def test_train(self):
with tempfile.TemporaryDirectory() as working_dir, \
tempfile.NamedTemporaryFile() as tf_record:
preprocessing.make_dataset_from_sgf(
utils_test.BOARD_SIZE, 'example_game.sgf', tf_record.name)
dualnet.train(
working_dir, [tf_record.name], 1, model_params.DummyMiniGoParams())
def test_train(self):
with tempfile.TemporaryDirectory() as working_dir, \
tempfile.NamedTemporaryFile() as tf_record:
preprocessing.make_dataset_from_sgf(
utils_test.BOARD_SIZE, 'example_game.sgf', tf_record.name)
dualnet.train(
working_dir, [tf_record.name], 1, model_params.DummyMiniGoParams())
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 test_make_dataset_from_sgf(self):
with tempfile.NamedTemporaryFile() as sgf_file, \
tempfile.NamedTemporaryFile() as record_file:
sgf_file.write(TEST_SGF.encode('utf8'))
sgf_file.seek(0)
preprocessing.make_dataset_from_sgf(
utils_test.BOARD_SIZE, sgf_file.name, record_file.name)
recovered_data = self.extract_data(record_file.name)
start_pos = go.Position(utils_test.BOARD_SIZE)
first_move = coords.from_sgf('fd')
next_pos = start_pos.play_move(first_move)
second_move = coords.from_sgf('cf')
expected_data = [
(
features.extract_features(utils_test.BOARD_SIZE, start_pos),
preprocessing._one_hot(utils_test.BOARD_SIZE, coords.to_flat(
utils_test.BOARD_SIZE, first_move)), -1
),
(
features.extract_features(utils_test.BOARD_SIZE, next_pos),
preprocessing._one_hot(utils_test.BOARD_SIZE, coords.to_flat(
utils_test.BOARD_SIZE, second_move)), -1
)
]
self.assertEqualData(expected_data, recovered_data)
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 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 test_make_dataset_from_sgf(self):
with tempfile.NamedTemporaryFile() as sgf_file, \
tempfile.NamedTemporaryFile() as record_file:
sgf_file.write(TEST_SGF.encode('utf8'))
sgf_file.seek(0)
preprocessing.make_dataset_from_sgf(sgf_file.name,
record_file.name)
recovered_data = self.extract_data(record_file.name)
start_pos = go.Position()
first_move = coords.from_sgf('fd')
next_pos = start_pos.play_move(first_move)
second_move = coords.from_sgf('cf')
expected_data = [
(features.extract_features(start_pos),
preprocessing._one_hot(coords.to_flat(first_move)), -1),
(features.extract_features(next_pos),
preprocessing._one_hot(coords.to_flat(second_move)), -1)
]
self.assertEqualData(expected_data, recovered_data)
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)
