import tensorflow as tf

        loss_base_1 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_1, logits=output_1))
        loss_base_2 = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_2, logits=output_2))
        reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
        loss_total = loss_base_1 + loss_base_2 + tf.reduce_sum(reg_losses)

    with tf.variable_scope("evaluation"):
        accuracy_1 = tf.reduce_mean(tf.cast(tf.equal(
            tf.argmax(output_1, axis=-1),
            tf.argmax(y_1, axis=-1)), tf.float32), name="accuracy_1")
        accuracy_2 = tf.reduce_mean(tf.cast(tf.equal(
