def main(num_users=5,
loc_ep=10,
Numb_Glob_Iters=100,
lamb=0,
learning_rate=0.01,
alg='fedprox',
weight=True,
batch_size=0,
dataset="mnist"):
# suppress tf warnings
tf.logging.set_verbosity(tf.logging.WARN)
# parse command line arguments
options, learner, optimizer = read_options(num_users, loc_ep,
Numb_Glob_Iters, lamb,
learning_rate, alg, weight,
batch_size, dataset)
# read data
train_path = os.path.join('data', options['dataset'], 'data', 'train')
test_path = os.path.join('data', options['dataset'], 'data', 'test')
dataset = read_data(train_path, test_path)
# call appropriate trainer
t = optimizer(options, learner, dataset)
t.train()
def main(num_users=5,
loc_ep=10,
Numb_Glob_Iters=100,
lamb=0,
learning_rate=0.01,
hyper_learning_rate=0.01,
alg='fedprox',
weight=True,
batch_size=0,
dataset="mnist"):
# suppress tf warnings
tf.logging.set_verbosity(tf.logging.WARN)
model = MODEL_TYPE + ".py"
if (DATA_SET == "cifar100"):
learning_rate = 0.001
# parse command line arguments
options, learner_model, trainer = read_options(
num_users, loc_ep, Numb_Glob_Iters, lamb, learning_rate,
hyper_learning_rate, alg, weight, batch_size, dataset, model)
# read data
train_path = os.path.join('data', options['dataset'], 'data', 'train')
test_path = os.path.join('data', options['dataset'], 'data', 'test')
dataset = read_data(train_path, test_path)
# call appropriate trainer
t = trainer(options, learner_model, dataset)
t.train()
def main():
# suppress tf warnings
tf.logging.set_verbosity(tf.logging.WARN)
# parse command line arguments
options, learner, optimizer = read_options()
# read data
train_path = os.path.join('data', options['dataset'], 'data', 'train')
test_path = os.path.join('data', options['dataset'], 'data', 'test')
dataset = read_data(train_path, test_path)
# call appropriate trainer
for i in range(options['times']):
# Set seeds
random.seed(1 + i)
np.random.seed(12 + i)
tf.set_random_seed(123 + i)
print('......time for runing......', i)
t = optimizer(options, learner, dataset)
t.train(i)
average_data(num_users=options['clients_per_round'],
loc_ep1=options['num_epochs'],
Numb_Glob_Iters=options['num_rounds'],
lamb=options['lamb'],
learning_rate=options['learning_rate'],
hyper_learning_rate=options['hyper_learning_rate'],
algorithms=options['optimizer'],
batch_size=options['batch_size'],
dataset=options['dataset'],
rho=options['rho'],
times=options['times'])
def main():
# suppress tf warnings
tf.logging.set_verbosity(tf.logging.WARN)
# parse command line arguments
options, learner, optimizer = read_options()
# read data
train_path = os.path.join('data', options['dataset'], 'data', 'train')
test_path = os.path.join('data', options['dataset'], 'data', 'test')
dataset = read_data(train_path, test_path)
# call appropriate trainer
t = optimizer(options, learner, dataset)
t.train()
def main(num_users=5, loc_ep=10, alg='fedprox', weight = False):
# suppress tf warnings
tf.logging.set_verbosity(tf.logging.WARN)
# parse command line arguments
options, learner, optimizer = read_options(num_users, loc_ep, alg, weight)
# read data
train_path = os.path.join('data', options['dataset'], 'data', 'train')
test_path = os.path.join('data', options['dataset'], 'data', 'test')
dataset = read_data(train_path, test_path)
# call appropriate trainer
t = optimizer(options, learner, dataset)
t.train()
def main(num_users=5,
loc_ep=10,
Numb_Glob_Iters=100,
lamb=0,
learning_rate=0.01,
hyper_learning_rate=0.01,
alg='fedprox',
weight=True,
batch_size=0,
times=10,
rho=0,
dataset="mnist"):
# suppress tf warnings
tf.logging.set_verbosity(tf.logging.WARN)
# parse command line arguments
options, learner, optimizer = read_options(
num_users, loc_ep, Numb_Glob_Iters, lamb, learning_rate,
hyper_learning_rate, alg, weight, batch_size, times, rho, dataset)
# read data
train_path = os.path.join('data', options['dataset'], 'data', 'train')
test_path = os.path.join('data', options['dataset'], 'data', 'test')
dataset = read_data(train_path, test_path)
# call appropriate trainer
for i in range(times):
# Set seeds
random.seed(1 + i)
np.random.seed(12 + i)
tf.set_random_seed(123 + i)
print('......time for runing......', i)
t = optimizer(options, learner, dataset)
t.train(i)
average_data(num_users=num_users,
loc_ep1=loc_ep,
Numb_Glob_Iters=Numb_Glob_Iters,
lamb=lamb,
learning_rate=learning_rate,
hyper_learning_rate=hyper_learning_rate,
algorithms=alg,
batch_size=batch_size,
dataset=dataset,
rho=rho,
times=times)
def main():
# suppress tf warnings
tf.logging.set_verbosity(tf.logging.WARN)
# parse command line arguments
options, learner, optimizer = read_options()
# read data
path = "/".join(os.path.abspath(__file__).split('/')[:-1])
log_path = os.path.join(os.path.abspath('.'), 'out_new',
options['dataset'])
if not os.path.exists(log_path):
os.makedirs(log_path)
train_path = os.path.join(path, 'data/train')
test_path = os.path.join(path, 'data/test')
dataset = read_data(train_path, test_path)
# call trainer
t = optimizer(options, learner, dataset)
t.train()
