def train_student(nb_teachers):
"""
This function trains a student using predictions made by an ensemble of
teachers. The student and teacher models are trained using the same
neural network architecture.
:param dataset: string corresponding to mnist, cifar10, or svhn
:param nb_teachers: number of teachers (in the ensemble) to learn from
:return: True if student training went well
"""
# Call helper function to prepare student data using teacher predictions
stdnt_dataset = prepare_student_data(nb_teachers, save=True)
# Unpack the student dataset
stdnt_data, stdnt_labels, stdnt_test_data, stdnt_test_labels = stdnt_dataset
if config.resnet:
dir_path = os.path.join(config.save_model, config.dataset)
dir_path = os.path.join(dir_path,
'pate_num_teacher_' + str(config.nb_teachers))
#dir_path = os.path.join(config.save_model,'pate_'+str(config.nb_teachers))
utils.mkdir_if_missing(dir_path)
filename = os.path.join(dir_path, '_stndent_resnet.checkpoint.pth.tar')
print('stdnt_label used for train', stdnt_labels.shape)
network.train_each_teacher(config.student_epoch, stdnt_data, stdnt_labels,
stdnt_test_data, stdnt_test_labels, filename)
final_preds = network.pred(stdnt_test_data, filename)
precision = hamming_accuracy(final_preds, stdnt_test_labels, torch=False)
print('Precision of student after training: ' + str(precision))
return True
def train_student(nb_teachers):
"""
This function trains a student using predictions made by an ensemble of
teachers. The student and teacher models are trained using the same
neural network architecture.
:param dataset: string corresponding to celeba
:param nb_teachers: number of teachers (in the ensemble) to learn from
:return: True if student training went well
"""
# Call helper function to prepare student data using teacher predictions
stdnt_dataset = prepare_student_data(nb_teachers, save=True)
# Unpack the student dataset
stdnt_data, stdnt_labels, stdnt_test_data, stdnt_test_labels = stdnt_dataset
dir_path = os.path.join(config.save_model, config.dataset)
dir_path = os.path.join(dir_path,
'knn_num_neighbor_' + str(config.nb_teachers))
utils.mkdir_if_missing(dir_path)
if config.resnet:
filename = os.path.join(
dir_path,
str(config.nb_teachers) + '_stdnt_resnet.checkpoint.pth.tar')
print('stdnt_label used for train', stdnt_labels.shape)
network.train_each_teacher(config.student_epoch, stdnt_data, stdnt_labels,
stdnt_test_data, stdnt_test_labels, filename)
return True
def train_teacher():
"""
This function trains a teacher (teacher id) among an ensemble of nb_teachers
models for the dataset specified.
:param dataset: string corresponding to dataset (svhn, cifar10)
:param nb_teachers: total number of teachers in the ensemble
:param teacher_id: id of the teacher being trained
:return: True if everything went well
"""
# If working directories do not exist, create them
#assert utils.mkdir_if_missing(config.data_dir)
#assert utils.mkdir_if_misshing(config.train_dir)
print("Initializing dataset {}".format(config.dataset))
dataset = data_manager.init_img_dataset(
root=config.data_dir, name=config.dataset,
)
# Load the dataset
for i in range(0,config.nb_teachers):
# Retrieve subset of data for this teacher
if config.dataset == 'celeba':
data, labels = dataset._data_partition(config.nb_teachers,i)
print("Length of training data: " + str(len(data)))
# Define teacher checkpoint filename and full path
print('data.shape for each teacher')
dir_path = os.path.join(config.save_model,'pate_'+config.dataset+str(config.nb_teachers))
utils.mkdir_if_missing(dir_path)
#filename = os.path.join(dir_path, str(config.nb_teachers) + '_teachers_' + str(i) + '_resnet.checkpoint.pth.tar')
filename = os.path.join(dir_path, str(config.nb_teachers) + '_teachers_' + str(i) + config.arch+'.checkpoint.pth.tar')
print('save_path for teacher{} is {}'.format(i,filename))
network.train_each_teacher(config.teacher_epoch,data, labels, dataset.test_data, dataset.test_label, filename)
return True
def train_tracher():
"""
Partition the entire private (training) data into config.nb_teacher subsets and train config.nb_teacher teacher models
"""
# Load the dataset
if config.dataset == 'mnist':
train_dataset = dataset.MNIST(root=config.data_dir, train=True, download=True)
test_dataset = dataset.MNIST(root=config.data_dir, train=False, download=True)
ori_train_data= [ data[0] for idx, data in enumerate(train_dataset)]
ori_test_data = [ data[0] for idx, data in enumerate(test_dataset)]
test_labels = test_dataset.targets
train_labels = train_dataset.targets
elif config.dataset =='svhn':
train_dataset = dataset.SVHN(root=config.data_dir, split='train', download=True)
extra_dataset = dataset.SVHN(root=config.data_dir, split='extra', download=True)
test_dataset = dataset.SVHN(root=config.data_dir, split='test', download=True)
ori_train_data = np.concatenate((train_dataset.data,extra_dataset.data),axis=0)
print('ori data shape', ori_train_data.shape)
ori_train_data = np.transpose(ori_train_data, (0, 2, 3, 1))
print('orig data shape', ori_train_data.shape)
#ori_train_data= [ data[0] for idx, data in enumerate(train_dataset.data)]
#for data in extra_dataset.data:
# ori_train_data.append(data)
#ori_test_data = [ data[0] for idx, data in enumerate(test_dataset.data)]
ori_test_data = np.transpose(test_dataset.data, (0,2,3,1))
test_labels = test_dataset.labels
extra_labels = extra_dataset.labels
train_labels = [ll for ll in train_dataset.labels]
for ll in extra_labels:
train_labels.append(ll)
batch_len = int(len(ori_train_data)/config.nb_teachers)
for i in range(0,1):
dir_path = os.path.join(config.save_model,'pate_'+str(config.nb_teachers))
utils.mkdir_if_missing(dir_path)
filename = os.path.join(dir_path, str(config.nb_teachers) + '_teachers_' + str(i) + config.arch+'.checkpoint.pth.tar')
print('save_path for teacher{} is {}'.format(i,filename))
start = i * batch_len
end = (i + 1) * batch_len
t_data = ori_train_data[start : end]
t_labels = train_labels[start: end]
network.train_each_teacher(config.teacher_epoch, t_data, t_labels, ori_test_data, test_labels, filename)