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)
