Tensorflow学习笔记3:TensorBoard可视化学习

TensorBoard简介

Tensorflow发布包中提供了TensorBoard,用于展示Tensorflow任务在计算过程中的Graph、定量指标图以及附加数据。大致的效果如下所示, 

tensorboard_graphs

TensorBoard工作机制

TensorBoard 通过读取 TensorFlow 的事件文件来运行。TensorFlow 的事件文件包括了你会在 TensorFlow 运行中涉及到的主要数据。关于TensorBoard的详细介绍请参考TensorBoard:可视化学习。下面做个简单介绍。

Tensorflow的API中提供了一种叫做Summary的操作,用于将Tensorflow计算过程的相关数据序列化成字符串Tensor。例如标量数据的图表scalar_summary或者梯度权重的分布histogram_summary

通过tf.train.SummaryWriter来将序列化后的Summary数据保存到磁盘指定目录(通过参数logdir指定)。此外,SummaryWriter构造函数还包含了一个可选参数GraphDef,通过指定该参数,可以在TensorBoard中展示Tensorflow中的Graph(如上图所示)。

大致的代码框架如下所示:

merged_summary_op = tf.merge_all_summaries()
summary_writer = tf.train.SummaryWriter('/tmp/mnist_logs', sess.graph)
total_step = 0
while training:
    total_step += 1
    session.run(training_op)
    if total_step % 100 == 0:
        summary_str = session.run(merged_summary_op)
        summary_writer.add_summary(summary_str, total_step)

启动TensorBoard的命令如下,

python tensorflow/tensorboard/tensorboard.py --logdir=/tmp/mnist_logs

其中--logdir命令行参数指定的路径必须跟SummaryWriter的logdir参数值保持一致,TensorBoard才能够正确读取到Tensorflow的事件文件。

启动Tensorflow后,我们在浏览器中输入http://localhost:6006 即可访问TensorBoard页面了。

通过MNIST实例来验证TensorBoard

tensorflow/tensorflow的源代码目录tensorflow/examples/tutorials/mnist目录下提供了手写数字MNIST识别样例代码。该样例代码同样包含了SummaryWriter的相关代码,我们可以使用该样例代码来验证一下TensorBoard的效果。

首先,克隆一下tensorflow的代码库到本地,

$ git clone https://github.com/tensorflow/tensorflow.git
$ cd tensorflow/examples/tutorials/mnist/
$ emacs fully_connected_feed.py

对fully_connected_feed.py的代码做一下下面两个地方的修改:

  1. 将29、30行的import语句修改一下

    import input_data
    import mnist
  2. 将154行的FLAGS.train_dir修改成'/opt/tensor':

    # Instantiate a SummaryWriter to output summaries and the Graph.
    summary_writer = tf.train.SummaryWriter('/opt/tensor', sess.graph)

样例代码准备好了,下面我们如何启动TensorBoard。

Tensorflow官方的Docker镜像tensorflow/tensorflow提供了一个可快速使用Tensorflow的途径。不过该镜像默认启动的是jupyter。我们通过下面命令通过该镜像启动TensorBoard,并且将我们准备好的MNIST样例代码通过volume挂载到容器中。

lienhuadeMacBook-Pro:tensorflow lienhua34$ docker run -d -p 6006:6006 --name=tensorboard -v /Users/lienhua34/Programs/python/tensorflow/tensorflow/examples/tutorials/mnist:/tensorflow/mnist tensorflow/tensorflow tensorboard --logdir=/opt/tensor
50eeb7282f60c10ed52d26f34feeb3472cf36d83c546357801c45e14939adf1a
lienhuadeMacBook-Pro:tensorflow lienhua34$ 
lienhuadeMacBook-Pro:tensorflow lienhua34$ docker ps -a
CONTAINER ID        IMAGE                                    COMMAND                  CREATED             STATUS                   PORTS                              NAMES
50eeb7282f60        tensorflow/tensorflow                    "tensorboard --logdir"   49 minutes ago      Up 4 seconds             0.0.0.0:6006->6006/tcp, 8888/tcp   tensorboard

此时,我们在浏览器中输入http://localhost:6006/ ,得到下面的效果, 

tensorboard_home

因为我们还没有运行MNIST的样例代码,所以TensorBoard提示没有数据。下面我们将进入tensorboard容器中运行MNIST的样例代码,

lienhuadeMacBook-Pro:tensorflow lienhua34$ docker exec -ti tensorboard /bin/bash
root@50eeb7282f60:/notebooks# cd /tensorflow/mnist/                                                                                                                                 
root@50eeb7282f60:/tensorflow/mnist# python fully_connected_feed.py 
Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
Step 0: loss = 2.31 (0.010 sec)
Step 100: loss = 2.13 (0.007 sec)
Step 200: loss = 1.90 (0.008 sec)
Step 300: loss = 1.56 (0.008 sec)
Step 400: loss = 1.37 (0.007 sec)
Step 500: loss = 0.99 (0.005 sec)
Step 600: loss = 0.82 (0.004 sec)
Step 700: loss = 0.77 (0.004 sec)
Step 800: loss = 0.83 (0.004 sec)
Step 900: loss = 0.54 (0.004 sec)
Training Data Eval:
  Num examples: 55000  Num correct: 47055  Precision @ 1: 0.8555
Validation Data Eval:
  Num examples: 5000  Num correct: 4303  Precision @ 1: 0.8606
Test Data Eval:
  Num examples: 10000  Num correct: 8639  Precision @ 1: 0.8639
Step 1000: loss = 0.52 (0.010 sec)
Step 1100: loss = 0.58 (0.444 sec)
Step 1200: loss = 0.44 (0.005 sec)
Step 1300: loss = 0.42 (0.005 sec)
Step 1400: loss = 0.69 (0.005 sec)
Step 1500: loss = 0.43 (0.004 sec)
Step 1600: loss = 0.43 (0.006 sec)
Step 1700: loss = 0.39 (0.004 sec)
Step 1800: loss = 0.34 (0.004 sec)
Step 1900: loss = 0.34 (0.004 sec)
Training Data Eval:
  Num examples: 55000  Num correct: 49240  Precision @ 1: 0.8953
Validation Data Eval:
  Num examples: 5000  Num correct: 4506  Precision @ 1: 0.9012
Test Data Eval:
  Num examples: 10000  Num correct: 8987  Precision @ 1: 0.8987
root@50eeb7282f60:/tensorflow/mnist# ls -l /opt/tensor
total 76
-rw-r--r-- 1 root root 77059 Oct 25 14:53 events.out.tfevents.1477407177.50eeb7282f60

通过上面的运行结果,我们看到MNIST样例代码正常运行,而且在/opt/tensor目录下也生成了Tensorflow的事件文件events.out.tfevents.1477407177.50eeb7282f60。此时我们刷新一下TensorBoard的页面,看到的效果如下, 

tensorboard_event

tensorboard_histograms

 

如果想看到TensorBoard展示的丰富信息,可以使用mnist目录下的mnist_with_summaries.py文件。

(done)

posted on 2016-10-26 22:59  lienhua34  阅读(37038)  评论(0编辑  收藏  举报

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