python一維卷積函數(shù) 一維卷積的卷積核

怎樣用python構(gòu)建一個(gè)卷積神經(jīng)網(wǎng)絡(luò)

用keras框架較為方便

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首先安裝anaconda，然后通過pip安裝keras

以下轉(zhuǎn)自wphh的博客。

#coding:utf-8

'''

GPU?run?command:

THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32?python?cnn.py

CPU?run?command:

python?cnn.py

2016.06.06更新：

這份代碼是keras開發(fā)初期寫的，當(dāng)時(shí)keras還沒有現(xiàn)在這么流行，文檔也還沒那么豐富，所以我當(dāng)時(shí)寫了一些簡單的教程。

現(xiàn)在keras的API也發(fā)生了一些的變化，建議及推薦直接上keras.io看更加詳細(xì)的教程。

'''

#導(dǎo)入各種用到的模塊組件

from?__future__?import?absolute_import

from?__future__?import?print_function

from?keras.preprocessing.image?import?ImageDataGenerator

from?keras.models?import?Sequential

from?keras.layers.core?import?Dense,?Dropout,?Activation,?Flatten

from?keras.layers.advanced_activations?import?PReLU

from?keras.layers.convolutional?import?Convolution2D,?MaxPooling2D

from?keras.optimizers?import?SGD,?Adadelta,?Adagrad

from?keras.utils?import?np_utils,?generic_utils

from?six.moves?import?range

from?data?import?load_data

import?random

import?numpy?as?np

np.random.seed(1024)??#?for?reproducibility

#加載數(shù)據(jù)

data,?label?=?load_data()

#打亂數(shù)據(jù)

index?=?[i?for?i?in?range(len(data))]

random.shuffle(index)

data?=?data[index]

label?=?label[index]

print(data.shape[0],?'?samples')

#label為0~9共10個(gè)類別，keras要求格式為binary?class?matrices,轉(zhuǎn)化一下，直接調(diào)用keras提供的這個(gè)函數(shù)

label?=?np_utils.to_categorical(label,?10)

###############

#開始建立CNN模型

###############

#生成一個(gè)model

model?=?Sequential()

#第一個(gè)卷積層，4個(gè)卷積核，每個(gè)卷積核大小5*5。1表示輸入的圖片的通道,灰度圖為1通道。

#border_mode可以是valid或者full，具體看這里說明：

#激活函數(shù)用tanh

#你還可以在model.add(Activation('tanh'))后加上dropout的技巧:?model.add(Dropout(0.5))

model.add(Convolution2D(4,?5,?5,?border_mode='valid',input_shape=(1,28,28)))?

model.add(Activation('tanh'))

#第二個(gè)卷積層，8個(gè)卷積核，每個(gè)卷積核大小3*3。4表示輸入的特征圖個(gè)數(shù)，等于上一層的卷積核個(gè)數(shù)

#激活函數(shù)用tanh

#采用maxpooling，poolsize為(2,2)

model.add(Convolution2D(8,?3,?3,?border_mode='valid'))

model.add(Activation('tanh'))

model.add(MaxPooling2D(pool_size=(2,?2)))

#第三個(gè)卷積層，16個(gè)卷積核，每個(gè)卷積核大小3*3

#激活函數(shù)用tanh

#采用maxpooling，poolsize為(2,2)

model.add(Convolution2D(16,?3,?3,?border_mode='valid'))?

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2,?2)))

#全連接層，先將前一層輸出的二維特征圖flatten為一維的。

#Dense就是隱藏層。16就是上一層輸出的特征圖個(gè)數(shù)。4是根據(jù)每個(gè)卷積層計(jì)算出來的：(28-5+1)得到24,(24-3+1)/2得到11，(11-3+1)/2得到4

#全連接有128個(gè)神經(jīng)元節(jié)點(diǎn),初始化方式為normal

model.add(Flatten())

model.add(Dense(128,?init='normal'))

model.add(Activation('tanh'))

#Softmax分類，輸出是10類別

model.add(Dense(10,?init='normal'))

model.add(Activation('softmax'))

#############

#開始訓(xùn)練模型

##############

#使用SGD?+?momentum

#model.compile里的參數(shù)loss就是損失函數(shù)(目標(biāo)函數(shù))

sgd?=?SGD(lr=0.05,?decay=1e-6,?momentum=0.9,?nesterov=True)

model.compile(loss='categorical_crossentropy',?optimizer=sgd,metrics=["accuracy"])

