TensorBoard 使用

TensorBoard是一个可视化工具，能够有效地展示Tensorflow在运行过程中的计算图、各种指标随着时间的变化趋势以及训练中使用到的数据信息。

D:\python\1.py文件内容：
import tensorflow as tf
with tf.name_scope('graph') as scope:
matrix1 = tf.constant([[3., 3.]],name ='matrix1') #1 row by 2 column
matrix2 = tf.constant([[2.],[2.]],name ='matrix2') # 2 row by 1 column
product = tf.matmul(matrix1, matrix2,name='product')
sess = tf.Session()
writer = tf.summary.FileWriter("logs/", sess.graph)
init = tf.global_variables_initializer()
sess.run(init)

cd /opt/aiaasdata/aicode

D:\python>python 1.py
D:\python>tensorboard --logdir logs

浏览器访问：http://localhost:6006

如果需要指定端口：
tensorboard --logdir logs --port=6007
tensorboard --logdir logs2 --port=6007
tensorboard --logdir logs3 --port=6006
tensorboard --logdir logs_fancy
tensorboard --logdir=/tmp/tensorflow/logs/mnist_with_summaries --port=6008

tensorboard --logdir=D:/python/logs --port=6007

tensorboard --logdir=/opt/workspace/code/logs/ --port=6007