maxframe.tensor.full_like#

maxframe.tensor.full_like(a, fill_value, dtype=None, gpu=None, order='K')[source]#

Return a full tensor with the same shape and type as a given tensor.

Parameters:

  • a (array_like) – The shape and data-type of a define these same attributes of the returned tensor.

  • fill_value (scalar) – Fill value.

  • dtype (data-type, optional) – Overrides the data type of the result.

  • gpu (bool, optional) – Allocate the tensor on GPU if True, None as default

  • order ({'C', 'F', 'A', or 'K'}, optional) – Overrides the memory layout of the result. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible.

Returns:

out – Tensor of fill_value with the same shape and type as a.

Return type:

Tensor

See also

empty_like

Return an empty tensor with shape and type of input.

ones_like

Return a tensor of ones with shape and type of input.

zeros_like

Return a tensor of zeros with shape and type of input.

full

Return a new tensor of given shape filled with value.

Examples

>>> import maxframe.tensor as mt
>>> x = mt.arange(6, dtype=int)
>>> mt.full_like(x, 1).execute()
array([1, 1, 1, 1, 1, 1])
>>> mt.full_like(x, 0.1).execute()
array([0, 0, 0, 0, 0, 0])
>>> mt.full_like(x, 0.1, dtype=mt.double).execute()
array([ 0.1,  0.1,  0.1,  0.1,  0.1,  0.1])
>>> mt.full_like(x, mt.nan, dtype=mt.double).execute()
array([ nan,  nan,  nan,  nan,  nan,  nan])

>>> y = mt.arange(6, dtype=mt.double)
>>> mt.full_like(y, 0.1).execute()
array([ 0.1,  0.1,  0.1,  0.1,  0.1,  0.1])