Handling Class Imbalance in TensorFlow

When dealing with imbalanced classes, the following methods can be used:

1. Undersampling: Randomly removing samples from the majority class to balance the number of samples between the majority and minority classes, reducing the dominance of the majority class in the dataset.
2. Oversampling involves duplicating or artificially creating new samples for minority classes, in order to increase the number of samples in those minority classes and balance the overall class distribution.
3. Utilize a weighted loss function during model training, assigning different loss weights to samples from different categories to prioritize minority class samples.
4. Utilize ensemble learning by combining predictions from multiple models through voting or weighted averaging to improve overall predictive performance.
5. Synthesizing samples using generative adversarial networks (GANs) involves generating new samples of minority classes in order to increase the quantity of samples in those classes.
6. Utilizing anomaly detection: Viewing the majority class as normal samples and the minority class as anomaly samples, identify the minority class samples through anomaly detection algorithms.
7. Utilize adaptive learning rate adjustment strategy: dynamically adjust the learning rate based on the distribution of samples from different categories to help the model better adapt to imbalanced data.

These are some common methods for handling class imbalance issues. Choose the appropriate method based on the specific situation.