What is Batch Normalization?
Batch normalization is a technique used in training deep neural networks to improve their performance and stability. It addresses the problem of internal covariate shift, which occurs when the distribution of inputs to a layer changes during training due to updates in the parameters of the previous layers.
How It Works
During training, batch normalization normalizes the inputs of each layer by calculating the mean and variance of the mini-batch data. This normalization helps to stabilize the learning process by ensuring that the inputs to the layer maintain a consistent distribution throughout training.
Key Components
- Mean and Variance Calculation: The mean and variance of the mini-batch are computed for each feature.
- Normalization: The inputs are normalized by subtracting the mean and dividing by the square root of the variance (plus a small constant for numerical stability).
- Learnable Parameters: Two parameters, scale (γ) and shift (β), are introduced to allow the model to recover the original representation if necessary.
Benefits
Batch normalization has several advantages, including:
- Accelerated training speed.
- Improved convergence rates.
- Reduced sensitivity to hyperparameters.
- Regularization effect, leading to higher accuracy.
Overall, batch normalization has become a standard technique in the deep learning community, utilized in various architectures to enhance performance and robustness.