首先简单认识tensorboard
简单的生成tensorboard文件
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
tensorboard 可视化生成tensorboard文件
"""
import tensorflow as tf
def add_layer(inputs, in_size, out_size, activation_function=None):
# add one more layer and return the output of this layer
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b, )
return outputs
# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])
# add hidden layer
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
# add output layer
prediction = add_layer(l1, 10, 1, activation_function=None)
# the error between prediciton and real data
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
sess = tf.Session()
"""
tensorboard文件生成的位置,文件位置为../logs
"""
writer = tf.summary.FileWriter("../logs/", sess.graph)
# important step
sess.run(tf.initialize_all_variables())
运行程序后, 可以在tensorboard文件生成的位置的文件夹下找到名为events.out.tfevents.1499943764.m类似的文件
打开终端输入:
tensorboard --logdir='../logs/'
logdir为程序中定义的log的文件夹路径
看到如下运行结果
$ tensorboard --logdir='../logs/'
Starting TensorBoard 41 on port 6006
打开浏览器输入http://0.0.0.0:6006, 即可打开可视化界面,左下角有文件的路径
上面只是生成了一个界面,下面详细的定义每个tensorboard中每个标签页输出的图像