mars.tensor.zeros

mars.tensor.zeros(shape, dtype=None, chunk_size=None, gpu=False, sparse=False, order='C')[source]

Return a new tensor of given shape and type, filled with zeros.

Parameters
  • shape (int or sequence of ints) – Shape of the new tensor, e.g., (2, 3) or 2.

  • 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.

Returns

out – Tensor of zeros with the given shape, dtype, and order.

Return type

Tensor

See also

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.

Examples

>>> 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')])