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)
