import tensorflow as tf

            print('------valid_classification_report-----')
            print(classification_report(valid_true_total, valid_pre_total, target_names=target_names))
            print('------valid_classification_report-----')
            print('------valid_confusion_matrix-----')
            cm = confusion_matrix(y_true=valid_true_total, y_pred=valid_pre_total)
            print(cm)
            print('------valid_confusion_matrix-----')
        
        coord.request_stop()
        coord.join(threads)

def predict_time(loop=100):
    feed_dict={
        testnum:1
    }
    with tf.Session(config=config) as sess:
        sess.run(init)
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)
        tf.train.Saver().restore(sess,path)
        total=0.0
        for i in range(loop):
            a = datetime.now()
            accuracy_np = sess.run([accuracy],feed_dict=feed_dict)
            b = datetime.now()
            c = (b - a).microseconds
            total+=c
        print('predict_time(ms): ',total/(loop*1000))
        coord.request_stop()
        coord.join(threads)
