TensorFlow逻辑回归

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
#from tensorflow.examples.tutorials.mnist import input_data
import input_data
#导入实验所需的数据
mnist = input_data.read_data_sets("D:大二Java大三寒假作业大三寒假作业深度学习算法部分",one_hot = True)
#设置训练参数
learning_rate=0.01
training_epochs=25
batch_size=100
display_step=1

#构造计算图，使用占位符placeholder函数构造变量x，y，
x=tf.placeholder(tf.float32,[None,784])
y=tf.placeholder(tf.float32,[None,10])
#使用Variable函数，设置模型的初始权重
W=tf.Variable(tf.zeros([784,10]))
b=tf.Variable(tf.zeros([10]))
#构造逻辑回归模型
pred=tf.nn.softmax(tf.matmul(x,W)+b)
#构造代价函数cost
cost=tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred),reduction_indices=1))
#使用梯度下降法求最小值，即最优解
optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
#初始化全部变量
init=tf.global_variables_initializer()
#.使用tf.Session()创建Session会话对象，会话封装了Tensorflow运行时的状态和控制
with tf.Session() as sess:
    sess.run(init)
    #调用会话对象sess的run方法，运行计算图，即开始训练模型
    for epoch in range(training_epochs):
        avg_cost = 0
        total_batch = int(mnist.train.num_examples / batch_size)
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, y: batch_ys})
            avg_cost += c  / total_batch
        if (epoch+1) % display_step == 0:
            print("Epoch:", '%04d' % (epoch + 1), "Cost:","{:.09f}".format(avg_cost))
    print("Optimization Finished!")
    #测试模型
    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    #评估模型的准确度
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print("Accuracy:", accuracy.eval({x: mnist.test.images[:3000], y: mnist.test.labels[:3000]}))