What is the impact of batch size on model accuracy?

What is the impact of batch size on model accuracy?

The batch size in training a model can significantly affect its accuracy. A smaller batch size often leads to more accurate models because it allows the model to learn from more diverse data points in each iteration. However, it can also increase training time. Conversely, larger batch sizes can speed up training but may lead to less accurate models due to less frequent updates.

Why does a smaller batch size improve accuracy?

Smaller batch sizes provide more frequent updates to the model's weights, allowing it to learn from a wider variety of examples in each iteration. This can help the model generalize better to unseen data, reducing overfitting.

Are there any downsides to using a small batch size?

Yes, while smaller batch sizes can improve accuracy, they also increase the training time and can lead to more noisy gradient estimates, which might make the training process less stable.

What is the optimal batch size for training?

The optimal batch size can vary depending on the specific dataset and model architecture. It's often recommended to experiment with different sizes, typically ranging from 16 to 256, to find the best balance between training speed and model performance.