np.resize和np.reshape()的区别

resize:给定一个数组，和特定维度，将会返回一个给定维度形式的新数组。

       如果新数组比原数组大，则将会copy原数组中的值对新数组进行填充

    >>> a=np.array([[0,1],[2,3]])
    >>> np.resize(a,(2,3))
    array([[0, 1, 2],
           [3, 0, 1]])
    >>> np.resize(a,(1,4))
    array([[0, 1, 2, 3]])
    >>> np.resize(a,(2,4))
    array([[0, 1, 2, 3],
           [0, 1, 2, 3]])

reshape:在不改变原数组数据的情况下，将它reshape成一个新的维度。

如果给定的数组数据和需要reshape的形状不符合时，将会报错。

     >>> a = np.zeros((10, 2))
     # A transpose makes the array non-contiguous
     >>> b = a.T
     # Taking a view makes it possible to modify the shape without modifying
     # the initial object.
     >>> c = b.view()
     >>> c.shape = (20)
     AttributeError: incompatible shape for a non-contiguous array

    >>> a = np.arange(6).reshape((3, 2))
    >>> a
    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, 6, order='F')
    array([1, 4, 2, 5, 3, 6])

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