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【深度学习】实验07 使用TensorFlow完成逻辑回归

news 2025/7/6 17:23:13

文章目录

使用TensorFlow完成逻辑回归
- 1. 环境设定
- 2. 数据读取
- 3. 准备好placeholder
- 4. 准备好参数/权重
- 5. 计算多分类softmax的loss function
- 6. 准备好optimizer
- 7. 在session里执行graph里定义的运算
附：系列文章

使用TensorFlow完成逻辑回归

TensorFlow是一种开源的机器学习框架，由Google Brain团队于2015年开发。它被广泛应用于图像和语音识别、自然语言处理、推荐系统等领域。

TensorFlow的核心是用于计算的数据流图。在数据流图中，节点表示数学操作，边表示张量（多维数组）。将操作和数据组合在一起的数据流图可以使 TensorFlow 对复杂的数学模型进行优化，同时支持分布式计算。

TensorFlow提供了Python，C++，Java，Go等多种编程语言的接口，让开发者可以更便捷地使用TensorFlow构建和训练深度学习模型。此外，TensorFlow还具有丰富的工具和库，包括TensorBoard可视化工具、TensorFlow Serving用于生产环境的模型服务、Keras高层封装API等。

TensorFlow已经发展出了许多优秀的模型，如卷积神经网络、循环神经网络、生成对抗网络等。这些模型已经在许多领域取得了优秀的成果，如图像识别、语音识别、自然语言处理等。

除了开源的TensorFlow，Google还推出了基于TensorFlow的云端机器学习平台Google Cloud ML，为用户提供了更便捷的训练和部署机器学习模型的服务。

解决分类问题里最普遍的baseline model就是逻辑回归，简单同时可解释性好，使得它大受欢迎，我们来用tensorflow完成这个模型的搭建。

1. 环境设定

import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'import warnings
warnings.filterwarnings("ignore")import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time

2. 数据读取

#使用tensorflow自带的工具加载MNIST手写数字集合
mnist = input_data.read_data_sets('./data/mnist', one_hot=True)

Extracting ./data/mnist/train-images-idx3-ubyte.gz
Extracting ./data/mnist/train-labels-idx1-ubyte.gz
Extracting ./data/mnist/t10k-images-idx3-ubyte.gz
Extracting ./data/mnist/t10k-labels-idx1-ubyte.gz

#查看一下数据维度
mnist.train.images.shape

(55000, 784)

#查看target维度
mnist.train.labels.shape

(55000, 10)

3. 准备好placeholder

batch_size = 128
X = tf.placeholder(tf.float32, [batch_size, 784], name='X_placeholder') 
Y = tf.placeholder(tf.int32, [batch_size, 10], name='Y_placeholder')

4. 准备好参数/权重

w = tf.Variable(tf.random_normal(shape=[784, 10], stddev=0.01), name='weights')
b = tf.Variable(tf.zeros([1, 10]), name="bias")

logits = tf.matmul(X, w) + b

5. 计算多分类softmax的loss function

# 求交叉熵损失
entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y, name='loss')
# 求平均
loss = tf.reduce_mean(entropy)

6. 准备好optimizer

这里的最优化用的是随机梯度下降，我们可以选择AdamOptimizer这样的优化器

learning_rate = 0.01
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)

7. 在session里执行graph里定义的运算

#迭代总轮次
n_epochs = 30with tf.Session() as sess:# 在Tensorboard里可以看到图的结构writer = tf.summary.FileWriter('../graphs/logistic_reg', sess.graph)start_time = time.time()sess.run(tf.global_variables_initializer())	n_batches = int(mnist.train.num_examples/batch_size)for i in range(n_epochs): # 迭代这么多轮total_loss = 0for _ in range(n_batches):X_batch, Y_batch = mnist.train.next_batch(batch_size)_, loss_batch = sess.run([optimizer, loss], feed_dict={X: X_batch, Y:Y_batch}) total_loss += loss_batchprint('Average loss epoch {0}: {1}'.format(i, total_loss/n_batches))print('Total time: {0} seconds'.format(time.time() - start_time))print('Optimization Finished!')# 测试模型preds = tf.nn.softmax(logits)correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(Y, 1))accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32))n_batches = int(mnist.test.num_examples/batch_size)total_correct_preds = 0for i in range(n_batches):X_batch, Y_batch = mnist.test.next_batch(batch_size)accuracy_batch = sess.run([accuracy], feed_dict={X: X_batch, Y:Y_batch}) total_correct_preds += accuracy_batch[0]print('Accuracy {0}'.format(total_correct_preds/mnist.test.num_examples))writer.close()

