Python中神经网络模型 python神经网络模型运作机制

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mob6454cc70cb6b 2024-01-08 17:41:34

文章标签 Python中神经网络模型 python 神经网络深度学习 cnn 文章分类 Python 后端开发

Hopfield神经网络用python实现讲解？

神经网络结构具有以下三个特点：神经元之间全连接，并且为单层神经网络。每个神经元既是输入又是输出，导致得到的权重矩阵相对称，故可节约计算量。

在输入的激励下，其输出会产生不断的状态变化，这个反馈过程会一直反复进行。

假如Hopfield神经网络是一个收敛的稳定网络，则这个反馈与迭代的计算过程所产生的变化越来越小，一旦达到了稳定的平衡状态，Hopfield网络就会输出一个稳定的恒值。

Hopfield网络可以储存一组平衡点，使得当给定网络一组初始状态时，网络通过自行运行而最终收敛于这个设计的平衡点上。

当然，根据热力学上，平衡状态分为stable state和metastable state, 这两种状态在网络的收敛过程中都是非常可能的。为递归型网络，t时刻的状态与t-1时刻的输出状态有关。

之后的神经元更新过程也采用的是异步更新法(Asynchronous)。Hopfield神经网络用python实现。

谷歌人工智能写作项目：神经网络伪原创

Python中神经网络模型 python神经网络模型运作机制_cnn

怎么用python训练神经网络

如何用 Python 构建神经网络择时模型

import mathimport random(0)def rand(a,b): #随机函数return (b-a)*random.random()+adef make_matrix(m,n,fill=0.0):#创建一个指定大小的矩阵mat = []for i in range(m):mat.append([fill]*n)return mat#定义sigmoid函数和它的导数def sigmoid(x):return 1.0/((-x))def sigmoid_derivate(x):return x*(1-x) #sigmoid函数的导数class BPNeuralNetwork:def __init__(self):#初始化变量self.input_n = 0self.hidden_n = 0self.output_n = 0self.input_cells = []self.hidden_cells = []self.output_cells = []self.input_weights = []self.output_weights = []self.input_correction = []self.output_correction = []#三个列表维护：输入层，隐含层，输出层神经元def setup(self,ni,nh,no):self.input_n = ni+1 #输入层+偏置项self.hidden_n = nh #隐含层self.output_n = no #输出层#初始化神经元self.input_cells = [1.0]*self.input_nself.hidden_cells= [1.0]*self.hidden_nself.output_cells= [1.0]*self.output_n#初始化连接边的边权self.input_weights = make_matrix(self.input_n,self.hidden_n) #邻接矩阵存边权：输入层->隐藏层self.output_weights = make_matrix(self.hidden_n,self.output_n) #邻接矩阵存边权：隐藏层->输出层#随机初始化边权：为了反向传导做准备--->随机初始化的目的是使对称失效for i in range(self.input_n):for h in range(self.hidden_n):self.input_weights[i][h] = rand(-0.2 , 0.2) #由输入层第i个元素到隐藏层第j个元素的边权为随机值for h in range(self.hidden_n):for o in range(self.output_n):self.output_weights[h][o] = rand(-2.0, 2.0) #由隐藏层第i个元素到输出层第j个元素的边权为随机值#保存校正矩阵，为了以后误差做调整self.input_correction = make_matrix(self.input_n , self.hidden_n)self.output_correction = make_matrix(self.hidden_n,self.output_n)#输出预测值def predict(self,inputs):#对输入层进行操作转化样本for i in range(self.input_n-1):self.input_cells[i] = inputs[i] #n个样本从0~n-1#计算隐藏层的输出，每个节点最终的输出值就是权值*节点值的加权和for j in range(self.hidden_n):total = 0.0for i in range(self.input_n):total+=self.input_cells[i]*self.input_weights[i][j]# 此处为何是先i再j，以隐含层节点做大循环，输入样本为小循环，是为了每一个隐藏节点计算一个输出值，传输到下一层self.hidden_cells[j] = sigmoid(total) #此节点的输出是前一层所有输入点和到该点之间的权值加权和for k in range(self.output_n):total = 0.0for j in range(self.hidden_n):total+=self.hidden_cells[j]*self.output_weights[j][k]self.output_cells[k] = sigmoid(total) #获取输出层每个元素的值return self.output_cells[:] #最后输出层的结果返回#反向传播算法：调用预测函数，根据反向传播获取权重后前向预测，将结果与实际结果返回比较误差def back_propagate(self,case,label,learn,correct):#对输入样本做预测self.predict(case) #对实例进行预测output_deltas = [0.0]*self.output_n #初始化矩阵for o in range(self.output_n):error = label[o] - self.output_cells[o] #正确结果和预测结果的误差：0,1，-1output_deltas[o]= sigmoid_derivate(self.output_cells[o])*error#误差稳定在0~1内#隐含层误差hidden_deltas = [0.0]*self.hidden_nfor h in range(self.hidden_n):error = 0.0for o in range(self.output_n):error+=output_deltas[o]*self.output_weights[h][o]hidden_deltas[h] = sigmoid_derivate(self.hidden_cells[h])*error#反向传播算法求W#更新隐藏层->输出权重for h in range(self.hidden_n):for o in range(self.output_n):change = output_deltas[o]*self.hidden_cells[h]#调整权重：上一层每个节点的权重学习*变化+矫正率self.output_weights[h][o] += learn*change + correct*self.output_correction[h][o]#更新输入->隐藏层的权重for i in range(self.input_n):for h in range(self.hidden_n):change = hidden_deltas[h]*self.input_cells[i]self.input_weights[i][h] += learn*change + correct*self.input_correction[i][h]self.input_correction[i][h] = change#获取全局误差error = 0.0for o in range(len(label)):error = 0.5*(label[o]-self.output_cells[o])**2 #平方误差函数return errordef train(self,cases,labels,limit=10000,learn=0.05,correct=0.1):for i in range(limit): #设置迭代次数error = 0.0for j in range(len(cases)):#对输入层进行访问label = labels[j]case = cases[j]error+=self.back_propagate(case,label,learn,correct) #样例，标签，学习率，正确阈值def test(self): #学习异或cases = [[0, 0],[0, 1],[1, 0],[1, 1],] #测试样例labels = [[0], [1], [1], [0]] #标签self.setup(2,5,1) #初始化神经网络：输入层，隐藏层，输出层元素个数self.train(cases,labels,10000,0.05,0.1) #可以更改for case in cases:print(self.predict(case))if __name__ == '__main__':nn = BPNeuralNetwork()()。

