Tensorflow timeline trace

根据

https://github.com/tensorflow/tensorflow/issues/1824

简单进行了测试

修改运行的脚本增加如下关键代码

例如mnist_softmax.py

from __future__ import absolute_import
 
from __future__ import division
 
from __future__ import print_function
 
# Import data
 
from tensorflow.examples.tutorials.mnist import input_data
 
from tensorflow.python.client import timeline
 
import tensorflow as tf
 
flags = tf.app.flags
 
FLAGS = flags.FLAGS
 
flags.DEFINE_string('data_dir', '/tmp/data/', 'Directory for storing data')
 
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
 
# Create the model
 
x = tf.placeholder(tf.float32, [None, 784])
 
W = tf.Variable(tf.zeros([784, 10]))
 
b = tf.Variable(tf.zeros([10]))
 
y = tf.nn.softmax(tf.matmul(x, W) + b)
 
# Define loss and optimizer
 
y_ = tf.placeholder(tf.float32, [None, 10])
 
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
 
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
 
# Train
 
intiOp = tf.initialize_all_variables()
 
# Init run_metadata
 
run_metadata = tf.RunMetadata()
 
# Open file to save trace
 
trace_file = open('/tmp/timeline.ctf.json', 'w')
 
sess = tf.Session()
 
sess.run(intiOp)
 
for i in range(500):
 
  batch_xs, batch_ys = mnist.train.next_batch(100)
 
  sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys},
 
           options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE),
 
           run_metadata=run_metadata)
 
# Test trained model
 
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
 
#timeline
 
trace = timeline.Timeline(step_stats=run_metadata.step_stats)
 
trace_file.write(trace.generate_chrome_trace_format())

打开chrome浏览器输入

chrome://tracing/

选择Load按钮加载输出的json文件

W,S按键可以缩放，A,D按键可以移动，具体帮助点击右上角“？”按钮