# 导入必要的库 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  # 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   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.matmul(X, W) + B  # 修正：移除softmax  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(tf.nn.softmax(y_pred), 1))  # 修正：在计算准确率时应用softmax 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     for epoch in range(20):         total_batch = int(len(train_img) / batch_size)          for batch in range(total_batch):             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))