def test_record_new_flags(self):
with utils_impl.record_new_flags() as hparam_flags:
flags.DEFINE_string('exp_name', 'name',
'Unique name for the experiment.')
flags.DEFINE_float('learning_rate', 0.1,
'Optimizer learning rate.')
self.assertCountEqual(hparam_flags, ['exp_name', 'learning_rate'])
def test_convert_flag_names_to_odict(self):
with utils_impl.record_new_flags() as hparam_flags:
flags.DEFINE_integer('flag1', 1, 'This is the first flag.')
flags.DEFINE_float('flag2', 2.0, 'This is the second flag.')
hparam_odict = utils_impl.lookup_flag_values(hparam_flags)
expected_odict = collections.OrderedDict(flag1=1, flag2=2.0)
self.assertEqual(hparam_odict, expected_odict)
import os.path
from absl import app
from absl import flags
import tensorflow as tf
import tensorflow_federated as tff
from compression import sparsity
from utils import training_loop
from utils import training_utils
from utils import utils_impl
from utils.datasets import emnist_dataset
from utils.models import emnist_models
from tensorflow_model_optimization.python.core.internal import tensor_encoding as te
with utils_impl.record_new_flags():
# Training hyperparameters
flags.DEFINE_integer('clients_per_round', 2,
'How many clients to sample per round.')
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 used on the client.')
flags.DEFINE_boolean(
'only_digits', True, 'Whether to use the digit-only '
'EMNIST dataset (10 characters) or the extended EMNIST '
'dataset (62 characters).')
# Optimizer configuration (this defines one or more flags per optimizer).
utils_impl.define_optimizer_flags('server')
utils_impl.define_optimizer_flags('client')
from absl import app
from absl import flags
from absl import logging
import pandas as pd
import tensorflow as tf
import tensorflow_federated as tff
import tree
from flars import flars_fedavg
from flars import flars_optimizer
from utils import checkpoint_manager
from utils import utils_impl
from utils.models import emnist_models
from tensorboard.plugins.hparams import api as hp
with utils_impl.record_new_flags() as hparam_flags:
# Metadata
flags.DEFINE_string(
'exp_name', 'emnist', 'Unique name for the experiment, suitable for use '
'in filenames.')
# Training hyperparameters
flags.DEFINE_boolean(
'digit_only_emnist', True,
'Whether to train on the digits only (10 classes) data '
'or the full data (62 classes).')
flags.DEFINE_integer('total_rounds', 500, 'Number of total training rounds.')
flags.DEFINE_integer('rounds_per_eval', 1, 'How often to evaluate')
flags.DEFINE_integer(
'rounds_per_checkpoint', 25,
'How often to emit a state checkpoint. Higher numbers '
