def testing(num): trainData = samples.loadImagesFile("data/facedata/facedatatrain", num, 60, 70) trainLabels = samples.loadLabelsFile("data/facedata/facedatatrainlabels", num) testData = samples.loadImagesFile("data/facedata/facedatatest", 150, 60, 70) testLabels = samples.loadLabelsFile("data/facedata/facedatatestlabels", 151) validData = samples.loadImagesFile("data/facedata/facedatavalidation", 301, 60, 70) validLabels = samples.loadLabelsFile("data/facedata/facedatavalidationlabels", 301) perceptron=PerceptronClassifier(trainData, trainLabels,0) perceptron.train(trainData, trainLabels,10) print "===================================" print "Test Data" guess=perceptron.classify(testData) samples.verify(perceptron, guess, testLabels) print "===================================" print "Validation Data" guess=perceptron.classify(validData) samples.verify(perceptron,guess,validLabels)
def testing(num): trainData = samples.loadImagesFile("data/digitdata/trainingimages", num, 28, 28) trainLabels = samples.loadLabelsFile("data/digitdata/traininglabels", num) testData = samples.loadImagesFile("data/digitdata/testimages", 1000, 28, 28) testLabels = samples.loadLabelsFile("data/digitdata/testlabels", 1000) validData = samples.loadImagesFile("data/digitdata/validationimages", 1000, 28, 28) validLabels = samples.loadLabelsFile("data/digitdata/validationlabels", 1000) perceptron=PerceptronClassifier(trainData, trainLabels,0) perceptron.train(trainData, trainLabels,10) print "***********************************" print "*************Test Data*************" guess=perceptron.classify(testData) samples.verify(perceptron, guess, testLabels) print "***********************************" print "************Valid Data*************" guess=perceptron.classify(validData) samples.verify(perceptron,guess,validLabels)
def testing(num): trainData = samples.loadImagesFile("data/digitdata/trainingimages", num, 28, 28) trainLabels = samples.loadLabelsFile("data/digitdata/traininglabels", num) testData = samples.loadImagesFile("data/digitdata/testimages", 1000, 28, 28) testLabels = samples.loadLabelsFile("data/digitdata/testlabels", 1000) validData = samples.loadImagesFile("data/digitdata/validationimages", 1000, 28, 28) validLabels = samples.loadLabelsFile("data/digitdata/validationlabels", 1000) perceptron=PerceptronClassifier(trainData, trainLabels,0) perceptron.train(trainData, trainLabels,10) print "===================================" print "Test Data" guess=perceptron.classify(testData) samples.verify(perceptron, guess, testLabels) print "===================================" print "Validation Data" guess=perceptron.classify(validData) samples.verify(perceptron,guess,validLabels)
def testing(num): trainData = samples.loadImagesFile("data/facedata/facedatatrain", num, 60, 70) trainLabels = samples.loadLabelsFile("data/facedata/facedatatrainlabels", num) testData = samples.loadImagesFile("data/facedata/facedatatest", 150, 60, 70) testLabels = samples.loadLabelsFile("data/facedata/facedatatestlabels", 151) validData = samples.loadImagesFile("data/facedata/facedatavalidation", 301, 60, 70) validLabels = samples.loadLabelsFile( "data/facedata/facedatavalidationlabels", 301) perceptron = PerceptronClassifier(trainData, trainLabels, 0) perceptron.train(trainData, trainLabels, 10) print "===================================" print "Test Data" guess = perceptron.classify(testData) samples.verify(perceptron, guess, testLabels) print "===================================" print "Validation Data" guess = perceptron.classify(validData) samples.verify(perceptron, guess, validLabels)
n_train = len(trainingDataList) classifier.set_weights(range(2), FaceData.FACE_DATUM_WIDTH, FaceData.FACE_DATUM_HEIGHT) # Conduct training and testing percentages, runtimes = ([], []) for n, n_samples in enumerate( range(n_train // 10, n_train + 1, n_train // 10)): start_time = time.time() print('Training with {}% of data'.format((n + 1) * 10)) idx = np.random.choice(n_train, n_samples, replace=False) train_img_sample = np.array(trainingDataList)[idx].tolist() train_labels_sample = np.array( face_data.face_train_labels)[idx].tolist() errors = classifier.train(train_img_sample, train_labels_sample) print('errors over 3 iterations', errors) print('Validating...') validation_guesses = classifier.classify(validationDataList) correct = [ validation_guesses[i] == face_data.face_validation_labels[i] for i in range(len(face_data.face_validation_labels)) ].count(True) print(str(correct), 'correct out of ', str(len(face_data.face_validation_labels))) print('Testing...') test_guesses = classifier.classify(testDataList) correct = [ test_guesses[i] == face_data.face_test_labels[i] for i in range(len(face_data.face_test_labels)) ].count(True)