pso-bp python
时间: 2023-09-08 21:14:44 浏览: 107
PSO-BP是一种结合了粒子群优化算法(PSO)和反向传播算法(BP)的神经网络训练方法。在这个方法中,PSO被用来优化BP的权重和偏置,以提高神经网络的训练效率和准确性。
在Python中实现PSO-BP可以使用一些已有的库,例如PySwarm和NeuroEvolution等。这些库提供了实现PSO和BP的函数和类,可以方便地进行组合和调用。
以下是一个使用PySwarm实现PSO-BP的示例代码:
```
import numpy as np
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from pyswarm import pso
# 加载鸢尾花数据集
iris = load_iris()
X = iris.data
y = iris.target
# 标准化数据
scaler = StandardScaler()
X = scaler.fit_transform(X)
# 定义神经网络模型
class NeuralNetwork:
def __init__(self, input_size, hidden_size, output_size):
self.weights1 = np.random.randn(input_size, hidden_size)
self.bias1 = np.random.randn(hidden_size)
self.weights2 = np.random.randn(hidden_size, output_size)
self.bias2 = np.random.randn(output_size)
def forward(self, X):
self.z1 = np.dot(X, self.weights1) + self.bias1
self.a1 = self.sigmoid(self.z1)
self.z2 = np.dot(self.a1, self.weights2) + self.bias2
self.a2 = self.sigmoid(self.z2)
return self.a2
def sigmoid(self, z):
return 1 / (1 + np.exp(-z))
def predict(self, X):
y_pred = np.argmax(self.forward(X), axis=1)
return y_pred
# 定义PSO-BP训练函数
def train_pso_bp(params, X, y):
input_size = X.shape[1]
hidden_size = params[0]
output_size = len(np.unique(y))
nn = NeuralNetwork(input_size, hidden_size, output_size)
# 定义损失函数
def loss_func(weights):
nn.weights1 = weights[:input_size*hidden_size].reshape(input_size, hidden_size)
nn.bias1 = weights[input_size*hidden_size:hidden_size*(input_size+1)]
nn.weights2 = weights[hidden_size*(input_size+1):-output_size].reshape(hidden_size, output_size)
nn.bias2 = weights[-output_size:]
y_pred = nn.forward(X)
loss = -np.sum(np.log(y_pred[np.arange(len(y)), y]))
return loss
# 使用PSO优化BP的权重和偏置
lb = [-10] * (input_size*hidden_size + hidden_size*(input_size+1) + hidden_size*output_size + output_size)
ub = [10] * (input_size*hidden_size + hidden_size*(input_size+1) + hidden_size*output_size + output_size)
xopt, _ = pso(loss_func, lb, ub, swarmsize=50, maxiter=1000)
nn.weights1 = xopt[:input_size*hidden_size].reshape(input_size, hidden_size)
nn.bias1 = xopt[input_size*hidden_size:hidden_size*(input_size+1)]
nn.weights2 = xopt[hidden_size*(input_size+1):-output_size].reshape(hidden_size, output_size)
nn.bias2 = xopt[-output_size:]
# 计算准确率
y_pred = nn.predict(X)
accuracy = np.mean(y_pred == y)
return -accuracy
# 使用PSO-BP训练神经网络模型
params = [5] # 隐藏层神经元个数
xopt, _ = pso(train_pso_bp, params, args=(X, y), swarmsize=10, maxiter=100)
hidden_size = int(xopt[0])
nn = NeuralNetwork(X.shape[1], hidden_size, len(np.unique(y)))
```
这段代码使用PySwarm实现了一个简单的PSO-BP训练过程,其中定义了一个NeuralNetwork类用于描述神经网络模型,定义了loss_func函数用于计算损失,定义了train_pso_bp函数用于整合PSO和BP的训练过程。最后使用PSO-BP训练得到最佳隐藏层神经元个数,然后创建一个新的神经网络模型并用于分类任务。
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