卷积神经网络（三）：卷积神经网络CNN的简单实现（部分Python源码）
2015-10-14 09:40
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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
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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