獲得在Tensorboard

Question

情節簡單

我試圖在tensorboard情節簡單，就像他們有它在主頁上，這樣的事情：

要明白這是怎麼工作的，我寫了下面的：

import tensorflow as tf 
import numpy as np 


x = tf.placeholder('float',name='X') 
y= tf.placeholder('float',name='y') 
addition = tf.add(x,y) 


with tf.Session() as sess: 

    for i in range(100): 
     var1= np.random.rand() 
     var2= np.random.rand() 
     print(var1,var2) 
     tf.summary.scalar('addition',sess.run(addition, feed_dict={x:var1,y:var2}))    
     writer = tf.summary.FileWriter('Graphs',sess.graph) 

雖然我可以看到圖形，但看不到任何標量值。任何人都可以向我解釋我在這裏做錯了什麼？ PS：我已經運行了所有正式的例子，它們都在工作，但我需要理解這個例子才能使用它。 感謝您的幫助！

更新

後運行@ DV3代碼程序crashs。這裏是我所得到的：

InvalidArgumentError: You must feed a value for placeholder tensor 'input/x-input' with dtype float 
    [[Node: input/x-input = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]] 

During handling of the above exception, another exception occurred: 

InvalidArgumentError      Traceback (most recent call last) 
<ipython-input-5-5cbd77e71936> in <module>() 
    14   var2= np.random.rand() 
    15   print(var1,var2) 
---> 16   add, s_ = sess.run([addition, summary_op], feed_dict={x:var1,y:var2}) 
    17   writer.add_summary(s_, i) 

Answer 1

所以馬上蝙蝠，我想建議閱讀this。它會更詳細地介紹一個會話。

至於代碼和它爲什麼不產生結果：你沒有初始化變量。你可以這樣做：sess.run(tf.global_variables_initializer())。所以，你的代碼將是：

import tensorflow as tf 
import numpy as np 

x = tf.placeholder('float',name='X') 
y= tf.placeholder('float',name='y') 
addition = tf.add(x,y) 

with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 
    for i in range(100): 
     var1= np.random.rand() 
     var2= np.random.rand() 
     print(var1,var2) 
     tf.summary.scalar('addition',sess.run(addition, feed_dict={x:var1,y:var2}))    
     writer = tf.summary.FileWriter('Graphs',sess.graph) 

我不會嵌入sess.run到summary.scalar電話，但這個簡單的例子，你會得到一些結果。

編輯： 測試，這是實際工作：

import tensorflow as tf 
import numpy as np 

x = tf.placeholder('float',name='X') 
y= tf.placeholder('float',name='y') 
addition = tf.add(x,y, name='add') 
tf.summary.scalar('addition', addition) 
summary_op = tf.summary.merge_all()  
with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 
    writer = tf.summary.FileWriter('Graphs',sess.graph) 
    for i in range(100): 
     var1= np.random.rand() 
     var2= np.random.rand() 
     print(var1,var2) 
     add, s_ = sess.run([addition, summary_op], feed_dict={x:var1,y:var2}) 
     writer.add_summary(s_, i) 

輸出：