import numpy as np

import matplotlib.pyplot as plt

import tensorflow as tf

#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

#定义两个placeholder

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

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

#定义神经网络中间层

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

L1=tf.nn.tanh(Wx_plus_b_L1)

#定义神经网络输出层

Weights_L2=tf.Variable(tf.random_normal([10,1]))

biases_L2=tf.Variable(tf.zeros([1,1]))

Wx_plus_b_L2=tf.matmul(L1,Weights_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))

#使用梯度下降法训练

train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)

with tf.Session() as sess:

    #变量初始化

    sess.run(tf.global_variables_initializer())

    for _ in range(2000):

        sess.run(train_step,feed_dict={x:x_data,y:y_data})

    #获取预测值

    prediction_value=sess.run(prediction,feed_dict={x:x_data})

    #画图

    plt.figure()

    plt.scatter(x_data,y_data)

    plt.plot(x_data,prediction_value,'r-',lw=5)

    plt.show()