def train(cfg, tub_names, new_model_path, base_model_path=None): """ use the specified data in tub_names to train an artifical neural network saves the output trained model as model_name """ X_keys = ['cam/image_array'] y_keys = ['user/angle', 'user/throttle'] new_model_path = os.path.expanduser(new_model_path) kl = KerasLinear() if base_model_path is not None: base_model_path = os.path.expanduser(base_model_path) kl.load(base_model_path) print('tub_names', tub_names) if not tub_names: tub_names = os.path.join(cfg.DATA_PATH, '*') tubgroup = TubGroup(tub_names) train_gen, val_gen = tubgroup.get_train_val_gen(X_keys, y_keys, batch_size=cfg.BATCH_SIZE, train_frac=cfg.TRAIN_TEST_SPLIT) total_records = len(tubgroup.df) total_train = int(total_records * cfg.TRAIN_TEST_SPLIT) total_val = total_records - total_train print('train: %d, validation: %d' % (total_train, total_val)) steps_per_epoch = total_train // cfg.BATCH_SIZE print('steps_per_epoch', steps_per_epoch) kl.train(train_gen, val_gen, saved_model_path=new_model_path, steps=steps_per_epoch, train_split=cfg.TRAIN_TEST_SPLIT)
def train(cfg, tub_names, new_model_path, base_model_path=None): """ 引数 tub_names 似て指定されたパスに格納されている tub データを学習データとして トレーニングを行い、引数 new_model_path にて指定されたパスへ学習済みモデルファイルを格納する。 引数: cfg 個別車両設定オブジェクト、`config.py`がロードされたオブジェクト。 tub_names 学習データとして使用するtubディレクトリのパスを指定する。 new_model_path トレーニング後モデルファイルとして保管するパスを指定する。 base_model_path ファインチューニングを行う場合、ベースとなるモデルファイルを指定する。 戻り値 なし """ # モデルの入力データとなる項目 X_keys = ['cam/image_array'] # モデルの出力データとなる項目 y_keys = ['user/angle', 'user/throttle'] # トレーニング後モデルファイルとして保管するパスをフルパス化 new_model_path = os.path.expanduser(new_model_path) # トレーニング後モデルファイルとして保管するパスをフルパス化 kl = KerasLinear() # ファインチューニングを行う場合は base_model_path にベースモデルファイルパスが指定されている if base_model_path is not None: # ベースモデルファイルパスをフルパス化 base_model_path = os.path.expanduser(base_model_path) # ベースモデルファイルを読み込む kl.load(base_model_path) print('tub_names', tub_names) # 引数tub_names 指定がない場合 if not tub_names: # config.py 上に指定されたデータファイルパスを使用 tub_names = os.path.join(cfg.DATA_PATH, '*') # Tub データ群をあらわすオブジェクトを生成 tubgroup = TubGroup(tub_names) # トレーニングデータGenerator、評価データGeneratorを生成 train_gen, val_gen = tubgroup.get_train_val_gen(X_keys, y_keys, batch_size=cfg.BATCH_SIZE, train_frac=cfg.TRAIN_TEST_SPLIT) # 全学習データ件数を取得 total_records = len(tubgroup.df) # トレーニングデータ件数の取得 total_train = int(total_records * cfg.TRAIN_TEST_SPLIT) # 評価データ件数の取得 total_val = total_records - total_train print('train: %d, validation: %d' % (total_train, total_val)) # 1epochごとのステップ数の取得 steps_per_epoch = total_train // cfg.BATCH_SIZE print('steps_per_epoch', steps_per_epoch) # トレーニングの開始 kl.train(train_gen, val_gen, saved_model_path=new_model_path, steps=steps_per_epoch, train_split=cfg.TRAIN_TEST_SPLIT)
