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