文章目录
- 建立'计算图'
- [tensorflow placeholder](#tensorflow placeholder)
- tensorboard
- 建立一维与二维张量
- 矩阵的基本运算
github地址https://github.com/fz861062923/TensorFlow
建立'计算图'
python
#建立'计算图'
import tensorflow as tf
x=tf.constant(2,name='x')#建立常量，有点像C
y=tf.Variable(x+5,name='y')#建立变量
python
#执行'计算图'
with tf.Session() as sess:
init=tf.global_variables_initializer()#初始化global变量
sess.run(init)
print('x=',sess.run(x))
print('y=',sess.run(y))x= 2
y= 7
python
x<tf.Tensor 'x:0' shape=() dtype=int32>tensorflow placeholder
正如这个名字一样，hold on,hold on,告诉计算机等等在把值传给你，嘻嘻嘻嘻
python
a=tf.placeholder('int32')
b=tf.placeholder('int32')
c=tf.multiply(a,b)
with tf.Session() as sess:
init=tf.global_variables_initializer()
sess.run(init)
print('c=',sess.run(c,feed_dict={a:6,b:7}))c= 42tensorflow数值运算常用的方法
- tf.add(x,y)
- tf.subtract(x,y)#减法
- tf.multiply(x,y)
- tf.divide(x,y)
- tf.mod(x,y)#余数
- tf.sqrt(x,name=None)
- tf.abs(x,name=None)
tensorboard
正如其名，可视化已经建立的计算图
python
#承接上面的session
#下面代码将显示在tensorboard的数据写在log文件中
tf.summary.merge_all()#将显示在board的数据整合
train_writer=tf.summary.FileWriter('log/c',sess.graph)#写入log文件中启动tensorboard的方法
- activate tensorflow(虚拟环境名称)
- tensorboard --logdir=c:\python\log\c
- 用浏览器打开http://lacalhost:6006/
建立一维与二维张量
建立一维张量
python
ts_x=tf.Variable([0.4,0.2,0.4])
with tf.Session() as sess:
init=tf.global_variables_initializer()
sess.run(init)
x=sess.run(ts_x)
print(x)[0.4 0.2 0.4]
python
x.shape(3,)建立二维张量
python
ts_x=tf.Variable([[0.4,0.2,0.4]])
with tf.Session() as sess:
init=tf.global_variables_initializer()
sess.run(init)
x=sess.run(ts_x)
print(x)[[0.4 0.2 0.4]]
python
x.shape(1, 3)建立新的二维张量
python
ts_x=tf.Variable([[0.4,0.2],
[0.3,0.4],
[-0.5,0.2]])
with tf.Session() as sess:
init=tf.global_variables_initializer()
sess.run(init)
x=sess.run(ts_x)
print(x)[[ 0.4 0.2]
[ 0.3 0.4]
[-0.5 0.2]]
python
x.shape(3, 2)矩阵的基本运算
矩阵的加法
python
x=tf.Variable([[1.,1.,1.]])
w=tf.Variable([[-0.1,-0.2],
[-0.3,0.4],
[0.5,0.6]])
xw=tf.matmul(x,w)
with tf.Session() as sess:
init=tf.global_variables_initializer()
sess.run(init)
print(sess.run(xw))[[0.09999999 0.8 ]]矩阵乘法与加法
python
x=tf.Variable([[1.,1.,1.]])
w=tf.Variable([[-0.1,-0.2],
[-0.3,0.4],
[0.5,0.6]])
b=tf.Variable([[0.1,0.2]])
xwb=tf.matmul(x,w)+b
with tf.Session() as sess:
init=tf.global_variables_initializer()
sess.run(init)
print(sess.run(xwb))[[0.19999999 1. ]]