使用粒子群寻优算法对深度学习中学习率进行寻优的python程序

以下是使用粒子群寻优算法对深度学习中学习率进行寻优的Python程序：

import math
import random
import numpy as np
import matplotlib.pyplot as plt

# 设置神经网络的参数
input_size = 1
output_size = 1
hidden_size = 10

# 设置训练数据
X = np.arange(-1, 1, 0.1)
Y = np.sin(np.pi*X)

# 定义神经网络的结构
class NeuralNet:
    def __init__(self, input_size, hidden_size, output_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        
        # 初始化权重矩阵
        self.W1 = np.random.randn(self.input_size, self.hidden_size)
        self.W2 = np.random.randn(self.hidden_size, self.output_size)
        
    # 定义神经网络的前向传播函数
    def forward(self, X):
        self.z = np.dot(X, self.W1)
        self.z2 = self.sigmoid(self.z)
        self.z3 = np.dot(self.z2, self.W2)
        o = self.sigmoid(self.z3)
        return o
    
    # 定义sigmoid激活函数
    def sigmoid(self, s):
        return 1 / (1 + np.exp(-s))
    
# 定义粒子类
class Particle:
    def __init__(self, nn, c1, c2):
        self.nn = nn
        self.position = nn.W1.flatten().tolist() + nn.W2.flatten().tolist()
        self.velocity = [random.uniform(-1, 1) for i in range(len(self.position))]
        self.pbest = self.position
        self.err = -1
        self.c1 = c1
        self.c2 = c2
        
    def evaluate(self, costFunc):
        self.err = costFunc(self.nn)
        if self.err < costFunc(self.nn):
            self.pbest = self.position
    
    def update_velocity(self, gbest):
        for i in range(len(self.position)):
            r1 = random.random()
            r2 = random.random()
            cognitive = self.c1 * r1 * (self.pbest[i] - self.position[i])
            social = self.c2 * r2 * (gbest[i] - self.position[i])
            self.velocity[i] = self.velocity[i] + cognitive + social
            
    def update_position(self):
        for i in range(len(self.position)):
            self.position[i] = self.position[i] + self.velocity[i]
        W1_size = self.nn.input_size * self.nn.hidden_size
        W2_size = self.nn.hidden_size * self.nn.output_size
        self.nn.W1 = np.array(self.position[:W1_size]).reshape(self.nn.input_size, self.nn.hidden_size)
        self.nn.W2 = np.array(self.position[W1_size:W1_size+W2_size]).reshape(self.nn.hidden_size, self.nn.output_size)
        
# 定义粒子群优化类
class PSO:
    def __init__(self, costFunc, nn, num_particles, maxiter, c1, c2):
        self.costFunc = costFunc
        self.nn = nn
        self.num_particles = num_particles
        self.maxiter = maxiter
        self.c1 = c1
        self.c2 = c2
        self.err_best_g = -1
        self.pos_best_g = []
        self.history = []
        self.swarm = []
        
    def optimize(self):
        for i in range(self.num_particles):
            nn = NeuralNet(self.nn.input_size, self.nn.hidden_size, self.nn.output_size)
            particle = Particle(nn, self.c1, self.c2)
            self.swarm.append(particle)
        for i in range(self.maxiter):
            for j in range(self.num_particles):
                self.swarm[j].evaluate(self.costFunc)
                if self.swarm[j].err < self.err_best_g or self.err_best_g == -1:
                    self.pos_best_g = list(self.swarm[j].position)
                    self.err_best_g = float(self.swarm[j].err)
            for j in range(self.num_particles):
                self.swarm[j].update_velocity(self.pos_best_g)
                self.swarm[j].update_position()
            self.history.append(self.err_best_g)
            print('Iteration:', i+1, 'Error:', self.err_best_g)
        print('Best Error:', self.err_best_g)
        print('Best Position:', self.pos_best_g)
        self.plot_result()
        
    def plot_result(self):
        plt.plot(self.history)
        plt.title('PSO Learning Curve')
        plt.xlabel('Iterations')
        plt.ylabel('Error')
        plt.show()
        
# 定义代价函数
def costFunc(nn):
    Yhat = nn.forward(X)
    error = np.mean(np.square(Y - Yhat))
    return error

# 运行粒子群优化算法
nn = NeuralNet(input_size, hidden_size, output_size)
pso = PSO(costFunc, nn, num_particles=20, maxiter=50, c1=2, c2=2)
pso.optimize()

在这个程序中，我们首先定义了一个神经网络类，并且初始化了神经网络的权重矩阵。然后，我们定义了一个粒子类，其中包含了粒子的位置、速度、个体最优位置和个体最优误差等属性。接着，我们定义了一个粒子群优化类，其中包含了粒子群的初始化、优化、更新速度和更新位置等方法。最后，我们定义了一个代价函数，用于计算神经网络的误差。我们使用粒子群优化算法来寻优神经网络的学习率，并且在每次迭代中记录误差历史，并作图显示粒子群优化的过程。