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torch.unsqueeze(input, dim) → 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.
28 de jul. de 2019 · Learn how to use torch.unsqueeze() to add a dimension of size one to a tensor at a specified position. See examples, explanations and comparisons with torch.squeeze() and numpy.squeeze().
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9 de abr. de 2022 · unsqueeze turns an n-dimensionsal tensor into an n+1-dimensional one, by adding an extra dimension of zero depth. However, since it is ambiguous which axis the new dimension should lie across (i.e. in which direction it should be "unsqueezed"), this needs to be specified by the dim argument.
2 de abr. de 2024 · In essence, unsqueeze is a function used to manipulate the dimensionality of tensors in PyTorch, a deep learning framework built on Python. It adds a new dimension of size 1 (essentially an empty dimension) at a specified location within the tensor.
torch.unsqueeze(input, dim) → 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.
