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How to use Sparse Categorical Crossentropy in Keras
- Sparse categorical crossentropy is a loss function in Keras that allows leaving the integers as they are without requiring one-hot encoding.
- The blog explains how to build a CNN using sparse categorical crossentropy with an example on the MNIST dataset.
- In traditional multiclass classification with Keras, categorical crossentropy necessitates one-hot encoding of target vectors.
- One-hot encoding involves converting integer targets into categorical format before using categorical crossentropy.
- For integer targets that are too large for one-hot encoding, sparse categorical crossentropy can be used.
- The formula for categorical crossentropy involves computing natural logarithms of class predictions and actual targets.
- Sparse categorical crossentropy is an integer-based version of categorical crossentropy.
- The blog provides code examples for creating a CNN with sparse categorical crossentropy using the MNIST dataset.
- The tutorial includes setting up model configurations, loading and preparing MNIST data, model architecture, compilation using sparse categorical crossentropy, and model fitting.
- By following the tutorial, one can train a CNN with sparse categorical crossentropy in Keras for multiclass classification.
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