torch.Tensor — PyTorch 1.10.1 documentation
pytorch.org › docs › stabletorch.ByteTensor. /. 1. Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10 significand bits. Useful when precision is important at the expense of range. 2. Sometimes referred to as Brain Floating Point: uses 1 sign, 8 exponent, and 7 significand bits. Useful when range is important, since it has the same number of exponent bits ...
torch.Tensor.fill_ — PyTorch 1.10.1 documentation
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torch.Tensor.fill_diagonal_ — PyTorch 1.10.1 documentation
pytorch.org › torchtorch.Tensor.fill_diagonal_. Fill the main diagonal of a tensor that has at least 2-dimensions. When dims>2, all dimensions of input must be of equal length. This function modifies the input tensor in-place, and returns the input tensor. wrap ( bool) – the diagonal ‘wrapped’ after N columns for tall matrices.
torch.Tensor — PyTorch 1.10.1 documentation
https://pytorch.org/docs/stable/tensorstorch.ByteTensor. /. 1. Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10 significand bits. Useful when precision is important at the expense of range. 2. Sometimes referred to as Brain Floating Point: uses 1 sign, 8 exponent, and 7 significand bits. Useful when range is important, since it has the same number of exponent bits ...
torch.full_like — PyTorch 1.10.1 documentation
https://pytorch.org/docs/stable/generated/torch.full_like.htmltorch.full_like¶ torch. full_like (input, fill_value, \*, dtype=None, layout=torch.strided, device=None, requires_grad=False, memory_format=torch.preserve_format) → Tensor ¶ Returns a tensor with the same size as input filled with fill_value. torch.full_like(input, fill_value) is equivalent to torch.full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device).