tensorlayer/layers/utils.py
def print_all_variables(train_only=False):
    """Print information of trainable or all variables,
    without ``tl.layers.initialize_global_variables(sess)``.

    Parameters
    ----------
    train_only : boolean
        Whether print trainable variables only.
            - If True, print the trainable variables.
            - If False, print all variables.

    """
    # tvar = tf.trainable_variables() if train_only else tf.all_variables()
    if train_only:
        t_vars = tf.trainable_variables()
        logging.info("  [*] printing trainable variables")

    else:
        t_vars = tf.global_variables()
        logging.info("  [*] printing global variables")

    for idx, v in enumerate(t_vars):
        logging.info("  var {:3}: {:15}   {}".format(idx, str(v.get_shape()), v.name))
def print_all_variables(train_only=False):
    """Print information of trainable or all variables,
    without ``tl.layers.initialize_global_variables(sess)``.

    Parameters
    ----------
    train_only : boolean
        Whether print trainable variables only.
            - If True, print the trainable variables.
            - If False, print all variables.

    """
    # tvar = tf.trainable_variables() if train_only else tf.all_variables()
    if train_only:
        t_vars = tf.trainable_variables()
        logging.info("  [*] printing trainable variables")

    else:
        t_vars = tf.global_variables()
        logging.info("  [*] printing global variables")

    for idx, v in enumerate(t_vars):
        logging.info("  var {:3}: {:15}   {}".format(idx, str(v.get_shape()), v.name))
Print information of trainable or all variables,
    without ``tl.layers.initialize_global_variables(sess)``.

    Parameters
    ----------
    train_only : boolean
        Whether print trainable variables only.
            - If True, print the trainable variables.
            - If False, print all variables.