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SeaRecluse
V2EX  ›  Python

Python * 运算会比 np.matmul 效率高吗?

  •  
  •   SeaRecluse · 2019-03-25 10:16:30 +08:00 · 2467 次点击
    这是一个创建于 2075 天前的主题,其中的信息可能已经有所发展或是发生改变。

    对比浮点数矩阵运算测试。
    a = np.array([
    [
    [0.12,0.23,0.34,0.45], [0.56,0.67,0.78,0.89], [0.91,0.1011,0.1112,0.1213], [0.1314,0.1415,0.1516,0.1617] ],
    [
    [0.12,0.23,0.34,0.45], [0.56,0.67,0.78,0.89], [0.91,0.1011,0.1112,0.1213], [0.1314,0.1415,0.1516,0.1617] ],
    [
    [0.12,0.23,0.34,0.45], [0.56,0.67,0.78,0.89], [0.91,0.1011,0.1112,0.1213], [0.1314,0.1415,0.1516,0.1617] ],
    [
    [0.12,0.23,0.34,0.45], [0.56,0.67,0.78,0.89], [0.91,0.1011,0.1112,0.1213], [0.1314,0.1415,0.1516,0.1617] ],
    ])

    b = np.array([
    [
    [0.12,0.23,0.34,0.45], [0.56,0.67,0.78,0.89], [0.91,0.1011,0.1112,0.1213], [0.1314,0.1415,0.1516,0.1617] ],
    [
    [0.12,0.23,0.34,0.45], [0.56,0.67,0.78,0.89], [0.91,0.1011,0.1112,0.1213], [0.1314,0.1415,0.1516,0.1617] ],
    [
    [0.12,0.23,0.34,0.45], [0.56,0.67,0.78,0.89], [0.91,0.1011,0.1112,0.1213], [0.1314,0.1415,0.1516,0.1617] ],
    [
    [0.12,0.23,0.34,0.45], [0.56,0.67,0.78,0.89], [0.91,0.1011,0.1112,0.1213], [0.1314,0.1415,0.1516,0.1617] ],
    ])

    A: c = a * b
    B: c = np.matmul(a,b)
    C: c = a @ b

    测试下来耗时 A < B < C ???
    这是啥情况,原生*运算比 numpy 优化过的速度还快???

    10 条回复    2019-03-29 21:47:35 +08:00
    silkriver
        1
    silkriver  
       2019-03-25 10:30:45 +08:00   ❤️ 1
    你这全都是 numpy 优化过的速度…… ufunc 了解一下
    * 和 np.multiply 等价
    @ 和 np.matmul 等价
    enenaaa
        2
    enenaaa  
       2019-03-25 10:39:43 +08:00   ❤️ 1
    一看就是没学过 c++的, 重载运算符啊。
    SeaRecluse
        3
    SeaRecluse  
    OP
       2019-03-25 10:44:47 +08:00
    @silkriver 另一个问题是,在进行如下操作时:

    a = a * 1000
    b = b * 1000

    α:c = a * b / 1000000
    β:c = a @ b /1000000

    为什么 time(A) < time(α),time(C) > time(β)呢😂?
    其实本来是想验证浮点数计算的优化的,后者才是正式表达的矩阵乘法运算吧~
    SeaRecluse
        4
    SeaRecluse  
    OP
       2019-03-25 10:46:04 +08:00
    @enenaaa 你这样让我的写的第一个 cout << "Hello World" << endl; 很尴尬😂
    silhouette
        5
    silhouette  
       2019-03-25 11:11:19 +08:00 via Android
    兄弟,运算符重载了解一下
    silkriver
        6
    silkriver  
       2019-03-25 12:28:55 +08:00
    这很正常因为矩阵乘法运算量更大
    menc
        7
    menc  
       2019-03-25 17:14:17 +08:00
    @SeaRecluse 写过 CPP 还不知道重载运算符不是更尴尬么。。
    SeaRecluse
        8
    SeaRecluse  
    OP
       2019-03-25 17:26:49 +08:00
    @menc 知道啊,不过写动态语言写多了完全忽略掉了 [捂脸
    menc
        9
    menc  
       2019-03-26 15:46:13 +08:00
    @SeaRecluse python 的运算符重载也是很常用的东西
    necomancer
        10
    necomancer  
       2019-03-29 21:47:35 +08:00
    * 不是矩阵乘法,计算量不一样。

    In [1]:
    ...: a = random.random((1000,1000))

    In [2]:
    ...: b = random.random((1000,1000))

    In [3]: np.allclose(a@b, np.matmul(a,b))
    Out[3]: True

    In [4]: np.allclose(a*b, np.matmul(a,b))
    Out[4]: False

    In [5]: %timeit a@b
    34.7 ms ± 1.91 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

    In [6]: %timeit np.matmul(a,b)
    34.6 ms ± 1.14 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

    In [7]: np.__version__
    Out[7]: '1.15.1'
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