SparseCategorialCrossentropy or CategoricalCrossEntropy

To understand the differences and use cases for SparseCategoricalCrossentropy and CategoricalCrossentropy in TensorFlow, let’s break down each one:

CategoricalCrossentropy

• Use Case: This loss function is used when you have one-hot encoded labels. One-hot encoding means that each label is represented as a vector with a length equal to the number of classes, where the correct class is marked with a 1 and all other classes are marked with 0s.
• Example: If you have three classes, a label might look like [0, 1, 0] for class 2.
• Functionality: It calculates the cross entropy loss between the true labels and the predicted probabilities.

SparseCategoricalCrossentropy

• Use Case: This loss function is used when your labels are integers instead of one-hot encoded vectors. Each label is represented by a single integer corresponding to the correct class.
• Example: If you have three classes, a label might simply be 1 for class 2.
• Functionality: It also calculates the cross-entropy loss but expects the labels to be in integer form, which can be more memory efficient.

Key Differences

• Input Format: The main difference is the format of the labels. CategoricalCrossentropy requires one-hot encoded labels, while SparseCategoricalCrossentropy works with integer labels.
• Efficiency: SparseCategoricalCrossentropy can be more efficient in terms of memory and computation, especially when dealing with a large number of classes.

When to Use Which

• Use CategoricalCrossentropy if your labels are already one-hot encoded or if you prefer to work with one-hot encoded labels for any specific reason.
• Use SparseCategoricalCrossentropy if your labels are integers, which is often the case when labels are directly loaded from datasets.