   Average loss epoch 0: 0.36748782022571785    Average loss epoch 1: 0.2978815356126198    Average loss epoch 2: 0.27840628396797845    Average loss epoch 3: 0.2783186247437706    Average loss epoch 4: 0.2783641471138923    Average loss epoch 5: 0.2750668214473413           Average loss epoch 6: 0.2687560408126502    Average loss epoch 7: 0.2713795114126239    Average loss epoch 8: 0.2657588795522154    Average loss epoch 9: 0.26322007090686916    Average loss epoch 10: 0.26289192279735646    Average loss epoch 11: 0.26248606019989873       Average loss epoch 12: 0.2604622903056356    Average loss epoch 13: 0.26015280702939403    Average loss epoch 14: 0.2581879366319496    Average loss epoch 15: 0.2590309207117085    Average loss epoch 16: 0.2630510463581219    Average loss epoch 17: 0.25501730025578767    Average loss epoch 18: 0.2547102673000945    Average loss epoch 19: 0.258298404375851    Average loss epoch 20: 0.2549241428330784    Average loss epoch 21: 0.2546788509283866    Average loss epoch 22: 0.259556887067837    Average loss epoch 23: 0.25428259843365575    Average loss epoch 24: 0.25442713139565676    Average loss epoch 25: 0.2553852511383159    Average loss epoch 26: 0.2503043229415978    Average loss epoch 27: 0.25468004046828596    Average loss epoch 28: 0.2552785321479633    Average loss epoch 29: 0.2506257003663859    Total time: 28.603315353393555 seconds    Optimization Finished!    Accuracy 0.9187

附：系列文章

序号	文章目录	直达链接
1	波士顿房价预测	https://want595.blog.csdn.net/article/details/132181950
2	鸢尾花数据集分析	https://want595.blog.csdn.net/article/details/132182057
3	特征处理	https://want595.blog.csdn.net/article/details/132182165
4	交叉验证	https://want595.blog.csdn.net/article/details/132182238
5	构造神经网络示例	https://want595.blog.csdn.net/article/details/132182341
6	使用TensorFlow完成线性回归	https://want595.blog.csdn.net/article/details/132182417
7	使用TensorFlow完成逻辑回归	https://want595.blog.csdn.net/article/details/132182496
8	TensorBoard案例	https://want595.blog.csdn.net/article/details/132182584
9	使用Keras完成线性回归	https://want595.blog.csdn.net/article/details/132182723
10	使用Keras完成逻辑回归	https://want595.blog.csdn.net/article/details/132182795
11	使用Keras预训练模型完成猫狗识别	https://want595.blog.csdn.net/article/details/132243928
12	使用PyTorch训练模型	https://want595.blog.csdn.net/article/details/132243989
13	使用Dropout抑制过拟合	https://want595.blog.csdn.net/article/details/132244111
14	使用CNN完成MNIST手写体识别(TensorFlow)	https://want595.blog.csdn.net/article/details/132244499
15	使用CNN完成MNIST手写体识别(Keras)	https://want595.blog.csdn.net/article/details/132244552
16	使用CNN完成MNIST手写体识别(PyTorch)	https://want595.blog.csdn.net/article/details/132244641
17	使用GAN生成手写数字样本	https://want595.blog.csdn.net/article/details/132244764
18	自然语言处理	https://want595.blog.csdn.net/article/details/132276591