tensorplay.functional.full_like

tensorplay.functional.full_like(input, fill_value, \*, dtype=None, layout=tensorplay.strided, device=None, requires_grad=False, memory_format=tensorplay.preserve_format) → Tensor

Returns a tensor with the same size as input filled with fill_value. tensorplay.full_like(input, fill_value) is equivalent to tensorplay.full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device).

Parameters:

• input (Tensor) – the size of input will determine size of the output tensor.

• fill_value – the number to fill the output tensor with.

Keyword Arguments:

• dtype (tensorplay.dtype, optional) – the desired data type of returned Tensor. Default: if None, defaults to the dtype of input.

• layout (tensorplay.layout, optional) – the desired layout of returned tensor. Default: if None, defaults to the layout of input.

• device (tensorplay.device, optional) – the desired device of returned tensor. Default: if None, defaults to the device of input.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

• memory_format (tensorplay.memory_format, optional) – the desired memory format of returned Tensor. Default: tensorplay.preserve_format.

Example:

>>> x = tensorplay.ones(2, 3)
>>> tensorplay.full_like(x, 3.141592)
tensor([[ 3.1416,  3.1416,  3.1416],
        [ 3.1416,  3.1416,  3.1416]])
>>> tensorplay.full_like(x, 7)
tensor([[7., 7., 7.],
        [7., 7., 7.]])
>>> tensorplay.full_like(x, 0.5, dtype=tensorplay.int32)
tensor([[0, 0, 0],
        [0, 0, 0]], dtype=tensorplay.int32)
>>> y = tensorplay.randn(3, 4, dtype=tensorplay.float64)
>>> tensorplay.full_like(y, -1.0)
tensor([[-1., -1., -1., -1.],
        [-1., -1., -1., -1.],
        [-1., -1., -1., -1.]], dtype=tensorplay.float64)