numpy.expand_dims(a, axis)

Expand the shape of an array.

Insert a new axis that will appear at the axis position in the expanded array shape.

 

Parameters:
a : array_like

Input array.

axis : int

Position in the expanded axes where the new axis is placed.
Returns:
res : ndarray

Output array. The number of dimensions is one greater than that of the input array.

 

Examples

>>> x = np.array([1,2])
>>> x.shape
(2,)

The following is equivalent to x[np.newaxis,:] or x[np.newaxis]:

>>> y = np.expand_dims(x, axis=0)
>>> y
array([[1, 2]])
>>> y.shape
(1, 2)
>>> y = np.expand_dims(x, axis=1)  # Equivalent to x[:,np.newaxis]
>>> y
array([[1],
       [2]])
>>> y.shape
(2, 1)

Note that some examples may use None instead of np.newaxis. These are the same objects:

>>> np.newaxis is None
True


 

 

torch.unsqueeze(input, dim, out=None) → Tensor

Returns a new tensor with a dimension of size one inserted at the specified position.

The returned tensor shares the same underlying data with this tensor.

A dim value within the range [-input.dim() - 1, input.dim() + 1) can be used. Negative dimwill correspond to unsqueeze() applied at dim = dim + input.dim() + 1.

Parameters:
  • input (Tensor) – the input tensor
  • dim (int) – the index at which to insert the singleton dimension
  • out (Tensor, optional) – the output tensor

Example:

>>> x = torch.tensor([1, 2, 3, 4])
>>> torch.unsqueeze(x, 0)
tensor([[ 1,  2,  3,  4]])
>>> torch.unsqueeze(x, 1)
tensor([[ 1],
        [ 2],
        [ 3],
        [ 4]])