Logistic Regression on MNIST dataset - opencv

Question

In this post you can find a very good tutorial on how to apply SVM classifier to MNIST dataset. I was wondering if I could use logistic regression instead of SVM classifier. So I searhed for Logistic regression in openCV, And I found that the syntax for both classifiers are almost identical. So I guessed that I could just comment out these parts:
cv::Ptr<cv::ml::SVM> svm = cv::ml::SVM::create();
svm->setType(cv::ml::SVM::C_SVC);
svm->setKernel(cv::ml::SVM::POLY);//LINEAR, RBF, SIGMOID, POLY
svm->setTermCriteria(cv::TermCriteria(cv::TermCriteria::MAX_ITER, 100, 1e-6));
svm->setGamma(3);
svm->setDegree(3);
svm->train( trainingMat , cv::ml::ROW_SAMPLE , labelsMat );
and replace it with:
cv::Ptr<cv::ml::LogisticRegression> lr1 = cv::ml::LogisticRegression::create();
lr1->setLearningRate(0.001);
lr1->setIterations(10);
lr1->setRegularization(cv::ml::LogisticRegression::REG_L2);
lr1->setTrainMethod(cv::ml::LogisticRegression::BATCH);
lr1->setMiniBatchSize(1);
lr1->train( trainingMat, cv::ml::ROW_SAMPLE, labelsMat);
But first I got this error:
OpenCV Error: Bad argument(data and labels must be a floating point matrix)
Then I changed
cv::Mat labelsMat(labels.size(), 1, CV_32S, labelsArray);
to:
cv::Mat labelsMat(labels.size(), 1, CV_32F, labelsArray);
And now I get this error: OpenCV Error: bad argument(data should have atleast two classes)
I have 10 classes (0,1,...,9) but I don't know why I get this error. My codes are almost identical with the ones in the mentioned tutorial.

Answer 1

In Python you could do something like this:
import matplotlib.pyplot as plt
# Import datasets, classifiers and performance metrics
from sklearn import datasets, svm, metrics
from sklearn.linear_models import LogisticRegression
# The digits dataset
digits = datasets.load_digits()
# The data that we are interested in is made of 8x8 images of digits, let's
# have a look at the first 3 images, stored in the `images` attribute of the
# dataset. If we were working from image files, we could load them using
# pylab.imread. Note that each image must have the same size. For these
# images, we know which digit they represent: it is given in the 'target' of
# the dataset.
images_and_labels = list(zip(digits.images, digits.target))
for index, (image, label) in enumerate(images_and_labels[:4]):
plt.subplot(2, 4, index + 1)
plt.axis('off')
plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
plt.title('Training: %i' % label)
# To apply a classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))
Choose which one of the classifiers you like below
# Create a classifier: a support vector classifier
classifier = svm.SVC(gamma=0.001)
# create a Logistic Regression Classifier
classifier = LogisticRegression(C=1.0)
# We learn the digits on the first half of the digits
classifier.fit(data[:n_samples / 2], digits.target[:n_samples / 2])
# Now predict the value of the digit on the second half:
expected = digits.target[n_samples / 2:]
predicted = classifier.predict(data[n_samples / 2:])
print("Classification report for classifier %s:\n%s\n"
% (classifier, metrics.classification_report(expected, predicted)))
print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted))
images_and_predictions = list(zip(digits.images[n_samples / 2:], predicted))
for index, (image, prediction) in enumerate(images_and_predictions[:4]):
plt.subplot(2, 4, index + 5)
plt.axis('off')
plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
plt.title('Prediction: %i' % prediction)
plt.show()
You can see the whole code here