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

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