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_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)