前言
理论知识：UFLDL教程和http://www.cnblogs.com/tornadomeet/archive/2013/04/09/3009830.html
实验环境：win7， matlab2015b，16G内存，2T机械硬盘
实验内容：Exercise:Convolution and Pooling。从2000张64*64的RGB图片（它是the STL10 Dataset的一个子集）中提取特征作为训练数据集，训练softmax分类器，然后从3200张64*64的RGB图片（它是the STL10 Dataset的另一个子集）中提取特征作为测试数据集，输入到前面已经训练好的softmax分类器，把这3200张图片分为4类：airplane, car, cat, dog。
实验基础说明：
1.怎么样从2000张64*64的RGB图片中提取特征得到训练集，怎么样从3200张64*64的RGB图片中提取特征得到测试集？
因为这里的RGB图片是64*64，尺寸较大，而不是前面所有实验中用的8*8的小图像块，如果用Deep Learning九之深度学习UFLDL教程：linear decoder_exercise（斯坦福大学深度学习教程）中的方法直接从大尺寸图片中提取特征，那么运算量就太大，所以，为了减小运算量，不能直接从大尺寸图片中提取特征，而是要用间接的减小运算量的方法，这个方法利用了自然图像的固有特性：自然图像的一部分的统计特性与其他部分是一样的。这个固有特性也就意味着：从自然图像的某一部分A中学习的特征L_feature也能用在该自然图像的另一部分B上。也就是说，对于这个自然图像上的所有位置，我们都能使用同样的学习特征。那么究竟怎么样从这个大尺寸自然图像提取出它的L_feature特征呢（这个特征是从它的一部分A上学习到的）？答案是卷积！即把这个特征和大尺寸图片相卷，就可以提取这个大尺寸图片的L_feature特征，至于原因请看下面。这个L_feature特征维数只会比大尺寸图片稍微小一点（假设从小图像块中学习到的特征是8*8，大尺寸图片是64*64，那么这个L_feature特征就是（64－8+1）*（64－8+1），即：57*57），如果把这些特征作训练数据集，那么运算量还是很大，输入层神经元节点数还是要57*57*3个，所以我们再对这些特征用池化的方法（即：假设池化的维数是19，池化就是把57*57的特征依次分为19*19的9个小部分，然后把每个小部分变为一个值（如果这个值是每个小部分的平均值就是平均池化，是最大值就是最大池化），从而把这个57*57的特征变为了3*3的特征），从而最后从2000张64*64的RGB图片中提取出了3*3的特征得到了训练数据集，同理，可得到测试数据集。
具体方法：
①是从the STL10 Dataset中随机抽样选出10万张8*8的RGB小图像块（即：Sampled 8×8 patches from the STL-10 dataset），然后对它们进行预处理，具体包括：先对它们进行0均值化（注意：不是每个样本各自单独0均值化），再对它们进行ZCA白化。
②是对预处理后的数据利用线性解码器提取出M个颜色特征。
前两步已经在Deep Learning九之深度学习UFLDL教程：linear decoder_exercise（斯坦福大学深度学习教程）中实现。
③是把2000张64*64的 RGB图片中的每一张图片都分别与第二步中提取出的M个特征相卷积，从而就能在每张64*64的 RGB图片中都提取出在第二步中学习到的M个特征，共得到2000*M个卷积特征。
④是把这2000*M个特征进行池化，减小它的维数，从而得到了训练数据集。同理，可得到测试数据集。
后两步就是本节实验的内容。
2.为什么从自然图像上的一部分A提取出L_feature特征后，要提取这整张自然图像的L_feature特征的方法是：把这个特征和这个自然图像相卷积？
首先，我们要明确知道：
① 卷积运算图解：
② 从数据集Y中提取特征F（F是从数据集X中通过稀疏自动编码器提取到的特征）的方法：
如果用数据集X训练稀疏自动编码器，得到稀疏自动编码器的权重参数是opttheta，从而就提取到特征F，F就是稀疏自动编码器的激活值，即F＝sigmod（X*opttheta），而把数据集Y通过该训练过稀疏自动编码器得到的激活值就是从数据集Y中提取的特征F，即：F＝sigmod（Y*opttheta）。
