遗传神经网络经费遗传神经网络代码

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码海无压 2024-01-15 22:04:49

文章标签 遗传神经网络经费 python 遗传神经网络权重 Network 数据 文章分类 神经网络人工智能

最近在忙着写论文，开题答辩也已经顺利的通过了，接下来我打算为读博做打算
–小记
最近在写一篇论文，其中我打算使用遗传算法加上神经网络来优化数据，但是我的数据有点少，于是我就上Mnist数据集上找了一个数据，其实也不是我找的，主要是我找了一个源码。然后我在他的程序上做了修改，这应该不算抄袭吧？罪过，罪过。
话不多说上程序
GA.py

from numpy import *
class GA:
    def __init__(self,sizes=[2,3,1]):
        '''
        :param sizes:对神经网络权重进行部分隐藏 利用ｄｒｏｐｏｕｔ
        '''
        self.sizes = sizes
        self.weights = [random.randn(y, x) for x, y in zip(sizes[:-1], sizes[1:])]
        self.a = []
        self.totalWeight = []
        self.accuracy = [0,0]
    def newWeight(self):
        '''
        重新生成一个新的权重用于测试　
        :return:
        '''
        self.weights = [random.randn(y, x) for x, y in zip(self.sizes[:-1], self.sizes[1:])]
        print(self.weights)

    def variation(self):
        '''
       突变
        :return:改变权重
        '''
        layers = len(self.sizes)
        for i in range(layers-1):
            # print(layers)
            # number=self.sizes[i]*self.sizes[i+1]
            self.a.append([random.randint(0,self.sizes[i+1]),random.randint(0,self.sizes[i])])

        for j in range(layers-1): 
            self.weights[j][self.a[j][0]][self.a[j][1]]=0

        self.totalWeight.append(self.weights)
        self.a = []
    def cross(self,newAccuracy=[1,2]):
        '''
        交叉
        :param newAccuracy:
        :return:
        '''
        #找到最大的两个数
        newAccuracy = self.accuracy
        a1 = newAccuracy.index(max(newAccuracy))
        newAccuracy[newAccuracy.index(max(newAccuracy))] = 0
        a2 = newAccuracy.index(max(newAccuracy))
        #对最大的两个权重进行交叉
        self.totalWeight[a1]
        self.totalWeight[a2]
        layers = len(self.sizes)
        for i in range(layers-2):
            if i/2==0:
                self.weights[i] = self.totalWeight[a1][i]
            else:
                self.weights[i] = self.totalWeight[a2][i]

if __name__ =="__main__":
    ga = GA()
    print(type(ga.weights))
    ga.variation()
    ga.newWeight()
    ga.variation()
    ga.cross()
    print(ga.weights)

接下来是神经网络的程序
NN.py

# coding=UTF-8
import numpy as np
import random
from GA import *

def sigmoid(x):
    s = 1/(1+np.exp(-x))
    # s = (np.exp(x)-np.exp(-x))/(np.exp(x)+np.exp(-x))
    return s

def sigmoid_derivative(x):
    s = 1/(1+ np.exp(-x))
    ds = s*(1 - s)
    # s = (np.exp(x) - np.exp(-x)) / (np.exp(x) + np.exp(-x))
    # ds = 1 - s ** 2
    return ds


class Network:
    def __init__(self, sizes):
        '''
        :param sizes: 每层神经元的个数，
        例如：第一层２个神经元，第二层３神经元：
        net = Network[2, 3, 1] 输入层：２个，隐藏层：３个，输出层：１个．　
        '''
        self.num_layers = len(sizes)
        self.sizes = sizes
        self.biases = [np.random.randn(y,1) for y in sizes[1:]]
        self.weights = [np.random.randn(y,x)#服从正态分布（均值０，方差1）中生成
                       for x, y in zip(sizes[:-1], sizes[1:])]
        #zip:　产生一个新的ｌｉｓｔ，取两个ｌｉｓｔ的值，从第一个依次往下取，循环取出

    def feedforward(self, a):
        '''
        Return the output of the network if 'a' is input.
        :param self:
        :param a:
        :return:
        '''
        for b,w in zip(self.biases,self.weights):
            a = sigmoid(np.dot(w, a)+b)
        return a

    def SGD(self, training_data, epochs, mini_batch_size,eta,test_data=None):
        '''
        随机梯度下降算法
        :param training_data: 训练集ｌｉｓｔ　（ｘ，ｙ）
        :param epochs: 训练的次数
        :param min_batch_size: 最小块的实例
        :param eta: 学习率
        :param test_data:测试集
        :return:
        '''
        #利用遗传算法对权重重新进行赋值
        ga = GA(self.sizes)
        ga.variation()
        ga.newWeight()
        ga.variation()
        ga.cross()
        self.weights = ga.weights

