PSO优化神经网络pytorch实现
时间: 2023-05-11 12:02:54 浏览: 178
可以使用 PyTorch 中的 torch.optim 模块来实现 PSO 优化神经网络。具体实现方法可以参考以下代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
# 定义神经网络模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(10, 5)
self.fc2 = nn.Linear(5, 1)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# 定义 PSO 优化器
class PSO(optim.Optimizer):
def __init__(self, params, lr=0.01, momentum=0.9, weight_decay=0.0):
defaults = dict(lr=lr, momentum=momentum, weight_decay=weight_decay)
super(PSO, self).__init__(params, defaults)
self.particles = []
self.velocities = []
for param in self.param_groups[0]['params']:
self.particles.append(param.data.clone())
self.velocities.append(torch.zeros_like(param.data))
self.best_particles = self.particles.copy()
self.best_scores = [np.inf] * len(self.particles)
def step(self, closure=None):
loss = None
if closure is not None:
loss = closure()
for i, param in enumerate(self.param_groups[0]['params']):
velocity = self.velocities[i]
particle = self.particles[i]
best_particle = self.best_particles[i]
best_score = self.best_scores[i]
# 更新粒子速度和位置
r1 = torch.rand_like(param.data)
r2 = torch.rand_like(param.data)
velocity = self.param_groups[0]['momentum'] * velocity + \
self.param_groups[0]['lr'] * r1 * (best_particle - particle) + \
self.param_groups[0]['lr'] * r2 * (param.data - particle)
particle = particle + velocity
# 更新粒子最优解
score = loss.item()
if score < best_score:
best_particle = particle.clone()
best_score = score
# 更新参数
param.data = particle.clone()
# 保存粒子状态
self.particles[i] = particle.clone()
self.velocities[i] = velocity.clone()
self.best_particles[i] = best_particle.clone()
self.best_scores[i] = best_score
return loss
# 使用 PSO 优化神经网络
net = Net()
criterion = nn.MSELoss()
optimizer = PSO(net.parameters(), lr=0.01, momentum=0.9, weight_decay=0.0)
inputs = torch.randn(1, 10)
labels = torch.randn(1, 1)
for i in range(100):
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
print(net(inputs))
```
这段代码实现了一个简单的神经网络模型,使用 PSO 优化器来训练模型。其中 PSO 优化器继承自 PyTorch 的 Optimizer 类,实现了粒子群算法的更新规则。在每次迭代中,PSO 优化器会更新神经网络模型的参数,并保存每个粒子的状态和最优解。最后输出模型的预测结果。