希望你能提供帮助，因为这个让我头疼。
我正在python（3.3）中开发一个小型捕食者 - 食饵模拟，它使用一个简单的前馈神经网络。今天，我将执行大脑每个“滴答”的功能从纯python更改为numpy数组，以便在我使用更大的脑时优化它。
我用cProfile检查了（整个主程序“循环”）的性能速度，正如我所料，“tick”功能（在大脑类内部）更快。但是，在运行程序时，我注意到，实际上，使用numpy的速度较慢（约12秒内有100个循环，而~9中有100个循环）。
为什么会这样？这是代码：
原始实施：
class Brain:
def __init__(self, inputs, hidden, outputs):
self.input_num = inputs
self.hidden_num = hidden
self.output_num = outputs
self.h_weight = []
self.o_weight = []
for _ in range(self.input_num * self.hidden_num):
self.h_weight.append(random.random()*2-1)
for _ in range(self.hidden_num * self.output_num):
self.o_weight.append(random.random()*2-1)
def tick(self):
input_num = self.input_num
hidden_num = self.hidden_num
output_num = self.output_num
hidden = [0]*hidden_num
output = [0]*output_num
inputs = self.input
h_weight = self.h_weight
o_weight = self.o_weight
e = math.e
count = -1
for x in range(hidden_num):
temp = 0
for y in range(input_num):
count += 1
temp -= inputs[y] * h_weight[count]
hidden[x] = 1/(1+e**(temp))
count = -1
for x in range(output_num):
temp = 0
for y in range(hidden_num):
count += 1
temp -= hidden[y] * o_weight[count]
output[x] = 1/(1+e**(temp))
self.output = output
新实施（使用numpy）：
class Brain:
def __init__(self, inputs, hidden, outputs):
self.input_num = inputs
self.hidden_num = hidden
self.output_num = outputs
self.h_weights = random.random((self.hidden_num, self.input_num))
self.o_weights = random.random((self.output_num, self.hidden_num))
self.h_activation = zeros((self.hidden_num, 1), dtype=float)
self.o_activation = zeros((self.output_num, 1), dtype=float)
self.i_output = zeros((self.input_num, 1), dtype=float)
self.h_output = zeros((self.hidden_num, 1), dtype=float)
self.o_output = zeros((self.output_num, 1), dtype=float)
def tick(self):
i_output = self.input
h_weights = self.h_weights
o_weights = self.o_weights
h_activation = dot(h_weights, i_output)
h_output = tanh(h_activation)
o_activation = dot(o_weights, h_output)
o_output = tanh(o_activation)
self.output = o_output
这是程序的主循环，我有时间（忽略其他函数，它们对“brain.tick（）”函数没有影响）。它在另一个类中，这也是无关紧要的：
def update(self):
GUI.update()
if not self.pause:
self.tick += 1
if self.tick % 1000 == 0:
if self.globalSelection:
self.newGeneration(self.creatures)
else:
for specie in self.species:
if not specie.isPlant:
creatureList = [creature for creature in self.creatures if creature.specie == specie]
self.newGeneration(creatureList)
for creature in self.creatures:
if not creature.specie.isPlant:
if self.useHunger and creature.hunger < 1:
creature.hunger += 1/240
creature.setInputs()
creature.brain.tick()
creature.move(creature.brain.output[0]*50-25, creature.brain.output[1]*8)
creature.interactions()
threading.Timer(self.tickrate, self.update).start()
现在大脑被设置为5个输入，200个隐藏和2个输出，仅用于测试速度。 以下是cProfile的结果：
原件：
3312 function calls in 0.094 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.094 0.094 <string>:1(<module>)
200 0.006 0.000 0.007 0.000 __init__.py:263(move)
200 0.024 0.000 0.024 0.000 __init__.py:286(setInputs)
