tensorflow入门Day3-TensorBoard

#-*- coding: utf-8 -*-import tensorflow as tf#TB是tensorflow自带的一个强大的可视化工具tf.set_random_seed(1234)'''Step 1: Name variables and make use of scopes for organizing your graphStep 2: Place summaries for those values you want to keep trackStep 3: Let TF manage all summaries with tf.merge_all_summaries()Step 4: Create a writer pointing to the desired directoryStep 5: Add the merged summaries to the writerStep 6: Launch TensorBoardUse the command ➙ $ tensorboard --logdir=/tmp/regression/run1Then browse ➙ http://localhost:6006/ or http://127.0.0.1:6006/○If using Docker you may need to add the parameter -p 6006:6006'''with tf.name_scope('data'):with tf.name_scope('x'):x = tf.random_normal([100], mean=0.0, stddev=0.9, name='rand_x')  #[100]尺寸，均值，标准差with tf.name_scope('y'):y_true = x * tf.constant(0.1, name='real_scope') + tf.constant(0.3, name='real_bias') + tf.random_normal([100], mean=0.0, stddev=0.05, name='rand_y')'''random_normal: 正太分布随机数，均值mean,标准差stddev truncated_normal:截断正态分布随机数，均值mean,标准差stddev,不过只保留[mean-2*stddev,mean+2*stddev]范围内的随机数 random_uniform:均匀分布随机数，范围为[minval,maxval]'''with tf.name_scope('W'):W = tf.Variable(tf.random_uniform([], minval=-1.0, maxval=1.0))#step2:记录你想追踪的参数tf.scalar_summary('function/W', W)with tf.name_scope('b'):b = tf.Variable(tf.zeros([]))tf.scalar_summary('function/b', b)with tf.name_scope('function'):y_pred = W * x + bwith tf.name_scope('error'):loss = tf.reduce_mean(tf.square(y_pred-y_true))tf.scalar_summary('error', loss)train = tf.train.GradientDescentOptimizer(0.05).minimize(loss)init = tf.initialize_all_variables()sess = tf.Session()#step3:merged = tf.merge_all_summaries()#tf.train.SummaryWriter用于写入包含了图表本身和即时数据具体值的事件文件#step4:writer = tf.train.SummaryWriter('/tmp/regression/run1', sess.graph)sess.run(init)#slop:坡；intercept：截距.for step in range(1,101):_, slope, intercept, error = sess.run([train, W, b, loss])#step5:if step % 10 == 0:summary_str = sess.run(merged)writer.add_summary(summary_str, step)print('Step %.3d; W = %.5f; b= %.5f; loss = %.5f' % (step, slope, intercept, error))

Graph示例：

事件示例：