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# torch.squeeze

torch.squeeze(input, dim=None) → Tensor

  Returns a tensor with all specified dimensions of `input` of size 1 removed.

  For example, if input is of shape: (A×1×B×C×1×D)(A \times 1 \times B \times C \times 1 \times D)(A×1×B×C×1×D) then the input.squeeze() will be of shape: (A×B×C×D)(A \times B \times C \times D)(A×B×C×D).

  When `dim` is given, a squeeze operation is done only in the given dimension(s). If input is of shape: (A×1×B)(A \times 1 \times B)(A×1×B), `squeeze(input, 0)` leaves the tensor unchanged, but `squeeze(input, 1)` will squeeze the tensor to the shape (A×B)(A \times B)(A×B).

  Note

  The returned tensor shares the storage with the input tensor, so changing the contents of one will change the contents of the other.

  Warning

  If the tensor has a batch dimension of size 1, then squeeze(input) will also remove the batch dimension, which can lead to unexpected errors. Consider specifying only the dims you wish to be squeezed.

  Parameters:

    - input (Tensor) – the input tensor.

    - dim (int or tuple of ints, optional) –

      if given, the input will be squeezed

        only in the specified dimensions.

      Changed in version 2.0: `dim` now accepts tuples of dimensions.

  Example:

      >>> x = torch.zeros(2, 1, 2, 1, 2)
      >>> x.size()
      torch.Size([2, 1, 2, 1, 2])
      >>> y = torch.squeeze(x)
      >>> y.size()
      torch.Size([2, 2, 2])
      >>> y = torch.squeeze(x, 0)
      >>> y.size()
      torch.Size([2, 1, 2, 1, 2])
      >>> y = torch.squeeze(x, 1)
      >>> y.size()
      torch.Size([2, 2, 1, 2])
      >>> y = torch.squeeze(x, (1, 2, 3))
      torch.Size([2, 2, 2])

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