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 = 30
    with 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 = 0
            for _ 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_batch
            print('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 = 0
        
        for 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

     
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