- Mastering TensorFlow 1.x
- Armando Fandango
- 248字
- 2021-06-25 22:50:58
A TensorBoard minimal example
- Start by defining the variables and placeholders for our linear model:
# Assume Linear Model y = w * x + b
# Define model parameters
w = tf.Variable([.3], name='w',dtype=tf.float32)
b = tf.Variable([-.3], name='b', dtype=tf.float32)
# Define model input and output
x = tf.placeholder(name='x',dtype=tf.float32)
y = w * x + b
- Initialize a session, and within the context of this session, do the following steps:
- Initialize global variables
- Create tf.summary.FileWriter that would create the output in the tflogs folder with the events from the default graph
- Fetch the value of node y, effectively executing our linear model
with tf.Session() as tfs:
tfs.run(tf.global_variables_initializer())
writer=tf.summary.FileWriter('tflogs',tfs.graph)
print('run(y,{x:3}) : ', tfs.run(y,feed_dict={x:3}))
- We see the following output:
run(y,{x:3}) : [ 0.60000002]
As the program executes, the logs are collected in the tflogs folder that would be used by TensorBoard for visualization. Open the command line interface, navigate to the folder from where you were running the ch-01_TensorFlow_101 notebook, and execute the following command:
tensorboard --logdir='tflogs'
You would see an output similar to this:
Starting TensorBoard b'47' at http://0.0.0.0:6006
Open a browser and navigate to http://0.0.0.0:6006. Once you see the TensorBoard dashboard, don't worry about any errors or warnings shown and just click on the GRAPHS tab at the top. You will see the following screen:
TensorBoard console
You can see that TensorBoard has visualized our first simple model as a computation graph:
Computation graph in TensorBoard
Let's now try to understand how TensorBoard works in detail.
推薦閱讀
- Learning SQL Server Reporting Services 2012
- Aftershot Pro:Non-destructive photo editing and management
- 電腦維護與故障排除傻瓜書(Windows 10適用)
- Android NDK Game Development Cookbook
- 基于Proteus和Keil的C51程序設計項目教程(第2版):理論、仿真、實踐相融合
- SDL Game Development
- 數字道路技術架構與建設指南
- Large Scale Machine Learning with Python
- Hands-On Machine Learning with C#
- STM32嵌入式技術應用開發全案例實踐
- Arduino BLINK Blueprints
- 筆記本電腦使用、維護與故障排除從入門到精通(第5版)
- Hands-On Artificial Intelligence for Banking
- 基于PROTEUS的電路設計、仿真與制板
- 筆記本電腦芯片級維修從入門到精通(圖解版)