[source] # Batch Norm Batch Norm layers normalize the activations of the previous layer such that the mean activation is *close* to 0 and the standard deviation is *close* to 1. Adding Batch Norm reduces the amount of covariate shift within the network which makes it possible to use higher learning rates and thus converge faster under some circumstances. ## Parameters | # | Name | Default | Type | Description | |---|---|---|---|---| | 1 | decay | 0.9 | float | The decay rate of the previous running averages of the global mean and variance. | | 2 | betaInitializer | Constant | Initializer | The initializer of the beta parameter. | | 3 | gammaInitializer | Constant | Initializer | The initializer of the gamma parameter. | ## Example ```php use Rubix\ML\NeuralNet\Layers\BatchNorm; use Rubix\ML\NeuralNet\Initializers\Constant; use Rubix\ML\NeuralNet\Initializers\Normal; $layer = new BatchNorm(0.7, new Constant(0.), new Normal(1.)); ``` ## References [^1]: S. Ioffe et al. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.