[pytorch]API总结、速查 [操作系统入门]

编程

torch.numel(input) → int

Returns the total number of elements in the input tensor. Document

torch.from_numpy(ndarray) → Tensor

Creates a Tensor from a numpy.ndarray.

torch.range(start=0, end, step=1, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

torch.range(1, 4) -> tensor([ 1., 2., 3., 4.])

torch.heaviside(input, values, *, out=None) → Tensor

>>> values = torch.tensor([0.5])

>>> torch.heaviside(input, values)

tensor([0.0000, 0.5000, 1.0000])

>>> values = torch.tensor([1.2, -2.0, 3.5])

>>> torch.heaviside(input, values)

tensor([0., -2., 1.])

torch.cat

torch.chunk

torch.stack

torch.gather

torch.index_select

torch.masked_select

torch.narrow(input, dim, start, length) → Tensor

torch.split(tensor, split_size_or_sections, dim=0)

torch.t

torch.take(input, index)

torch.transpose(input, dim0, dim1)

torch.unbind(input, dim=0)

torch.unsqueeze(input, dim)

torch.where(condition, x, y) → Tensor

>>> x = torch.randn(3, 2)

>>> y = torch.ones(3, 2)

>>> x

tensor([[-0.4620, 0.3139],

[ 0.3898, -0.7197],

[ 0.0478, -0.1657]])

>>> torch.where(x > 0, x, y)

tensor([[ 1.0000, 0.3139],

[ 0.3898, 1.0000],

[ 0.0478, 1.0000]])

>>> x = torch.randn(2, 2, dtype=torch.double)

>>> x

tensor([[ 1.0779, 0.0383],

[-0.8785, -1.1089]], dtype=torch.float64)

>>> torch.where(x > 0, x, 0.)

tensor([[1.0779, 0.0383],

[0.0000, 0.0000]], dtype=torch.float64)

数学运算

abs/absolute | acos/arccos | add | bitwise_not | bitwise_and | bitwise_or | bitwise_xor | ceil | clamp/clip | div/divide | exp

| trunk/fix | floor | fmod | logical_and/logical_or/logical_xor | mul/multiply | lerp | neg/negative | pow | round | sign | sqrt |...

其他

随机数生成器

[pytorch] API总结、速查

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