1.输入命令开启TensorBoard:
(tensorflow) C:\Users\IRay>python D:\software\anaconda\envs\tensorflow\Lib\site-packages\tensorflow\tensorboard\tensorboard.py --logdir=D:\tmp\tensorflow\mnist\logs\fully_connected_feed\
2.如果安装了TensorBoard,可以直接使用命令:
(tensorflow) C:\Users\IRay>tensorboard --logdir=D:\tmp\tensorflow\mnist\logs\fully_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:\Users\IRay>D: (tensorflow) D:\>tensorboard --logdir=D:\tmp\tensorflow
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}))
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