import tensorflow as tf

        return (model, graph, sess, saver, monitored_values)

    def _load_tf_vars(self, params):
        m = self._model

        utils.logger.log('Loading TF vars...')

        tf_vars = tf.global_variables()
        values = self._sess.run(tf_vars) # Get current values for vars

        # Build feed dict for op for loading vars
        # For each var, use current value of param in session if not in params
        var_feeddict = {
            m.var_phs[tf_var.name]: params[tf_var.name]
