为什么PyGad fitness_function在类内部不工作?

Question

我试图训练一个遗传算法，但由于某种原因，它不工作，当它存储在一个类内。我有两段相同的代码，但是存储在类中的代码失败了。它返回这个…

raise ValueError("The fitness function must accept 2 parameters:
1) A solution to calculate its fitness value.
2) The solution's index within the population.
The passed fitness function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount))
ValueError: The fitness function must accept 2 parameters:
1) A solution to calculate its fitness value.
2) The solution's index within the population.
The passed fitness function named 'fitness_func' accepts 3 parameter(s).

这是一个不能工作的简化版本。

import torch
import torch.nn as nn
import pygad.torchga
import pygad
class NN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super().__init__()
self.linear1 = nn.Linear(input_size, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
self.linear3 = nn.Linear(hidden_size, hidden_size)
self.linear4 = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = self.linear1(x)
x = self.linear2(x)
x = self.linear3(x)
x = self.linear4(x)
return x
class Coin:
def __init__(self):
self.NeuralNet = NN(1440, 1440, 3)
def fitness_func(self, solution, solution_idx):
return 0
def trainModel(self):
torch_ga = pygad.torchga.TorchGA(model=self.NeuralNet, num_solutions=10)
ga_instance = pygad.GA(num_generations=10,
num_parents_mating=2,
initial_population=torch_ga.population_weights,
fitness_func=self.fitness_func)
ga_instance.run()
if __name__ == "__main__":
coin = Coin()
coin.trainModel()

这是一个有效的简化版本。

import torch
import torch.nn as nn
import pygad.torchga
import pygad
class NN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super().__init__()
self.linear1 = nn.Linear(input_size, hidden_size)
self.linear2 = nn.Linear(hidden_size, hidden_size)
self.linear3 = nn.Linear(hidden_size, hidden_size)
self.linear4 = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = self.linear1(x)
x = self.linear2(x)
x = self.linear3(x)
x = self.linear4(x)
return x

def fitness_func(solution, solution_idx):
return 0
def trainModel():
NeuralNet = NN(1440, 1440, 3)
torch_ga = pygad.torchga.TorchGA(model=NeuralNet, num_solutions=10)
ga_instance = pygad.GA(num_generations=10,
num_parents_mating=2,
initial_population=torch_ga.population_weights,
fitness_func=fitness_func)
ga_instance.run()
if __name__ == "__main__":
trainModel()

这两个应该是一样的，但它们不是

Answer 1

当你看pygad代码时，你可以看到它显式地检查适应度函数是否有两个参数

# Check if the fitness function accepts 2 paramaters.
if (fitness_func.__code__.co_argcount == 2):
self.fitness_func = fitness_func
else:
self.valid_parameters = False
raise ValueError("The fitness function must accept 2 parameters:n1) A solution to calculate its fitness value.n2) The solution's index within the population.nnThe passed fitness function named '{funcname}' accepts {argcount} parameter(s).".format(funcname=fitness_func.__code__.co_name, argcount=fitness_func.__code__.co_argcount))

所以如果你想在类中使用它你需要把它变成一个静态方法这样你就不需要传入self:

@staticmethod
def fitness_func(solution, solution_idx):
return 0