Layer normalization is used to normalize inputs across the batch dimension.Group of answer choicesTrueFalse

The statement is False.

Explanation:

Layer normalization is a technique used to normalize the inputs across the feature dimension, not the batch dimension. This method was introduced to address some limitations of batch normalization, particularly in recurrent neural networks (RNNs) where the batch size can be very small or even equal to one.

In layer normalization, the mean and variance are computed for each training example independently. This means that normalization is applied across all features for each individual sample, rather than across the entire batch of samples. Specifically, for a given input vector $x$ with features $x_1, x_2, \ldots, x_n$, the normalized output $\hat{x}$ can be calculated as follows:

$\hat{x} = \frac{x - \mu}{\sigma + \epsilon}$

Where:

• $\mu$ is the mean of the features: $\mu = \frac{1}{n} \sum_{i=1}^{n} x_i$
• $\sigma$ is the standard deviation: $\sigma = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (x_i - \mu)^2}$
• $\epsilon$ is a small constant added for numerical stability.

This allows layer normalization to be more effective for certain types of networks where batch normalization might not perform well.