官术网_书友最值得收藏!

A TensorBoard minimal example

  1. 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
  1. 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}))
  1. 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.

主站蜘蛛池模板: 灵石县| 措勤县| 嵊州市| 保康县| 墨玉县| 玛沁县| 军事| 商河县| 阜新市| 高平市| 桐柏县| 恩平市| 瑞金市| 塔城市| 正镶白旗| 大邑县| 浦东新区| 凭祥市| 微博| 阿城市| 贵溪市| 青川县| 江口县| 乐山市| 庄浪县| 文水县| 定结县| 柘荣县| 奉贤区| 名山县| 昔阳县| 阿拉尔市| 旺苍县| 措勤县| 呼玛县| 宜都市| 灌阳县| 晋州市| 望江县| 平定县| 云浮市|