import tensorflow as tf

                [loss, opti, batch_label, batch_image, inf],feed_dict=feed_dict)
            if i > 0 and i % report_step == 0:
                accuracy_np = sess.run([accuracy],feed_dict=feed_dict)
                print(i, accuracy_np, loss_np)
            if i > 0 and i % save_step == 0:
                tf.train.Saver().save(sess, path)
        tf.train.Saver().save(sess, path)
        coord.request_stop()
        coord.join(threads)