这一点实际上已经在Deep Learning七之深度学习UFLDL教程：Self-Taught Learning_Exercise（斯坦福大学深度学习教程）的“实验内容及步骤”中的第一、二点提到。
③ 我们要清楚从自然图像的某一部分A中提取L_feature特征的方法是线性解码器，它的第一层实际上是一个稀疏自动编码器（假设用A来训练该稀疏自动编码器得到其网络参数为opttheta1），我们说的这个L_feature特征实际上就是这个第一层稀疏自动编码器的激活值，即：L_feature＝sigmod（A*opttheta1）。
其次，在清楚以上三点的情况下，我们才能进行如下说明：
假设这个L_feature特征大小是8*8，要从这整张自然图像中提取L_feature特征的方法是：从这整张自然图像上依次抽取8*8区域Q通过前面提到的网络参数为opttheta1的稀疏自动编码器，即可得到从Q上提取的L_feature特征，即为其激活值：L_feature＝sigmod（Q*opttheta1）。这些所有8*8区域提取的L_feature特征组合在一起，就是这整张自然图像上提取的L_feature特征。这个过程就是Ng在讲解中说明的，把这个L_feature特征作为探测器，应用到这个图像的任意地方中去的过程。这个过程如下：
而这以上整个过程，基本正好符合卷积运算，所以我们把得到特征叫卷积特征，即：这个过程基本正好是opttheta1与整张自然图像的卷积过程，只两个不同之处：
a. 卷积运算时，opttheta1的倒序依次与区域Q相乘，而我们实际计算L_feature特征时opttheta1不是倒序的。所以为了能直接运用卷积，我们可先把opttheta1倒序再与整张自然图像进行卷积，得到的就正好是L_feature特征。所以，在cnnConvolve.m中的cnnConvolve函数有这句代码来倒序：
feature = rot90(squeeze(feature),2);
当然，Ng用的是这句：
feature = flipud(fliplr(squeeze(feature)));
相比起来， rot90的运行速度更快，我在这里做了改进。
b. 整个卷积运算过程实际上还包含了使用边缘补 0 部分进行计算的卷积结果部分，而我们并不需要这个部分，所以我们在cnnConvolve.m中的cnnConvolve函数中有：
convolvedImage = convolvedImage + conv2(im, feature, ‘valid’);
参数valid使返回在卷积过程中，未使用边缘补 0 部分进行计算的卷积结果部分。
综上，所以把这个特征和这个自然图像相卷积即可提取这整张自然图像的L_feature特征。
3.一些matlab函数
squeeze: 移除单一维
使用方法 ：B=squeeze(A)
返回和矩阵A相同元素但所有单一维都移除的矩阵B，单一维是满足size(A,dim)=1的维。
squeeze命令对二维数组是不起作用的;
如果A是一行或列向量或一标量(1*1)值，则B=A。
例如：2*1*3 数组Y = rand(2,1,3). 这个数组有单一维 —就是每页仅仅一列：
Y =
Y(:,:,1) =
0.5194
0.8310
Y(:,:,2) =
0.0346
0.0535
Y(:,:,3) =
0.5297
0.6711
命令Z = squeeze(Y)结果是2*3矩阵:
Z =
0.5194 0.0346 0.5297
0.8310 0.0535 0.6711
rot90(X)
Ng教程中用的是：W = flipud(fliplr(W));
这个函数可用rot90(W,2)代替，因为它的运行速度更快。估计是Ng写这个教程的时候在2011年，rot90这个函数在matlab中还没出现，好像是在2012年才出现的。
用法：rot90(X)，其中X表示一个矩阵。
功能：rot90函数是matlab中使一个矩阵逆时针旋转90度的函数。Y=rot90(X)表示使矩阵X逆时针旋转90度，作为新的矩阵Y，但矩阵X本身不变。
rot90(x,2),其中X表示一个矩阵。功能：将矩阵x旋转180度，形成新的矩阵，但x本身不变。
rot90(x,n),其中x表示一个矩阵，n为正整数，默认功能：将矩阵x逆时针旋转90*n度，形成新矩阵，x本身不变。
conv2
格式：C=conv2(A,B)
C=conv2(Hcol,Hrow,A)
C=conv2(…,’shape’)
说明：
C=conv2(A,B) ，conv2 的算矩阵 A 和 B 的卷积，若 [Ma,Na]＝size(A), [Mb,Nb]=size(B), 则 size(C)=[Ma+Mb-1,Na+Nb-1];
