Is there .all() or .any() equivalent in python Tensorflow












5















Trying to find a similar operation to .any(), .all() methods that will work on a tensor. Here is a scenario:



a = tf.Variable([True, False, True], dtype=tf.bool)



# this is how I do it right now

has_true = a.reduce_sum(tf.cast(a, tf.float32)) 

# this is what I'm looking for

has_true = a.any()


Currently converting my boolean tensor into int using reduce_sum to see if there are any truths in it. Is there a cleaner way to perform this operation?






python tensorflow







share|improve this question



























    5















    Trying to find a similar operation to .any(), .all() methods that will work on a tensor. Here is a scenario:



    a = tf.Variable([True, False, True], dtype=tf.bool)
    

    
# this is how I do it right now
    
has_true = a.reduce_sum(tf.cast(a, tf.float32)) 
    
# this is what I'm looking for
    
has_true = a.any()


    Currently converting my boolean tensor into int using reduce_sum to see if there are any truths in it. Is there a cleaner way to perform this operation?






    python tensorflow







    share|improve this question

























      5












      5








      5


      1






      Trying to find a similar operation to .any(), .all() methods that will work on a tensor. Here is a scenario:



      a = tf.Variable([True, False, True], dtype=tf.bool)
      

      
# this is how I do it right now
      
has_true = a.reduce_sum(tf.cast(a, tf.float32)) 
      
# this is what I'm looking for
      
has_true = a.any()


      Currently converting my boolean tensor into int using reduce_sum to see if there are any truths in it. Is there a cleaner way to perform this operation?






      python tensorflow







      share|improve this question














      Trying to find a similar operation to .any(), .all() methods that will work on a tensor. Here is a scenario:



      a = tf.Variable([True, False, True], dtype=tf.bool)
      

      
# this is how I do it right now
      
has_true = a.reduce_sum(tf.cast(a, tf.float32)) 
      
# this is what I'm looking for
      
has_true = a.any()


      Currently converting my boolean tensor into int using reduce_sum to see if there are any truths in it. Is there a cleaner way to perform this operation?






      python tensorflow




      python tensorflow






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 20 '18 at 20:31









      nikolaevranikolaevra

      696




      696
























          1 Answer
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          oldest

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          6














          There are tf.reduce_any and tf.reduce_all methods:



          sess = tf.Session()
          

          
a = tf.Variable([True, False, True], dtype=tf.bool)
          
sess.run(tf.global_variables_initializer())
          

          
sess.run(tf.reduce_any(a))
          
# True
          
sess.run(tf.reduce_all(a))
          
# False





          share|improve this answer





















          • 2





            Thanks, just what I needed!

            – nikolaevra
            Nov 20 '18 at 20:41











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          1 Answer
          1






          active

          oldest

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          active

          oldest

          votes






          active

          oldest

          votes









          6














          There are tf.reduce_any and tf.reduce_all methods:



          sess = tf.Session()
          

          
a = tf.Variable([True, False, True], dtype=tf.bool)
          
sess.run(tf.global_variables_initializer())
          

          
sess.run(tf.reduce_any(a))
          
# True
          
sess.run(tf.reduce_all(a))
          
# False





          share|improve this answer





















          • 2





            Thanks, just what I needed!

            – nikolaevra
            Nov 20 '18 at 20:41
















          6














          There are tf.reduce_any and tf.reduce_all methods:



          sess = tf.Session()
          

          
a = tf.Variable([True, False, True], dtype=tf.bool)
          
sess.run(tf.global_variables_initializer())
          

          
sess.run(tf.reduce_any(a))
          
# True
          
sess.run(tf.reduce_all(a))
          
# False





          share|improve this answer





















          • 2





            Thanks, just what I needed!

            – nikolaevra
            Nov 20 '18 at 20:41














          6












          6








          6







          There are tf.reduce_any and tf.reduce_all methods:



          sess = tf.Session()
          

          
a = tf.Variable([True, False, True], dtype=tf.bool)
          
sess.run(tf.global_variables_initializer())
          

          
sess.run(tf.reduce_any(a))
          
# True
          
sess.run(tf.reduce_all(a))
          
# False





          share|improve this answer















          There are tf.reduce_any and tf.reduce_all methods:



          sess = tf.Session()
          

          
a = tf.Variable([True, False, True], dtype=tf.bool)
          
sess.run(tf.global_variables_initializer())
          

          
sess.run(tf.reduce_any(a))
          
# True
          
sess.run(tf.reduce_all(a))
          
# False






          share|improve this answer














          share|improve this answer



          share|improve this answer








          edited Nov 20 '18 at 20:43

























          answered Nov 20 '18 at 20:38









          PsidomPsidom

          124k1286127




          124k1286127








          • 2





            Thanks, just what I needed!

            – nikolaevra
            Nov 20 '18 at 20:41














          • 2





            Thanks, just what I needed!

            – nikolaevra
            Nov 20 '18 at 20:41








          2




          2





          Thanks, just what I needed!

          – nikolaevra
          Nov 20 '18 at 20:41





          Thanks, just what I needed!

          – nikolaevra
          Nov 20 '18 at 20:41


















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