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卷积神经网络（三）：卷积神经网络CNN的简单实现（部分Python源码）
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卷積神經(jīng)網(wǎng)絡(luò)（三）：卷積神經(jīng)網(wǎng)絡(luò)CNN的簡單實(shí)現(xiàn)（部分Python源碼） - xuanyuansen的專欄 - 博客頻道 - CSDN.NET
http://blog.csdn.net/xuanyuansen/article/details/41924377
上周末利用python簡單實(shí)現(xiàn)了一個卷積神經(jīng)網(wǎng)絡(luò)，只包含一個卷積層和一個maxpooling層，pooling層后面的多層神經(jīng)網(wǎng)絡(luò)采用了softmax形式的輸出。實(shí)驗(yàn)輸入仍然采用MNIST圖像使用10個feature map時，卷積和pooling的結(jié)果分別如下所示。
部分源碼如下：
[python]?view plaincopy
?? ''? ? ? ?? import?numpy?? import?struct?? import?matplotlib.pyplot?as?plt?? import?math?? import?random?? import?copy?? ?? from?BasicMultilayerNeuralNetwork?import?BMNN2?? ?? ?? def?sigmoid(inX):?? ????if?1.0+numpy.exp(-inX)==?0.0:?? ????????return?999999999.999999999?? ????return?1.0/(1.0+numpy.exp(-inX))?? def?difsigmoid(inX):?? ????return?sigmoid(inX)*(1.0-sigmoid(inX))?? def?tangenth(inX):?? ????return?(1.0*math.exp(inX)-1.0*math.exp(-inX))/(1.0*math.exp(inX)+1.0*math.exp(-inX))?? ?? def?cnn_conv(in_image,?filter_map,B,type_func='sigmoid'):?? ?????? ?????? ?????? ?????? ????shape_image=numpy.shape(in_image)?? ?????? ????shape_filter=numpy.shape(filter_map)?? ????if?shape_filter[1]>shape_image[0]?or?shape_filter[2]>shape_image[1]:?? ????????raise?Exception?? ????shape_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):?? ?????????? ????????c_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.dot(in_image[r_idx:r_idx+shape_filter[1],c_idx:c_idx+shape_filter[2]],c_filter)?? ????????????????sum_temp=numpy.sum(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?Exception???????? ????return?out_feature?? ?? def?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]=numpy.amax(temp_matrix)?? ????????????????max_index=numpy.argmax(temp_matrix)?? ?????????????????? ?????????????????? ????????????????max_index_Matirx[k_idx,pooling_size*r_idx+max_index/pooling_size,pooling_size*c_idx+max_index%pooling_size]=1?? ????return?out_pooling,max_index_Matirx?? ?? def?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?? ?? def?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?Exception?? ????for?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_conv?? ?? def?backErrorfromConvToInput(theta,inputImage):?? ????k1,row,col=numpy.shape(theta)?? ?????? ????i_row,i_col=numpy.shape(inputImage)?? ????if?row>i_row?or?col>?i_col:?? ????????raise?Exception?? ????filter_row=i_row-row+1?? ????filter_col=i_col-col+1?? ????detaW=numpy.zeros((k1,filter_row,filter_col))?? ?????? ????for?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]?? ?????????????????? ?????????????????? ?????????????????? ????????????????theta_rotate=numpy.rot90(theta[k_idx,:,:],?2)?? ?????????????????? ????????????????dotMatrix=numpy.dot(subInputMatrix,theta_rotate)?? ????????????????detaW[k_idx,idx_row,idx_col]=numpy.sum(dotMatrix)?? ????detaB=numpy.zeros((k1,1))?? ????for?k_idx?in?range(0,k1):?? ????????detaB[k_idx]=numpy.sum(theta[k_idx,:,:])?? ????return?detaW,detaB?? ?? def?loadMNISTimage(absFilePathandName,datanum=60000):?? ????images=open(absFilePathandName,'rb')?? ????buf=images.read()?? ????index=0?? ????magic,?numImages?,?numRows?,?numColumns?