        if test_data:
            n_test = len(test_data)
        n = len(training_data)
        for j in range(epochs):
            ga.variation()
            ga.cross()
            self.weights = ga.weights
            random.shuffle(training_data)#洗牌，打乱list里面的元素　来实现抽取的效果
            mini_batches=[
                training_data[k:k+mini_batch_size]
                for k in range(0, n, mini_batch_size)
            ]
            for mini_batch in mini_batches:
                self.update_mini_batch(mini_batch,eta)
            if test_data:
                ga.accuracy.append(self.evaluate(test_data)/n_test)#把当前的准确率压入列表
                ga.totalWeight.append(self.weights)#把当前的权重值压入
                print("Epoch {0}: {1} / {2}".format(
                    j, self.evaluate(test_data),n_test))
            else:
                print("Epoch {0} complete".format(j))

    def update_mini_batch(self, mini_batch, eta):
        '''
        完成权重和偏向的更新
        :param mini_batch: 单个一个小块例如１００张图片 a list of tuples (x,y)
        :param eta: 学习率 learning rate
        :return:
        '''
        nabla_b = [np.zeros(b.shape) for b in self.biases] #偏向的初始化
        nabla_w = [np.zeros(w.shape) for w in self.weights]#权重的初始化

        for x, y in mini_batch:
            delta_nabla_b, delta_nabla_w = self.backprop(x,y)#b,w的偏导数　
            nabla_b = [nb+dnb for nb, dnb in zip(nabla_b,delta_nabla_b)]
            nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]

        self.weights = [w-(eta/len(mini_batch))*nw
                        for w, nw in zip(self.weights, nabla_w)]

        self.biases = [b - (eta/len(mini_batch))*nb
                        for b, nb in zip(self.biases, nabla_b)]

    def evaluate(self, test_data):
        '''
        验证正确率
        :param test_data:测试集
        :return:
        '''
        test_results = [(np.argmax(self.feedforward(x)),y)
                         for (x, y) in test_data ]
        return sum(int (x == y) for (x,y) in test_results)

    def backprop(self, x, y):
        '''

        :param x: 784维的向量
        :param y: 10维的向量
        :return: 偏重和权重的向量
        '''

        nabla_b = [np.zeros(b.shape) for b in self.biases]
        nabla_w = [np.zeros(w.shape) for w in self.weights]

        #feedworward
        activation = x #设置输入层的输出值　ａｃｔｉｖａｔｉｏｎ
        activations = [x]  #设置所有层的输出值
        zs = [] #z=wx+b  储存所有的ｚ向量　每一层

        for b, w in zip(self.biases, self.weights):
            z = np.dot(w,activation)+b
            zs.append(z)
            activation = sigmoid(z)
            activations.append(activation)

        #backward pass 这个就是第三步最后一层的输出ｅｒｒｏｒ　
        delta = self.cost_derivative(activations[-1],y)*sigmoid_derivative(zs[-1])
        nabla_b[-1] = delta # 最后一层ｃｏｓｔ函数对偏向的导数
        nabla_w[-1] = np.dot(delta,activations[-2].transpose()) #最后一层ｃｏｓｔ函数对权重的偏导
        #
        # l 是从输出层往回反　反向更新
        for l in range(2, self.num_layers):
            z = zs[-l]
            sp = sigmoid_derivative(z)
            delta =  np.dot(self.weights[-l+1].transpose(),delta)*sp
            nabla_b[-l] = delta
            nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())
        return (nabla_b, nabla_w)

    def cost_derivative(self, output_activation, y):
        '''
        :param param:
        :param y:
        :return:
        '''
        return (output_activation - y)

class EntropyCost(object):

    @staticmethod
    def fn(a,y):
        return np.sum(np.nan_to_num(-y*np.log(a)-(1-y)*np.log(1-a)))

    @staticmethod
    def delta(z, a, y):
        return (a-y)

class QuadraicCost(object):

    @staticmethod
    def fn(a,y):
        return 0.5*np.linalg.norm(a-y)**2

    @staticmethod
    def delta(z, a, y):
        return (a-y) * sigmoid_derivative(z)


if __name__ == "__main__":
    net = Network([2,3,1])
    print('num_layers:'+str(net.num_layers))
    print('sizes:'+str(net.sizes))
    # print('biases:'+str(net.biases))
    print("weight"+str(net.weights))

接下来是测试用的
这里我就不公开我的论文数据了，因为这个项目还没有结，而且是市政府的项目，我怕导师有意见。所以这里我采用Mnist的数据集

import mnist_loader
import time
from NN import *

if __name__ == "__main__":
    start = time.clock()
    training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
    training_data, validation_data, test_data = list(training_data),list(validation_data),list(test_data)
    print('training data')
    print(type(training_data))
    print(training_data[0][0].shape)
    print(training_data[0])

    print("validation data")#验证集
    print(len(validation_data))

    print("test data")
    print(len(test_data))
    print("-------------------------------")

    net = Network([784, 30,10])
    net.SGD(training_data, 30, 10, 3, test_data=test_data)

遗传神经网络经费遗传神经网络代码_Network