if __name__ == '__main__':
iterations = 3
legalLabels = range(2)
face_data = FaceData("facedata")
classifier = PerceptronClassifier(legalLabels, iterations)
featureFunction = face_data.basic_feature_extractor
trainingDataList = list(map(featureFunction, face_data.face_train_imgs))
validationDataList = list(
map(featureFunction, face_data.face_validation_imgs))
testDataList = list(map(featureFunction, face_data.face_test_imgs))
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)
if __name__ == '__main__':
iterations = 3
legalLabels = range(10)
digit_data = DigitData("digitdata")
classifier = PerceptronClassifier(legalLabels, iterations)
featureFunction = digit_data.basic_feature_extractor_digit
trainingDataList = list(map(featureFunction, digit_data.digit_train_imgs))
validationDataList = list(
map(featureFunction, digit_data.digit_validation_imgs))
testDataList = list(map(featureFunction, digit_data.digit_test_imgs))
n_train = len(trainingDataList)
classifier.set_weights(range(10), DigitData.DIGIT_DATUM_WIDTH,
DigitData.DIGIT_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(
digit_data.digit_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)
