What are the common mistakes with batch size in machine learning?

What are the common mistakes with batch size in machine learning?

Common mistakes include using too large or too small batch sizes, ignoring the impact on training time, and not considering hardware limitations.

How does batch size affect model performance?

Batch size can influence convergence speed, generalization, and the stability of the training process. Smaller batches can lead to noisy gradients, while larger batches may converge faster but risk overfitting.

What is the ideal batch size?

The ideal batch size varies depending on the dataset and model architecture, but a common practice is to start with powers of 2, like 32 or 64, and adjust based on performance.

Can I use a different batch size for training and validation?

Yes, it's common to use a larger batch size for validation to speed up the evaluation process, but ensure it doesn't affect the results.

What tools can help me determine the best batch size?

You can use tools like TensorBoard for monitoring training metrics, or libraries like Keras that allow you to experiment with different batch sizes easily.