=?struct.unpack_from('>IIII'?,?buf?,?index)?? ????print?magic,?numImages?,?numRows?,?numColumns?? ????index?+=?struct.calcsize('>IIII')?? ????if?magic?!=?2051:?? ????????raise?Exception?? ????datasize=int(784*datanum)?? ????datablock=">"+str(datasize)+"B"?? ?????? ????nextmatrix=struct.unpack_from(datablock?,buf,?index)?? ????nextmatrix=numpy.array(nextmatrix)/255.0?? ?????? ?????? ????nextmatrix=nextmatrix.reshape(datanum,1,numRows,numColumns)???? ????return?nextmatrix,?numImages?? ?????? def?loadMNISTlabels(absFilePathandName,datanum=60000):?? ????labels=open(absFilePathandName,'rb')?? ????buf=labels.read()?? ????index=0?? ????magic,?numLabels??=?struct.unpack_from('>II'?,?buf?,?index)?? ????print?magic,?numLabels?? ????index?+=?struct.calcsize('>II')?? ????if?magic?!=?2049:?? ????????raise?Exception?? ?????? ????datablock=">"+str(datanum)+"B"?? ?????? ????nextmatrix=struct.unpack_from(datablock?,buf,?index)?? ????nextmatrix=numpy.array(nextmatrix)?? ????return?nextmatrix,?numLabels?? ?? def?simpleCNN(numofFilter,filter_size,pooling_size=2,maxIter=1000,imageNum=500):?? ????decayRate=0.01?? ????MNISTimage,num1=loadMNISTimage("F:\Machine?Learning\UFLDL\data\common\\train-images-idx3-ubyte",imageNum)?? ????print?num1?? ????row,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()?? ?????? ????rng?=?numpy.random.RandomState(23455)?? ????W_shp?=?(numofFilter,?filter_size,?filter_size)?? ????W_bound?=?numpy.sqrt(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=0?? ????while?cIter<maxIter:?? ????????cIter?+=?1?? ????????ImageNum=random.randint(0,imageNum-1)?? ????????conv_out_map=cnn_conv(MNISTimage[ImageNum,0,:,:],?W_k,?B,"sigmoid")?? ????????out_pooling,max_index_Matrix=cnn_maxpooling(conv_out_map,2,"max")?? ????????pool_shape?=?numpy.shape(out_pooling)?? ????????MLP_input=out_pooling.reshape(1,1,out_Di)?? ?????????? ????????DetaW,DetaB,temperror=MLP.backwardPropogation(MLP_input,ImageNum)?? ????????if?cIter%50?==0?:?? ????????????print?cIter,"Temp?error:?",temperror?? ?????????? ?????????? ?????????? ????????theta_pool=MLP.Theta[MLP.Nl-2]*MLP.weightMatrix[0].transpose()?? ?????????? ?????????? ????????temp=numpy.zeros((1,1,out_Di))?? ????????temp[0,:,:]=theta_pool?? ????????back_theta_pool=temp.reshape(pool_shape)?? ?????????? ?????????? ????????error_conv=backErrorfromPoolToConv(back_theta_pool,max_index_Matrix,conv_out_map,2)?? ?????????? ?????????? ????????conv_DetaW,conv_DetaB=backErrorfromConvToInput(error_conv,MNISTimage[ImageNum,0,:,:])?? ?????????? ?????????? ?????????? ????????temp=W_k-?decayRate*conv_DetaW?? ????????W_k=copy.deepcopy(temp)?? ?????????? ????????temp?=?B?