def train(cfg, tub_names, new_model_path, base_model_path=None ): """ use the specified data in tub_names to train an artifical neural network saves the output trained model as model_name """ X_keys = ['cam/image_array'] y_keys = ['user/angle', 'user/throttle'] def train_record_transform(record): """ convert categorical steering to linear and apply image augmentations """ record['user/angle'] = dk.util.data.linear_bin(record['user/angle']) # TODO add augmentation that doesn't use opencv return record def val_record_transform(record): """ convert categorical steering to linear """ record['user/angle'] = dk.util.data.linear_bin(record['user/angle']) return record new_model_path = os.path.expanduser(new_model_path) kl = KerasLinear() if base_model_path is not None: base_model_path = os.path.expanduser(base_model_path) kl.load(base_model_path) print('tub_names', tub_names) if not tub_names: tub_names = os.path.join(cfg.DATA_PATH, '*') tubgroup = TubGroup(tub_names) train_gen, val_gen = tubgroup.get_train_val_gen(X_keys, y_keys, batch_size=cfg.BATCH_SIZE, train_frac=cfg.TRAIN_TEST_SPLIT) total_records = len(tubgroup.df) total_train = int(total_records * cfg.TRAIN_TEST_SPLIT) total_val = total_records - total_train print('train: %d, validation: %d' % (total_train, total_val)) steps_per_epoch = total_train // cfg.BATCH_SIZE print('steps_per_epoch', steps_per_epoch) kl.train(train_gen, val_gen, saved_model_path=new_model_path, steps=steps_per_epoch, train_split=cfg.TRAIN_TEST_SPLIT)
def train(cfg, tub_names, model_name): ''' use the specified data in tub_names to train an artifical neural network saves the output trained model as model_name ''' X_keys = ['cam/image_array'] y_keys = ['user/angle', 'user/throttle'] #use these offsets from the current frame as points to learn the future #steering values. frames = [0, 20, 40, 120] new_y_keys = [] for iFrame in frames: for key in y_keys: new_y_keys.append(key + "_" + str(iFrame)) y_keys = new_y_keys kl = KerasLinear(num_outputs=len(y_keys)) tubgroup = TubGroup(tub_names) train_gen, val_gen = tubgroup.get_train_val_gen( X_keys, y_keys, batch_size=cfg.BATCH_SIZE, train_frac=cfg.TRAIN_TEST_SPLIT) model_path = os.path.expanduser(model_name) total_records = len(tubgroup.df) total_train = int(total_records * cfg.TRAIN_TEST_SPLIT) total_val = total_records - total_train print('train: %d, validation: %d' % (total_train, total_val)) steps_per_epoch = total_train // cfg.BATCH_SIZE print('steps_per_epoch', steps_per_epoch) kl.train(train_gen, val_gen, saved_model_path=model_path, steps=steps_per_epoch, train_split=cfg.TRAIN_TEST_SPLIT)
def train(cfg, tub_names, model_name): ''' use the specified data in tub_names to train an artifical neural network saves the output trained model as model_name ''' X_keys = ['cam/image_array'] y_keys = ['user/angle', 'user/throttle'] def rt(record): record['user/angle'] = dk.utils.linear_bin(record['user/angle']) return record kl = KerasLinear(num_outputs=2) print('tub_names', tub_names) if not tub_names: tub_names = os.path.join(cfg.DATA_PATH, '*') tubgroup = TubGroup(tub_names) train_gen, val_gen = tubgroup.get_train_val_gen( X_keys, y_keys, batch_size=cfg.BATCH_SIZE, train_frac=cfg.TRAIN_TEST_SPLIT) model_path = os.path.expanduser(model_name) total_records = len(tubgroup.df) total_train = int(total_records * cfg.TRAIN_TEST_SPLIT) total_val = total_records - total_train print('train: %d, validation: %d' % (total_train, total_val)) steps_per_epoch = total_train // cfg.BATCH_SIZE print('steps_per_epoch', steps_per_epoch) kl.train(train_gen, val_gen, saved_model_path=model_path, steps=steps_per_epoch, train_split=cfg.TRAIN_TEST_SPLIT)