.2}Understanding Activation Functions in Neural Networks - ↑
^{3.0}^{3.1}^{3.2}^{3.3}A Gentle Introduction to the Rectified Linear Unit (ReLU) - ↑ Activation Functions in Neural Networks
- ↑ Layer activation functions
- ↑
^{6.0}^{6.1}^{6.2}^{6.3}Fundamentals Of Deep Learning - ↑
^{7.0}^{7.1}^{7.2}^{7.3}Activation function - ↑ Activation Function - an overview
- ↑
^{9.0}^{9.1}^{9.2}Activation functions in Neural Networks - ↑
^{10.0}^{10.1}^{10.2}^{10.3}Activation Functions in a Neural Network explained - ↑
^{11.0}^{11.1}^{11.2}^{11.3}7 Types of Activation Functions in Neural Network - ↑ Radiology Reference Article
- ↑
^{13.0}^{13.1}The Influence of the Activation Function in a Convolution Neural Network Model of Facial Expression Recognition - ↑
^{14.0}^{14.1}^{14.2}^{14.3}CS231n Convolutional Neural Networks for Visual Recognition - ↑ Nonlinear Activation Functions in a Backpropagation Neural Network
- ↑
^{16.0}^{16.1}^{16.2}^{16.3}Neural Networks: Structure - ↑ Activation Function
- ↑ Why do Neural Networks Need an Activation Function?
- ↑
^{19.0}^{19.1}The Sigmoid Activation Function: Activation in Multilayer Perceptron Neural Networks - ↑
^{20.0}^{20.1}Comparison of activation function on extreme learning machine (ELM) performance for classifying the active compound - ↑
^{21.0}^{21.1}An Adaptive Sigmoidal Activation Function Cascading Neural Networks - ↑ Reconfigurable all-optical nonlinear activation functions for neuromorphic photonics
- ↑
^{23.0}^{23.1}^{23.2}^{23.3}An Introduction to Rectified Linear Unit (ReLU) - ↑
^{24.0}^{24.1}^{24.2}^{24.3}Activation Functions in Neural Networks: An Overview - ↑
^{25.0}^{25.1}^{25.2}Symmetric Power Activation Functions for Deep Neural Networks - ↑ Thesis: Evaluation of the smoothing activation function in neural networks for business applications
- ↑
^{27.0}^{27.1}Activation Functions and Optimizers for Deep Learning Models - ↑
^{28.0}^{28.1}Neural networks: activation functions. - ↑ Benchmarking deep learning activation functions on MNIST
- ↑
^{30.0}^{30.1}^{30.2}^{30.3}Deep Neural Networks with Multistate Activation Functions

## 메타데이터

### 위키데이터

- ID : Q4677469