用PSO优化DBN的隐藏层神经元和层数的代码

由于缺少具体的数据和问题描述，以下代码仅提供一种基本的实现思路，需要用户根据具体问题进行修改和调整。

首先，导入必要的库：

import numpy as np
import math
import random

定义DBN类，包含初始化、训练和预测三个方法：

class DBN:
    def __init__(self, sizes, learning_rate=0.1, epochs=100, batch_size=10, hidden_layers=1, hidden_neurons=10):
        self.sizes = sizes
        self.learning_rate = learning_rate
        self.epochs = epochs
        self.batch_size = batch_size
        self.hidden_layers = hidden_layers
        self.hidden_neurons = hidden_neurons
        self.weights = []
        self.biases = []
        for i in range(len(sizes)-1):
            self.weights.append(np.random.randn(sizes[i+1], sizes[i]))
            self.biases.append(np.random.randn(sizes[i+1], 1))

    def sigmoid(self, z):
        return 1.0/(1.0+np.exp(-z))

    def train(self, X):
        n = X.shape[0]
        for epoch in range(self.epochs):
            for i in range(0, n, self.batch_size):
                X_batch = X[i:i+self.batch_size]
                activations = [X_batch.T]
                for j in range(self.hidden_layers):
                    z = np.dot(self.weights[j], activations[-1]) + self.biases[j]
                    a = self.sigmoid(z)
                    activations.append(a)
                delta = (activations[-1] - X_batch.T) * activations[-1] * (1-activations[-1])
                for j in range(self.hidden_layers-1, -1, -1):
                    grad_w = np.dot(delta, activations[j].T) / self.batch_size
                    grad_b = np.mean(delta, axis=1, keepdims=True)
                    delta = np.dot(self.weights[j].T, delta) * activations[j] * (1-activations[j])
                    self.weights[j] -= self.learning_rate * grad_w
                    self.biases[j] -= self.learning_rate * grad_b

    def predict(self, X):
        activations = [X.T]
        for j in range(self.hidden_layers):
            z = np.dot(self.weights[j], activations[-1]) + self.biases[j]
            a = self.sigmoid(z)
            activations.append(a)
        return activations[-1].T

接下来，定义PSO类，包含初始化、粒子更新和PSO优化三个方法：

class PSO:
    def __init__(self, dbn, pop_size=10, max_iter=50, c1=2, c2=2, w=0.8):
        self.dbn = dbn
        self.pop_size = pop_size
        self.max_iter = max_iter
        self.c1 = c1
        self.c2 = c2
        self.w = w
        self.particles = []
        for i in range(self.pop_size):
            particle = {}
            particle['position'] = np.array([random.randint(1, 10)] * dbn.hidden_layers)
            particle['velocity'] = np.array([0] * dbn.hidden_layers)
            particle['fitness'] = float('inf')
            self.particles.append(particle)
        self.global_best = {'position': np.array([random.randint(1, 10)] * dbn.hidden_layers), 'fitness': float('inf')}

    def update_particle(self, particle):
        new_position = particle['position'] + particle['velocity']
        new_position = np.maximum(new_position, 1)
        new_position = np.minimum(new_position, 10)
        particle['position'] = new_position

    def optimize(self, X):
        for iteration in range(self.max_iter):
            for particle in self.particles:
                self.dbn.hidden_layers = len(particle['position'])
                self.dbn.hidden_neurons = int(particle['position'][0])
                self.dbn.train(X)
                fitness = 1 / (1 + self.dbn.predict(X).mean())
                if fitness < particle['fitness']:
                    particle['fitness'] = fitness
                    particle['position'] = np.array([self.dbn.hidden_neurons] * self.dbn.hidden_layers)
                if fitness < self.global_best['fitness']:
                    self.global_best['fitness'] = fitness
                    self.global_best['position'] = np.array([self.dbn.hidden_neurons] * self.dbn.hidden_layers)
            for particle in self.particles:
                r1 = np.random.rand(self.dbn.hidden_layers)
                r2 = np.random.rand(self.dbn.hidden_layers)
                particle['velocity'] = self.w * particle['velocity'] \
                    + self.c1 * r1 * (particle['position'] - particle['position']) \
                    + self.c2 * r2 * (self.global_best['position'] - particle['position'])
                self.update_particle(particle)
        self.dbn.hidden_layers = len(self.global_best['position'])
        self.dbn.hidden_neurons = int(self.global_best['position'][0])
        self.dbn.train(X)

最后，定义主函数，包含数据读取、参数设置和PSO优化：

if __name__ == '__main__':
    X = np.loadtxt('data.txt')
    dbn = DBN(sizes=[X.shape[1], 5, 1], learning_rate=0.1, epochs=50, batch_size=10)
    pso = PSO(dbn, pop_size=10, max_iter=50, c1=2, c2=2, w=0.8)
    pso.optimize(X)
    print('Optimal number of hidden layers:', pso.dbn.hidden_layers)
    print('Optimal number of hidden neurons:', pso.dbn.hidden_neurons)
    print('Global best fitness:', pso.global_best['fitness'])