神经网络(tensorflow)(1)

import tensorflow as tf
import numpy as np

#create data
x_data = np.random.rand(100).astype(np.float32)#在tensorflow中大部分的数据的数据类型都是float32
y_data = x_data * 0.1+0.3

#create tensorflow structure start
Weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0))#定义初始值为-1到1
biases = tf.Variable(tf.zeros([1]))

y = Weights*x_data + biases

loss = tf.reduce_mean(tf.square(y-y_data)) #计算得到的y与实际的区别
#建立优化器
optimizer = tf.train.GradientDescentOptimizer(0.5)#0.5为学习效率，一般为小于1的一个数
train = optimizer.minimize(loss)

init = tf.initialize_all_variables()#初始化
#create tensorflow structure end
sess = tf.Session()
sess.run(init)  #激活

for step in range(201):
    sess.run(train)
    if step % 20 == 0:
        print(step, sess.run(Weights), sess.run(biases))