TensorFlow实现Logistic回归_Python

TensorFlow实现Logistic回归

2021-04-01 00:18不凡De老五 Python

这篇文章主要为大家详细介绍了TensorFlow实现Logistic回归的相关资料，具有一定的参考价值，感兴趣的小伙伴们可以参考一下

本文实例为大家分享了TensorFlow实现Logistic回归的具体代码，供大家参考，具体内容如下

1.导入模块

									import numpy as np

									import pandas as pd

									from pandas import Series,DataFrame

									from matplotlib import pyplot as plt

									%matplotlib inline

									#导入tensorflow

									import tensorflow as tf

									#导入MNIST(手写数字数据集)

									from tensorflow.examples.tutorials.mnist import input_data

2.获取训练数据和测试数据

									import ssl 

									ssl._create_default_https_context = ssl._create_unverified_context

									mnist = input_data.read_data_sets('./TensorFlow',one_hot=True)

									test = mnist.test

									test_images = test.images

									train = mnist.train

									images = train.images

3.模拟线性方程

									#创建占矩阵位符X,Y

									X = tf.placeholder(tf.float32,shape=[None,784])

									Y = tf.placeholder(tf.float32,shape=[None,10])

									#随机生成斜率W和截距b

									W = tf.Variable(tf.zeros([784,10]))

									b = tf.Variable(tf.zeros([10]))

									#根据模拟线性方程得出预测值

									y_pre = tf.matmul(X,W)+b

									#将预测值结果概率化

									y_pre_r = tf.nn.softmax(y_pre)

4.构造损失函数

									# -y*tf.log(y_pre_r) --->-Pi*log(Pi)  信息熵公式

									cost = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(y_pre_r),axis=1))

5.实现梯度下降，获取最小损失函数

									#learning_rate:学习率,是进行训练时在最陡的梯度方向上所采取的「步」长；

									learning_rate = 0.01

									optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

6.TensorFlow初始化，并进行训练

									#定义相关参数

									#训练循环次数

									training_epochs = 25

									#batch 一批，每次训练给算法10个数据

									batch_size = 10

									#每隔5次，打印输出运算的结果

									display_step = 5

									#预定义初始化

									init = tf.global_variables_initializer()

									#开始训练

									with tf.Session() as sess:

									  #初始化

									  sess.run(init)

									  #循环训练次数

									  for epoch in range(training_epochs):

									    avg_cost = 0.

									    #总训练批次total_batch =训练总样本量/每批次样本数量

									    total_batch = int(train.num_examples/batch_size)

									    for i in range(total_batch):

									      #每次取出100个数据作为训练数据

									      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(batch_xs.shape,batch_ys.shape)

									      print('epoch:','%04d'%(epoch+1),'cost=','{:.9f}'.format(avg_cost))

									  print('Optimization Finished!')

									  #7.评估效果

									  # Test model

									  correct_prediction = tf.equal(tf.argmax(y_pre_r,1),tf.argmax(Y,1))

									  # Calculate accuracy for 3000 examples

									  # tf.cast类型转换

									  accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

									  print("Accuracy:",accuracy.eval({X: mnist.test.images[:3000], Y: mnist.test.labels[:3000]}))