nn_batch_norm2d  R Documentation 
Applies Batch Normalization over a 4D input (a minibatch of 2D inputs additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.
nn_batch_norm2d(
num_features,
eps = 1e05,
momentum = 0.1,
affine = TRUE,
track_running_stats = TRUE
)
num_features 

eps 
a value added to the denominator for numerical stability. Default: 1e5 
momentum 
the value used for the running_mean and running_var
computation. Can be set to 
affine 
a boolean value that when set to 
track_running_stats 
a boolean value that when set to 
y = \frac{x  \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
The mean and standarddeviation are calculated perdimension over
the minibatches 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. The standarddeviation is calculated
via the biased estimator, equivalent to torch_var(input, unbiased=FALSE)
.
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 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.
Input: (N, C, H, W)
Output: (N, C, H, W)
(same shape as input)
This momentum
argument is different from one used in optimizer
classes and the conventional notion of momentum. Mathematically, the
update rule for running statistics here is
\hat{x}_{\mbox{new}} = (1  \mbox{momentum}) \times \hat{x} + \mbox{momentum} \times x_t
,
where \hat{x}
is the estimated statistic and x_t
is the
new observed value.
Because the Batch Normalization is done over the C
dimension, computing statistics
on (N, H, W)
slices, it's common terminology to call this Spatial Batch Normalization.
if (torch_is_installed()) {
# With Learnable Parameters
m < nn_batch_norm2d(100)
# Without Learnable Parameters
m < nn_batch_norm2d(100, affine = FALSE)
input < torch_randn(20, 100, 35, 45)
output < m(input)
}
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