C=conv2(Hcol,Hrow,A) 中，矩阵 A 分别与 Hcol 向量在列方向和 Hrow 向量在行方向上进行卷积；
C=conv2(…,’shape’) 用来指定 conv2返回二维卷积结果部分，参数 shape 可取值如下：
full 为缺省值，返回二维卷积的全部结果；
same 返回二维卷积结果中与 A 大小相同的中间部分；
valid 返回在卷积过程中，未使用边缘补 0 部分进行计算的卷积结果部分，当 size(A)>size(B)时，size(C)=[Ma-Mb+1,Na-Nb+1]
permute
语法格式：
B = permute(A,order)
按照向量order指定的顺序重排A的各维。B中元素和A中元素完全相同。但由于经过重新排列，在A、B访问同一个元素使用的下标就不一样了。order中的元素必须各不相同
三维：
a=rand(2,3,4); %这是一个三维数组，各维的长度分别为：2，3，4
%现在交换第一维和第二维：
permute(A,[2,1,3]) %变成3*2*4的矩阵
二维：
二维的更形象，a=[1,2+j;3+2*j,4+5*j];permute(a,[2,1])，相当于把行(x)、列(y)互换；有别于转置(a’),你试一下就知道了。所以就叫非共轭转置。
4.优秀的编程技巧：
①在Ng的代码中，总是有检查的习惯，无论是前面的梯度计算还是本节实验中的卷积和池化等，Ng都会在计算完后想办法来验证前面的计算是否正确，这是一个良好的习惯，起码可以保证这些关键步骤没有错误。
②可用类似语句来检查代码：
assert(mod(hiddenSize, stepSize) == 0, ‘stepSize should divide hiddenSize’);
以及
if abs(features(featureNum, 1) – convolvedFeatures(featureNum, imageNum, imageRow, imageCol)) > 1e-9
fprintf(‘Convolved feature does not match activation from autoencoder\n’);
end
5.
代价函数 
为：
，其中 
计算梯度需要用到的公式：
，其中y是期望的输出。
其中， 
令 
, 
疑问
1.从代码中可看出，为什么对10万张小图像块要经过预处理（0均值化和ZCA白化），而对2000张和3200张64*64RGB图片却未进行预处理？感觉自己对什么时候该进行预处理，什么时候不用进行预处理，为什么这样，都没完全掌握！比如：在Deep Learning四之深度学习UFLDL教程：PCA in 2D_Exercise（斯坦福大学深度学习教程）中为什么二维数据不用进行0均值化，而自然图像就要先0均值化？
实验步骤
1.初始化参数，加载上一节实验结果，即：10万张8*8的RGB小图像块中提取的颜色特征，并把特征可视化。
2.先加载8张64*64的图片（用来测试卷积和池化是否正确），再实现卷积函数cnnConvolve.m，并检查该函数是否正确。
3.实现池化函数cnnPool.m，并检查该函数是否正确。
4.加载2000张64*64RGB图片，利用前面实现的卷积函数从中提取出卷积特征convolvedFeaturesThis后，再利用池化函数从convolvedFeaturesThis中提取出池化特征pooledFeaturesTrain，把它作为softmax分类器的训练数据集；加载3200张64*64RGB图片，利用前面实现的卷积函数从中提取出卷积特征convolvedFeaturesThis后，再利用池化函数从convolvedFeaturesThis中提取出池化特征pooledFeaturesTest，把它作为softmax分类器的测试数据集。
5.用训练数据集pooledFeaturesTrain及其标签训练softmax分类器，得到模型参数softmaxModel。
6.利用训练过的模型参数为pooledFeaturesTest的softmax分类器对测试数据集pooledFeaturesTest进行分类，即得到3200张64*64RGB图片的分类结果。
运行结果
Accuracy: 80.313%
所有训练数据和测试数据的卷积和池化特征的提取所用时间为：
Elapsed time is 2644.920372 seconds.
