用python实现GWO优化BPNN

好的，下面是一个简单的用Python实现GWO优化BPNN的代码示例，需要使用PyTorch库来实现BPNN模型的训练：

import torch
import random
import numpy as np

# 定义BPNN模型
class BPNN(torch.nn.Module):
    def __init__(self, input_dim, output_dim, hidden_dims=[10, 5]):
        super(BPNN, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.hidden_dims = hidden_dims
        
        # 定义神经网络结构
        self.layers = torch.nn.ModuleList()
        self.layers.append(torch.nn.Linear(input_dim, hidden_dims[0]))
        for i in range(1, len(hidden_dims)):
            self.layers.append(torch.nn.Linear(hidden_dims[i-1], hidden_dims[i]))
        self.layers.append(torch.nn.Linear(hidden_dims[-1], output_dim))
        
        # 初始化权重和偏置
        for layer in self.layers:
            torch.nn.init.normal_(layer.weight, mean=0.0, std=0.1)
            torch.nn.init.constant_(layer.bias, 0.0)
            
    def forward(self, x):
        y = x
        for layer in self.layers:
            y = layer(y)
            y = torch.sigmoid(y)
        return y

# 定义适应度函数
def fitness(net, x, t):
    y = net(x)
    loss = torch.nn.functional.binary_cross_entropy(y, t)
    return loss.item()

# 初始化参数
N = 5 # 灰狼数量
max_iter = 100 # 最大迭代次数
c1 = 2 # 学习因子1
c2 = 2 # 学习因子2
c3 = 2 # 学习因子3
a = 2 # 衰减因子

# 加载数据
x = torch.Tensor(np.random.rand(100, 10)) # 输入数据
t = torch.Tensor(np.random.randint(0, 2, (100, 1))).float() # 输出数据

# 初始化灰狼群体
wolf = []
for i in range(N):
    net = BPNN(10, 1) # 创建一个2层神经网络
    pos = []
    for param in net.parameters():
        pos.append(param.data.clone().detach().numpy().ravel()) # 获取当前灰狼的权重和偏置参数
    wolf.append({'net': net, 'pos': pos})

# 初始化最优解
best_pos = wolf[0]['pos']
best_fitness = float('inf')

# 迭代更新
for iter in range(max_iter):
    # 更新每个灰狼的位置
    for i in range(N):
        # 随机选择3个不同的灰狼
        r = random.sample(range(N), 3)
        A = wolf[r[0]]['pos']
        B = wolf[r[1]]['pos']
        C = wolf[r[2]]['pos']
        
        # 计算A、B、C三个灰狼的距离
        D_alpha = abs(c1 * np.array(A) - np.array(wolf[i]['pos']))
        D_beta = abs(c2 * np.array(B) - np.array(wolf[i]['pos']))
        D_delta = abs(c3 * np.array(C) - np.array(wolf[i]['pos']))
        
        # 更新灰狼位置
        wolf[i]['pos'] = (np.array(A) + np.array(B) + np.array(C)) / 3 - a * (D_alpha + D_beta + D_delta)
        
        # 更新灰狼适应度
        for j, param in enumerate(wolf[i]['net'].parameters()):
            param.data = torch.Tensor(wolf[i]['pos'][j].reshape(param.data.shape))
        fitness_i = fitness(wolf[i]['net'], x, t)
        if fitness_i < best_fitness: # 更新最优解
            best_fitness = fitness_i
            best_pos = wolf[i]['pos'].copy()
        
    # 输出当前迭代结果
    print('Iteration {}: Best Fitness = {}'.format(iter, best_fitness))

# 使用最优解进行预测
net = BPNN(10, 1)
for j, param in enumerate(net.parameters()):
    param.data = torch.Tensor(best_pos[j].reshape(param.data.shape))
y = net(x) # 使用最优解的BPNN模型进行预测

需要注意的是，这只是一个简单的示例代码，实际情况下需要根据具体问题进行适当修改。