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Training loss: Difference between revisions
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#[[Categorical cross-entropy]] (CCE): Used in multiclass classification problems to predict one of several classes, this statistic measures the difference between a predicted probability distribution and an actual one-hot encoded class label. | #[[Categorical cross-entropy]] (CCE): Used in multiclass classification problems to predict one of several classes, this statistic measures the difference between a predicted probability distribution and an actual one-hot encoded class label. | ||
#Softmax Cross-Entropy Loss: This approach is used for multiclass classification problems with mutually exclusive classes. It calculates the categorical cross-entropy loss for each class and then takes its average across all classes. | #Softmax Cross-Entropy Loss: This approach is used for multiclass classification problems with mutually exclusive classes. It calculates the categorical cross-entropy loss for each class and then takes its average across all classes. | ||
#KL-Divergence: This statistic measures the difference in probability distributions. It's commonly employed when training generative models such as Generative Adversarial Networks (GANs). | |||
==How Training Loss is Used== | ==How Training Loss is Used== | ||