What is Batch Normalization? Batch Normalization Explained.
Batch normalization is a technique commonly used in deep neural networks to improve training efficiency and stability. It aims to address the internal covariate shift, which refers to the change in the distribution of network activations during training.
During the training process of a neural network, the distribution of inputs to each layer can shift, making it challenging for subsequent layers to learn effectively. This phenomenon is known as the internal covariate shift. Batch normalization helps mitigate this issue by normalizing the inputs to each layer.
Here’s how batch normalization works:
Calculation within a batch: Given a mini-batch of inputs during training, batch normalization calculates the mean and variance of the activations across the batch for each input feature.
Normalization: The mean and variance are used to normalize the activations within the batch, ensuring that they have zero mean and unit variance. This step helps stabilize the distribution of the inputs.
Scaling and shifting: After normalization, the activations are scaled and shifted by learnable parameters, known as gamma and beta, respectively. These parameters allow the network to learn the optimal scale and shift for each feature.
Backpropagation: During backpropagation, the gradients are calculated not only concerning the weights but also concerning the mean, variance, gamma, and beta parameters. This allows the network to learn the appropriate adjustments to the normalization and scaling factors.
The benefits of batch normalization include:
Improved training speed: By reducing the internal covariate shift, batch normalization helps networks converge faster during training. It allows for the use of higher learning rates, leading to quicker convergence.
Enhanced generalization: Batch normalization acts as a regularizer, reducing the reliance on other forms of regularization techniques like dropout. It helps prevent overfitting and improves the generalization capability of the network.
Robustness to initialization: Batch normalization reduces the sensitivity of the network to the choice of initial weights. It helps stabilize the gradients, making the network less dependent on careful weight initialization.
Gradient flow: Normalizing the inputs within each batch helps maintain a consistent gradient flow throughout the network. This enables smoother and more efficient backpropagation, which aids in training deep networks.
Batch normalization has become a standard component in many deep learning architectures and has significantly contributed to the success of deep neural networks across various domains.
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