def initialize_common_flags():
"""Define the common flags across models."""
common_hparams_flags.define_common_hparams_flags()
common_tpu_flags.define_common_tpu_flags()
# Parameters for MultiWorkerMirroredStrategy
flags.DEFINE_string(
'worker_hosts',
default=None,
help='Comma-separated list of worker ip:port pairs for running '
'multi-worker models with distribution strategy. The user would '
'start the program on each host with identical value for this flag.')
flags.DEFINE_integer(
'task_index', 0,
'If multi-worker training, the task_index of this worker.')
from hyperparameters import common_hparams_flags
from hyperparameters import common_tpu_flags
from hyperparameters import flags_to_params
from hyperparameters import params_dict
import imagenet_input
import mnasnet_models
import utils
from configs import mnasnet_config
from tensorflow.contrib.tpu.python.tpu import async_checkpoint
from tensorflow.contrib.training.python.training import evaluation
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.python.estimator import estimator
from tensorflow.python.keras import backend as K
common_tpu_flags.define_common_tpu_flags()
common_hparams_flags.define_common_hparams_flags()
FLAGS = flags.FLAGS
FAKE_DATA_DIR = 'gs://cloud-tpu-test-datasets/fake_imagenet'
# Model specific flags
flags.DEFINE_string(
'model_name',
default=None,
help=(
'The model name to select models among existing MnasNet configurations.'
))
flags.DEFINE_enum(
'mode', 'train_and_eval',