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.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
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
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