1.输入命令开启TensorBoard：
(tensorflow) C:UsersIRay>python D:softwareanacondaenvs ensorflowLibsite-packages ensorflow ensorboard ensorboard.py --logdir=D: mp ensorflowmnistlogsfully_connected_feed
2.如果安装了TensorBoard，可以直接使用命令：
(tensorflow) C:UsersIRay>tensorboard --logdir=D: mp ensorflowmnistlogsfully_connected_feed
3.输入命令后，结果显示：
Starting TensorBoard b'47' at http://0.0.0.0:6006 (Press CTRL+C to quit)
4.此时，到网页上输入地址即可打开，有可能出现意外（IE解析问题），则使用如下地址打开：
http://localhost:6006/
如果发现网页显示 “No scalar data was found”等信息，说明未正确打开记录文件。
需要将terminal的工作路径修改到events log files所在路径，同时注意：logdir=后面所接的文件路径不需要引号（可以使用双引号，单引号会出错）
(tensorflow) C:UsersIRay>D: (tensorflow) D:>tensorboard --logdir=D: mp ensorflow
注意清空spyder（或重启），否则会造成events记录叠加。
使用summary设置记录Tensor的代码如下：使用MNIST多层神经网络做例子
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 11 10:16:34 2017
multy layers softmax regression
@author: Wangjc
"""
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
#need to show the full address, or error occus.
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
#use read_data_sets to download and load the mnist data set. if has the data, then load.
#need a long time about 5 minutes
sess = tf.InteractiveSession()
#link the back-end of C++ to compute.
#in norm cases, we should create the map and then run in the sussion.
#now, use a more convenient class named InteractiveSession which could insert compute map when running map.
x=tf.placeholder("float",shape=[None,784])
y_=tf.placeholder("float",shape=[None,10])
def weight_variable(shape):
#use normal distribution numbers with stddev 0.1 to initial the weight
initial=tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
#use constant value of 0.1 to initial the bias
initial=tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x,W):
#convolution by filter of W,with step size of 1, 0 padding size
#x should have the dimension of [batch,height,width,channels]
#other dimension of strides or ksize is the same with x
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
def max_pool_2x2(x):
#pool by windows of ksize,with step size of 2, 0 padding size
return tf.nn.max_pool(x,ksize=[1,2,2,1],
strides=[1,2,2,1],padding='SAME')
#------------------------------------------------
x_image = tf.reshape(x, [-1,28,28,1])
#to use conv1, need to convert x to 4D, in form of [batch,height,width,channels]
# -1 means default
with tf.name_scope('conv1'):
#use 'with' and name_scope to define a name space which will show in tensorboard as a ragion
with tf.name_scope('weight'):
W_conv1=weight_variable([5,5,1,32])
tf.summary.histogram('conv1'+'/weight',W_conv1)
#summary the variation ('name', value)
with tf.name_scope('bias'):
b_conv1=bias_variable([32])
tf.summary.histogram('conv1'+'/bias',b_conv1)
#build the first conv layer:
#get 32 features from every 5*5 patch, so the shape is [5,5,1(channel),32]
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
with tf.name_scope('pool1'):
h_pool1 = max_pool_2x2(h_conv1)
#--------------------------------------------
with tf.name_scope('conv2'):
with tf.name_scope('weight'):
W_conv2=weight_variable([5,5,32,64])
tf.summary.histogram('weight',W_conv2)
with tf.name_scope('bias'):
b_conv2=bias_variable([64])
tf.summary.histogram('bias',b_conv2)
#build the 2nd conv layer:
#get 64 features from every 5*5 patch, so the shape is [5,5,32(channel),64]
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
with tf.name_scope('pool2'):
h_pool2 = max_pool_2x2(h_conv2)
#----------------------------------------
#image size reduce to 7*7 by pooling
#we add a full connect layer contains 1027 nuere
#need to flat pool tensor for caculate
with tf.name_scope('fc1'):
with tf.name_scope('weight'):
W_fc1 = weight_variable([7*7*64, 1024])
tf.summary.histogram('weight',W_fc1)
with tf.name_scope('bias'):
b_fc1 = bias_variable([1024])
tf.summary.histogram('bias',b_fc1)
h_pool2_flat = tf.reshape(h_pool2,[-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1) + b_fc1)
#------------------------------------
#output layer
with tf.name_scope('out'):
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)
#to decrease overfit, we add dropout before output layer.
#use placeholder to represent the porbability of a neure's output value unchange
with tf.name_scope('weight'):
W_fc2 = weight_variable([1024, 10])
tf.summary.histogram('weight',W_fc2)
with tf.name_scope('bias'):
b_fc2 = bias_variable([10])
tf.summary.histogram('bias',b_fc2)
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
#---------------------------------
#train and evaluate the module
#use a ADAM
cross_entropy=-tf.reduce_sum(y_*tf.log(y_conv))
tf.summary.scalar('cross_entropy',cross_entropy)
##summary the constant ('name', value)
train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
#sess = tf.Session()
merged=tf.summary.merge_all()
#merge all the summary nodes
writer=tf.summary.FileWriter('D:/tmp/tensorflow/mnist/',sess.graph)
# assign the event file write directory
sess.run(tf.global_variables_initializer())
for i in range(500):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_:batch[1],keep_prob:1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
result=sess.run(merged,feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
#the merged summary need to be run
writer.add_summary(result,i)
#add the result to summary
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print("test accuracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))