特征可视化结果：
代码
cnnExercise.m
%% CS294A/CS294W Convolutional Neural Networks Exercise% Instructions
% ------------
%
% This file contains code that helps you get started on the
% convolutional neural networks exercise. In this exercise, you will only
% need to modify cnnConvolve.m and cnnPool.m. You will not need to modify
% this file.%%======================================================================
%% STEP : Initialization
% Here we initialize some parameters used for the exercise.imageDim = ; % image dimension
imageChannels = ; % number of channels (rgb, so )patchDim = ; % patch dimension
numPatches = ; % number of patchesvisibleSize = patchDim * patchDim * imageChannels; % number of input units
outputSize = visibleSize; % number of output units
hiddenSize = ; % number of hidden units epsilon = 0.1; % epsilon for ZCA whiteningpoolDim = ; % dimension of pooling region%%======================================================================
%% STEP : Train a sparse autoencoder (with a linear decoder) to learn
% features from color patches. If you have completed the linear decoder
% execise, use the features that you have obtained from that exercise,
% loading them into optTheta. Recall that we have to keep around the
% parameters used in whitening (i.e., the ZCA whitening matrix and the
% meanPatch)% --------------------------- YOUR CODE HERE --------------------------
% Train the sparse autoencoder and fill the following variables with
% the optimal parameters:optTheta = zeros(*hiddenSize*visibleSize+hiddenSize+visibleSize, );
ZCAWhite = zeros(visibleSize, visibleSize);
meanPatch = zeros(visibleSize, );
load STL10Features.mat;% --------------------------------------------------------------------% Display and check to see that the features look good
W = reshape(optTheta(:visibleSize * hiddenSize), hiddenSize, visibleSize);
b = optTheta(*hiddenSize*visibleSize+:*hiddenSize*visibleSize+hiddenSize);displayColorNetwork( (W*ZCAWhite)');%%======================================================================
%% STEP : Implement and test convolution and pooling
% In this step, you will implement convolution and pooling, and test them
% on a small part of the data set to ensure that you have implemented
% these two functions correctly. In the next step, you will actually
% convolve and pool the features with the STL10 images.%% STEP 2a: Implement convolution
% Implement convolution in the function cnnConvolve in cnnConvolve.m% Note that we have to preprocess the images in the exact same way
% we preprocessed the patches before we can obtain the feature activations.load stlTrainSubset.mat % loads numTrainImages, trainImages, trainLabels%% 只用8张图片来测试卷积和池化是否正确 Use only the first images for testing
convImages = trainImages(:, :, :, :); % 格式：trainImages(r, c, channel, image number)% NOTE: Implement cnnConvolve in cnnConvolve.m first!
convolvedFeatures = cnnConvolve(patchDim, hiddenSize, convImages, W, b, ZCAWhite, meanPatch);%% STEP 2b: Checking your convolution
% To ensure that you have convolved the features correctly, we have
% provided some code to compare the results of your convolution with
% activations from the sparse autoencoder% For random points
for i = :
featureNum = randi([, hiddenSize]);
imageNum = randi([, ]);
imageRow = randi([, imageDim - patchDim + ]);
imageCol = randi([, imageDim - patchDim + ]); patch = convImages(imageRow:imageRow + patchDim - , imageCol:imageCol + patchDim - , :, imageNum);
patch = patch(:); % 将patch矩阵从3维矩阵转换为一个列向量
patch = patch - meanPatch;
patch = ZCAWhite * patch; % 白化后的数据 features = feedForwardAutoencoder(optTheta, hiddenSize, visibleSize, patch); if abs(features(featureNum, ) - convolvedFeatures(featureNum, imageNum, imageRow, imageCol)) > 1e-
fprintf('Convolved feature does not match activation from autoencoder\n');
fprintf('Feature Number : %d\n', featureNum);
fprintf('Image Number : %d\n', imageNum);
fprintf('Image Row : %d\n', imageRow);
fprintf('Image Column : %d\n', imageCol);
fprintf('Convolved feature : %0.5f\n', convolvedFeatures(featureNum, imageNum, imageRow, imageCol));
fprintf('Sparse AE feature : %0.5f\n', features(featureNum, ));
error('Convolved feature does not match activation from autoencoder');
end
enddisp('Congratulations! Your convolution code passed the test.');%% STEP 2c: Implement pooling
% Implement pooling in the function cnnPool in cnnPool.m% NOTE: Implement cnnPool in cnnPool.m first!
pooledFeatures = cnnPool(poolDim, convolvedFeatures);%% STEP 2d: Checking your pooling
% To ensure that you have implemented pooling, we will use your pooling
% function to pool over a test matrix and check the results.testMatrix = reshape(:, , );
expectedMatrix = [mean(mean(testMatrix(:, :))) mean(mean(testMatrix(:, :))); ...
mean(mean(testMatrix(:, :))) mean(mean(testMatrix(:, :))); ];testMatrix = reshape(testMatrix, , , , );pooledFeatures = squeeze(cnnPool(, testMatrix));if ~isequal(pooledFeatures, expectedMatrix)
disp('Pooling incorrect');
disp('Expected');
disp(expectedMatrix);
disp('Got');
disp(pooledFeatures);
else
disp('Congratulations! Your pooling code passed the test.');
end%%======================================================================
%% STEP : Convolve and pool with the dataset
% In this step, you will convolve each of the features you learned with
% the full large images to obtain the convolved features. You will then
% pool the convolved features to obtain the pooled features for
% classification.
