Tensorflow 为什么keras模型不那么准确，也不被认可？

我下载了mnist数据集（jpg），并用.hdf（.h5）创建了一个模型文件。
使用.h5模型识别图像，但识别率较低。
.
实际编译时精度较低。
我做错什么了吗？

该图像使用了28x28灰度图像。

import os
import cv2
import numpy as np
import tensorflow as tf
from PIL import Image
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
import random

FILENAME = 'model.h5'

WIDTH = 28
HEIGHT = 28
EPOCHES = 100
BATCH_SIZE = 32

def create_dataset(img_folder):
    img_data_array=[]
    class_name=[]
   
    for path in os.listdir(img_folder):
        if path == ".DS_Store":
            continue
        for file in os.listdir(os.path.join(img_folder, path)):
            if file == ".DS_Store":
                continue
            image_path = os.path.join(img_folder, path,  file)

            image = cv2.imread( image_path, cv2.IMREAD_UNCHANGED)


            image = cv2.resize(image, (HEIGHT, WIDTH),interpolation = cv2.INTER_AREA)
            image = np.array(image)
            
            image = image.astype('float32')
            image /= 255 
            img_data_array.append(image)
            class_name.append(path)
    return img_data_array, class_name

img_data, class_name = create_dataset(r'/Users/animalman/Documents/test/grayscale/train')
test, test_class_name = create_dataset(r'/Users/animalman/Documents/test/grayscale/test')

target_dict = {k: v for v, k in enumerate(np.unique(class_name))}
target_val = [target_dict[class_name[i]] for i in range(len(class_name))]

test_dict = {k: v for v, k in enumerate(np.unique(test_class_name))}
test_val = [test_dict[test_class_name[i]] for i in range(len(test_class_name))]

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(512, activation=tf.nn.relu),
    tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# tensor
history = model.fit(x=tf.cast(np.array(img_data), tf.float64), y=tf.cast(list(map(int,target_val)),tf.int32), epochs=EPOCHES, batch_size=BATCH_SIZE, validation_split=0.33)

evaluate = model.evaluate(x=tf.cast(np.array(img_data), tf.float64), y=tf.cast(list(map(int,target_val)),tf.int32), batch_size=BATCH_SIZE)
print('Train:', evaluate)

test_evaluate = model.evaluate(x=tf.cast(np.array(test), tf.float64), y=tf.cast(list(map(int,test_val)),tf.int32), batch_size=BATCH_SIZE)
print('Test:', test_evaluate)

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

test_loss, test_acc = model.evaluate(x_test, y_test)
print('mnist', test_acc)


model.save(FILENAME)

…
…
纪元98/100 1257/1257[=========================================================-3s 2ms/步长-损耗：5.5190e-08-精度：1.0000-val_损耗：43.3440-val_精度：0.1135
纪元99/100 1257/1257[============================================================-3s 2ms/步长-损耗：4.0746e-08-精度：1.0000-val_损耗：43.3764-val_精度：0.1136
纪元100/100 1257/1257[======================================================-3s 2ms/步-损耗：2.3033e-08-精度：1.0000-val_损耗：43.4628-val_精度：0.1136

..
..

列车：[14.343465805053711，0.7074833512306213]
313/313[=============================================-0s 579us/步长-损耗：14.7582-精度：0.6990
测试：[14.7581863403320331，0.699000009536743]
313/313[===================================================-0s 850us/步-损耗：3887.2236-精度：0.6991
mnist:0.6991000175476074从这里开始

Epoch 100/100 1257/1257 [==============================] - 3s 2ms/step - loss: 2.3033e-08 - accuracy: 1.0000 - val_loss: 43.4628 - val_accuracy: 0.1136

您可以看到培训acc为

1.0

，验证acc为

0.1136

->您的模型对训练数据集进行了过度拟合

---

关于过度拟合：

是否可以通过从mnist中提取数据来过度拟合已使用的数据？这不是我的代码问题吗？