What is cross entropy? Minimization of cross entropy algorithm

Machine learning and deep learning models are often used to solve regression and classification problems. In supervised learning, the model learns during training how to map inputs to probabilistic outputs. In order to optimize the performance of the model, a loss function is often used to evaluate the difference between the predicted results and the true labels, among which cross-entropy is a common loss function. It measures the difference between the probability distribution predicted by the model and the true labels. By minimizing the cross-entropy, the model can predict the output more accurately.

What is Cross Entropy

Cross entropy is a measure of the difference between two probability distributions for a given set of random variables or events.

Cross entropy is a commonly used loss function, mainly used to optimize classification models. The performance of the model can be measured by the value of the loss function. The lower the loss, the better the model. The core idea of ​​the cross-entropy loss function is to compare the probability of each predicted category with the expected output (0 or 1) of the actual category and calculate the loss. When the gap between the predicted probability and the actual expected value is larger, the loss score will be larger; conversely, when the gap is smaller, the loss score will be smaller. During the model training process, we hope that the loss score is as small as possible, and the cross-entropy loss of a perfect model is 0.

Algorithm minimization of cross entropy

The loss function can be minimized by optimizing the model parameters. A common method is to use gradient descent The algorithm searches for the optimal solution in the parameter space.

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