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', 'imu_array']
y_keys = ['user/angle', 'user/throttle']
def rt(rec):
rec['imu_array'] = np.array([
rec['imu/acl_x'], rec['imu/acl_y'], rec['imu/acl_z'],
rec['imu/gyr_x'], rec['imu/gyr_y'], rec['imu/gyr_z'],
rec['imu/temp']
])
return rec
kl = KerasIMU()
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,
record_transform=rt,
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', 'imu/imu_vec']
y_keys = ['user/angle', 'user/throttle']
def rt(record):
#record['user/angle'] = dk.utils.linear_bin(record['user/angle']) # Comment out if angle is not binned
record['imu/imu_vec'] = np.array(
ast.literal_eval(record['imu/imu_vec'].split('/')[-1]))
return record
#kl = KerasCategorical()
kl = KerasIMU()
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,
record_transform=rt,
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)