In Keras, loss functions are used to measure the difference between the predicted values and the true values during the training process, which reflects the performance of the model. The goal of training the model is to minimize the loss function, making the predicted values of the model as close as possible to the true values. Common loss functions include Mean Squared Error (MSE), Cross Entropy, etc. Choosing the appropriate loss function is crucial for the training and performance of the model.<\/p>\n","protected":false},"excerpt":{"rendered":"
In Keras, loss functions are used to measure the difference between the predicted values and the true values during the training process, which reflects the performance of the model. The goal of training the model is to minimize the loss function, making the predicted values of the model as close as possible to the true […]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_import_markdown_pro_load_document_selector":0,"_import_markdown_pro_submit_text_textarea":"","footnotes":""},"categories":[1],"tags":[3014,3010,3013,3012,3011],"class_list":["post-3970","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-deep-learning-loss","tag-keras-loss-function","tag-keras-training","tag-machine-learning-metrics","tag-neural-network-optimization"],"yoast_head":"\n