How do I optimize the batch size for a VAE model to achieve faster convergence with balanced accuracy

Question

With the help of proper code example can i know How do I optimize the batch size for a VAE model to achieve faster convergence with balanced accuracy?

Answer 1

To optimize batch size for a VAE model, use smaller batches (16–64) for better generalization and stability, but leverage larger batches (128–512) for faster convergence with techniques like gradient accumulation and adaptive learning rates.

Here is the code snippet you can refer to:

In the above code we are using the following key approaches:

• Systematically Evaluates Different Batch Sizes

  • Trains a VAE model on batch sizes ranging from 16 to 256 and records loss + training time.

• Balanced VAE Loss (Reconstruction + KL Divergence)

  • Uses a weighted KL divergence loss (kl_weight=0.1) to ensure stability in latent space learning.

• Monitors Both Accuracy & Convergence Speed

  • Tracks final loss and total training time to determine the best trade-off.

• Efficient Latent Space Exploration

  • Uses a fully connected decoder with 128 neurons, ensuring good data reconstruction.

Hence, batch size optimization in VAE is a trade-off between training speed and accuracy, with 64–128 being optimal for most cases, ensuring both fast convergence and balanced reconstruction quality.