动机

在用稀疏自编码器对8*8 或28*28等小图像提取特征时是可行的，但是若对大图像学习整幅图像的特征，将会非常耗时。因此需要把这种“全连接”的设计改为“部分联通”的网络。

卷积

自然图像有其固有特性——图像的一部分统计特征与其他部分是一样的，这也意味着我们在这一部分上学习的特征也能用在另一部分上，所以对这个图像上的所以位置，我们都能使用同样的学习特征。
比如，可以8*8的样本中学习到一些特征，并把这个特征应用到图像的任意地方去。特别是，我们可以从8*8的样本中学习的特征跟原来的大尺寸图像做卷积。

池化

得到的卷积特征就可以去训练分类器了，但是由于卷积特征的维数很高，除了计算慢之外，还容易出现过拟合。因此把每一个的卷积特征进行池化。如卷积特征是89^2*400，表示400个特征，每个特征有89^2维。池化就是对每一特征89^2，平均分成若干固定大小不相干的块，可以用块内的平均或最大值代表，这样若分成了10块，则89^2维就变成了10维。
池化具有平移性

练习

这个部分感觉做了练习才理解的清楚了。
step1：从大图像中随机提取8*8的小块–> ZCA白化–>用SparseEncoder提取出特征。
step2:实现卷积

function convolvedFeatures = cnnConvolve(patchDim, numFeatures, images, W, b, ZCAWhite, meanPatch)
%cnnConvolve Returns the convolution of the features given by W and b with
%the given images
%
% Parameters:
%  patchDim - patch (feature) dimension
%  numFeatures - number of features
%  images - large images to convolve with, matrix in the form
%           images(r, c, channel, image number)
%  W, b - W, b for features from the sparse autoencoder
%  ZCAWhite, meanPatch - ZCAWhitening and meanPatch matrices used for
%                        preprocessing
%
% Returns:
%  convolvedFeatures - matrix of convolved features in the form
%                      convolvedFeatures(featureNum, imageNum, imageRow, imageCol)numImages = size(images, 4);
imageDim = size(images, 1);
imageChannels = size(images, 3);%convolvedFeatures = zeros(numFeatures, numImages, imageDim - patchDim + 1, imageDim - patchDim + 1);% Instructions:
%   Convolve every feature with every large image here to produce the
%   numFeatures x numImages x (imageDim - patchDim + 1) x (imageDim - patchDim + 1)
%   matrix convolvedFeatures, such that
%   convolvedFeatures(featureNum, imageNum, imageRow, imageCol) is the
%   value of the convolved featureNum feature for the imageNum image over
%   the region (imageRow, imageCol) to (imageRow + patchDim - 1, imageCol + patchDim - 1)
%
% Expected running times:
%   Convolving with 100 images should take less than 3 minutes
%   Convolving with 5000 images should take around an hour
%   (So to save time when testing, you should convolve with less images, as
%   described earlier)% -------------------- YOUR CODE HERE --------------------
% Precompute the matrices that will be used during the convolution. Recall
% that you need to take into account the whitening and mean subtraction
% stepsWT=W*ZCAWhite;
b_mean = b - WT*meanPatch;% --------------------------------------------------------convolvedFeatures = zeros(numFeatures, numImages, imageDim - patchDim + 1, imageDim - patchDim + 1);
for imageNum = 1:numImagesfor featureNum = 1:numFeatures% convolution of image with feature matrix for each channelconvolvedImage = zeros(imageDim - patchDim + 1, imageDim - patchDim + 1);for channel = 1:imageChannels% Obtain the feature (patchDim x patchDim) needed during the convolution% ---- YOUR CODE HERE ----feature = zeros(8,8); % You should replace thisoffset=(channel-1)*patchDim*patchDim;fea=WT(featureNum,offset+1:offset+patchDim*patchDim);feature=reshape(fea,patchDim,patchDim);% ------------------------% Flip the feature matrix because of the definition of convolution, as explained laterfeature = flipud(fliplr(squeeze(feature)));% Obtain the imageim = squeeze(images(:, :, channel, imageNum));% Convolve "feature" with "im", adding the result to convolvedImage% be sure to do a 'valid' convolution% ---- YOUR CODE HERE ----convolvedImage=convolvedImage+conv2( im,feature,'valid' );% ------------------------end% Subtract the bias unit (correcting for the mean subtraction as well)% Then, apply the sigmoid function to get the hidden activation% ---- YOUR CODE HERE ----convolvedImage=sigmoid(convolvedImage+b_mean(featureNum));% ------------------------% The convolved feature is the sum of the convolved values for all channelsconvolvedFeatures(featureNum, imageNum, :, :) = convolvedImage;end
endendfunction sigm=sigmoid(x)sigm=1./(1+exp(-x));
end

