tensorfow模拟函数拟合过程

tensorflow真是把机器学习变成了纯工程化的东西了    本文用TensorFlow   模拟函数拟合   用matplotlib  画出动图  便于直观感受

最终您会看到拟合的动图

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

x_data=np.random.rand(100).astype(np.float32);
#print(x_data);
y_data=x_data*0.1+0.3;
Weith=tf.Variable(tf.random_uniform([1],-1.0,1.0));
Baise=tf.Variable(tf.zeros([1]));
y=Weith*x_data+Baise;
#定义损失函数
loss=tf.reduce_mean(tf.square(y-y_data));
#学习效率
optimizer=tf.train.GradientDescentOptimizer(0.5);

//按照损失函数最小化进行训练
train=optimizer.minimize(loss);
inint=tf.initialize_all_variables();
sess=tf.Session();
sess.run(inint);


//动图模拟过程
fig2 = plt.figure()
axes1 = fig2.add_subplot(111)
axes1.plot(x_data,y_data);
line,= axes1.plot(x_data,y_data,color='red')

def update(datay):
    line.set_ydata(datay)

    return line,

def data_gen():
    for step in range(201):

        sess.run(train);
        yield sess.run(Weith)*x_data+sess.run(Baise);

ani = animation.FuncAnimation(fig2, update, data_gen, interval=0.1*1000)
plt.show()