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我目前正在研究一個open source ANN（爲了好玩和作爲一種學習體驗），最近我做了我認爲對我的代碼做了一個相對較小的更改，但由於某種原因，它使ANN運行速度提高了16倍。 （根據我至少測試）是什麼讓我的ANN大幅減少運行時間？
ANN/ANN5.py：（老ANN）
from random import uniform
class Neuron(object):
def __init__(self, parents=[]):
self.parents = [{
'neuron': parent,
'weight': uniform(-1, 1),
'slope': uniform(-1, 1),
} for parent in parents]
def calculate(self, increment=0):
self.output = sum([parent['neuron'].output * (parent['weight'] + increment * parent['slope']) for parent in self.parents]) > 0
def mutate(self, increment):
for parent in self.parents:
parent['weight'] += increment * parent['slope']
parent['slope'] = uniform(-1, 1)
def get_genome(self):
return [parent['weight'] for parent in self.parents]
def set_genome(self, value):
for i, parent in enumerate(self.parents):
parent['weight'] = value[i]
genome = property(get_genome, set_genome)
class NeuralNetwork(object):
def __init__(self, inputs, outputs, hidden, rows):
self.bias = Neuron()
self.neurons = []
for row in xrange(rows):
if row == 0:
self.neurons.append([Neuron(parents=[]) for input_ in xrange(inputs)])
elif row == rows - 1:
self.neurons.append([Neuron(parents=self.neurons[row - 1] + [self.bias]) for output in xrange(outputs)])
else:
self.neurons.append([Neuron(parents=self.neurons[row - 1] + [self.bias]) for column in xrange(hidden)])
self.bias.output = True
def calculate(self, inputs, increment=0):
for i, neuron_row in enumerate(self.neurons):
for j, neuron in enumerate(neuron_row):
if i == 0:
neuron.output = inputs[j]
else:
neuron.calculate(increment=increment)
return [neuron.output for neuron in self.neurons[-1]]
def mutate(self, increment):
for neuron_row in self.neurons:
for neuron in neuron_row:
neuron.mutate(increment=increment)
def get_genome(self):
genome = []
for neuron_row in self.neurons[1:]:
genome.append([neuron.genome for neuron in neuron_row])
return genome
def set_genome(self, value):
for i, neuron_row in enumerate(self.neurons[1:]):
for j, neuron in enumerate(neuron_row):
neuron.genome = value[i][j]
genome = property(get_genome, set_genome)
ANN/ANN.py：（新ANN）
from random import uniform
class Neuron(object):
def __init__(self, parents=[]):
self.parents = [{
'neuron': parent,
'weight': uniform(-1, 1),
'slope': uniform(-1, 1),
} for parent in parents]
def calculate(self, increment=0):
self.output = sum([parent['neuron'].output * (parent['weight'] + increment * parent['slope']) for parent in self.parents]) > 0
def mutate(self, increment):
for parent in self.parents:
parent['weight'] += increment * parent['slope']
parent['slope'] = uniform(-1, 1)
def get_genome(self):
return [parent['weight'] for parent in self.parents]
def set_genome(self, value):
for i, parent in enumerate(self.parents):
parent['weight'] = value[i]
genome = property(get_genome, set_genome)
class NeuralNetwork(object):
def __init__(self, inputs, outputs, hidden, rows):
self.bias = Neuron()
self.neurons = [[Neuron(parents=[]) for input_ in xrange(inputs)]]
for row in xrange(rows - 2):
self.neurons.append([Neuron(parents=self.neurons[-1] + [self.bias]) for output in xrange(outputs)])
self.neurons.append([Neuron(parents=self.neurons[-1] + [self.bias]) for output in xrange(outputs)])
self.bias.output = True
