To set the initial and final beta values for training a machine learning model, you need to consider the learning rate dynamics. Beta values are often used in optimization algorithms like Adam, which employs momentum (first moment) and adaptive learning rates (second moment). The initial beta values typically range between 0 and 1, with common starting points being around 0.9 for the first moment (beta1) and 0.999 for the second moment (beta2). These values are chosen based on empirical results and help stabilize updates during training. A higher beta value for beta1 means that the algorithm gives more weight to past gradients, which can help in smoothing out the updates.
The final beta values often depend on the specific task and dataset you are working with. For most cases, you set the final values to the same as the initial ones. However, you may choose to experiment with reducing beta1 slightly as training progresses. This can sometimes help in fine-tuning the learning process, allowing the model to adapt more quickly to new patterns without losing the momentum gained during earlier epochs. Setting a learning rate schedule can complement this approach, gradually decreasing the learning rate as training progresses.
Additionally, it's important to monitor model performance throughout training to determine if the chosen beta values are effective. For instance, if you observe that the training loss plateaus too early or the model overfits, adjusting beta values or introducing a learning rate decay might be necessary. Ultimately, it's a good practice to start with standard values, then adjust based on empirical results from your training run. This iterative approach allows you to find the best settings tailored to your specific project requirements.