Does batch_size in Keras have any effects in results’ quality?

Answer: Yes, batch size in Keras can affect the model’s training stability, convergence speed, and generalization ability, potentially influencing the results’ quality.

In Keras, as in other deep learning frameworks, the batch size—the number of training samples processed before the model is updated—plays a critical role in the training process and can significantly impact the quality of the results. Here’s a breakdown of how batch size can influence various aspects of training:

Aspect	Small Batch Size	Large Batch Size
Convergence Speed	May converge faster due to more frequent updates.	Slower convergence as updates are less frequent.
Generalization	Tends to generalize better due to more noise in the gradient updates, which can prevent overfitting.	May lead to poorer generalization as the gradient estimates are more accurate and can lead to sharper minima.
Memory Usage	Lower memory requirements, enabling training with limited computational resources.	Higher memory requirements, which can be prohibitive on devices with limited memory.
Stability	Training can be less stable due to the higher variance in gradient updates.	Training is more stable with smoother updates.

Conclusion:

Choosing the right batch size is a balance between computational efficiency, convergence speed, memory usage, and model generalization. Smaller batch sizes can lead to better generalization and faster convergence but at the cost of stability and higher computational demand. Conversely, larger batch sizes offer computational efficiency and stability but may impact the model’s ability to generalize well to unseen data. Therefore, the optimal batch size is context-dependent and should be determined through experimentation, considering the specific goals and constraints of the training process.