%
% Because the convolved features matrix is very large, we will do the
% convolution and pooling features at a time to avoid running out of
% memory. Reduce this number if necessarystepSize = ;
assert(mod(hiddenSize, stepSize) == , 'stepSize should divide hiddenSize');load stlTrainSubset.mat % loads numTrainImages, trainImages, trainLabels
load stlTestSubset.mat % loads numTestImages, testImages, testLabelspooledFeaturesTrain = zeros(hiddenSize, numTrainImages, ...
floor((imageDim - patchDim + ) / poolDim), ...
floor((imageDim - patchDim + ) / poolDim) );
pooledFeaturesTest = zeros(hiddenSize, numTestImages, ...
floor((imageDim - patchDim + ) / poolDim), ...
floor((imageDim - patchDim + ) / poolDim) );tic();for convPart = :(hiddenSize / stepSize) featureStart = (convPart - ) * stepSize + ;
featureEnd = convPart * stepSize; fprintf('Step %d: features %d to %d\n', convPart, featureStart, featureEnd);
Wt = W(featureStart:featureEnd, :);
bt = b(featureStart:featureEnd); fprintf('Convolving and pooling train images\n');
convolvedFeaturesThis = cnnConvolve(patchDim, stepSize, ...
trainImages, Wt, bt, ZCAWhite, meanPatch);
pooledFeaturesThis = cnnPool(poolDim, convolvedFeaturesThis);
pooledFeaturesTrain(featureStart:featureEnd, :, :, :) = pooledFeaturesThis;
toc();
clear convolvedFeaturesThis pooledFeaturesThis; fprintf('Convolving and pooling test images\n');
convolvedFeaturesThis = cnnConvolve(patchDim, stepSize, ...
testImages, Wt, bt, ZCAWhite, meanPatch);
pooledFeaturesThis = cnnPool(poolDim, convolvedFeaturesThis);
pooledFeaturesTest(featureStart:featureEnd, :, :, :) = pooledFeaturesThis;
toc(); clear convolvedFeaturesThis pooledFeaturesThis;end% You might want to save the pooled features since convolution and pooling takes a long time
save('cnnPooledFeatures.mat', 'pooledFeaturesTrain', 'pooledFeaturesTest');
toc();%%======================================================================
%% STEP : Use pooled features for classification
% Now, you will use your pooled features to train a softmax classifier,
% using softmaxTrain from the softmax exercise.
% Training the softmax classifer for iterations should take less than
% minutes.% Add the path to your softmax solution, if necessary
% addpath /path/to/solution/% Setup parameters for softmax
softmaxLambda = 1e-;
numClasses = ;
% Reshape the pooledFeatures to form an input vector for softmax
softmaxX = permute(pooledFeaturesTrain, [ ]); % 把pooledFeaturesTrain的第2维移到最后
softmaxX = reshape(softmaxX, numel(pooledFeaturesTrain) / numTrainImages,...
numTrainImages);
softmaxY = trainLabels;options = struct;
options.maxIter = ;
softmaxModel = softmaxTrain(numel(pooledFeaturesTrain) / numTrainImages,...
numClasses, softmaxLambda, softmaxX, softmaxY, options);%%======================================================================
%% STEP : Test classifer
% Now you will test your trained classifer against the test imagessoftmaxX = permute(pooledFeaturesTest, [ ]);
softmaxX = reshape(softmaxX, numel(pooledFeaturesTest) / numTestImages, numTestImages);
softmaxY = testLabels;[pred] = softmaxPredict(softmaxModel, softmaxX);
acc = (pred(:) == softmaxY(:));
acc = sum(acc) / size(acc, );
fprintf('Accuracy: %2.3f%%\n', acc * );% You should expect to get an accuracy of around % on the test images.
