mxnet.np.reshape¶
-
reshape
(a, newshape, order='C', out=None)¶ Gives a new shape to an array without changing its data. This function always returns a copy of the input array if
out
is not provided.- Parameters
a (ndarray) – Array 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 array of that length. One shape dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions.
order ({'C'}, 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. Other order types such as ‘F’/’A’ may be added in the future.
- Returns
reshaped_array – It will be always a copy of the original array. This behavior is different from the official NumPy
reshape
operator where views of the original array may be generated.- Return type
ndarray
See also
ndarray.reshape()
Equivalent method.
Examples
>>> a = np.arange(6).reshape((3, 2)) >>> a array([[0., 1.], [2., 3.], [4., 5.]])
>>> np.reshape(a, (2, 3)) # C-like index ordering array([[0., 1., 2.], [3., 4., 5.]])
>>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape array([[0., 1., 2.], [3., 4., 5.]])
>>> a = np.array([[1,2,3], [4,5,6]]) >>> np.reshape(a, 6) array([1., 2., 3., 4., 5., 6.])
>>> np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2 array([[1., 2.], [3., 4.], [5., 6.]])