pso优化bp算法python代码

pso优化bp算法python代码
PSO优化BP算法Python代码是一种利用粒子群优化算法优化BP 算法的Python代码。

通过将粒子群优化算法与BP算法相结合，可以更好地解决BP算法在训练过程中容易陷入局部最优解的问题，从而提高模型的准确性和收敛速度。

以下是一个简单的PSO优化BP算法Python代码示例：
```python
import numpy as np
import random
# 定义BP神经网络类
class BPNN:
def __init__(self, n_input, n_hidden, n_output):
self.n_input = n_input
self.n_hidden = n_hidden
self.n_output = n_output
self.w1 = np.random.rand(n_input, n_hidden)
self.b1 = np.random.rand(n_hidden)
self.w2 = np.random.rand(n_hidden, n_output)
self.b2 = np.random.rand(n_output)
def sigmoid(self, x):
return 1.0 / (1.0 + np.exp(-x))
def forward(self, x):
y1 = np.dot(x, self.w1) + self.b1
z1 = self.sigmoid(y1)
y2 = np.dot(z1, self.w2) + self.b2
z2 = self.sigmoid(y2)
return z2
def predict(self, X):
Y = np.zeros((X.shape[0], self.n_output)) for i in range(X.shape[0]):
Y[i] = self.forward(X[i])
return Y
# 定义粒子类
class Particle:
def __init__(self, dim):
self.position = np.random.rand(dim)
self.velocity = np.random.rand(dim)
self.fitness = float('inf')
self.best_position = self.position.copy() self.best_fitness = float('inf')
def update_fitness(self, fitness):
self.fitness = fitness
if fitness < self.best_fitness:
self.best_fitness = fitness
self.best_position = self.position.copy()
# 定义粒子群优化算法类
class PSO:
def __init__(self, func, dim, n_particles, max_iter, lb, ub, w=0.729, c1=1.49445, c2=1.49445):
self.func = func
self.dim = dim
self.n_particles = n_particles
self.max_iter = max_iter
self.lb = lb
self.ub = ub
self.w = w
self.c1 = c1
self.c2 = c2
self.particles = [Particle(dim) for i in
range(n_particles)]
self.gbest_position = np.zeros(dim)
self.gbest_fitness = float('inf')
def optimize(self):
for i in range(self.max_iter):
for j in range(self.n_particles):
# 更新速度和位置
self.particles[j].velocity = self.w *
self.particles[j].velocity +
self.c1 * random.random() *
(self.particles[j].best_position -
self.particles[j].position) +
self.c2 * random.random() * (self.gbest_position - self.particles[j].position)
self.particles[j].position += self.particles[j].velocity # 边界处理
self.particles[j].position[self.particles[j].position < self.lb] = self.lb
self.particles[j].position[self.particles[j].position > self.ub] = self.ub
# 计算适应度
fitness = self.func(self.particles[j].position)
# 更新个体最优解和全局最优解
self.particles[j].update_fitness(fitness)
if fitness < self.gbest_fitness:
self.gbest_fitness = fitness
self.gbest_position = self.particles[j].position.copy() # 定义损失函数
def loss_function(theta, X, Y):
n_input, n_hidden, n_output = 2, 3, 1
nn = BPNN(n_input, n_hidden, n_output)
nn.w1 = theta[0:6].reshape(n_input, n_hidden)
nn.b1 = theta[6:9].reshape(n_hidden)
nn.w2 = theta[9:12].reshape(n_hidden, n_output)
nn.b2 = theta[12:].reshape(n_output)
Y_pred = nn.predict(X)
return np.mean((Y_pred - Y)**2)
# 生成数据
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
Y = np.array([[0], [1], [1], [0]])
# 定义搜索范围
lb = np.array([-5] * 12)
ub = np.array([5] * 12)
# 定义PSO算法
pso = PSO(loss_function, dim=12, n_particles=20,
max_iter=100, lb=lb, ub=ub)
# 运行PSO算法
pso.optimize()
# 输出结果
print('Global best fitness:', pso.gbest_fitness)
print('Global best position:', pso.gbest_position)
# 计算模型预测结果
theta = pso.gbest_position
n_input, n_hidden, n_output = 2, 3, 1
nn = BPNN(n_input, n_hidden, n_output)
nn.w1 = theta[0:6].reshape(n_input, n_hidden)
nn.b1 = theta[6:9].reshape(n_hidden)
nn.w2 = theta[9:12].reshape(n_hidden, n_output)
nn.b2 = theta[12:].reshape(n_output)
Y_pred = nn.predict(X)
print('Predicted output:', Y_pred)
```
该代码实现了一个简单的BP神经网络来解决异或问题，并使用粒子群优化算法来优化BP神经网络的权重和偏置，从而得到更好的模型预测结果。