我正在尝试使用稀疏自动编码器来训练卷积神经网络,以便计算卷积层的滤波器。我正在使用UFLDL代码来构建补丁和训练CNN网络。我的代码如下:
===========================================================================
imageDim = 30; % image dimension
imageChannels = 3; % number of channels (rgb, so 3)
patchDim = 10; % patch dimension
numPatches = 100000; % number of patches
visibleSize = patchDim * patchDim * imageChannels; % number of input units
outputSize = visibleSize; % number of output units
hiddenSize = 400; % number of hidden units
epsilon = 0.1; % epsilon for ZCA whitening
poolDim = 10; % dimension of pooling region
optTheta = zeros(2*hiddenSize*visibleSize+hiddenSize+visibleSize, 1);
ZCAWhite = zeros(visibleSize, visibleSize);
meanPatch = zeros(visibleSize, 1);
load patches_16_1
===========================================================================
% Display and check to see that the features look good
W = reshape(optTheta(1:visibleSize * hiddenSize), hiddenSize, visibleSize);
b = optTheta(2*hiddenSize*visibleSize+1:2*hiddenSize*visibleSize+hiddenSize);
displayColorNetwork( (W*ZCAWhite));
stepSize = 100;
assert(mod(hiddenSize, stepSize) == 0, stepSize should divide hiddenSize);
load train.mat % loads numTrainImages, trainImages, trainLabels
load train.mat % loads numTestImages, testImages, testLabels
% size 30x30x3x8862
numTestImages = 8862;
numTrainImages = 8862;
pooledFeaturesTrain = zeros(hiddenSize, numTrainImages, floor((imageDim - patchDim + 1) / poolDim), floor((imageDim - patchDim + 1) / poolDim) );
pooledFeaturesTest = zeros(hiddenSize, numTestImages, ...
floor((imageDim - patchDim + 1) / poolDim), ...
floor((imageDim - patchDim + 1) / poolDim) );
tic();
testImages = trainImages;
for convPart = 1:(hiddenSize / stepSize)
featureStart = (convPart - 1) * stepSize + 1;
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
我在计算卷积层和池化层时遇到了问题。我得到pooledFeaturesTrain(featureStart : featureEnd,:)= pooledFeaturesThis;下标赋值维度不匹配。路径通常会计算出来,它们是:
我正在尝试理解convPart变量到底在做什么以及pooledFeaturesThis到底在做什么。其次,我注意到我的问题是pooledFeaturesTrain(featureStart:featureEnd, :, :, :) = pooledFeaturesThis;
这一行中的不匹配,在那里我得到变量不匹配的消息。pooledFeaturesThis的THe大小为100x3x2x2,其中pooledFeaturesTrain的大小为400x8862x2x2。pooledFeaturesTrain到底代表了什么?是每个过滤器的2x2结果吗?可以在here中找到CnnConvolve:
编辑:我稍微修改了一下我的代码,它就可以工作了。然而,我有点担心代码的理解。
发布于 2015-05-25 07:49:49
好的,在这一行中,您设置的是池化区域。
poolDim = 10; % dimension of pooling region
这部分意味着,对于每一层中的每个内核,您将获取10x10像素的图像和池化区域。从你的代码看,你正在应用一个均值函数,这意味着它是一个面片,计算均值,并在下一层输出它……也就是从100x100到10x10的图像。在您的网络中,您正在重复convolution+pooling,直到根据此输出得到一个2x2图像(顺便说一句,根据我的经验,这通常不是一个好的做法)。
400x8862x2x2
无论如何,回到你的代码。请注意,在训练开始时,您需要执行以下初始化:
pooledFeaturesTrain = zeros(hiddenSize, numTrainImages, floor((imageDim - patchDim + 1) / poolDim), floor((imageDim - patchDim + 1) / poolDim) );
所以你的错误是非常简单和正确的--保存convolution+pooling输出的矩阵的大小并不是你初始化的矩阵的大小。
现在的问题是如何修复它。我认为懒人修复它的方法是删除初始化。它会极大地减慢你的代码,并且如果你有超过1层的话也不能保证工作。
我建议你应该让pooledFeaturesTrain成为一个三维数组的结构。所以不是这样
pooledFeaturesTrain(featureStart:featureEnd, :, :, :) = pooledFeaturesThis;
你可以像这样做更多的事情:
pooledFeaturesTrain{n}(:, :, :) = pooledFeaturesThis;
其中n是当前层。
CNN网络并不像人们所说的那样容易--即使它们没有崩溃,让它们得到良好的训练也是一项壮举。我强烈建议阅读CNNs的理论-它将使编码和调试变得更容易。
祝你好运!:)
https://stackoverflow.com/questions/30194055
复制相似问题