Python define_lr_schedule_flags Examples

Example #1

def setUpModule():
    # Create flags here to ensure duplicate flags are not created.
    optimizer_utils.define_optimizer_flags(TEST_SERVER_FLAG_PREFIX)
    optimizer_utils.define_optimizer_flags(TEST_CLIENT_FLAG_PREFIX)
    optimizer_utils.define_lr_schedule_flags(TEST_SERVER_FLAG_PREFIX)
    optimizer_utils.define_lr_schedule_flags(TEST_CLIENT_FLAG_PREFIX)

Example #2

from tensorflow_federated.python.research.optimization.shared import fed_avg_schedule
from tensorflow_federated.python.research.optimization.shared import optimizer_utils
from tensorflow_federated.python.research.optimization.stackoverflow import federated_stackoverflow
from tensorflow_federated.python.research.optimization.stackoverflow_lr import federated_stackoverflow_lr
from tensorflow_federated.python.research.utils import utils_impl

_SUPPORTED_TASKS = [
    'cifar100', 'emnist_cr', 'emnist_ae', 'shakespeare', 'stackoverflow_nwp',
    'stackoverflow_lr'
]

with utils_impl.record_hparam_flags() as optimizer_flags:
    # Defining optimizer flags
    optimizer_utils.define_optimizer_flags('client')
    optimizer_utils.define_optimizer_flags('server')
    optimizer_utils.define_lr_schedule_flags('client')
    optimizer_utils.define_lr_schedule_flags('server')

with utils_impl.record_hparam_flags() as shared_flags:
    # Federated training hyperparameters
    flags.DEFINE_integer('client_epochs_per_round', 1,
                         'Number of epochs in the client to take per round.')
    flags.DEFINE_integer('client_batch_size', 20, 'Batch size on the clients.')
    flags.DEFINE_integer('clients_per_round', 10,
                         'How many clients to sample per round.')
    flags.DEFINE_integer('client_datasets_random_seed', 1,
                         'Random seed for client sampling.')
    flags.DEFINE_integer('total_rounds', 200,
                         'Number of total training rounds.')

    # Training loop configuration