def rl_loop():
# monkeypatch the hyperparams so that we get a quickly executing network.
dual_net.get_default_hyperparams = lambda **kwargs: {
'k': 1, 'fc_width': 2, 'num_shared_layers': 1, 'l2_strength': 2e-4, 'momentum': 0.9}
with tempfile.TemporaryDirectory() as base_dir:
model_save_file = os.path.join(base_dir, 'models', '000000-bootstrap')
selfplay_dir = os.path.join(base_dir, 'data', 'selfplay')
model_selfplay_dir = os.path.join(selfplay_dir, '000000-bootstrap', 'worker1')
gather_dir = os.path.join(base_dir, 'data', 'training_chunks')
sgf_dir = os.path.join(base_dir, 'sgf', '000000-bootstrap')
os.mkdir(os.path.join(base_dir, 'data'))
print("Creating random initial weights...")
dual_net.DualNetworkTrainer(model_save_file).bootstrap()
print("Playing some games...")
# Do two selfplay runs to test gather functionality
main.selfplay(
load_file=model_save_file,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
readouts=10)
main.selfplay(
load_file=model_save_file,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
readouts=10)
print("Gathering game output...")
main.gather(input_directory=selfplay_dir, output_directory=gather_dir)
print("Training on gathered game data... (ctrl+C to quit)")
main.train(gather_dir, save_file=model_save_file, num_steps=10000)
def test_train(self):
with tempfile.TemporaryDirectory() as model_dir, \
tempfile.NamedTemporaryFile() as tf_record:
preprocessing.make_dataset_from_sgf(
'tests/example_game.sgf', tf_record.name)
model_save = os.path.join(model_dir, 'test_model')
n = dual_net.DualNetworkTrainer(model_save, **fast_hparams)
n.train([tf_record.name], num_steps=1)
def train(chunk_dir, save_file, load_file=None, generation_num=0,
logdir=None, num_steps=None):
tf_records = gfile.Glob(os.path.join(chunk_dir, '*.tfrecord.zz'))
tf_records = [f for f in tf_records
if (generation_num - 50) < int(os.path.basename(f)[:6]) <= generation_num]
n = dual_net.DualNetworkTrainer(save_file)
with timer("Training"):
n.train(tf_records, init_from=load_file,
logdir=logdir, num_steps=num_steps)
def test_inference(self):
with tempfile.TemporaryDirectory() as model_dir:
model_path = os.path.join(model_dir, 'blah')
n = dual_net.DualNetworkTrainer(model_path, **fast_hparams)
n.bootstrap()
n1 = dual_net.DualNetwork(model_path, **fast_hparams)
n1.run(go.Position())
# In the past we've had issues initializing two separate NNs
# in the same process... just double check that two DualNetwork
# instances can live side by side.
n2 = dual_net.DualNetwork(model_path, **fast_hparams)
n2.run(go.Position())
def rl_loop():
"""Run the reinforcement learning loop
This is meant to be more of an integration test than a realistic way to run
the reinforcement learning.
"""
# monkeypatch the hyperparams so that we get a quickly executing network.
dual_net.get_default_hyperparams = lambda **kwargs: {
'k': 8,
'fc_width': 16,
'num_shared_layers': 1,
'l2_strength': 1e-4,
'momentum': 0.9
}
dual_net.TRAIN_BATCH_SIZE = 16
#monkeypatch the shuffle buffer size so we don't spin forever shuffling up positions.
preprocessing.SHUFFLE_BUFFER_SIZE = 10000
with tempfile.TemporaryDirectory() as base_dir:
model_save_file = os.path.join(base_dir, 'models', '000000-bootstrap')
selfplay_dir = os.path.join(base_dir, 'data', 'selfplay')
model_selfplay_dir = os.path.join(selfplay_dir, '000000-bootstrap')
gather_dir = os.path.join(base_dir, 'data', 'training_chunks')
sgf_dir = os.path.join(base_dir, 'sgf', '000000-bootstrap')
os.mkdir(os.path.join(base_dir, 'data'))
print("Creating random initial weights...")
dual_net.DualNetworkTrainer(model_save_file).bootstrap()
print("Playing some games...")
# Do two selfplay runs to test gather functionality
main.selfplay(load_file=model_save_file,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=0,
readouts=10)
main.selfplay(load_file=model_save_file,
output_dir=model_selfplay_dir,
output_sgf=sgf_dir,
holdout_pct=0,
readouts=10)
print("Gathering game output...")
main.gather(input_directory=selfplay_dir, output_directory=gather_dir)
print("Training on gathered game data... (ctrl+C to quit)")
main.train(gather_dir,
save_file=model_save_file,
num_steps=10000,
logdir="logs",
verbosity=2)
def train(chunk_dir,
save_file,
load_file=None,
generation_num=0,
logdir=None,
num_steps=None,
verbosity=1):
tf_records = sorted(gfile.Glob(os.path.join(chunk_dir, '*.tfrecord.zz')))
tf_records = tf_records[-1 * (WINDOW_SIZE // EXAMPLES_PER_RECORD):]
print("Training from:", tf_records[0], "to", tf_records[-1])
n = dual_net.DualNetworkTrainer(save_file, logdir=logdir)
with timer("Training"):
n.train(tf_records,
init_from=load_file,
num_steps=num_steps,
verbosity=verbosity)
def validate(*tf_record_dirs, load_file=None, logdir=None, num_steps=100):
"""Computes the error terms for a set of holdout data specified by
`holdout_dir`, using the model specified at `load_file` and logging TB
metrics to the dir in `logdir`, using `num_steps` batches of examples
"""
n = dual_net.DualNetworkTrainer(logdir=logdir)
with timer("Building lists of holdout files"):
tf_records = [
item for sublist in map(
lambda path: gfile.Glob(os.path.join(path, '*.zz')),
tf_record_dirs) for item in sublist
]
with timer("Validating from {} to {}".format(
os.path.basename(tf_records[0]),
os.path.basename(tf_records[-1]))):
n.validate(tf_records,
batch_size=dual_net.TRAIN_BATCH_SIZE,
init_from=load_file,
num_steps=num_steps)
def bootstrap(save_file):
dual_net.DualNetworkTrainer(save_file).bootstrap()
