if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--mode", dest="mode", default="cluster")
args = parser.parse_args()
config = {
"hyperparameters": {
"hidden_size": 2,
"learning_rate": 0.1,
"global_batch_size": 4,
"optimizer": "sgd",
"shuffle": False,
}
}
context = estimator.init(
config=config, mode=experimental.Mode(args.mode), context_dir=str(pathlib.Path.cwd())
)
batch_size = context.get_per_slot_batch_size()
shuffle = context.get_hparam("shuffle")
context.serving_input_receiver_fns = build_serving_input_receiver_fns()
context.train_and_evaluate(
build_estimator(context),
tf.estimator.TrainSpec(
xor_input_fn(context=context, batch_size=batch_size, shuffle=shuffle), max_steps=1
),
tf.estimator.EvalSpec(xor_input_fn(context=context, batch_size=batch_size, shuffle=False)),
)
parser.add_argument("--local", action="store_true")
parser.add_argument("--test", action="store_true")
args = parser.parse_args()
config = {
"hyperparameters": {
"hidden_size": 2,
"learning_rate": 0.1,
"global_batch_size": 4,
"optimizer": "sgd",
"shuffle": False,
}
}
context = estimator.init(config=config,
local=args.local,
test=args.test,
context_dir=str(pathlib.Path.cwd()))
batch_size = context.get_per_slot_batch_size()
shuffle = context.get_hparam("shuffle")
context.serving_input_receiver_fns = build_serving_input_receiver_fns()
context.train_and_evaluate(
build_estimator(context),
tf.estimator.TrainSpec(xor_input_fn(context=context,
batch_size=batch_size,
shuffle=shuffle),
max_steps=1),
tf.estimator.EvalSpec(
xor_input_fn(context=context, batch_size=batch_size,
shuffle=False)),
)