def test_train(self):
with tempfile.TemporaryDirectory() as working_dir, \
tempfile.NamedTemporaryFile() as tf_record:
flags.FLAGS.model_dir = working_dir
preprocessing.make_dataset_from_sgf('tests/example_game.sgf',
tf_record.name)
dual_net.train([tf_record.name], steps=1)
def train(working_dir):
model_num, model_name = fsdb.get_latest_model()
print("Training on gathered game data, initializing from {}".format(model_name))
new_model_num = model_num + 1
new_model_name = shipname.generate(new_model_num)
print("New model will be {}".format(new_model_name))
training_file = os.path.join(
fsdb.golden_chunk_dir(), str(new_model_num) + '.tfrecord.zz')
while not gfile.Exists(training_file):
print("Waiting for", training_file)
time.sleep(1*60)
print("Using Golden File:", training_file)
try:
save_file = os.path.join(fsdb.models_dir(), new_model_name)
print("Training model")
dual_net.train(training_file)
print("Exporting model to ", save_file)
dual_net.export_model(working_dir, save_file)
except Exception as e:
import traceback
logging.error(traceback.format_exc())
print(traceback.format_exc())
logging.exception("Train error")
sys.exit(1)
def train(tf_records: 'list of files of tf_records to train on',
model_save_path: 'Where to export the completed generation.'):
print("Training on:", tf_records[0], "to", tf_records[-1])
with utils.logged_timer("Training"):
dual_net.train(*tf_records)
print("== Training done. Exporting model to ", model_save_path)
dual_net.export_model(flags.FLAGS.model_dir, model_save_path)
freeze_graph(model_save_path)
def train(working_dir: 'tf.estimator working directory.',
tf_records: 'list of files of tf_records to train on',
model_save_path: 'Where to export the completed generation.'):
print("Training on:", tf_records[0], "to", tf_records[-1])
with utils.logged_timer("Training"):
dual_net.train(working_dir, tf_records)
print("== Training done. Exporting model to ", model_save_path)
dual_net.export_model(working_dir, model_save_path)
freeze_graph(model_save_path)
def train(
working_dir: 'tf.estimator working directory.',
tf_records: 'list of files of tf_records to train on',
model_save_path: 'Where to export the completed generation.',
generation_num: 'Which generation you are training.'=0):
print("Training on:", tf_records[0], "to", tf_records[-1])
with timer("Training"):
dual_net.train(working_dir, tf_records, generation_num)
dual_net.export_model(working_dir, model_save_path)
freeze_graph(model_save_path)
def train(working_dir: 'tf.estimator working directory.',
chunk_dir: 'Directory where gathered training chunks are.',
model_save_path: 'Where to export the completed generation.',
generation_num: 'Which generation you are training.' = 0):
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])
with timer("Training"):
dual_net.train(working_dir, tf_records, generation_num)
dual_net.export_model(working_dir, model_save_path)
def train(
working_dir: 'tf.estimator working directory.',
chunk_dir: 'Directory where gathered training chunks are.',
model_save_path: 'Where to export the completed generation.',
generation_num: 'Which generation you are training.'=0):
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])
with timer("Training"):
dual_net.train(working_dir, tf_records, generation_num)
dual_net.export_model(working_dir, model_save_path)
def test_train(self):
with tempfile.TemporaryDirectory() as working_dir, \
tempfile.NamedTemporaryFile() as tf_record:
preprocessing.make_dataset_from_sgf('tests/example_game.sgf',
tf_record.name)
dual_net.train(working_dir, [tf_record.name], 1, **fast_hparams)
def test_train(self):
with tempfile.TemporaryDirectory() as working_dir, \
tempfile.NamedTemporaryFile() as tf_record:
preprocessing.make_dataset_from_sgf(
'tests/example_game.sgf', tf_record.name)
dual_net.train(working_dir, [tf_record.name], 1, **fast_hparams)
