tensorlayer/layers/core.py
def print_params(self, details=True, session=None):
        """Print all info of parameters in the network"""
        for i, p in enumerate(self.all_params):
            if details:
                try:
                    val = p.eval(session=session)
                    logging.info(
                        "  param {:3}: {:20} {:15}    {} (mean: {:<18}, median: {:<18}, std: {:<18})   ".
                        format(i, p.name, str(val.shape), p.dtype.name, val.mean(), np.median(val), val.std())
                    )
                except Exception as e:
                    logging.info(str(e))
                    raise Exception(
                        "Hint: print params details after tl.layers.initialize_global_variables(sess) "
                        "or use network.print_params(False)."
                    )
            else:
                logging.info("  param {:3}: {:20} {:15}    {}".format(i, p.name, str(p.get_shape()), p.dtype.name))
        logging.info("  num of params: %d" % self.count_params())
def print_params(self, details=True, session=None):
        """Print all info of parameters in the network"""
        for i, p in enumerate(self.all_params):
            if details:
                try:
                    val = p.eval(session=session)
                    logging.info(
                        "  param {:3}: {:20} {:15}    {} (mean: {:<18}, median: {:<18}, std: {:<18})   ".
                        format(i, p.name, str(val.shape), p.dtype.name, val.mean(), np.median(val), val.std())
                    )
                except Exception as e:
                    logging.info(str(e))
                    raise Exception(
                        "Hint: print params details after tl.layers.initialize_global_variables(sess) "
                        "or use network.print_params(False)."
                    )
            else:
                logging.info("  param {:3}: {:20} {:15}    {}".format(i, p.name, str(p.get_shape()), p.dtype.name))
        logging.info("  num of params: %d" % self.count_params())
Print all info of parameters in the network