Linear Regression 回归主要分为线性回归和逻辑回归。线性回归主要解决连续值预测问题，逻辑回归主要解决分类问题。
假定输入与输出之间有线性关系，给定样本x,假设其输出如下，还可以加上一个bias

loss function 选取的损失函数为MSE，即均方误差函数


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MSE 数据集 数据集是随便从网上搜了一个工作时间和薪水对应的csv文件，见下图


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代码部分

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.model_selection import train_test_split raw = pd.read_csv("../data/Salary_Data.csv")#X和Y是numpy.array类型 X = raw["YearsExperience"].values Y = raw["Salary"].values#划分训练集和测试集 x_train,x_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.3)##对x和y进行归一化 # x_train = (X_train -X_train.min())/(X_train.max()-X_train.min()) y_train = (Y_train - Y_train.min())/(Y_train.max()-Y_train.min()) # print(type(x_train)) y_test = (Y_test - Y_test.min())/(Y_test.max()-Y_test.min()) n_numbers = x_train.shape[0]x = tf.placeholder(dtype=tf.float32,name="x") y = tf.placeholder(dtype=tf.float32,name="y")w = tf.get_variable("w",shape=[],initializer=tf.zeros_initializer) b = tf.get_variable("b",shape=[],initializer=tf.zeros_initializer)pred = tf.multiply(w,x)+b###MSE作为损失函数 loss = tf.reduce_sum(tf.square(pred-y))/(2*n_numbers)optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(loss) init_op =tf.initialize_all_variables() epoches = 1000 display = 50 with tf.Session() as sess: sess.run(init_op) for i in range(epoches): for (x_data,y_data) in zip(x_train,y_train): sess.run(optimizer,feed_dict={x:x_data,y:y_data}) if (i+1) % display == 0: print("after {} epoch of training,loss is {},w is {},b is {}".format(i,sess.run(loss,feed_dict={x:x_train,y:y_train}),sess.run(w),sess.run(b))) plt.plot(x_train, y_train, 'ro', label='Original data') plt.plot(x_train, sess.run(w) * x_train + sess.run(b), label='Fitted line') plt.legend() plt.show()##测试集 testing_cost = sess.run(tf.reduce_sum(tf.square(pred-y))/(2*n_numbers),feed_dict={x:x_test,y:y_test}) print("testing cost is {:.9f}".format(testing_cost)) plt.plot(x_test, y_test, 'bo', label='Testing data') plt.plot(train_X, sess.run(w) * train_X + sess.run(b), label='Fitted line') plt.legend() plt.show()

3. tf.Variable() vs tf.get_variable()创建变量
   以下两句等价，区别在于tf.Variable的变量名是一个可选项，通过name=’v’的形式给出。但是tf.get_variable必须指定变量名。

v = tf.get_variable('v', shape=[1], initializer=tf.constant_initializer(1.0)) v = tf.Variable(tf.constant(1.0, shape=[1], name='v')

4. initialize_all_variable()这个方法可以自动处理变量之间的依赖关系
   TypeError: Fetch argument .wrapped at 0x00000231D706B268> has invalid type , must be a string or Tensor. (Can not convert a function into a Tensor or Operation.)