Expand the shape of an array.
Insert a new axis that will appear at the axis position in the expanded array shape.
| Parameters: |
|
|---|---|
| Returns: |
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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: |
|---|
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]])