在Python中结合reduce和map的最干净方法 [英] Cleanest way to combine reduce and map in Python

问题描述

我正在做一些深度学习，我想获取所有隐藏层的值.所以我最终编写了这样的函数:

I'm doing a little deep learning, and I want to grab the values of all hidden layers. So I end up writing functions like this:

def forward_pass(x, ws, bs):
    activations = []
    u = x
    for w, b in zip(ws, bs):
        u = np.maximum(0, u.dot(w)+b)
        activations.append(u)
    return activations

如果我不必获取中间值，那么我将使用更为冗长的形式:

If I didn't have to get the intermediate values, I'd use the much less verbose form:

out = reduce(lambda u, (w, b): np.maximum(0, u.dot(w)+b), zip(ws, bs), x)

Bam.全部一条线，美观小巧.但是我不能保留任何中间值.

Bam. All one line, nice and compact. But I can't keep any of the intermediate values.

那么，有什么方法可以让我的蛋糕(漂亮的紧凑型单缸)也可以吃掉(返回中间值)?

So, what is there any way to have my cake (nice compact one-liner) and eat it too (return intermediate values)?

推荐答案

实际上，您可以使用 result = [y for y in in [initial] for y for x in input in [f]中的y的怪异模式来执行此操作.(x，y)]] .请注意，第一个和第三个 for 并不是真正的循环，而是赋值-我们可以在[value] 中使用 for var来理解分配 value 的方法.到 var .例如:

You can actually do this using the somewhat weird pattern of result = [y for y in [initial] for x in inputs for y in [f(x, y)]]. Note that the first and third for are not really loops but assignments - we can use for var in [value] in a comprehension to assign value to the var. For example:

def forward_pass(x, ws, bs):
    activations = []
    u = x
    for w, b in zip(ws, bs):
        u = np.maximum(0, u.dot(w)+b)
        activations.append(u)
    return activations

相当于:

def forward_pass(x, ws, bs):
    return [u for u in [x] for w, b in zip(ws, bs) for u in [np.maximum(0, u.dot(w)+b)]]

Python 3.8 +:
Python 3.8引入了海象"运算符:= ，这为我们提供了另一种选择:

Python 3.8+:
Python 3.8 introduces the "walrus" operator :=, which gives us another option:

def forward_pass(x, ws, bs):
    u = x
    return [u:=np.maximum(0, u.dot(w)+b) for w, b in zip(ws, bs)]

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