cnnConvolve.m
function convolvedFeatures = cnnConvolve(patchDim, numFeatures, images, W, b, ZCAWhite, meanPatch)
%卷积特征提取：把每一个特征都与每一张大尺寸图片images相卷积，并返回卷积结果
%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)
% 表示第个featureNum特征与第imageNum张图片相卷的结果保存在矩阵
% convolvedFeatures(featureNum, imageNum, ：, ：)的第imageRow行第imageCol列,
% 而每行和列的大小都为imageDim - patchDim + numImages = size(images, ); % 图片数量
imageDim = size(images, ); % 每幅图片行数
imageChannels = size(images, ); % 每幅图片通道数patchSize = patchDim*patchDim;
assert(numFeatures == size(W,), 'W should have numFeatures rows');
assert(patchSize*imageChannels == size(W,), 'W should have patchSize*imageChannels cols');% Instructions:
% Convolve every feature with every large image here to produce the
% numFeatures x numImages x (imageDim - patchDim + ) x (imageDim - patchDim + )
% 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 - , imageCol + patchDim - )
%
% Expected running times:
% Convolving with images should take less than minutes
% Convolving with 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; % 等效的网络权值
b_mean = b - WT*meanPatch; % 等效偏置项% --------------------------------------------------------convolvedFeatures = zeros(numFeatures, numImages, imageDim - patchDim + , imageDim - patchDim + );
for imageNum = :numImages
for featureNum = :numFeatures % convolution of image with feature matrix for each channel
convolvedImage = zeros(imageDim - patchDim + , imageDim - patchDim + );
for channel = : % Obtain the feature (patchDim x patchDim) needed during the convolution
% ---- YOUR CODE HERE ----
offset = (channel-)*patchSize;
feature = reshape(WT(featureNum,offset+:offset+patchSize), patchDim, patchDim);%取一个权值图像块出来
im = images(:,:,channel,imageNum); % ------------------------ % Flip the feature matrix because of the definition of convolution, as explained later
feature = rot90(squeeze(feature),); % Obtain the image
im = squeeze(images(:, :, channel, imageNum)); % Convolve "feature" with "im", adding the result to convolvedImage
% be sure to do a 'valid' convolution
% ---- YOUR CODE HERE ---- convolvedoneChannel = conv2(im, feature, 'valid'); % 单个特征分别与所有图片相卷
convolvedImage = convolvedImage + convolvedoneChannel;% 直接把3通道的值加起来，理由：3通道相当于有3个feature-map，类似于cnn第2层以后的输入。 % ------------------------ 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 channels
convolvedFeatures(featureNum, imageNum, :, :) = convolvedImage;
end
endend
function sigm = sigmoid(x)
sigm = ./(+exp(-x));
end
cnnPool.m
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)
% 表示第个featureNum特征与第imageNum张图片的卷积特征池化后的结果保存在矩阵
% pooledFeatures(featureNum, imageNum, ：, ：)的第poolRow行第poolCol列
% numImages = size(convolvedFeatures, ); % 图片数量
numFeatures = size(convolvedFeatures, ); % 卷积特征数量
convolvedDim = size(convolvedFeatures, );% 卷积特征维数pooledFeatures = zeros(numFeatures, numImages, floor(convolvedDim / poolDim), floor(convolvedDim / poolDim));% -------------------- YOUR CODE HERE --------------------
% Instructions:
% Now pool the convolved features in regions of poolDim x poolDim,
% to obtain the
% numFeatures x numImages x (convolvedDim/poolDim) x (convolvedDim/poolDim)
% matrix pooledFeatures, such that
% pooledFeatures(featureNum, imageNum, poolRow, poolCol) is the
% value of the featureNum feature for the imageNum image pooled over the
% corresponding (poolRow, poolCol) pooling region
% (see http://ufldl/wiki/index.php/Pooling )
%
% Use mean pooling here.
% -------------------- YOUR CODE HERE --------------------resultDim = floor(convolvedDim / poolDim);
for imageNum = :numImages % 第imageNum张图片
for featureNum = :numFeatures % 第featureNum个特征
for poolRow = :resultDim
offsetRow = +(poolRow-)*poolDim;
for poolCol = :resultDim
offsetCol = +(poolCol-)*poolDim;
patch = convolvedFeatures(featureNum,imageNum,offsetRow:offsetRow+poolDim-,...
offsetCol:offsetCol+poolDim-);%取出一个patch
pooledFeatures(featureNum,imageNum,poolRow,poolCol) = mean(patch(:));%使用均值pool
end
end
end
endend
参考资料
http://www.cnblogs.com/tornadomeet/archive/2013/04/09/3009830.html
http://www.cnblogs.com/tornadomeet/archive/2013/03/25/2980766.html
——
