Can someone please explain me the difference between the two loss functions categorical crossentropy and sparse categorical crossentropy
Hello @Rukaiya_Bano and welcome back to DeepLearning.AI community,
Categorical crossentropy and sparse categorical crossentropy are two commonly used loss functions in machine learning models that involve multi-class classification. Here’s a brief explanation of each of these loss functions:
- Categorical Crossentropy: Categorical crossentropy is a loss function used for multi-class classification problems where the output variable is categorical, meaning it takes on one of a finite number of possible values. It measures the dissimilarity between the true probability distribution and the predicted probability distribution. It calculates the cross-entropy loss between the predicted probability distribution and the true probability distribution, which is a one-hot encoded vector representation of the target class. This loss function is suitable when the labels of each class are one-hot encoded.
- Sparse Categorical Crossentropy: Sparse categorical crossentropy is a variant of categorical crossentropy used when the true labels are not one-hot encoded, but instead are integers representing the class index. In other words, it’s used when the target variable is sparse, meaning it takes on a single integer value representing the class label, rather than a one-hot encoded vector. This loss function works by internally one-hot encoding the target variable and then computing the cross-entropy loss. It is generally used when the number of classes is large.
In summary, the main difference between categorical crossentropy and sparse categorical crossentropy is the way the true labels are represented. Categorical crossentropy is used when the labels are one-hot encoded, whereas sparse categorical crossentropy is used when the labels are integers representing the class index. Note most of the above definition is from ChatGPT
Happy Learning
Isaak
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