tensorborad

一：tensorboard简介
tensorboard是tensorflow的可视化工具，可以显示图、展示中间结果。详见：https://tensorflow.google.cn/get_started/summaries_and_tensorboard
二：tf.sumary.histogram
tf.sumary.histogram：直方图。
TensorBoard 直方图信息中心用于显示在 TensorFlow 图中某些 Tensor 随着时间推移而变化的分布。即，该信息中心可显示在不同时间点对应张量的许多张直方图图示。
tf.summary.histogram 基于任意大小和形状的张量，并将张量压缩成一个由许多分箱组成的直方图数据结构，这些分箱有各自的宽度和计数。

import tensorflow as tf

k = tf.placeholder(tf.float32)

# Make a normal distribution, with a shifting mean
mean_moving_normal = tf.random_normal(shape=[1000], mean=(5*k), stddev=1)
# Record that distribution into a histogram summary
tf.summary.histogram("normal/moving_mean", mean_moving_normal)

# Setup a session and summary writer
sess = tf.Session()
writer = tf.summary.FileWriter("/tmp/histogram_example")
#writer = tf.summary.FileWriter("/Users/dudu/Desktop/studykeras")
summaries = tf.summary.merge_all()

# Setup a loop and write the summaries to disk
N = 400
for step in range(N):
  k_val = step/float(N)
  summ = sess.run(summaries, feed_dict={k: k_val})
  writer.add_summary(summ, global_step=step)

执行后使用以下命令：
tensorboard –logdir=/tmp/histogram_example
tensorboard –logdir=/Users/dudu/Desktop/studykeras


三：tf.summary.scalar
一般在画loss,accuary时会用到这个函数。

import tensorflow as tf
import numpy as np

## 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'):
     with tf.name_scope('weights'):
            weight = tf.Variable(tf.random_uniform([1],-1.0,1.0))
            tf.summary.histogram('weight',weight)
     with tf.name_scope('biases'):
           bias = tf.Variable(tf.zeros([1]))
           tf.summary.histogram('bias',bias)
##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))
     tf.summary.scalar('loss',loss)
##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()
#merged
merged = tf.summary.merge_all()
##initialize
writer = tf.summary.FileWriter("/Users/dudu/Desktop/studykeras", sess.graph)
sess.run(init)
## Loop
for step  in  range(101):
    sess.run(train)
    rs=sess.run(merged)
    writer.add_summary(rs, step)

执行后使用以下命令：
tensorboard –logdir=/Users/dudu/Desktop/studykeras

四：tf.summary.image

import tensorflow as tf

# 获取图片数据
file = open('/Users/dudu/Desktop/studykeras/4.png', 'rb')
data = file.read()
file.close()

# 图片处理
image = tf.image.decode_png(data, channels=3)
image = tf.expand_dims(image, 0)

# 添加到日志中
sess = tf.Session()
writer = tf.summary.FileWriter('/Users/dudu/Desktop/studykeras')
summary_op = tf.summary.image("image1", image)

# 运行并写入日志
summary = sess.run(summary_op)
writer.add_summary(summary)

# 关闭
writer.close()
sess.close()

将图片保存为png格式

执行后使用以下命令：
tensorboard –logdir=/Users/dudu/Desktop/studykeras

本文参考了以下博客：
https://www.cnblogs.com/fydeblog/p/7429344.html
https://tensorflow.google.cn/programmers_guide/summaries_and_tensorboard
https://blog.csdn.net/smf0504/article/details/56369758
等等