LazyBatchNorm1d — PyTorch 2.7 documentation (original) (raw)
class torch.nn.LazyBatchNorm1d(eps=1e-05, momentum=0.1, affine=True, track_running_stats=True, device=None, dtype=None)[source][source]¶
A torch.nn.BatchNorm1d module with lazy initialization.
Lazy initialization based on the num_features argument of the BatchNorm1d that is inferred from the input.size(1). The attributes that will be lazily initialized are weight, bias,running_mean and running_var.
Check the torch.nn.modules.lazy.LazyModuleMixin for further documentation on lazy modules and their limitations.
Parameters
- eps (float) – a value added to the denominator for numerical stability. Default: 1e-5
- momentum (Optional_[_float]) – the value used for the running_mean and running_var computation. Can be set to
Nonefor cumulative moving average (i.e. simple average). Default: 0.1 - affine (bool) – a boolean value that when set to
True, this module has learnable affine parameters. Default:True - track_running_stats (bool) – a boolean value that when set to
True, this module tracks the running mean and variance, and when set toFalse, this module does not track such statistics, and initializes statistics buffersrunning_meanandrunning_varasNone. When these buffers areNone, this module always uses batch statistics. in both training and eval modes. Default:True
alias of BatchNorm1d