两者的功能是一致的，将多维数组降为一维，但是两者的区别是返回拷贝还是返回视图，np.flatten(0返回一份拷贝，对拷贝所做修改不会影响原始矩阵，而np.ravel()返回的是视图，修改时会影响原始矩阵
numpy.ravel(a, order = 'C'):Caxis越小的先变化；'F' axis越大的先变化
其中order : {‘C’,’F’, ‘A’, ‘K’}, optional
The elements of a are read using this index order. ‘C’ means to index the elements in row-major, C-style order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to index the elements in column-major, Fortran-style 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 axis indexing. ‘A’ means to read the elements in Fortran-like index order if a is Fortran contiguous in memory, C-like order otherwise. ‘K’ means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. By default, ‘C’ index order is used.
Examples
It is equivalent to reshape(-1, order=order).
>>>
>>> x = np.array([[1, 2, 3], [4, 5, 6]])
>>> print(np.ravel(x))
[1 2 3 4 5 6]
>>>
>>> print(x.reshape(-1))
[1 2 3 4 5 6]
>>>
>>> print(np.ravel(x, order='F'))
[1 4 2 5 3 6]
When order is ‘A’, it will preserve the array’s ‘C’ or ‘F’ ordering:
>>>
>>> print(np.ravel(x.T))
[1 4 2 5 3 6]
>>> print(np.ravel(x.T, order='A'))
[1 2 3 4 5 6]
When order is ‘K’, it will preserve orderings that are neither ‘C’ nor ‘F’, but won’t reverse axes:
>>>
>>> a = np.arange(3)[::-1]; a
array([2, 1, 0])
>>> a.ravel(order='C')
array([2, 1, 0])
>>> a.ravel(order='K')
array([2, 1, 0])
>>>
>>> a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a
array([[[ 0, 2, 4],
[ 1, 3, 5]],
[[ 6, 8, 10],
[ 7, 9, 11]]])
>>> a.ravel(order='C')
array([ 0, 2, 4, 1, 3, 5, 6, 8, 10, 7, 9, 11])
>>> a.ravel(order='K')
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])参考：https://docs.scipy.org/doc/numpy/reference/generated/numpy.ravel.html