softmax loss
时间: 2023-11-20 12:23:53 浏览: 38
Softmax loss, also known as cross-entropy loss, is a commonly used loss function in machine learning and deep learning for classification tasks. It is particularly useful when the output of the model is a probability distribution over multiple classes.
The softmax function is used to convert the output of the model into a probability distribution. The softmax function takes a vector of real-valued scores as input and outputs a vector of probabilities that add up to 1. The score for each class is exponentiated and normalized by the sum of all exponentiated scores. This ensures that the output probabilities are always positive and sum to 1.
The softmax loss measures the difference between the predicted probability distribution and the true distribution. It calculates the negative log-likelihood of the true class given the predicted probabilities. The goal of the training process is to minimize the softmax loss, which means maximizing the log-likelihood of the true class.
The softmax loss is given by the following formula:
L = -log(e^s_y / ∑e^s_i)
where s is the vector of scores for each class, y is the true class label, and i ranges over all classes.