200 0.001 0.000 0.001 0.000 __init__.py:360(interactions)
200 0.033 0.000 0.033 0.000 __init__.py:415(tick)
1 0.005 0.005 0.094 0.094 __init__.py:46(update)
1 0.007 0.007 0.020 0.020 __init__.py:471(update)
1 0.000 0.000 0.000 0.000 _weakrefset.py:79(add)
3 0.000 0.000 0.000 0.000 threading.py:127(__init__)
1 0.000 0.000 0.000 0.000 threading.py:160(_release_save)
1 0.000 0.000 0.000 0.000 threading.py:163(_acquire_restore)
1 0.000 0.000 0.000 0.000 threading.py:166(_is_owned)
1 0.000 0.000 0.000 0.000 threading.py:175(wait)
2 0.000 0.000 0.000 0.000 threading.py:297(__init__)
1 0.000 0.000 0.000 0.000 threading.py:305(is_set)
1 0.000 0.000 0.000 0.000 threading.py:325(wait)
1 0.000 0.000 0.000 0.000 threading.py:507(_newname)
1 0.000 0.000 0.000 0.000 threading.py:534(__init__)
1 0.000 0.000 0.000 0.000 threading.py:577(start)
1 0.000 0.000 0.000 0.000 threading.py:775(daemon)
1 0.000 0.000 0.000 0.000 threading.py:810(__init__)
1 0.000 0.000 0.000 0.000 threading.py:887(current_thread)
225 0.002 0.000 0.002 0.000 {built-in method aacircle}
200 0.001 0.000 0.001 0.000 {built-in method aaline}
200 0.000 0.000 0.000 0.000 {built-in method abs}
4 0.000 0.000 0.000 0.000 {built-in method allocate_lock}
200 0.001 0.000 0.001 0.000 {built-in method atan2}
400 0.001 0.000 0.001 0.000 {built-in method cos}
1 0.000 0.000 0.094 0.094 {built-in method exec}
225 0.001 0.000 0.001 0.000 {built-in method filled_circle}
4 0.000 0.000 0.000 0.000 {built-in method filled_polygon}
1 0.000 0.000 0.000 0.000 {built-in method get_ident}
1 0.000 0.000 0.000 0.000 {built-in method get_pressed}
1 0.000 0.000 0.000 0.000 {built-in method get}
2 0.000 0.000 0.000 0.000 {built-in method len}
200 0.000 0.000 0.000 0.000 {built-in method radians}
1 0.000 0.000 0.000 0.000 {built-in method round}
400 0.001 0.000 0.001 0.000 {built-in method sin}
1 0.000 0.000 0.000 0.000 {built-in method start_new_thread}
1 0.006 0.006 0.006 0.006 {built-in method update}
5 0.000 0.000 0.000 0.000 {method 'acquire' of '_thread.lock' objects}
1 0.000 0.000 0.000 0.000 {method 'add' of 'set' objects}
1 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}
2 0.000 0.000 0.000 0.000 {method 'blit' of 'pygame.Surface' objects}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
1 0.001 0.001 0.001 0.001 {method 'fill' of 'pygame.Surface' objects}
8 0.000 0.000 0.000 0.000 {method 'random_sample' of 'mtrand.RandomState' objects}
2 0.000 0.000 0.000 0.000 {method 'release' of '_thread.lock' objects}
3 0.000 0.000 0.000 0.000 {method 'render' of 'pygame.font.Font' objects}
新：
3322 function calls in 0.068 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.068 0.068 <string>:1(<module>)
200 0.005 0.000 0.006 0.000 __init__.py:265(move)
200 0.022 0.000 0.023 0.000 __init__.py:288(setInputs)
200 0.001 0.000 0.001 0.000 __init__.py:362(interactions)
200 0.005 0.000 0.014 0.000 __init__.py:417(tick)
1 0.005 0.005 0.068 0.068 __init__.py:47(update)
