在 Numpy 数组中，size 和 shape 都是属性，而非函数，所以调用的时候不要加括号，size 返回的是 里面的元素数，shape 返回的是真正的尺寸
RESHAPE and LINEAR INDEXING: Matlab always allows multi-dimensional arrays to be accessed using scalar or linear indices, NumPy does not. Linear indices are common in Matlab programs, e.g. find() on a matrix returns them, whereas NumPy’s find behaves differently.
When converting Matlab code it might be necessary to first reshape a matrix to a linear sequence, perform some indexing operations and then reshape back. As reshape (usually) produces views onto the same storage, it should be possible to do this fairly efficiently. Note that the scan order used by reshape in NumPy defaults to the ‘C’ order, whereas Matlab uses the Fortran order. If you are simply converting to a linear sequence and back this doesn’t matter. But if you are converting reshapes from Matlab code which relies on the scan order, then this Matlab code: z = reshape(x,3,4); should become z = x.reshape(3,4,order=’F’).copy() in NumPy.
NumPy 支持逻辑索引，而 MXNet 的 NDArray 不支持。
Numpy 的 == 符号，当左右两个变量相同大小的时候，会做 Element-wise 的比较，当两个变量大小不一样时，还是不会报错，只会说 False
即使是 dtype 为 uint8 的 numpy.ndarray，经过 nd.array() 后，dtype 会自动变成 numpy.float32，在可视化 Dataset 的输出时候要注意