mars.tensor.reshape(a, newshape, order='C')[source]#

Gives a new shape to a tensor without changing its data.

  • a (array_like) – Tensor to be reshaped.

  • newshape (int or tuple of ints) – The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D tensor of that length. One shape dimension can be -1. In this case, the value is inferred from the length of the tensor and remaining dimensions.

  • order ({'C', 'F', 'A'}, optional) – Read the elements of a using this index order, and place the elements into the reshaped array using this index order. ‘C’ means to read / write the elements using C-like index order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to read / write the elements using Fortran-like index order, with the first index changing fastest, and the last index changing slowest. Note that the ‘C’ and ‘F’ options take no account of the memory layout of the underlying array, and only refer to the order of indexing. ‘A’ means to read / write the elements in Fortran-like index order if a is Fortran contiguous in memory, C-like order otherwise.


reshaped_array – This will be a new view object if possible; otherwise, it will be a copy.

Return type


See also


Equivalent method.


It is not always possible to change the shape of a tensor without copying the data. If you want an error to be raised when the data is copied, you should assign the new shape to the shape attribute of the array:

>>> import mars.tensor as mt
>>> a = mt.arange(6).reshape((3, 2))
>>> a.execute()
array([[0, 1],
       [2, 3],
       [4, 5]])

You can think of reshaping as first raveling the tensor (using the given index order), then inserting the elements from the raveled tensor into the new tensor using the same kind of index ordering as was used for the raveling.

>>> mt.reshape(a, (2, 3)).execute()
array([[0, 1, 2],
       [3, 4, 5]])
>>> mt.reshape(mt.ravel(a), (2, 3)).execute()
array([[0, 1, 2],
       [3, 4, 5]])


>>> a = mt.array([[1,2,3], [4,5,6]])
>>> mt.reshape(a, 6).execute()
array([1, 2, 3, 4, 5, 6])
>>> mt.reshape(a, (3,-1)).execute()       # the unspecified value is inferred to be 2
array([[1, 2],
       [3, 4],
       [5, 6]])