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This article mainly shows you "how to use the loss function in Caffe", the content is easy to understand, clear, hope to help you solve your doubts, the following let the editor lead you to study and learn how to use the loss function in Caffe "this article.

Introduction to the Analytic Analysis of loss function in Caffe

In supervised machine learning, label data is needed, and at the same time, there is also a corresponding loss function (Loss Function).

In Caffe, some loss functions have been implemented, including the most common L2 loss function, contrast loss function, information gain loss function and so on. Take a note here to summarize the different loss functions used in Caffe and analyze their appropriate usage scenarios.

Euclidean distance loss function (Euclidean Loss)

Contrast loss function (Contrastive loss)

Hinge loss function (Hinge Loss) Information gain loss function (InformationGain Loss) polynomial Logic loss function (Multinomial Logistic Loss) Sigmoid Cross Entropy loss function (Sigmoid Cross Entropy Loss) Softmax+ loss function (Softmax With Loss) Summary

Euclidean distance loss function: generally applicable to regression problems, especially when the regression value is real.

Contrast loss function: used to train siamese networks.

Hinge loss: used in one-to-many classifications, similar to SVM.

Polynomial logic loss function: generally used in one-to-many classification tasks, directly taking the predicted probability distribution as input.

Sigmoid cross-entropy loss function: predict the probability distribution of the target.

Softmax+ loss function: application in one-to-many classification.

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