mnist数据集预测中输入图片的问题

from PIL import Image
import numpy as np
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# 下载参数
model_save_path = './checkpoint/mnist.ckpt'

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')])

model.load_weights(model_save_path)

preNum = int(input("input the number of test pictures:"))

# 预测过程
# 1.打开图片 Image.open
# 2.转换像素大小+图片类型
# 3.增强处理
# 4.除255.0符合0，1分布
# 5.增加batch维度

for i in range(preNum):
    image_path = input("the path of test picture:")
    img = Image.open(image_path)
    # 原图片为739*942的rgb彩图
    print(img)
    # 将图片转化为高质量28*28像素的图
    img = img.resize((28, 28), Image.ANTIALIAS)
    # L代表灰度图像
    img_arr = np.array(img.convert('L'))
# 灰度增强
    for i in range(28):
        for j in range(28):
            if img_arr[i][j] < 200:
                img_arr[i][j] = 255
            else:
                img_arr[i][j] = 0

    img_arr = img_arr / 255.0
    x_predict = img_arr[tf.newaxis, ...]
    result = model.predict(x_predict)

    pred = tf.argmax(result, axis=1)

    print('\n')
    tf.print(pred)