Hi,

I’m not sure if I should use `InstanceNorm1D`

or `BatchNorm1D`

in my network and I’d be grateful for some help.

I have an output `x`

of shape `(N, L)`

where `N`

is the number of elements in the batch and `L`

is the number of activations. I’d like to perform normalization for each `l`

in `L`

where the statistics are computed across `x[:,l]`

and there are separate parameters gamma and beta for each `l`

. Based on the docs it seems to me that both of the following layers will achieve the desired effect:

`torch.nn.BatchNorm1d(L, affine=True)`

`torch.nn.InstanceNorm1d(L, affine=true)`

and there would only be a difference if I had an output `(N, C, L)`

. Is this correct?

Thanks!