Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization
Arguments
- normalized_shape
(int or list): input shape from an expected input of size \([* \times \mbox{normalized\_shape}[0] \times \mbox{normalized\_shape}[1] \times \ldots \times \mbox{normalized\_shape}[-1]]\) If a single integer is used, it is treated as a singleton list, and this module will normalize over the last dimension which is expected to be of that specific size.
- eps
a value added to the denominator for numerical stability. Default: 1e-5
- elementwise_affine
a boolean value that when set to
TRUE, this module has learnable per-element affine parameters initialized to ones (for weights) and zeros (for biases). Default:TRUE.
Details
$$ y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta $$
The mean and standard-deviation are calculated separately over the last
certain number dimensions which have to be of the shape specified by
normalized_shape.
\(\gamma\) and \(\beta\) are learnable affine transform parameters of
normalized_shape if elementwise_affine is TRUE.
The standard-deviation is calculated via the biased estimator, equivalent to
torch_var(input, unbiased=FALSE).
Note
Unlike Batch Normalization and Instance Normalization, which applies
scalar scale and bias for each entire channel/plane with the
affine option, Layer Normalization applies per-element scale and
bias with elementwise_affine.
This layer uses statistics computed from input data in both training and evaluation modes.
Examples
if (torch_is_installed()) {
input <- torch_randn(20, 5, 10, 10)
# With Learnable Parameters
m <- nn_layer_norm(input$size()[-1])
# Without Learnable Parameters
m <- nn_layer_norm(input$size()[-1], elementwise_affine = FALSE)
# Normalize over last two dimensions
m <- nn_layer_norm(c(10, 10))
# Normalize over last dimension of size 10
m <- nn_layer_norm(10)
# Activating the module
output <- m(input)
}