손실 함수

수학노트
둘러보기로 가기 검색하러 가기

노트

  • The loss function is used to measure how good or bad the model is performing.[1]
  • Also, there is no fixed loss function that can be used in all places.[1]
  • Loss functions are mainly classified into two different categories that are Classification loss and Regression Loss.[1]
  • We implement this mechanism in the form of losses and loss functions.[2]
  • Neural networks are trained using an optimizer and we are required to choose a loss function while configuring our model.[2]
  • Different loss functions play slightly different roles in training neural nets.[2]
  • This article will explain the role of Keras loss functions in training deep neural nets.[2]
  • At its core, a loss function is incredibly simple: it’s a method of evaluating how well your algorithm models your dataset.[3]
  • If your predictions are totally off, your loss function will output a higher number.[3]
  • There are variety of pakages which surropt these loss function.[3]
  • This paper studies a variety of loss functions and output layer regularization strategies on image classification tasks.[4]
  • , we’ll be discussing what a loss function is and how it’s used in an artificial neural network.[5]
  • Recall that we’ve already introduced the idea of a loss function in our post on training a neural network.[5]
  • The loss function is what SGD is attempting to minimize by iteratively updating the weights in the network.[5]
  • This was just illustrating the math behind how one loss function, MSE, works.[5]
  • However, there is no universally accepted definition for other loss functions.[6]
  • Most approaches have focused solely on 0-1 loss functions and have produced significantly different definitions.[6]
  • Using this framework, bias and variance definitions are produced which generalize to any symmetric loss function.[6]
  • We illustrate these statistics on several loss functions with particular emphasis on 0-1 loss.[6]
  • The results obtained with their bi-temperature loss function was then compared to the vanilla logistic loss function.[7]
  • This loss function is adopted for the discriminator.[7]
  • As a result of this, GANs using this loss function are able to generate higher quality images than regular GANs.[7]
  • This loss function is used when images that look similar are being compared.[7]
  • We will use the term cost function for a single training example and loss function for the entire training dataset.[8]
  • Depending on the output variable we need to choose loss function to our model.[8]
  • MSE loss is popularly used loss functions in dealing with regression problems.[8]
  • The args and kwargs will be passed to loss_cls during the initialization to instantiate a loss function.[9]
  • + 1(e < 0)c 2 (e ) will be a loss function.[10]
  • Optimal forecasting of a time series model depends extensively on the specification of the loss function.[10]
  • Suppose the loss functions c 1 (·), c 2 (·) are used for forecasting Y t + h and for forecasting h (Y t + h ), respectively.[10]
  • Granger (1999) remarks that it would be strange behavior to use the same loss function for Y and h (Y ).[10]
  • Loss functions are used to train neural networks and to compute the difference between output and target variable.[11]
  • A critical component of training neural networks is the loss function.[11]
  • A loss function is a quantative measure of how bad the predictions of the network are when compared to ground truth labels.[11]
  • Some tasks use a combination of multiple loss functions, but often you’ll just use one.[11]
  • Loss functions are to be supplied in the loss parameter of the compile.keras.engine.training.[12]
  • How do you capture the difference between two distributions in GAN loss functions?[13]
  • The loss function used in the paper that introduced GANs.[13]
  • A GAN can have two loss functions: one for generator training and one for discriminator training.[13]
  • There are several ways to define the details of the loss function.[14]
  • There is one bug with the loss function we presented above.[14]
  • We can do so by extending the loss function with a regularization penalty \(R(W)\).[14]
  • The demo visualizes the loss functions discussed in this section using a toy 3-way classification on 2D data.[14]
  • In SLF, a generic loss function is formulated as a joint optimization problem of network weights and loss parameters.[15]
  • The loss function for linear regression is squared loss.[16]
  • The way you configure your loss functions can make or break the performance of your algorithm.[17]
  • In this article, we’ll talk about popular loss functions in PyTorch, and about building custom loss functions.[17]
  • Loss functions are used to gauge the error between the prediction output and the provided target value.[17]
  • A loss function tells us how far the algorithm model is from realizing the expected outcome.[17]
  • In fact, we can design our own (very) basic loss function to further explain how it works.[18]
  • For each prediction that we make, our loss function will simply measure the absolute difference between our prediction and the actual value.[18]
  • Notice how in the loss function we defined, it doesn’t matter if our predictions were too high or too low.[18]
  • A lot of the loss functions that you see implemented in machine learning can get complex and confusing.[18]
  • An optimization problem seeks to minimize a loss function.[19]
  • The use of a quadratic loss function is common, for example when using least squares techniques.[19]
  • The quadratic loss function is also used in linear-quadratic optimal control problems.[19]
  • One of these algorithmic changes was the replacement of mean squared error with the cross-entropy family of loss functions.[20]
  • Importantly, the choice of loss function is directly related to the activation function used in the output layer of your neural network.[20]
  • The choice of cost function is tightly coupled with the choice of output unit.[20]
  • The model can be updated to use the ‘mean_squared_logarithmic_error‘ loss function and keep the same configuration for the output layer.[21]
  • Loss functions are used to determine the error (aka “the loss”) between the output of our algorithms and the given target value.[22]
  • The quadratic loss is a commonly used symmetric loss function.[22]
  • The Cost function and Loss function refer to the same context.[23]
  • The cost function is a function that is calculated as the average of all loss function values.[23]
  • The Loss function is directly related to the predictions of your model that you have built.[23]
  • This is the most common Loss function used in Classification problems.[23]
  • The group of functions that are minimized are called “loss functions”.[24]
  • Loss function is used as measurement of how good a prediction model does in terms of being able to predict the expected outcome.[24]
  • A loss function is a mathematical function commonly used in statistics.[25]
  • There are many types of loss functions including mean absolute loss, mean squared error and mean bias error.[25]
  • Loss functions are at the heart of the machine learning algorithms we love to use.[26]
  • In this article, I will discuss 7 common loss functions used in machine learning and explain where each of them is used.[26]
  • Loss functions are one part of the entire machine learning journey you will take.[26]
  • Here, theta_j is the weight to be updated, alpha is the learning rate and J is the cost function.[26]
  • Machines learn by means of a loss function.[27]
  • If predictions deviates too much from actual results, loss function would cough up a very large number.[27]
  • Gradually, with the help of some optimization function, loss function learns to reduce the error in prediction.[27]
  • There’s no one-size-fits-all loss function to algorithms in machine learning.[27]
  • The loss function is the function that computes the distance between the current output of the algorithm and the expected output.[28]
  • This loss function is convex and grows linearly for negative values (less sensitive to outliers).[28]
  • The Hinge loss function was developed to correct the hyperplane of SVM algorithm in the task of classification.[28]
  • At the difference of the previous loss function, the square is replaced by an absolute value.[28]
  • Square Error (MSE) is the most commonly used regression loss function.[29]
  • Whenever we train a machine learning model, our goal is to find the point that minimizes loss function.[29]
  • Problems with both: There can be cases where neither loss function gives desirable predictions.[29]
  • Another way is to try a different loss function.[29]
  • Generally cost and loss functions are synonymous but cost function can contain regularization terms in addition to loss function.[30]
  • Loss function is a method of evaluating “how well your algorithm models your dataset”.[30]
  • Cost Function quantifies the error between predicted values and expected values and presents it in the form of a single real number.[30]
  • Depending on the problem Cost Function can be formed in many different ways.[30]
  • In this example, we’re defining the loss function by creating an instance of the loss class.[31]
  • Problems involving the prediction of more than one class use different loss functions.[31]
  • During the training process, one can weigh the loss function by observations or samples.[31]
  • It is usually a good idea to monitor the loss function, on the training and validation set as the model is training.[31]
  • Loss functions are typically created by instantiating a loss class (e.g. keras.losses.[32]

