Tensorboard
对于自己写的tensorflow程序,可以是使用tensorboard实现可视化。目前掌握的使用是:
- 编写代码
#!/usr/bin/env python3
import tensorflow as tf
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
## prepare the original data
with tf.name_scope('data'):
x_data = np.random.rand(100).astype(np.float32)
y_data = 0.3*x_data+0.1
##creat parameters
with tf.name_scope('parameters'):
weight = tf.Variable(tf.random_uniform([1],-1.0,1.0))
bias = tf.Variable(tf.zeros([1]))
##get y_prediction
with tf.name_scope('y_prediction'):
y_prediction = weight*x_data+bias
##compute the loss
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.square(y_data-y_prediction))
##creat optimizer
optimizer = tf.train.GradientDescentOptimizer(0.5)
#creat train ,minimize the loss
with tf.name_scope('train'):
train = optimizer.minimize(loss)
#creat init
with tf.name_scope('init'):
init = tf.global_variables_initializer()
##creat a Session
sess = tf.Session()
##initialize
writer = tf.summary.FileWriter("logs/", sess.graph)
sess.run(init)
## Loop
for step in range(101):
sess.run(train)
if step %10==0 :
print(step ,'weight:',sess.run(weight),'bias:',sess.run(bias))
- 运行代码
python 1.py
- 使用tensorboard命令
tensorboard --logdir logs
-
打开localhost:6006端口
tensorboard界面
未来遇到实际问题再来研究
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