torch (version 0.1.0)

nn_batch_norm1d: BatchNorm1D module

Description

Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D inputs with optional additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Usage

nn_batch_norm1d(
  num_features,
  eps = 1e-05,
  momentum = 0.1,
  affine = TRUE,
  track_running_stats = TRUE
)

Arguments

num_features

\(C\) from an expected input of size \((N, C, L)\) or \(L\) from input of size \((N, L)\)

eps

a value added to the denominator for numerical stability. Default: 1e-5

momentum

the value used for the running_mean and running_var computation. Can be set to NULL for cumulative moving average (i.e. simple average). Default: 0.1

affine

a boolean value that when set to TRUE, this module has learnable affine parameters. Default: TRUE

track_running_stats

a boolean value that when set to TRUE, this module tracks the running mean and variance, and when set to FALSE, this module does not track such statistics and always uses batch statistics in both training and eval modes. Default: TRUE

Shape

• Input: \((N, C)\) or \((N, C, L)\)

• Output: \((N, C)\) or \((N, C, L)\) (same shape as input)

Details

$$ y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta $$

The mean and standard-deviation are calculated per-dimension over the mini-batches and \(\gamma\) and \(\beta\) are learnable parameter vectors of size C (where C is the input size). By default, the elements of \(\gamma\) are set to 1 and the elements of \(\beta\) are set to 0.

Also by default, during training this layer keeps running estimates of its computed mean and variance, which are then used for normalization during evaluation. The running estimates are kept with a default :attr:momentum of 0.1. If track_running_stats is set to FALSE, this layer then does not keep running estimates, and batch statistics are instead used during evaluation time as well.

Examples

# NOT RUN {
if (torch_is_installed()) {
# With Learnable Parameters
m <- nn_batch_norm1d(100)
# Without Learnable Parameters
m <- nn_batch_norm1d(100, affine = FALSE)
input <- torch_randn(20, 100)
output <- m(input) 

}
# }