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# LazyBatchNorm1d

class torch.nn.LazyBatchNorm1d(eps=1e-05, momentum=0.1, affine=True, track_running_stats=True, device=None, dtype=None)

  A `torch.nn.BatchNorm1d` module with lazy initialization of 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 (float) – the value used for the running_mean and running_var computation. Can be set to `None` for 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 to `False`, this module does not track such statistics, and initializes statistics buffers `running_mean` and `running_var` as `None`. When these buffers are `None`, this module always uses batch statistics. in both training and eval modes. Default: `True`

  cls_to_become

    alias of `BatchNorm1d`

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