def save_npz_dict(save_list=None, name='model.npz', sess=None):
    """Input parameters and the file name, save parameters as a dictionary into .npz file.

    Use ``tl.files.load_and_assign_npz_dict()`` to restore.

    Parameters
    ----------
    save_list : list of parameters
        A list of parameters (tensor) to be saved.
    name : str
        The name of the `.npz` file.
    sess : Session
        TensorFlow Session.

    """
    if sess is None:
        raise ValueError("session is None.")
    if save_list is None:
        save_list = []

    save_list_names = [tensor.name for tensor in save_list]
    save_list_var = sess.run(save_list)
    save_var_dict = {save_list_names[idx]: val for idx, val in enumerate(save_list_var)}
    np.savez(name, **save_var_dict)
    save_list_var = None
    save_var_dict = None
    del save_list_var
    del save_var_dict
    logging.info("[*] Model saved in npz_dict %s" % name)
def save_npz_dict(save_list=None, name='model.npz', sess=None):
    """Input parameters and the file name, save parameters as a dictionary into .npz file.

    Use ``tl.files.load_and_assign_npz_dict()`` to restore.

    Parameters
    ----------
    save_list : list of parameters
        A list of parameters (tensor) to be saved.
    name : str
        The name of the `.npz` file.
    sess : Session
        TensorFlow Session.

    """
    if sess is None:
        raise ValueError("session is None.")
    if save_list is None:
        save_list = []

    save_list_names = [tensor.name for tensor in save_list]
    save_list_var = sess.run(save_list)
    save_var_dict = {save_list_names[idx]: val for idx, val in enumerate(save_list_var)}
    np.savez(name, **save_var_dict)
    save_list_var = None
    save_var_dict = None
    del save_list_var
    del save_var_dict
    logging.info("[*] Model saved in npz_dict %s" % name)
def save_npz_dict ( save_list = None , name = 'model.npz' , sess = None ) : if sess is None : raise ValueError ( "session is None." ) if save_list is None : save_list = [ ] save_list_names = [ tensor . name for tensor in save_list ] save_list_var = sess . run ( save_list ) save_var_dict = { save_list_names [ idx ] : val for idx , val in enumerate ( save_list_var ) } np . savez ( name , * * save_var_dict ) save_list_var = None save_var_dict = None del save_list_var del save_var_dict logging . info ( "[*] Model saved in npz_dict %s" % name )
Input parameters and the file name, save parameters as a dictionary into .npz file.

    Use ``tl.files.load_and_assign_npz_dict()`` to restore.

    Parameters
    ----------
    save_list : list of parameters
        A list of parameters (tensor) to be saved.
    name : str
        The name of the `.npz` file.
    sess : Session
        TensorFlow Session.
Input parameters and the file name save parameters as a dictionary into . npz file .