import tensorflow as tf

        # ce = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(y, x_recon))   # <haodong>: list , index , Error (only be used for softmax output)
        L2_w = tf.contrib.layers.l2_regularizer(lambda_l2_w)(self.train_params[0]) \
                + tf.contrib.layers.l2_regularizer(lambda_l2_w)(self.train_params[2])           # faster than the code below
        # L2_w = lambda_l2_w * tf.reduce_mean(tf.square(self.train_params[0])) + lambda_l2_w * tf.reduce_mean( tf.square(self.train_params[2]))
