(config_dict: dict = None)
Initialize TensorFlow session using good default settings.
Initialize TensorFlow session using good default settings.
Initialize TensorFlow session using good default settings .
def init_tf(config_dict: dict = None) -> None:
    """Initialize TensorFlow session using good default settings."""
    # Skip if already initialized.
    if tf.get_default_session() is not None:
        return

    # Setup config dict and random seeds.
    cfg = _sanitize_tf_config(config_dict)
    np_random_seed = cfg["rnd.np_random_seed"]
    if np_random_seed is not None:
        np.random.seed(np_random_seed)
    tf_random_seed = cfg["rnd.tf_random_seed"]
    if tf_random_seed == "auto":
        tf_random_seed = np.random.randint(1 << 31)
    if tf_random_seed is not None:
        tf.set_random_seed(tf_random_seed)

    # Setup environment variables.
    for key, value in cfg.items():
        fields = key.split(".")
        if fields[0] == "env":
            assert len(fields) == 2
            os.environ[fields[1]] = str(value)

    # Create default TensorFlow session.
    create_session(cfg, force_as_default=True)
def init_tf ( config_dict : dict = None ) -> None : # Skip if already initialized. if tf . get_default_session ( ) is not None : return # Setup config dict and random seeds. cfg = _sanitize_tf_config ( config_dict ) np_random_seed = cfg [ "rnd.np_random_seed" ] if np_random_seed is not None : np . random . seed ( np_random_seed ) tf_random_seed = cfg [ "rnd.tf_random_seed" ] if tf_random_seed == "auto" : tf_random_seed = np . random . randint ( 1 << 31 ) if tf_random_seed is not None : tf . set_random_seed ( tf_random_seed ) # Setup environment variables. for key , value in cfg . items ( ) : fields = key . split ( "." ) if fields [ 0 ] == "env" : assert len ( fields ) == 2 os . environ [ fields [ 1 ] ] = str ( value ) # Create default TensorFlow session. create_session ( cfg , force_as_default = True )