学习进度笔记4

观看Tensorflow案例实战视频课程04 常用基本操作

#最好使用float32格式
tf.zeros([3,4],int32)==>[[0,0,0,0],[0,0,0,0],[0,0,0,0]]

#'tensor' is [[1,2,3],[4,5,6]]
tf.zeros_like(tensor)==>[[0,0,0],[0,0,0]]
tf.ones([2,3],int32)==>[[1,1,1],[1,1,1]]

#'tensor' is [[1,2,3],[4,5,6]]
tf.ones_like(tensor)==>[[1,1,1],[1,1,1]]

#Contant 1-D Tensor populated with value list.
tensor=tf.constant([1,2,3,4,5,6,7])=>[1 2 3 4 5 6 7]

#Constant 2-D tensor populated with scalar value -1.
tensor=tf.constant(-1.0,shape=[2,3])=>[[-1. -1. -1.]
                                       [-1. -1. -1.]]

tf.linspace(10.0,12.0,3,name="linspace")=>[10.0 11.0 12.0]

#'start' is 3
#'limit' is 18
#'delta' is 3
tf.range(start,limit,delta)==>[3,6,9,12,15]

norm=tf.random_normal([2,3],mean=-1,stddev=4)

#Shuffle the first dimension of a tensor
c=tf.constant([[1,2],[3,4],[5,6]])
shuff=tf.random_shuffle(c)#洗牌操作

#Each time we run these ops, different results are generated
sess=tf.Session()
print(sess.run(norm))
print(sess.run(shuff))

state=tf.Variable(0)
new_value=tf.add(state,tf.constant(1))#state+1
update=tf.assign(state,new_value)#state=new_value

with tf.Session() as sess:
    sess.run(tf.global_varies_initializer())
    print(sess.run(state))
    for _ in range(3):
        sess.run(update)
        print(sess.run(state))

#tf.train.Saver
w=tf.Variable([[0.5,1.0]])
x=tf.Variable([[2.0],[1.0]])
y=tf.matmul(w,x)
init_op=tf.global_varies_initializer()
saver=tf.train.Saver()
with tf.Session() as sess:
    sess.run(init_op)
#Do some work with the model.
#Save the variables to disk.
    save_path=saver.save(sess,"C://tensorflow//model//test")
    print("Model saved in file:",save_path)

import numpy as np
a=np.zeros((3,3))
ta=tf.convert_to_tensor(a)#numpy格式转为tensorflow格式
with tf.Session() as sess:
    print(sess.run(ta))

input1=tf.placeholder(tf.float32)
input2=tf.placeholder(tf.float32)
output=tf.mul(input1,input2)
with tf.Session() as sess:
    print(sess.run([output],feed_dict={input1:[7.],input2:[2.]}))