class torch.nn.BatchNorm1d(num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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 .
The mean and standard-deviation are calculated per-dimension over the mini-batches and and are learnable parameter vectors of size
C is the input size). By default, the elements of are set to 1 and the elements of are set to 0. The standard-deviation is calculated via the biased estimator, equivalent to
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.
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.
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 , where is the estimated statistic and is the new observed value.
Because the Batch Normalization is done over the
C dimension, computing statistics on
(N, L) slices, it’s common terminology to call this Temporal Batch Normalization.
Nonefor cumulative moving average (i.e. simple average). Default: 0.1
True, this module has learnable affine parameters. Default:
True, this module tracks the running mean and variance, and when set to
False, this module does not track such statistics, and initializes statistics buffers
None. When these buffers are
None, this module always uses batch statistics. in both training and eval modes. Default:
>>> # With Learnable Parameters >>> m = nn.BatchNorm1d(100) >>> # Without Learnable Parameters >>> m = nn.BatchNorm1d(100, affine=False) >>> input = torch.randn(20, 100) >>> output = m(input)
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