为什么keras模型不那么准确，不被认可？

Question

我下载了mnist数据集（jpg），并用. hdf（. h5）创建了一个模型文件。
图像是使用. h5模型识别的，但是识别率很低...
。
实际编译时精度很低...
我做了吗有问题吗？。
。
图像使用了灰度的28x28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)

...

历元98/100 1257/1257[==========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================1.3.3.3 E-08-准确：1.3.3.3.3-3.3 E-08-准确：1.3.3.3 E-08-精确：1.3.3.3.3.3.3.3.3.3.3.3.3.4.4.4.4.4.28-val-val/u损失：val-val（val）损失：val（val）损失：损失：1.4.4.4.4.4628-val（val（val）val（val（val）损失：val（val）损失：val（val（val）损失：损失：损失：损失：损失：3.4.4.4.4.4628）val（val（val（val）准确：3.4.4.28）val（val（val）准确：val（val（val（val）准确：val）损失：val）0.699000009536743]
313/313[================================================-0s 850us/步-损耗：3887.2236-精度：0.6991
mnist:0.6991000175476074

Answer 1

从这里

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

您可以看到训练acc是1.0，验证acc是0.1136

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关于过度装配：https://en.wikipedia.org/wiki/Overfitting