소스

  1. ↑ 이동: 1.0 1.1 1.2 What Are Different Loss Functions Used as Optimizers in Neural Networks?
  2. ↑ 이동: 2.0 2.1 2.2 2.3 Keras Loss Functions
  3. ↑ 이동: 3.0 3.1 3.2 Types of Loss Function
  4. ↑ What's in a Loss Function for Image Classification?
  5. ↑ 이동: 5.0 5.1 5.2 5.3 Loss in a Neural Network explained
  6. ↑ 이동: 6.0 6.1 6.2 6.3 Variance and Bias for General Loss Functions
  7. ↑ 이동: 7.0 7.1 7.2 7.3 Research Guide: Advanced Loss Functions for Machine Learning Models
  8. ↑ 이동: 8.0 8.1 8.2 Hands-On Guide To Loss Functions Used To Evaluate A ML Algorithm
  9. ↑ Loss Functions
  10. ↑ 이동: 10.0 10.1 10.2 10.3 Encyclopedia.com
  11. ↑ 이동: 11.0 11.1 11.2 11.3 Loss functions — Apache MXNet documentation
  12. ↑ Model loss functions — loss_mean_squared_error
  13. ↑ 이동: 13.0 13.1 13.2 Generative Adversarial Networks
  14. ↑ 이동: 14.0 14.1 14.2 14.3 CS231n Convolutional Neural Networks for Visual Recognition
  15. ↑ Stochastic Loss Function
  16. ↑ Logistic Regression: Loss and Regularization
  17. ↑ 이동: 17.0 17.1 17.2 17.3 PyTorch Loss Functions: The Ultimate Guide
  18. ↑ 이동: 18.0 18.1 18.2 18.3 Introduction to Loss Functions
  19. ↑ 이동: 19.0 19.1 19.2 Loss function
  20. ↑ 이동: 20.0 20.1 20.2 Loss and Loss Functions for Training Deep Learning Neural Networks
  21. ↑ How to Choose Loss Functions When Training Deep Learning Neural Networks
  22. ↑ 이동: 22.0 22.1 Loss Function
  23. ↑ 이동: 23.0 23.1 23.2 23.3 Most Common Loss Functions in Machine Learning
  24. ↑ 이동: 24.0 24.1 Loss functions: Why, what, where or when?
  25. ↑ 이동: 25.0 25.1 Radiology Reference Article
  26. ↑ 이동: 26.0 26.1 26.2 26.3 Loss Function In Machine Learning
  27. ↑ 이동: 27.0 27.1 27.2 27.3 Common Loss functions in machine learning
  28. ↑ 이동: 28.0 28.1 28.2 28.3 What are Loss Functions?
  29. ↑ 이동: 29.0 29.1 29.2 29.3 5 Regression Loss Functions All Machine Learners Should Know
  30. ↑ 이동: 30.0 30.1 30.2 30.3 Cost, Activation, Loss Function|| Neural Network|| Deep Learning. What are these?
  31. ↑ 이동: 31.0 31.1 31.2 31.3 Keras Loss Functions: Everything You Need To Know
  32. ↑ Losses

메타데이터

위키데이터

Spacy 패턴 목록

  • [{'LOWER': 'loss'}, {'LEMMA': 'function'}]
  • [{'LOWER': 'loss'}, {'LEMMA': 'function'}]
  • [{'LOWER': 'error'}, {'LEMMA': 'function'}]
  • [{'LOWER': 'cost'}, {'LEMMA': 'function'}]
원본 주소 "https://wiki.mathnt.net/index.php?title=손실_함수&oldid=51640"