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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卷积神经网络（三）：卷积神经网络CNN的简单实现（部分Python源码） - xuanyuansen的专栏 - 博客频道 - CSDN.NET
http://blog.csdn.net/xuanyuansen/article/details/41924377
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 plaincopy
- #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
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