-?decayRate*conv_DetaB?? ????????B=copy.deepcopy(B)?? ?????????? ????????MLP.updatePara(DetaW,?DetaB,?1)?? ????return?W_k,B,MLP?? def?getTrainAccuracy(numofFilter,filter_size,pooling_size,ImageNum,W_k,B,MLP):?? ????MNISTimage,num1=loadMNISTimage("F:\Machine?Learning\UFLDL\data\common\\train-images-idx3-ubyte",ImageNum)?? ????MLP.setTrainDataNum(ImageNum)?? ????MLP.loadtrainlabel("F:\Machine?Learning\UFLDL\data\common\\train-labels-idx1-ubyte")?? ?????? ????row,col=numpy.shape(MNISTimage[0,0,:,:])?? ????iteration=0?? ????out_Di=numofFilter*((row-filter_size+1)/pooling_size)*((col-filter_size+1)/pooling_size)?? ????accuracycount=0?? ????while?iteration<ImageNum:?? ????????conv_out_map=cnn_conv(MNISTimage[iteration,0,:,:],?W_k,?B,"sigmoid")?? ????????out_pooling,max_index_Matrix=cnn_maxpooling(conv_out_map,2,"max")?? ?????????? ????????MLP_input=out_pooling.reshape(1,1,out_Di)?? ????????Atemp,Ztemp,errorsum=MLP.forwardPropogation(MLP_input,iteration)?? ????????TrainPredict=Atemp[MLP.Nl-2]?? ?????????? ????????Plist=TrainPredict.tolist()?? ????????LabelPredict=Plist[0].index(max(Plist[0]))?? ?????????? ?????????? ????????if?int(LabelPredict)?==?int(MLP.trainlabel[iteration]):?? ????????????accuracycount?+=?1?? ????????iteration?+=?1?? ????????if?iteration%50?==0?:?? ????????????print?iteration?? ????print?"accuracy:",?float(accuracycount)/float(ImageNum)?? ????return??float(accuracycount)/float(ImageNum)?? ?????? if?__name__?==?'__main__':?? ????MNISTimage,num1=loadMNISTimage("F:\Machine?Learning\UFLDL\data\common\\train-images-idx3-ubyte",1)?? ????MNISTlabel,num2=loadMNISTlabels("F:\Machine?Learning\UFLDL\data\common\\train-labels-idx1-ubyte",1)?? ????fig1?=?plt.figure("convolution")?? ????k=10?? ????filter_size=5?? ????rng?=?numpy.random.RandomState(23455)?? ????w_shp?=?(k,?filter_size,?filter_size)?? ????w_bound?=?numpy.sqrt(k?*?filter_size?*?filter_size)?? ????w_k=rng.uniform(low=-1.0?/?w_bound,high=1.0?/?w_bound,size=w_shp)?? ????B_shp?=?(k,)?? ????B=?numpy.asarray(rng.uniform(low=-.5,?high=.5,?size=B_shp))?? ?????? ????out_map=cnn_conv(MNISTimage[0,0,:,:],?w_k,?B,"sigmoid")?? ????for?idx?in?range(0,10):?? ????????plotwindow?=?fig1.add_subplot(2,5,idx+1)?? ????????plt.imshow(out_map[idx,:,:],?cmap='gray')?? ?????? ????fig2?=?plt.figure("max-pooling")?? ????out_pooling,max_index=cnn_maxpooling(out_map)?? ????for?idx?in?range(0,10):?? ????????plotwindow?=?fig2.add_subplot(2,5,idx+1)?? ????????plt.imshow(out_pooling[idx,:,:],?cmap='gray')?? ?????????? ????W_pool_shp?=?(k,)?? ????W_pool=?numpy.asarray(rng.uniform(low=-1,?high=1,?size=W_pool_shp))?? ????B_pool_shp?=?(k,)?? ????B_pool=?numpy.asarray(rng.uniform(low=-.5,?high=.5,?size=B_pool_shp))?? ????fig3?=?plt.figure("pooling")?? ????pooling=poolwithfunc(out_pooling,?W_pool,?B_pool)?? ????for?idx?in?range(0,10):?? ????????plotwindow?=?fig3.add_subplot(2,5,idx+1)?? ????????plt.imshow(pooling[idx,:,:],?cmap='gray')?? ?????? ?????? ????W_k,B,MLP=simpleCNN(5,5,2,2000,10000)?? ?????? ????accu=getTrainAccuracy(5,5,2,4000,W_k,B,MLP)?? ????print?accu?? ????pass??
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