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 datafrom 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 modelx = 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 optimizery_ = 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)
# TrainintiOp = tf.initialize_all_variables()
# Init run_metadatarun_metadata = tf.RunMetadata()
# Open file to save tracetrace_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 modelcorrect_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}))
#timelinetrace = timeline.Timeline(step_stats=run_metadata.step_stats)
trace_file.write(trace.generate_chrome_trace_format()) |
打开chrome浏览器输入
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