def calculate(self, inputs, increment=0):
for i, neuron_row in enumerate(self.neurons):
for j, neuron in enumerate(neuron_row):
if i == 0:
neuron.output = inputs[j]
else:
neuron.calculate(increment=increment)
return [neuron.output for neuron in self.neurons[-1]]
def mutate(self, increment):
for neuron_row in self.neurons:
for neuron in neuron_row:
neuron.mutate(increment=increment)
def get_genome(self):
genome = []
for neuron_row in self.neurons[1:]:
genome.append([neuron.genome for neuron in neuron_row])
return genome
def set_genome(self, value):
for i, neuron_row in enumerate(self.neurons[1:]):
for j, neuron in enumerate(neuron_row):
neuron.genome = value[i][j]
genome = property(get_genome, set_genome)
的差異從ANN/ANN5.py要ANN/ANN.py：
- self.neurons = []
- for row in xrange(rows):
- if row == 0:
- self.neurons.append([Neuron(parents=[]) for input_ in xrange(inputs)])
- elif row == rows - 1:
- self.neurons.append([Neuron(parents=self.neurons[row - 1] + [self.bias]) for output in xrange(outputs)])
- else:
- self.neurons.append([Neuron(parents=self.neurons[row - 1] + [self.bias]) for column in xrange(hidden)])
+ self.neurons = [[Neuron(parents=[]) for input_ in xrange(inputs)]]
+ for row in xrange(rows - 2):
+ self.neurons.append([Neuron(parents=self.neurons[-1] + [self.bias]) for output in xrange(outputs)])
+ self.neurons.append([Neuron(parents=self.neurons[-1] + [self.bias]) for output in xrange(outputs)])
（全部在NeuralNetwork的__init__）
tests.py：
from random import randint
from time import time
from ANN.ANN import NeuralNetwork
# from ANN.ANN2 import NeuralNetwork as NeuralNetwork2
# from ANN.ANN3 import NeuralNetwork as NeuralNetwork3
# from ANN.ANN4 import NeuralNetwork as NeuralNetwork4
from ANN.ANN5 import NeuralNetwork as NeuralNetwork5
def test(NeuralNetwork=NeuralNetwork):
time_ = time()
ANNs = []
for i in xrange(10):
ANNs.append(NeuralNetwork(inputs=49, outputs=3, hidden=49, rows=5))
for i, ANN in enumerate(ANNs[:1]):
for j in xrange(11):
for k in xrange(len(ANNs)/2):
for l in xrange(20):
ANN.calculate([randint(0, 1) for _ in xrange(49)], increment=j/10)
ANNs[k + len(ANNs)/2 * (i < len(ANNs)/2)].calculate([randint(0, 1) for _ in xrange(49)])
# print 'ANN {0} mutation {1:02d} opponent {2} turn {3:02d}'.format(i + 1, j + 1, k + 1, l + 1)
ANN.mutate(increment=randint(1, 100))
return time() - time_
if __name__ == '__main__':
print 'time: {0}'.format(test())
# print 'time 2: {0}'.format(test(NeuralNetwork2))
# print 'time 3: {0}'.format(test(NeuralNetwork3))
# print 'time 4: {0}'.format(test(NeuralNetwork4))
print 'time 5: {0}'.format(test(NeuralNetwork5))
我註釋掉ANN2，ANN3和ANN4，因爲他們是更老版本的ANN的，我存儲（僅限本地，他們都不是在Github上），以便比較性能。目前，我只擔心ANN5.py和ANN.py
我爲什麼for i, ANN in enumerate(ANNs[:1]):而不是for i, ANN in enumerate(ANNs):是因爲測試與後者花費的時間太長的原因之間的性能變化，我盤算了一下，結果仍然會是完全足夠，而無需重複過程10 ANN的（我偶爾會做所有10個測試，以確保）
當我最後一次運行這個tests.py是我得到：
time: 0.454416036606
time 5: 8.02504611015
，它總是給人有點接近於此。
我已經做了各種測試，比較ANN.py和ANN5.py的功能，到目前爲止他們在相同的情況下做了完全相同的事情。我已經使用基因組屬性來製作兩個完全相同的ANN，一個使用ANN.py中的NeuralNetwork類，另一個使用ANN5.py中的NeuralNetwork類，並且它們總是給出相同的輸入結果。
所以我的問題是，發生了什麼事？我意識到我的問題不是很確切，但我真的不知道爲什麼會有如此巨大的性能差異。我希望的是舊ANN（ANN5.py）只是在背景中做了一些非常低效的事情，這是因爲我初始化了ANN並且新的ANN（ANN.py）正在初始化它，但是我擔心新的ANN有一些完全缺失的東西，因爲某些原因，當我手動測試這兩個時沒有出現/有任何區別。