mars.tensor.zeros#
- mars.tensor.zeros(shape, dtype=None, chunk_size=None, gpu=None, sparse=False, order='C')[源代码]#
Return a new tensor of given shape and type, filled with zeros.
- 参数
shape (int or sequence of ints) – Shape of the new tensor, e.g.,
(2, 3)
or2
.dtype (data-type, optional) – The desired data-type for the array, e.g., mt.int8. Default is mt.float64.
chunk_size (int or tuple of int or tuple of ints, optional) – Desired chunk size on each dimension
gpu (bool, optional) – Allocate the tensor on GPU if True, False as default
sparse (bool, optional) – Create sparse tensor if True, False as default
order ({'C', 'F'}, optional, default: 'C') – Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory.
- 返回
out – Tensor of zeros with the given shape, dtype, and order.
- 返回类型
Tensor
参见
zeros_like
Return a tensor of zeros with shape and type of input.
ones_like
Return a tensor of ones with shape and type of input.
empty_like
Return a empty tensor with shape and type of input.
ones
Return a new tensor setting values to one.
empty
Return a new uninitialized tensor.
示例
>>> import mars.tensor as mt >>> mt.zeros(5).execute() array([ 0., 0., 0., 0., 0.])
>>> mt.zeros((5,), dtype=int).execute() array([0, 0, 0, 0, 0])
>>> mt.zeros((2, 1)).execute() array([[ 0.], [ 0.]])
>>> s = (2,2) >>> mt.zeros(s).execute() array([[ 0., 0.], [ 0., 0.]])
>>> mt.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]).execute() # custom dtype array([(0, 0), (0, 0)], dtype=[('x', '<i4'), ('y', '<i4')])