tencent_2.2_logistic_regression

蔚落 2021-10-25 14:16 288阅读 0赞

课程地址：[https://cloud.tencent.com/developer/labs/lab/10296/console][https_cloud.tencent.com_developer_labs_lab_10296_console]

首先我们需要先下载MNIST的数据集。使用以下的命令进行下载：

wget https://devlab-1251520893.cos.ap-guangzhou.myqcloud.com/t10k-images-idx3-ubyte.gz
    wget https://devlab-1251520893.cos.ap-guangzhou.myqcloud.com/t10k-labels-idx1-ubyte.gz
    wget https://devlab-1251520893.cos.ap-guangzhou.myqcloud.com/train-images-idx3-ubyte.gz
    wget https://devlab-1251520893.cos.ap-guangzhou.myqcloud.com/train-labels-idx1-ubyte.gz

logistic\_regression.py

#-*- coding:utf-8 -*-
    import time
    import numpy as np
    import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data
    
    MNIST = input_data.read_data_sets("./", one_hot=True)
    
    learning_rate = 0.01
    batch_size = 128
    n_epochs = 25
    
    X = tf.placeholder(tf.float32, [batch_size, 784])
    Y = tf.placeholder(tf.float32, [batch_size, 10])
    
    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
    
    entropy = tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=logits)
    loss = tf.reduce_mean(entropy) 
    
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
    
    init = tf.global_variables_initializer()
    
    with tf.Session() as sess:
        sess.run(init)
        n_batches = int(MNIST.train.num_examples/batch_size)
        for i in range(n_epochs): 
            for j in range(n_batches):
                X_batch, Y_batch = MNIST.train.next_batch(batch_size)
                _, loss_ = sess.run([optimizer, loss], feed_dict={ X: X_batch, Y: Y_batch})
                print "Loss of epochs[{0}] batch[{1}]: {2}".format(i, j, loss_)

![864195-20190808212953016-1757897271.png][]

修改logistic\_regression.py，增加测试准确率功能

#-*- coding:utf-8 -*-
    import numpy as np
    import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data
    
    MNIST = input_data.read_data_sets("./", one_hot=True)
    
    learning_rate = 0.01
    batch_size = 128
    n_epochs = 25
    
    X = tf.placeholder(tf.float32, [batch_size, 784])
    Y = tf.placeholder(tf.float32, [batch_size, 10])
    
    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
    
    entropy = tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=logits)
    loss = tf.reduce_mean(entropy)
    
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
    
    init = tf.global_variables_initializer()
    
    with tf.Session() as sess:
        sess.run(init)
    
        n_batches = int(MNIST.train.num_examples/batch_size)
        for i in range(n_epochs):
            for j in range(n_batches):
                X_batch, Y_batch = MNIST.train.next_batch(batch_size)
                _, loss_ = sess.run([optimizer, loss], feed_dict={ X: X_batch, Y: Y_batch})
                print "Loss of epochs[{0}] batch[{1}]: {2}".format(i, j, loss_)
    
        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)
            preds = tf.nn.softmax(tf.matmul(X_batch, w) + b)
            correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(Y_batch, 1))
            accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32))
    
            total_correct_preds += sess.run(accuracy)
    
        print "Accuracy {0}".format(total_correct_preds/MNIST.test.num_examples)

![864195-20190812141341401-2134811606.png][]

转载于:https://www.cnblogs.com/exciting/p/11323836.html

[https_cloud.tencent.com_developer_labs_lab_10296_console]: https://cloud.tencent.com/developer/labs/lab/10296/console
[864195-20190808212953016-1757897271.png]: /images/20211025/bf5fa108226443a8bea4f3765eb6cc8b.png
[864195-20190812141341401-2134811606.png]: /images/20211025/f7dcfe62c565496d87247fae05b827ac.png