TensorFlow之线性回归

通过tensorflow实现一个简易的线性回归，代码如下：


import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

#定义训练数据
train_x = np.linspace(-1,1,100)
train_y = 3*train_x + np.random.randn(*train_x.shape)*0.33 + 10

#定义输入输出
x = tf.placeholder(dtype=tf.float32)
y_ = tf.placeholder(dtype=tf.float32)

#定义权值和偏置
w = tf.Variable(0.0,name="weight")
b = tf.Variable(0.0,name="bias")

#定义预测输出
y = w*x + b

#定义损失函数和反向传播算法
loss = tf.square(y-y_)
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

#创建会话
with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    
    for i in range(10):
        for (X,Y) in zip(train_x,train_y):
            _,w_value,b_value = sess.run([train_step,w,b],feed_dict={x:X,y_:Y})
        print("step:{},w:{},b:{}".format(i+1,w_value,b_value))
        #绘图
        plt.plot(train_x,train_y,'+')
        plt.plot(train_x,w.eval()*train_x+b.eval())
        plt.show()