1 0.005 0.005 0.019 0.019 __init__.py:473(update)
1 0.000 0.000 0.000 0.000 _weakrefset.py:79(add)
3 0.000 0.000 0.000 0.000 threading.py:127(__init__)
1 0.000 0.000 0.000 0.000 threading.py:160(_release_save)
1 0.000 0.000 0.000 0.000 threading.py:163(_acquire_restore)
1 0.000 0.000 0.000 0.000 threading.py:166(_is_owned)
1 0.000 0.000 0.000 0.000 threading.py:175(wait)
2 0.000 0.000 0.000 0.000 threading.py:297(__init__)
1 0.000 0.000 0.000 0.000 threading.py:305(is_set)
1 0.000 0.000 0.000 0.000 threading.py:325(wait)
1 0.000 0.000 0.000 0.000 threading.py:507(_newname)
1 0.000 0.000 0.000 0.000 threading.py:534(__init__)
1 0.000 0.000 0.000 0.000 threading.py:577(start)
1 0.000 0.000 0.000 0.000 threading.py:775(daemon)
1 0.000 0.000 0.000 0.000 threading.py:810(__init__)
1 0.000 0.000 0.000 0.000 threading.py:887(current_thread)
225 0.002 0.000 0.002 0.000 {built-in method aacircle}
200 0.001 0.000 0.001 0.000 {built-in method aaline}
200 0.001 0.000 0.001 0.000 {built-in method abs}
4 0.000 0.000 0.000 0.000 {built-in method allocate_lock}
200 0.000 0.000 0.000 0.000 {built-in method atan2}
400 0.001 0.000 0.001 0.000 {built-in method cos}
400 0.008 0.000 0.008 0.000 {built-in method dot}
1 0.000 0.000 0.068 0.068 {built-in method exec}
225 0.001 0.000 0.001 0.000 {built-in method filled_circle}
4 0.000 0.000 0.000 0.000 {built-in method filled_polygon}
1 0.000 0.000 0.000 0.000 {built-in method get_ident}
1 0.000 0.000 0.000 0.000 {built-in method get_pressed}
1 0.000 0.000 0.000 0.000 {built-in method get}
2 0.000 0.000 0.000 0.000 {built-in method len}
200 0.000 0.000 0.000 0.000 {built-in method radians}
1 0.000 0.000 0.000 0.000 {built-in method round}
400 0.001 0.000 0.001 0.000 {built-in method sin}
1 0.000 0.000 0.000 0.000 {built-in method start_new_thread}
1 0.005 0.005 0.005 0.005 {built-in method update}
5 0.000 0.000 0.000 0.000 {method 'acquire' of '_thread.lock' objects}
1 0.000 0.000 0.000 0.000 {method 'add' of 'set' objects}
1 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}
2 0.000 0.000 0.000 0.000 {method 'blit' of 'pygame.Surface' objects}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
1 0.002 0.002 0.002 0.002 {method 'fill' of 'pygame.Surface' objects}
18 0.000 0.000 0.000 0.000 {method 'random_sample' of 'mtrand.RandomState' objects}
2 0.000 0.000 0.000 0.000 {method 'release' of '_thread.lock' objects}
3 0.000 0.000 0.000 0.000 {method 'render' of 'pygame.font.Font' objects}
如您所见，整个update（）函数（模拟的核心）和brain.tick（）似乎要快得多。那么，为什么在运行程序时，它会变慢？
干杯。
答案 0 :(得分：0)
在新实现中，使用每个Brain对象创建5个numpy数组：
self.h_activation = zeros((self.hidden_num, 1), dtype=float)
self.o_activation = zeros((self.output_num, 1), dtype=float)
self.i_output = zeros((self.input_num, 1), dtype=float)
self.h_output = zeros((self.hidden_num, 1), dtype=float)
self.o_output = zeros((self.output_num, 1), dtype=float)
这些属性未在代码的其他部分中引用。创建它们是一项潜在的高成本操作，在原始实现中似乎没有直接对应的操作。我不确定它会超过快速numpy计算的速度优势，但如果你创建了很多Brain个对象，那么值得一看。