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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

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

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