> ## Documentation Index
> Fetch the complete documentation index at: https://heart.metesahankurt.cloud/llms.txt
> Use this file to discover all available pages before exploring further.

# Batch Size

> How batch size affects update frequency, noise, and generalization.

# Batch Size Analysis

Batch size determines how many samples are processed before updating model weights. Four values were tested: 16, 32, 64, and 128.

## Trade-offs

| Batch Size | Gradient Updates per Epoch | Update Noise | Generalization |
| ---------- | -------------------------- | ------------ | -------------- |
| 16         | High                       | High (noisy) | Often better   |
| 32         | Moderate                   | Moderate     | Good balance   |
| 64         | Moderate-low               | Lower        | Moderate       |
| 128        | Low                        | Low (smooth) | May overfit    |

## How It Works

Each training step computes the gradient on a mini-batch and updates the weights. With a smaller batch:

* More updates happen per epoch (more gradient steps).
* Each gradient estimate is noisier (fewer samples to average over), but this noise can act as implicit regularization — helping the model escape sharp local minima and find flatter, better-generalizing solutions.

With a larger batch:

* Fewer updates per epoch.
* Gradient estimates are smoother and more accurate, but the model may converge to sharper minima that generalize less well.

## Practical Considerations

For a dataset of 1,025 samples:

* Batch size 16 or 32 keeps gradient updates frequent and noisy enough to regularize effectively.
* Batch size 128 reduces the number of updates per epoch significantly (only \~8 updates per epoch), which may slow learning or reduce generalization on a small dataset.

## Takeaway

> Smaller batch sizes (16–32) tend to work better on small datasets like this one. They provide more gradient updates and their inherent noise acts as a form of regularization alongside Dropout.