机器学习代码学习(一)->>first-dichotomy

Love The Way You Lie 2022-05-06 10:38 174阅读 0赞

#### 下面代码为: .ipynb文件, 可以在Jupyter上运行 ####

题目为:  
给了40组数据(x,y)坐标  
要求找出一条线进行分类(二分法)

from __future__ import print_function, division
    import tensorflow as tf
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    %matplotlib inline
    train= pd.read_csv('/Users/liyixin/Desktop/AI/first-dichotomy/diamond.csv')  #import csv檔案
    train_X = train['carats']  #x軸
    train_Y = train['price']   #y軸
    
    learning_rate = 0.01  #學習速率
    n_samples = train_X.shape[0]
    training_epochs = 1000  #訓練次數
    display_step = 50       #每50次,印出結果
    X = tf.placeholder(tf.float32) #Tensorflow中的佔位符，暫時儲存變量,用來傳入資料
    Y = tf.placeholder(tf.float32) #Tensorflow中的佔位符，暫時儲存變量,用來傳入資料
    
    W = tf.Variable(tf.random_normal([1]), name="weight", dtype=tf.float32) # W 指的是係數，斜率介於 -1 至 1 之間
    b = tf.Variable(tf.zeros([1]), name="bias", dtype=tf.float32)  # b 指的是截距，從 0 開始逼近任意數字
    
    
    pred = W*X+b # pred是預測值
    
    cost =tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples) #平均方差
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #梯度下降演算法,找最小值
    
    
    init =tf.global_variables_initializer() #初始化所有變數
    
    
    with tf.Session() as sess:
        sess.run(init)
    
        for epoch in range(training_epochs):
            for (x, y) in zip(train_X, train_Y):
                sess.run(optimizer, feed_dict={X: x, Y: y})
            if (epoch+1) % display_step == 0:
                c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
                print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
                    "W=", sess.run(W), "b=", sess.run(b))
    
                print("Optimization Finished!")
                training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
                print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')
    
                plt.plot(train_X, train_Y, 'ro', label="Original data")
                plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label="Fitted line")
                plt.legend()
                plt.show()