def train_teacher(FLAGS, dataset, nb_teachers, teacher_id):
"""
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 input.create_dir_if_needed(FLAGS.data_dir)
assert input.create_dir_if_needed(FLAGS.train_dir)
# Load the dataset
if dataset == 'svhn':
train_data,train_labels,test_data,test_labels = input.ld_svhn(extended=True)
elif dataset == 'cifar10':
train_data, train_labels, test_data, test_labels = input.ld_cifar10()
elif dataset == 'mnist':
train_data, train_labels, test_data, test_labels = input.ld_mnist()
else:
print("Check value of dataset flag")
return False
if FLAGS.cov_shift == True:
teacher_file_name = FLAGS.data + 'PCA_teacher' + FLAGS.dataset + '.pkl'
student_file_name = FLAGS.data + 'PCA_student' + FLAGS.dataset + '.pkl'
f = open(teacher_file_name, 'rb')
train_data = pickle.load(f)
f = open(student_file_name, 'rb')
test_data = pickle.load(f)
# Retrieve subset of data for this teacher
data, labels = input.partition_dataset(train_data,
train_labels,
nb_teachers,
teacher_id)
print("Length of training data: " + str(len(labels)))
# Define teacher checkpoint filename and full path
if FLAGS.deeper:
filename = str(nb_teachers) + 'pca_teachers_' + str(teacher_id) + '_deep.ckpt'
else:
filename = str(nb_teachers) + 'pca_teachers_' + str(teacher_id) + '.ckpt'
ckpt_path = FLAGS.train_dir + '/' + str(dataset) + '_' + filename
# Perform teacher training
assert deep_cnn.train(data, labels, ckpt_path)
# Append final step value to checkpoint for evaluation
ckpt_path_final = ckpt_path + '-' + str(FLAGS.max_steps - 1)
# Retrieve teacher probability estimates on the test data
teacher_preds = deep_cnn.softmax_preds(test_data, ckpt_path_final)
# Compute teacher accuracy
precision = metrics.accuracy(teacher_preds, test_labels)
print('Precision of teacher after training: ' + str(precision))
return True
def train_teacher(dataset, nb_teachers, teacher_id):
"""
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 input.create_dir_if_needed(FLAGS.data_dir)
assert input.create_dir_if_needed(FLAGS.train_dir)
# Load the dataset
if dataset == 'svhn':
train_data,train_labels,test_data,test_labels = input.ld_svhn(extended=True)
elif dataset == 'cifar10':
train_data, train_labels, test_data, test_labels = input.ld_cifar10()
elif dataset == 'mnist':
train_data, train_labels, test_data, test_labels = input.ld_mnist()
else:
print("Check value of dataset flag")
return False
# Retrieve subset of data for this teacher
data, labels = input.partition_dataset(train_data,
train_labels,
nb_teachers,
teacher_id)
print("Length of training data: " + str(len(labels)))
# Define teacher checkpoint filename and full path
if FLAGS.deeper:
filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '_deep.ckpt'
else:
filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.ckpt'
ckpt_path = FLAGS.train_dir + '/' + str(dataset) + '_' + filename
# Perform teacher training
assert deep_cnn.train(data, labels, ckpt_path)
# Append final step value to checkpoint for evaluation
ckpt_path_final = ckpt_path + '-' + str(FLAGS.max_steps - 1)
# Retrieve teacher probability estimates on the test data
teacher_preds = deep_cnn.softmax_preds(test_data, ckpt_path_final)
# Compute teacher accuracy
precision = metrics.accuracy(teacher_preds, test_labels)
print('Precision of teacher after training: ' + str(precision))
return True
def train_teacher(dataset, nb_teachers, teacher_id):
"""
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 input.create_dir_if_needed(FLAGS.data_dir)
assert input.create_dir_if_needed(FLAGS.train_dir)
print("teacher {}:".format(teacher_id))
# Load the dataset
if dataset == 'svhn':
train_data, train_labels, test_data, test_labels = input.ld_svhn(
extended=True)
elif dataset == 'cifar10':
train_data, train_labels, test_data, test_labels = input.ld_cifar10()
elif dataset == 'mnist':
train_data, train_labels, test_data, test_labels = input.ld_mnist()
else:
print("Check value of dataset flag")
return False
path = os.path.abspath('.')
path1 = path + '\\plts_nodisturb\\'
# 对标签进行干扰
import copy
train_labels1 = copy.copy(train_labels)
train_labels2 = disturb(train_labels, 0.1)
disturb(test_labels, 0.1)
#path1 = path + '\\plts_withdisturb\\'
# Retrieve subset of data for this teacher
#干扰前
data, labels = input.partition_dataset(train_data, train_labels,
nb_teachers, teacher_id)
from pca import K_S
import operator
print(operator.eq(train_labels1, train_labels2))
print("干扰前: ", K_S.tst_norm(train_labels1))
print("干扰后: ", K_S.tst_norm(train_labels2))
print(K_S.tst_samp(train_labels1, train_labels2))
print("Length of training data: " + str(len(labels)))
# Define teacher checkpoint filename and full path
if FLAGS.deeper:
filename = str(nb_teachers) + '_teachers_' + str(
teacher_id) + '_deep.ckpt'
else:
filename = str(nb_teachers) + '_teachers_' + str(teacher_id) + '.ckpt'
ckpt_path = FLAGS.train_dir + '/' + str(dataset) + '_' + filename
# Perform teacher training
losses = deep_cnn.train(data, labels, ckpt_path)
# Append final step value to checkpoint for evaluation
ckpt_path_final = ckpt_path + '-' + str(FLAGS.max_steps - 1)
# Retrieve teacher probability estimates on the test data
teacher_preds = deep_cnn.softmax_preds(test_data, ckpt_path_final)
# Compute teacher accuracy
precision = metrics.accuracy(teacher_preds, test_labels)
print('Precision of teacher after training: ' + str(precision))
print("each n step loss: ", losses)
#x = list(range(1, len(losses)+1))
#plt.plot(x, losses, 'bo-', markersize=20)
#plt.savefig(path1 + 'loss' + str(teacher_id) + '.jpg')
#plt.show()
#print("x: ",x)
#print("loss: ", losses)
return True
