#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2018/5/14 13:40
# @Author : HJH
# @Site :
# @File : add_layer.py
# @Software: PyCharm
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
def add_layer(inputs,in_size,out_size,activation_function=None):
Weights=tf.Variable(tf.random_normal([in_size,out_size]))
biases=tf.Variable(tf.zeros([1,out_size])+0.1)
Wx_plus_b=tf.matmul(inputs,Weights)+biases
if activation_function is None:
outputs=Wx_plus_b
else:
outputs=activation_function(Wx_plus_b,)
return outputs
if __name__=='__main__':
#创建一个300行的等差数列
X=np.linspace(-1,1,300)[:,np.newaxis]
noise=np.random.normal(0,0.05,X.shape)
y=np.square(X)-0.5+noise
xs=tf.placeholder(tf.float32,[None,1])
ys = tf.placeholder(tf.float32,[None,1])
l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction=add_layer(l1,10,1,activation_function=None)
loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init=tf.global_variables_initializer()
#使用matplotlib.pyplot可视化
with tf.Session() as sess:
sess.run(init)
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.scatter(X,y)
plt.ion()#
for i in range(1500):
sess.run(train_step,feed_dict={xs:X,ys:y})
if i%50==0:
# print(sess.run(loss,feed_dict={xs:X,ys:y}))
try:
ax.lines.remove(lines[0])
except Exception:
pass
prediction_value=sess.run(prediction,feed_dict={xs:X,ys:y})
lines=ax.plot(X,prediction_value,'r_',lw=5)
plt.pause(0.1)
plt.ioff()
plt.show()