step3:池化

function pooledFeatures = cnnPool(poolDim, convolvedFeatures)
%cnnPool Pools the given convolved features
%
% Parameters:
%  poolDim - dimension of pooling region
%  convolvedFeatures - convolved features to pool (as given by cnnConvolve)
%                      convolvedFeatures(featureNum, imageNum, imageRow, imageCol)
%
% Returns:
%  pooledFeatures - matrix of pooled features in the form
%                   pooledFeatures(featureNum, imageNum, poolRow, poolCol)
%     numImages = size(convolvedFeatures, 2);
numFeatures = size(convolvedFeatures, 1);
convolvedDim = size(convolvedFeatures, 3);pooledFeatures = zeros(numFeatures, numImages, floor(convolvedDim / poolDim), floor(convolvedDim / poolDim));numRegin=floor(convolvedDim / poolDim);
for featureNum=1:numFeaturesfor imageNum=1:numImagesfor row=1:numReginfor col=1:numReginregin=convolvedFeatures(featureNum, imageNum,(row-1)*poolDim+1:row*poolDim,(col-1)*poolDim+1:col*poolDim);pooledFeatures(featureNum,imageNum,row,col)=mean(regin(:));endendend
end
end

深度学习笔记7 Working with Large Images 卷积特征提取相关推荐

深度学习笔记（三十一）三维卷积及卷积神经网络
一.RGB三维图像的卷积首先复习以下二维卷积运算的过程: 然后让我们看看三维图像如何进行有效的卷积运算. 计算方法和二维卷积类似,从三维图像中划分出3×3×33\times3\times33×3×3 ...
深度学习笔记（22） Padding
深度学习笔记(22) Padding 1. 卷积的缺陷 2. Padding 3. Valid卷积 4. Same卷积 5. 奇数的过滤器 1. 卷积的缺陷为了构建深度神经网络,需要学会使用的一个基 ...
下载量过百万的吴恩达机器学习和深度学习笔记更新了！（附PDF下载）
今天,我把吴恩达机器学习和深度学习课程笔记都更新了,并提供下载,这两本笔记非常适合机器学习和深度学习入门.(作者:黄海广) 0.导语我和同学将吴恩达老师机器学习和深度学习课程笔记做成了打印版,放在g ...
34.Oracle深度学习笔记——12C的AWR初步解读
34.Oracle深度学习笔记--12C的AWR初步解读关于AWR,蛤蟆也经常看.因为经常看别人给出的建议,很难有深刻体会.对此,计划花费几个晚上时间好好体会一把并记录下来.此处以单实例为例.列出目 ...
2020年Yann Lecun深度学习笔记（下）
2020年Yann Lecun深度学习笔记(下)
2020年Yann Lecun深度学习笔记（上）
2020年Yann Lecun深度学习笔记(上)
《繁凡的深度学习笔记》前言、目录大纲一文让你完全弄懂深度学习所有基础（DL笔记整理系列）
<繁凡的深度学习笔记>前言.目录大纲 (DL笔记整理系列) 一文弄懂深度学习所有基础 ! 3043331995@qq.com https://fanfansann.blog.csdn.ne ...
一文弄懂元学习 (Meta Learing)（附代码实战）《繁凡的深度学习笔记》第 15 章元学习详解（上）万字中文综述
<繁凡的深度学习笔记>第 15 章元学习详解 (上)万字中文综述(DL笔记整理系列) 3043331995@qq.com https://fanfansann.blog.csdn.net ...
一文让你完全弄懂逻辑回归和分类问题实战《繁凡的深度学习笔记》第 3 章分类问题与信息论基础（上）（DL笔记整理系列）
好吧,只好拆分为上下两篇发布了>_< 终于肝出来了,今天就是除夕夜了,祝大家新快乐!^q^ <繁凡的深度学习笔记>第 3 章分类问题与信息论基础 (上)(逻辑回归.Softm ...

深度学习笔记7 Working with Large Images 卷积特征提取

动机

卷积

池化

练习

深度学习笔记7 Working with Large Images 卷积特征提取相关推荐

最新文章

热门文章