TensorFlow Model Fusion Methods Explained

In TensorFlow, the methods of model fusion mainly include the following several.

1. Voting: Taking the predictions of multiple models and selecting the result with the most votes as the final prediction. Voting methods can be either hard voting or soft voting.
2. Mean: Combine the prediction results of multiple models through weighted averaging to obtain the final prediction result. The weights can be adjusted based on the performance of the models.
3. Stacking: combining the predictions of multiple models as inputs, and then using another model (meta-model) to merge these predictions to obtain the final prediction.
4. Feature Fusion: Combining the output features of multiple models and inputting them into one model for training to obtain the final prediction results.
5. Bayesian optimization is used to find the optimal model fusion weights to achieve the best performance.

These methods can be selected and adjusted according to specific problems and datasets to improve the performance and generalization ability of the model.