怎样用python构建一个卷积神经网络模型

上周末利用python简单实现了一个卷积神经网络，只包含一个卷积层和一个maxpooling层，pooling层后面的多层神经网络采用了softmax形式的输出。

实验输入仍然采用MNIST图像使用10个feature map时，卷积和pooling的结果分别如下所示。

部分源码如下：[python] view plain copy#coding=utf-8'''''Created on 2014年11月30日@author: Wangliaofan'''import numpyimport structimport matplotlib.pyplot as pltimport mathimport randomimport copy#testfrom BasicMultilayerNeuralNetwork import BMNN2def sigmoid(inX):if (-inX)== 0.0:return 999999999.999999999return 1.0/((-inX))def difsigmoid(inX):return sigmoid(inX)*(1.0-sigmoid(inX))def tangenth(inX):return (1.0*(inX)-1.0*(-inX))/(1.0*(inX)+1.0*(-inX))def cnn_conv(in_image, filter_map,B,type_func='sigmoid'):#in_image[num,feature map,row,col]=>in_image[Irow,Icol]#features map[k filter,row,col]#type_func['sigmoid','tangenth']#out_feature[k filter,Irow-row+1,Icol-col+1]shape_image=numpy.shape(in_image)#[row,col]#print "shape_image",shape_imageshape_filter=numpy.shape(filter_map)#[k filter,row,col]if shape_filter[1]>shape_image[0] or shape_filter[2]>shape_image[1]:raise Exceptionshape_out=(shape_filter[0],shape_image[0]-shape_filter[1]+1,shape_image[1]-shape_filter[2]+1)out_feature=numpy.zeros(shape_out)k,m,n=numpy.shape(out_feature)for k_idx in range(0,k):#rotate 180 to calculate convc_filter=numpy.rot90(filter_map[k_idx,:,:], 2)for r_idx in range(0,m):for c_idx in range(0,n):#conv_temp=numpy.zeros((shape_filter[1],shape_filter[2]))(in_image[r_idx:r_idx+shape_filter[1],c_idx:c_idx+shape_filter[2]],c_filter)(conv_temp)if type_func=='sigmoid':out_feature[k_idx,r_idx,c_idx]=sigmoid(sum_temp+B[k_idx])elif type_func=='tangenth':out_feature[k_idx,r_idx,c_idx]=tangenth(sum_temp+B[k_idx])else:raise Exceptionreturn out_featuredef cnn_maxpooling(out_feature,pooling_size=2,type_pooling="max"):k,row,col=numpy.shape(out_feature)max_index_Matirx=numpy.zeros((k,row,col))out_row=int(numpy.floor(row/pooling_size))out_col=int(numpy.floor(col/pooling_size))out_pooling=numpy.zeros((k,out_row,out_col))for k_idx in range(0,k):for r_idx in range(0,out_row):for c_idx in range(0,out_col):temp_matrix=out_feature[k_idx,pooling_size*r_idx:pooling_size*r_idx+pooling_size,pooling_size*c_idx:pooling_size*c_idx+pooling_size]out_pooling[k_idx,r_idx,c_idx](temp_matrix)max_index=numpy.argmax(temp_matrix)#print max_index#print max_index/pooling_size,max_index%pooling_sizemax_index_Matirx[k_idx,pooling_size*r_idx+max_index/pooling_size,pooling_size*c_idx+max_index%pooling_size]=1return out_pooling,max_index_Matirxdef poolwithfunc(in_pooling,W,B,type_func='sigmoid'):k,row,col=numpy.shape(in_pooling)out_pooling=numpy.zeros((k,row,col))for k_idx in range(0,k):for r_idx in range(0,row):for c_idx in range(0,col):out_pooling[k_idx,r_idx,c_idx]=sigmoid(W[k_idx]*in_pooling[k_idx,r_idx,c_idx]+B[k_idx])return out_pooling#out_feature is the out put of convdef backErrorfromPoolToConv(theta,max_index_Matirx,out_feature,pooling_size=2):k1,row,col=numpy.shape(out_feature)error_conv=numpy.zeros((k1,row,col))k2,theta_row,theta_col=numpy.shape(theta)if k1!