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。下面做个简单介绍。
Tensorflow的API中提供了一种叫做的操作,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或者梯度权重的分布。
通过来将序列化后的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 = 0while 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
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:
-
将29、30行的import语句修改一下
import input_dataimport mnist
-
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/tensor50eeb7282f60c10ed52d26f34feeb3472cf36d83c546357801c45e14939adf1alienhuadeMacBook-Pro:tensorflow lienhua34$ lienhuadeMacBook-Pro:tensorflow lienhua34$ docker ps -aCONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES50eeb7282f60 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,
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/bashroot@50eeb7282f60:/notebooks# cd /tensorflow/mnist/ root@50eeb7282f60:/tensorflow/mnist# python fully_connected_feed.py Extracting data/train-images-idx3-ubyte.gzExtracting data/train-labels-idx1-ubyte.gzExtracting data/t10k-images-idx3-ubyte.gzExtracting data/t10k-labels-idx1-ubyte.gzStep 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: 0.8555Validation Data Eval: Num examples: 5000 Num correct: 4303 Precision @ 1: 0.8606Test Data Eval: Num examples: 10000 Num correct: 8639 Precision @ 1: 0.8639Step 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: 0.8953Validation Data Eval: Num examples: 5000 Num correct: 4506 Precision @ 1: 0.9012Test Data Eval: Num examples: 10000 Num correct: 8987 Precision @ 1: 0.8987root@50eeb7282f60:/tensorflow/mnist# ls -l /opt/tensortotal 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,
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。