with tensorflow .name_scope(layer_name): 

with tf.name_scope(layer_name): 
  with tf.name_scope('weights'): 

with tf.name_scope('weights'): 
  Weights = tf.Variable(tf.random_normal([in_size,out_size])) 

tf.histogram_summary(layer_name+"/weights",Weights) #name命名，Weights赋值 

tf.scalar_summary('loss',loss) #命名和赋值 

#合并到Summary中 
merged = tf.merge_all_summaries() 
#选定可视化存储目录 
writer = tf.train.SummaryWriter("/目录",sess.graph) 

result = sess.run(merged) #merged也是需要run的 
  writer.add_summary(result,i) 

tensorboard --logdir="/目录" 

import tensorflow as tf  
import numpy as np  
  
def add_layer(inputs,in_size,out_size,n_layer,activation_function=None): #activation_function=None线性函数  
  layer_name="layer%s" % n_layer  
  with tf.name_scope(layer_name):  
    with tf.name_scope('weights'):  
      Weights = tf.Variable(tf.random_normal([in_size,out_size])) #Weight中都是随机变量  
      tf.summary.histogram(layer_name+"/weights",Weights) #可视化观看变量  
    with tf.name_scope('biases'):  
      biases = tf.Variable(tf.zeros([1,out_size])+0.1) #biases推荐初始值不为0  
      tf.summary.histogram(layer_name+"/biases",biases) #可视化观看变量  
    with tf.name_scope('Wx_plus_b'):  
      Wx_plus_b = tf.matmul(inputs,Weights)+biases #inputs*Weight+biases  
      tf.summary.histogram(layer_name+"/Wx_plus_b",Wx_plus_b) #可视化观看变量  
    if activation_function is None:  
      outputs = Wx_plus_b  
    else:  
      outputs = activation_function(Wx_plus_b)  
    tf.summary.histogram(layer_name+"/outputs",outputs) #可视化观看变量  
    return outputs  
  
#创建数据x_data，y_data  
x_data = np.linspace(-1,1,300)[:,np.newaxis] #[-1,1]区间，300个单位，np.newaxis增加维度  
noise = np.random.normal(0,0.05,x_data.shape) #噪点  
y_data = np.square(x_data)-0.5+noise  
  
with tf.name_scope('inputs'): #结构化  
  xs = tf.placeholder(tf.float32,[None,1],name='x_input')  
  ys = tf.placeholder(tf.float32,[None,1],name='y_input')  
  
#三层神经，输入层（1个神经元），隐藏层（10神经元），输出层（1个神经元）  
l1 = add_layer(xs,1,10,n_layer=1,activation_function=tf.nn.relu) #隐藏层  
prediction = add_layer(l1,10,1,n_layer=2,activation_function=None) #输出层  
  
#predition值与y_data差别  
with tf.name_scope('loss'):  
  loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1])) #square()平方,sum()求和,mean()平均值  
  tf.summary.scalar('loss',loss) #可视化观看常量  
with tf.name_scope('train'):  
  train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) #0.1学习效率,minimize(loss)减小loss误差  
  
init = tf.initialize_all_variables()  
sess = tf.Session()  
#合并到Summary中  
merged = tf.summary.merge_all()  
#选定可视化存储目录  
writer = tf.summary.FileWriter("Desktop/",sess.graph)  
sess.run(init) #先执行init  
  
#训练1k次  
for i in range(1000):  
  sess.run(train_step,feed_dict={xs:x_data,ys:y_data})  
  if i%50==0:  
    result = sess.run(merged,feed_dict={xs:x_data,ys:y_data}) #merged也是需要run的  
    writer.add_summary(result,i) #result是summary类型的，需要放入writer中，i步数（x轴）