问使用min-max归一化后，训练mnist分类器的模型的准确率反而很低，这是为啥？

# 导入各种包
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import os
import pickle
from sklearn.preprocessing import StandardScaler
# 超参数设置
numClasses = 10
inputSize = 784
batch_size = 64
learning_rate = 0.05

# 下载数据集
mnist = input_data.read_data_sets('original_data/', one_hot=True)

train_img = mnist.train.images
train_label = mnist.train.labels
test_img = mnist.test.images
test_label = mnist.test.labels
train_img /= 255.0
test_img /= 255.0

# # 对train_img进行标准化
# scaler = StandardScaler().fit(train_img)
# train_img = scaler.transform(train_img)
# # 对test_img进行标准化
# test_img = scaler.transform(test_img)

X = tf.compat.v1.placeholder(tf.float32, shape=[None, inputSize])
y = tf.compat.v1.placeholder(tf.float32, shape=[None, numClasses])
W = tf.Variable(tf.random_normal([inputSize, numClasses], stddev=0.1))
B = tf.Variable(tf.constant(0.1), [numClasses])
# y_pred = tf.nn.softmax(tf.matmul(X, W) + B)
y_pred = tf.matmul(X, W) + B

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=y_pred)) + 0.01 * tf.nn.l2_loss(W)
opt = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_pred, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

saver = tf.train.Saver()
multiclass_parameters = {}
# 运行
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    # 开始训练 外循环控制轮数，内循环训练
    for epoch in range(20):
        total_batch = int(len(train_img) / batch_size)  # 总批次=训练的样本总数/批次大小

        for batch in range(total_batch):  # 一个批次的训练
            # 从训练集获取到一个批次的数据
            batch_input = train_img[batch * batch_size: (batch + 1) * batch_size]
            batch_label = train_label[batch * batch_size: (batch + 1) * batch_size]

            _, trainingLoss = sess.run([opt, loss], feed_dict={X: batch_input, y: batch_label})

        train_acc = sess.run(accuracy, feed_dict={X: train_img, y: train_label})
        print("Epoch %d Training Accuracy %g" % (epoch + 1, train_acc))

    test_acc = sess.run(accuracy, feed_dict={X: test_img, y: test_label})
    print(test_acc)