=k2:raise Exceptionfor idx_k in range(0,k1):for idx_row in range( 0, row):for idx_col in range( 0, col):error_conv[idx_k,idx_row,idx_col]=\max_index_Matirx[idx_k,idx_row,idx_col]*\float(theta[idx_k,idx_row/pooling_size,idx_col/pooling_size])*\difsigmoid(out_feature[idx_k,idx_row,idx_col])return error_convdef backErrorfromConvToInput(theta,inputImage):k1,row,col=numpy.shape(theta)#print "theta",k1,row,coli_row,i_col=numpy.shape(inputImage)if row>i_row or col> i_col:raise Exceptionfilter_row=i_row-row+1filter_col=i_col-col+1detaW=numpy.zeros((k1,filter_row,filter_col))#the same with conv valid in matlabfor k_idx in range(0,k1):for idx_row in range(0,filter_row):for idx_col in range(0,filter_col):subInputMatrix=inputImage[idx_row:idx_row+row,idx_col:idx_col+col]#print "subInputMatrix",numpy.shape(subInputMatrix)#rotate theta 180#print numpy.shape(theta)theta_rotate=numpy.rot90(theta[k_idx,:,:], 2)#print "theta_rotate",theta_rotate(subInputMatrix,theta_rotate)detaW[k_idx,idx_row,idx_col](dotMatrix)detaB=numpy.zeros((k1,1))for k_idx in range(0,k1):detaB[k_idx](theta[k_idx,:,:])return detaW,detaBdef loadMNISTimage(absFilePathandName,datanum=60000):images=open(absFilePathandName,'rb')()index=0magic, numImages , numRows , numColumns = struct.unpack_from('>IIII' , buf , index)print magic, numImages , numRows , numColumnsindex += struct.calcsize('>IIII')if magic != 2051:raise Exceptiondatasize=int(784*datanum)datablock=">"+str(datasize)+"B"#nextmatrix=struct.unpack_from('>47040000B' ,buf, index)nextmatrix=struct.unpack_from(datablock ,buf, index)nextmatrix=numpy.array(nextmatrix)/255.0#nextmatrix=nextmatrix.reshape(numImages,numRows,numColumns)#nextmatrix=nextmatrix.reshape(datanum,1,numRows*numColumns)nextmatrix=nextmatrix.reshape(datanum,1,numRows,numColumns)return nextmatrix, numImagesdef loadMNISTlabels(absFilePathandName,datanum=60000):labels=open(absFilePathandName,'rb')()index=0magic, numLabels = struct.unpack_from('>II' , buf , index)print magic, numLabelsindex += struct.calcsize('>II')if magic != 2049:raise Exceptiondatablock=">"+str(datanum)+"B"#nextmatrix=struct.unpack_from('>60000B' ,buf, index)nextmatrix=struct.unpack_from(datablock ,buf, index)nextmatrix=numpy.array(nextmatrix)return nextmatrix, numLabelsdef simpleCNN(numofFilter,filter_size,pooling_size=2,maxIter=1000,imageNum=500):decayRate=0.01MNISTimage,num1=loadMNISTimage("F:\Machine Learning\UFLDL\data\common\\train-images-idx3-ubyte",imageNum)print num1row,col=numpy.shape(MNISTimage[0,0,:,:])out_Di=numofFilter*((row-filter_size+1)/pooling_size)*((col-filter_size+1)/pooling_size)MLP=BMNN2.MuiltilayerANN(1,[128],out_Di,10,maxIter)MLP.setTrainDataNum(imageNum)MLP.loadtrainlabel("F:\Machine Learning\UFLDL\data\common\\train-labels-idx1-ubyte")MLP.initialweights()#MLP.printWeightMatrix()rng = numpy.random.RandomState(23455)W_shp = (numofFilter, filter_size, filter_size)W_bound = (numofFilter * filter_size * filter_size)W_k=rng.uniform(low=-1.0 / W_bound,high=1.0 / W_bound,size=W_shp)B_shp = (numofFilter,)B= numpy.asarray(rng.uniform(low=-.5, high=.5, size=B_shp))cIter=0while cIter。