tensorflow学习笔记——线性回归

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

#使用numpy生成200个随机点
x_data = np.linspace(-0.5,0.5,200)[:,np.newaxis]
noise = np.random.normal(0,0.02,x_data.shape)
y_data = np.square(x_data) + noise

x = tf.placeholder(tf.float32,[None,1])
y = tf.placeholder(tf.float32,[None,1])

#输入层到隐藏层
Weights_L1 = tf.Variable(tf.random_normal([1,10]))
biases_L1 = tf.Variable(tf.zeros([1,10]))
Wx_plus_b_L1 = tf.matmul(x,Weights_L1) + biases_L1
#激活函数                        
L1 = tf.nn.tanh(Wx_plus_b_L1)
#隐藏层到输出层
Weight_L2 = tf.Variable(tf.random_normal([10,1]))
biases_L2 = tf.Variable(tf.zeros([1,1]))

Wx_plus_b_L2 = tf.matmul(L1,Weight_L2) + biases_L2
prediction = tf.nn.tanh(Wx_plus_b_L2)
#二次代价函数
loss = tf.reduce_mean(tf.square(y-prediction))
#梯度下降法训练
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for _ in range(2000):
        sess.run(train_step,feed_dict={x:x_data,y:y_data})
    prediction_value = sess.run(prediction,feed_dict={x:x_data})
    
    plt.figure()
    plt.scatter(x_data,y_data)
    plt.plot(x_data,prediction_value,"r-",lw=5)
    plt.show()

运行结果如下：