# Is there any difference between x[:, None] and x.unsqueeze(1)?

Hi All,

I’ve got a quick question about reshaping `Tensor`s. In order to broadcast Tensors I’ve been using `x[:, None]` to add a new `dim` to my Tensors. However, this seems to be the same as `unsqueeze(1)`. Is this technically true, or is it slightly different in any way? For example, is it slower or quicker?

Thank you!

Hi Alpha!

Autgrad indicates that pytorch is smart enough to convert `x[:, None]`
to `x.unsqueeze (1)`:

``````>>> import torch
>>> torch.__version__
'1.9.0'
>>> x = torch.ones (2, 3, requires_grad = True)
>>> x.unsqueeze (1)
tensor([[[1., 1., 1.]],

>>> x[:, None]
tensor([[[1., 1., 1.]],

``````

But this seems to be something of a special case. Consider:

``````>>> x[:, None, :]
tensor([[[1., 1., 1.]],

>>> x[None, :]
tensor([[[1., 1., 1.],
``````

Best.

K. Frank

Hi @KFrank!

Thanks for the response! So, it seems that it should be ok when using it to unsqueeze new dims at the end dimensions but not any in between? (this is the special use case of it?) For example, `x[:, None]` is fine by `x[:, None, :]` isn’t?

I’m only using it at the moment in order to broadcast a batch of scalars over a batch of matrices. For example,

``````A = torch.randn(100, 2, 4, 4) #some matrcies
scalars = torch.randn(100, 2) #scalar scalars
scaled_A = scalar[:,:,None,None] * A
``````

Perhaps it might be safer to do this?

``````A = torch.randn(100, 2, 4, 4) #some matrcies
scalars = torch.randn(100, 2) #scalar scalars
scaled_A = scalar.unsqueeze(2).unsqueeze(3) * A
``````

Thank you for the help!