By fitting the loss function, It also increases the distance between classes to a certain extent. So, with some simple highschool level math, we have solved the numerical flaw in the basic Binary Cross-Entropy function and created a Stable Binary Cross-Entropy Loss and Cost function. 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. Take a moment to understand this and try to piece it together with the piecewise stable Binary Cross-Entropy Loss function from Fig.58. 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, Multi-class cross entropy loss is used in multi-class classification, such as the MNIST digits classification problem from Chapter 2, Deep Learning and. 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 The higher the difference between the two, the higher the loss. * Dichotomy : For example, judge whether a watermelon is good or bad The cross-entropy loss function measures your model’s performance by transforming its variables into real numbers, thereby evaluating the ’loss’ associated with them. * regression : The target variable is continuous, Such as predicting the sugar content of watermelon (0.00~1.00) It quantifies the degree of uncertainty in the model’s predicted value for the variable. In statistics, entropy refers to the disorder of the system. As the name implies, the basis of this is Entropy. * 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 ) Categorical Cross-Entropy loss is traditionally used in classification tasks. Supervised learning is mainly divided into two categories : Cross entropy loss function (CrossEntropy Loss)( Principle explanation )
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