diff --git a/test.py b/test.py
deleted file mode 100644
index 81d2c02..0000000
--- a/test.py
+++ /dev/null
@@ -1,103 +0,0 @@
-from PIL import Image
-import numpy as np
-import tensorflow as tf
-import matplotlib.pyplot as plt
-import forward
-from batchdealing import get_files
-
-
-# 获取一张图片
-def get_one_image(train):
-    # 输入参数：train,训练图片的路径
-    # 返回参数：image，从训练图片中随机抽取一张图片
-    n = len(train)
-    ind = np.random.randint(0, n)
-    img_dir = train[ind]  # 随机选择测试的图片
-
-    img = Image.open(img_dir)
-
-    # 显示图片，在jupyter notebook下当然也可以不用plt.show()
-    plt.imshow(img)
-    plt.show(img)
-    imag = img.resize([64, 64])  # 由于图片在预处理阶段以及resize，因此该命令可略
-    image = np.array(imag)
-    return image
-
-
-# 测试图片
-def evaluate_one_image(image_array):
-    with tf.Graph().as_default():
-        BATCH_SIZE = 1
-        N_CLASSES = 4
-
-        image = tf.cast(image_array, tf.float32)
-
-        # 线性缩放图像以具有零均值和单位范数。
-        image = tf.image.per_image_standardization(image)
-        image = tf.reshape(image, [1, 64, 64, 3])
-
-        # 构建卷积神经网络
-        logit = forward.inference(image, BATCH_SIZE, N_CLASSES)
-
-        # softmax函数的作用就是归一化
-        # 输入: 全连接层（往往是模型的最后一层）的值，一般代码中叫做logits。
-        # 输出: 归一化的值，含义是属于该位置的概率，一般代码叫做probs。
-        logit = tf.nn.softmax(logit)
-
-        x = tf.compat.v1.placeholder(tf.float32, shape=[64, 64, 3])
-
-        # you need to change the directories to yours. /Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/
-        logs_train_dir = '/Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/'
-
-        # tf.train.Saver() 保存和加载模型
-        saver = tf.compat.v1.train.Saver()
-
-        with tf.compat.v1.Session() as sess:
-
-            print("Reading checkpoints...")
-            ckpt = tf.train.get_checkpoint_state(logs_train_dir)
-            if ckpt and ckpt.model_checkpoint_path:
-                global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
-                saver.restore(sess, ckpt.model_checkpoint_path)
-                print('Loading success, global_step is %s' % global_step)
-            else:
-                print('No checkpoint file found')
-
-            # feed_dict的作用是给使用placeholder创建出来的tensor赋值。
-            # 其实，他的作用更加广泛：feed使用一个值临时替换一个op的输出结果。
-            # 你可以提供feed数据作为run()调用的参数。
-            # feed只在调用它的方法内有效,方法结束,feed就会消失。
-            # 当我们构建完图后，需要在一个会话中启动图，启动的第一步是创建一个Session对象。
-            # 为了取回（Fetch）操作的输出内容，可以在使用Session对象的run()调用执行图时，
-            # 传入一些tensor，这些tensor会帮助你取回结果。
-            prediction = sess.run(logit, feed_dict={x: image_array})
-
-            #  取出prediction中元素最大值所对应的索引，也就是最大的可能
-            max_index = np.argmax(prediction)
-
-            if max_index == 0:
-                print('This could be the first category %.6f' % prediction[:, 0])
-            elif max_index == 1:
-                print('This could be the second category %.6f' % prediction[:, 1])
-            elif max_index == 2:
-                print('This could be the third category %.6f' % prediction[:, 2])
-            elif max_index == 3:
-                print('This could be the fourth category %.6f' % prediction[:, 3])
-            elif max_index == 4:
-                print('This could be the fifth category %.6f' % prediction[:, 4])
-            elif max_index == 5:
-                print('This could be the sixth category %.6f' % prediction[:, 5])
-            elif max_index == 6:
-                print('This could be the seventh category %.6f' % prediction[:, 6])
-            elif max_index == 7:
-                print('This could be the eighth category %.6f' % prediction[:, 7])
-            else:
-                print('This could be the ninth category %.6f' % prediction[:, 9])
-
-
-if __name__ == '__main__':
-    train_dir = '/Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/'
-    train, train_label, val, val_label = get_files(train_dir, 0.3)
-    img = get_one_image(val)  # 通过改变参数train or val，进而验证训练集或测试集
-    evaluate_one_image(img)
-