tensorflow相关练习

1.产生随机的数据 y=0.1x +0.3 通过机器学习找到权重W和偏差b

import tensorflow as tf
import numpy as np

#creat data
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data*0.1 + 0.3

#creat tensorflow structure start
Weights  = tf.Variable(tf.random_uniform([1],-1.0,1.0))
biases = tf.Variable(tf.zeros([1]))

y = Weights*x_data + biases
loss = tf.reduce_mean(tf.square(y-y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)  #0.5表示学习效率
train = optimizer.minimize(loss)

init = tf.initialize_all_variables()

#create tensorflow structure end

sess = tf.Session();
sess.run(init);   #Very important

for step in range(10001):
    sess.run(train)
    if step % 20 == 0:
        print(step,sess.run(Weights),sess.run(biases));


        """
         #session的两种打开模式(1)
         sess = tf.Session()
         sess.run(product)
        
        #session的两种打开模式(2)
        with tf.Session() as sess:
            result2 = sess.run(product)
        print (result2)
        """

2.变量和赋值

import tensorflow as tf

#定义变量
state = tf.Variable(0,name='count')
# print(state.name)
one = tf.constant(1)
new_value = tf.add(state , one)
update = tf.assign(state,new_value)
init = tf.initialize_all_variables() #must have if define variable
with tf.Session() as sess:
    sess.run(init)
    for _ in range(8):
        sess.run(update)
        print(sess.run(state))
#赋值
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
output = tf.multiply(input1,input2)
with tf.Session() as sess:
    print(sess.run(output,feed_dict={input1:[7.],input2:[2.]}))