#調(diào)用fit方法，就是一個(gè)訓(xùn)練過程.?訓(xùn)練的epoch數(shù)設(shè)為10，batch_size為100．

#數(shù)據(jù)經(jīng)過隨機(jī)打亂shuffle=True。verbose=1，訓(xùn)練過程中輸出的信息，0、1、2三種方式都可以，無關(guān)緊要。show_accuracy=True，訓(xùn)練時(shí)每一個(gè)epoch都輸出accuracy。

#validation_split=0.2，將20%的數(shù)據(jù)作為驗(yàn)證集。

model.fit(data,?label,?batch_size=100,?nb_epoch=10,shuffle=True,verbose=1,validation_split=0.2)

"""

#使用data?augmentation的方法

#一些參數(shù)和調(diào)用的方法，請(qǐng)看文檔

datagen?=?ImageDataGenerator(

featurewise_center=True,?#?set?input?mean?to?0?over?the?dataset

samplewise_center=False,?#?set?each?sample?mean?to?0

featurewise_std_normalization=True,?#?divide?inputs?by?std?of?the?dataset

samplewise_std_normalization=False,?#?divide?each?input?by?its?std

zca_whitening=False,?#?apply?ZCA?whitening

rotation_range=20,?#?randomly?rotate?images?in?the?range?(degrees,?0?to?180)

width_shift_range=0.2,?#?randomly?shift?images?horizontally?(fraction?of?total?width)

height_shift_range=0.2,?#?randomly?shift?images?vertically?(fraction?of?total?height)

horizontal_flip=True,?#?randomly?flip?images

vertical_flip=False)?#?randomly?flip?images

#?compute?quantities?required?for?featurewise?normalization?

#?(std,?mean,?and?principal?components?if?ZCA?whitening?is?applied)

datagen.fit(data)

for?e?in?range(nb_epoch):

print('-'*40)

print('Epoch',?e)

print('-'*40)

print("Training...")

#?batch?train?with?realtime?data?augmentation

progbar?=?generic_utils.Progbar(data.shape[0])

for?X_batch,?Y_batch?in?datagen.flow(data,?label):

loss,accuracy?=?model.train(X_batch,?Y_batch,accuracy=True)

progbar.add(X_batch.shape[0],?values=[("train?loss",?loss),("accuracy:",?accuracy)]?)

"""

如何用python實(shí)現(xiàn)圖像的一維高斯濾波器

如何用python實(shí)現(xiàn)圖像的一維高斯濾波器

現(xiàn)在把卷積模板中的值換一下，不是全1了，換成一組符合高斯分布的數(shù)值放在模板里面，比如這時(shí)中間的數(shù)值最大，往兩邊走越來越小，構(gòu)造一個(gè)小的高斯包。實(shí)現(xiàn)的函數(shù)為cv2.GaussianBlur()。對(duì)于高斯模板，我們需要制定的是高斯核的高和寬（奇數(shù)），沿x與y方向的標(biāo)準(zhǔn)差(如果只給x，y=x，如果都給0，那么函數(shù)會(huì)自己計(jì)算)。高斯核可以有效的出去圖像的高斯噪聲。當(dāng)然也可以自己構(gòu)造高斯核，相關(guān)函數(shù)：cv2.GaussianKernel().

import cv2

import numpy as np

import matplotlib.pyplot as plt

img = cv2.imread(‘flower.jpg‘,0) #直接讀為灰度圖像

for i in range(2000): #添加點(diǎn)噪聲

temp_x = np.random.randint(0,img.shape[0])

temp_y = np.random.randint(0,img.shape[1])

img[temp_x][temp_y] = 255

blur = cv2.GaussianBlur(img,(5,5),0)

plt.subplot(1,2,1),plt.imshow(img,‘gray‘)#默認(rèn)彩色，另一種彩色bgr

plt.subplot(1,2,2),plt.imshow(blur,‘gray‘)

怎樣用python構(gòu)建一個(gè)卷積神經(jīng)網(wǎng)絡(luò)模型

上周末利用python簡單實(shí)現(xiàn)了一個(gè)卷積神經(jīng)網(wǎng)絡(luò)，只包含一個(gè)卷積層和一個(gè)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(gè)feature map時(shí)，卷積和pooling的結(jié)果分別如下所示。

部分源碼如下：

[python]?view plain?copy

#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?rowi_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?cItermaxIter:

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

新聞標(biāo)題：python一維卷積函數(shù) 一維卷積的卷積核
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