OpenCV神经网络Sigmoid输出 [英] OpenCV Neural Network Sigmoid Output
问题描述
我一直在使用OpenCV一段时间。我决定最近检查它的机器学习的力量。所以我最终实现了一个神经网络的面部识别。总结我的面部识别策略：
I have been using OpenCV for a quite time. I decided to check its power for Machine Learning lately. So I ended up with implementing a neural network for face recognition. To summarize my strategy for face recognition :
- 从某个面部数据库的csv读取图片。
-
- 应用PCA进行维数减少。
- 使用PCA的投影来训练网络。
-
使用训练有素的网络预测测试数据。
- Read images from a csv of some face database.
- Roll images to a Mat array row wise.
- Apply PCA for dimensionality reduction.
- Use projections of PCA to train the network.
Predict the test data using the trained network.
因此，在预测阶段之前一切正常。我使用最大响应输出单元来分类面部。所以通常OpenCV的sigmoid实现应该给出在-1到1的范围内的值，这在文档中说明。 1是类的最大闭包。在我得到接近0的精度后，我检查每个类的每个测试数据的输出响应。我被惊讶的价值观： 14.53，-1.7，#IND 。如果应用Sigmoid，我怎么能得到这些值？
So everything was OK until the prediction stage. I was using the max responsed output unit to classify the face. So normally OpenCV's sigmoid implementation should give values in range of -1 to 1 which is stated at the docs. 1 is the max closure to class. After I got nearly 0 accuracy I checked the output responses for each class for each test data. I was suprised with the values : 14.53, -1.7 , #IND . If sigmoid was applied, how could i get these values ? Where am i doing wrong ?
为了帮助你理解这个问题，对于想知道如何应用PCA和使用它与NN我想分享我的代码：
To help you understand the matter and for the ones wondering how to apply PCA and use it with NN I m sharing my code :
阅读csv ：
void read_csv(const string& filename, vector& images, vector& labels, char separator = ';')
{
std::ifstream file(filename.c_str(), ifstream::in);
if (!file)
{
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(1, error_message);
}
string line, path, classlabel;
while (getline(file, line))
{
stringstream liness(line);
getline(liness, path, separator);
getline(liness, classlabel);
if(!path.empty() && !classlabel.empty())
{
Mat im = imread(path, 0);
images.push_back(im);
labels.push_back(atoi(classlabel.c_str()));
}
}
}
逐行滚动图片
Mat rollVectortoMat(const vector<Mat> &data)
{
Mat dst(static_cast<int>(data.size()), data[0].rows*data[0].cols, CV_32FC1);
for(unsigned int i = 0; i < data.size(); i++)
{
Mat image_row = data[i].clone().reshape(1,1);
Mat row_i = dst.row(i);
image_row.convertTo(row_i,CV_32FC1, 1/255.);
}
return dst;
}
将标签向量转换为Mat标签 / p>
Converting vector of labels to Mat of labels
Mat getLabels(const vector<int> &data,int classes = 20)
{
Mat labels(data.size(),classes,CV_32FC1);
for(int i = 0; i <data.size() ; i++)
{
int cls = data[i] - 1;
labels.at<float>(i,cls) = 1.0;
}
return labels;
}
MAIN
int main()
{
PCA pca;
vector<Mat> images_train;
vector<Mat> images_test;
vector<int> labels_train;
vector<int> labels_test;
read_csv("train1k.txt",images_train,labels_train);
read_csv("test1k.txt",images_test,labels_test);
Mat rawTrainData = rollVectortoMat(images_train);
Mat rawTestData = rollVectortoMat(images_test);
Mat trainLabels = getLabels(labels_train);
Mat testLabels = getLabels(labels_test);
int pca_size = 500;
Mat trainData(rawTrainData.rows, pca_size,rawTrainData.type());
Mat testData(rawTestData.rows,pca_size,rawTestData.type());
pca(rawTrainData,Mat(),CV_PCA_DATA_AS_ROW,pca_size);
for(int i = 0; i < rawTrainData.rows ; i++)
pca.project(rawTrainData.row(i),trainData.row(i));
for(int i = 0; i < rawTestData.rows ; i++)
pca.project(rawTestData.row(i),testData.row(i));
Mat layers = Mat(3,1,CV_32SC1);
int sz = trainData.cols ;
layers.row(0) = Scalar(sz);
layers.row(1) = Scalar(1000);
layers.row(2) = Scalar(20);
CvANN_MLP mlp;
CvANN_MLP_TrainParams params;
CvTermCriteria criteria;
criteria.max_iter = 1000;
criteria.epsilon = 0.00001f;
criteria.type = CV_TERMCRIT_ITER | CV_TERMCRIT_EPS;
params.train_method = CvANN_MLP_TrainParams::BACKPROP;
params.bp_dw_scale = 0.1f;
params.bp_moment_scale = 0.1f;
params.term_crit = criteria;
mlp.create(layers,CvANN_MLP::SIGMOID_SYM);
int i = mlp.train(trainData,trainLabels,Mat(),Mat(),params);
int t = 0, f = 0;
for(int i = 0; i < testData.rows ; i++)
{
Mat response(1,20,CV_32FC1);
Mat sample = testData.row(i);
mlp.predict(sample,response);
float max = -1000000000000.0f;
int cls = -1;
for(int j = 0 ; j < 20 ; j++)
{
float value = response.at<float>(0,j);
if(value > max)
{
max = value;
cls = j + 1;
}
}
if(cls == labels_test[i])
t++;
else
f++;
}
return 0;
}
注意：我使用AT& T我的数据集的前20个类。
NOTE: I used AT&T 's first 20 class for my dataset.
推荐答案
感谢Canberk Baci的评论，我设法克服了Sigmoid输出差异。 问题似乎是默认参数mlp的创建函数，它将alpha和beta 0作为默认值。当它们都被给定为1时，Sigmoid函数的工作方式与文档中所述的一样，并且神经网络可以预测某事，但是当然会有错误。
Thanks to Canberk Baci's comment I managed to overcome sigmoid output discrepancy. Problem seems to be at default parameters of mlp 's create function which takes alpha and beta 0 as default. When they both are given as 1, sigmoid function works as it was stated in the docs and neural network can predict something but with errors of course.
神经网络：
通过修改一些参数，如动量等，没有任何光照校正算法，我得到％72精度的数据集（随机抽样936列车，262测试图像）前20个类的CroppedYaleB从opencv教程。对于其他因素提高精度;当我应用PCA时，我直接将减少的维度大小为500.这也可能降低精度，因为保留的方差可能低于％95或更差。因此，当我有空闲时间时，我将应用这些提高准确度：
By modifying some parameters like momentum etc, and without any illumunation correction algorithm, I got %72 accuracy on the dataset of (randomly sampled 936 train, 262 test images ) first 20 classes of CroppedYaleB from opencv tutorials. For the other factors to increase accuracy; when I applied PCA, I directly gave the reduced dimension size as 500. This may also reduce accuracy because retained variance may be below %95 or worse. So when I have free time I will apply these to increase accuracy :
- Tan Triggs照明校正
- 修改神经网络参数（我希望我们在OpenCV库中拥有更少的参数化NN）
我分享了这些内容，因此有人可能想知道如何提高NN的分类精确度。
I shared these so that someone may wonder how to increase the classification accuracy of NN. I hope it helps.
您可以在这里追踪这个问题：
http://code.opencv.org/issues/3583
By the way you can track the issue about this here: http://code.opencv.org/issues/3583
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