Cross-entropy is the default loss function to use for binary classification problems. The strength of down-weighting is proportional to the size of the gamma parameter. Although an MLP is used in these examples, the same loss functions can be used when training CNN and RNN models for binary classification. Focal Loss is the same as cross entropy except easy-to-classify observations are down-weighted in the loss calculation. By fitting the loss function, It also increases the distance between classes to a certain extent. Wrapping a general loss function inside of BaseLoss provides extra functionalities to your loss functions. Predicted as bad melon (0) The probability is 1 − P 1-P 1−P: P ( y = 0 ∣ x ) = 1 − P P(y=0|x)=1-P P(y=0īe, P ( y ∣ x ) = P y ( 1 − P ) 1 − y P(y|x)=P^y(1-P)^pi,k Denotes the second i i i The second sample is predicted to be the third k k k The probability of a tag value. Here are some advantages and disadvantages of. It is predicted to be a good melon (1) The probability is P P P: P ( y = 1 ∣ x ) = P P(y=1|x)=P P(y=1∣x)=P, Cross entropy is a commonly used error measure when training neural networks, especially in classification tasks. In the case of dichotomy, Only two values can be predicted for each category, Suppose the prediction is good melon (1) The probability is P P P, Bad melon (0) The probability is 1 − P 1-P 1−P:īe, The general form of cross entropy loss is, among y Label : The cross entropy loss function is the most commonly used loss function in classification, Cross entropy is used to measure the difference between two probability distributions, It is used to measure the difference between the learned distribution and the real distribution. * Multiclassification : Such as judging a watermelon variety, Black Beauty, Te Xiaofeng, Annong 2, etc * Dichotomy : For example, judge whether a watermelon is good or bad * regression : The target variable is continuous, Such as predicting the sugar content of watermelon (0.00~1.00) The cross entropy loss is the negative of the first, multiplied by the logarithm of the. T công thc trên ra rút ra các nhn xét sau: Hàm loss t giá tr nh nht khi PQ. Xem P là phân phi úng (true distribution), Q là phân phi hin ti. * classification : The target variable is discrete, For example, judge whether a watermelon is good or bad, Then the target variable can only be 1( Good melon ),0( Bad melon ) To recap: y is the actual label, and is the classifiers output. Nói luyên thuyên nãy gi, ly ví d trong machine learning, ta s dùng cross-entropy nh là 1 loss function. Supervised learning is mainly divided into two categories : Cross entropy loss function (CrossEntropy Loss)( Principle explanation )
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