卷積神經網路（三）：卷積神經網路CNN的簡單實現（部分Python原始碼）
阿新 • • 發佈：2019-01-29
上週末利用python簡單實現了一個卷積神經網路，只包含一個卷積層和一個maxpooling層，pooling層後面的多層神經網路採用了softmax形式的輸出。實驗輸入仍然採用MNIST影象使用10個feature map時，卷積和pooling的結果分別如下所示。
部分原始碼如下：
#coding=utf-8 ''' Created on 2014年11月30日 @author: Wangliaofan ''' import numpy import struct import matplotlib.pyplot as plt import math import random import copy #test 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'): #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_image shape_filter=numpy.shape(filter_map)#[k filter,row,col] 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): #rotate 180 to calculate conv 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.zeros((shape_filter[1],shape_filter[2])) 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) #print max_index #print max_index/pooling_size,max_index%pooling_size 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 #out_feature is the out put of conv 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) #print "theta",k1,row,col 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)) #the same with conv valid in matlab 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] #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 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('>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, 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('>60000B' ,buf, index) 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() #MLP.printWeightMatrix() 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) #print numpy.shape(MLP_input) DetaW,DetaB,temperror=MLP.backwardPropogation(MLP_input,ImageNum) if cIter%50 ==0 : print cIter,"Temp error: ",temperror #print numpy.shape(MLP.Theta[MLP.Nl-2]) #print numpy.shape(MLP.Ztemp[0]) #print numpy.shape(MLP.weightMatrix[0]) theta_pool=MLP.Theta[MLP.Nl-2]*MLP.weightMatrix[0].transpose() #print numpy.shape(theta_pool) #print "theta_pool",theta_pool temp=numpy.zeros((1,1,out_Di)) temp[0,:,:]=theta_pool back_theta_pool=temp.reshape(pool_shape) #print "back_theta_pool",numpy.shape(back_theta_pool) #print "back_theta_pool",back_theta_pool error_conv=backErrorfromPoolToConv(back_theta_pool,max_index_Matrix,conv_out_map,2) #print "error_conv",numpy.shape(error_conv) #print error_conv conv_DetaW,conv_DetaB=backErrorfromConvToInput(error_conv,MNISTimage[ImageNum,0,:,:]) #print "W_k",W_k #print "conv_DetaW",conv_DetaW #print "conv_DetaB",conv_DetaB temp=W_k- decayRate*conv_DetaW W_k=copy.deepcopy(temp) #print "W_k",W_k temp = B - decayRate*conv_DetaB B=copy.deepcopy(B) #print "B",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") #MNISTlabel,num2=loadMNISTimage("F:\Machine Learning\UFLDL\data\common\\train-images-idx3-ubyte",ImageNum) 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") #pool_shape = numpy.shape(out_pooling) MLP_input=out_pooling.reshape(1,1,out_Di) Atemp,Ztemp,errorsum=MLP.forwardPropogation(MLP_input,iteration) TrainPredict=Atemp[MLP.Nl-2] #print TrainPredict Plist=TrainPredict.tolist() LabelPredict=Plist[0].index(max(Plist[0])) #print "LabelPredict",LabelPredict #print "trainLabel",MLP.trainlabel[iteration] 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)) #print B 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') #plt.show() 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') #plt.show() W_k,B,MLP=simpleCNN(5,5,2,2000,10000) #MLP.printWeightMatrix() accu=getTrainAccuracy(5,5,2,4000,W_k,B,MLP) print accu pass