Cannot interpret feed_dict key as Tensor












0















I have a list of placeholders called "enqueue_ops" and a list of methods called "feed_fns", each of which returns a feed_dict.



The queue runner of my graph is defined as:



queue_runner = feeding_queue_runner.FeedingQueueRunner(

            queue=queue, enqueue_ops=enqueue_ops,

            feed_fns=feed_fns)


However I got an error of 



TypeError: Cannot interpret feed_dict key as Tensor: The name 'face_detection/x1' refers to an Operation, not a Tensor. Tensor names must be of the form "<op_name>:<output_index>".


But why are they looking at my feed_dict keys, while my feed_dict values are tensors that they don't want to look at?



Thanks!!!






tensorflow







share|improve this question


















  • 3





    You should add the code which raises the error, but it seems you need to add ":0" at the end of the tensor name, like "face_detection/x1:0"

    – Olivier Moindrot
    Jul 16 '16 at 14:14
















0















I have a list of placeholders called "enqueue_ops" and a list of methods called "feed_fns", each of which returns a feed_dict.



The queue runner of my graph is defined as:



queue_runner = feeding_queue_runner.FeedingQueueRunner(

            queue=queue, enqueue_ops=enqueue_ops,

            feed_fns=feed_fns)


However I got an error of 



TypeError: Cannot interpret feed_dict key as Tensor: The name 'face_detection/x1' refers to an Operation, not a Tensor. Tensor names must be of the form "<op_name>:<output_index>".


But why are they looking at my feed_dict keys, while my feed_dict values are tensors that they don't want to look at?



Thanks!!!






tensorflow







share|improve this question


















  • 3





    You should add the code which raises the error, but it seems you need to add ":0" at the end of the tensor name, like "face_detection/x1:0"

    – Olivier Moindrot
    Jul 16 '16 at 14:14














0












0








0








I have a list of placeholders called "enqueue_ops" and a list of methods called "feed_fns", each of which returns a feed_dict.



The queue runner of my graph is defined as:



queue_runner = feeding_queue_runner.FeedingQueueRunner(

            queue=queue, enqueue_ops=enqueue_ops,

            feed_fns=feed_fns)


However I got an error of 



TypeError: Cannot interpret feed_dict key as Tensor: The name 'face_detection/x1' refers to an Operation, not a Tensor. Tensor names must be of the form "<op_name>:<output_index>".


But why are they looking at my feed_dict keys, while my feed_dict values are tensors that they don't want to look at?



Thanks!!!






tensorflow







share|improve this question














I have a list of placeholders called "enqueue_ops" and a list of methods called "feed_fns", each of which returns a feed_dict.



The queue runner of my graph is defined as:



queue_runner = feeding_queue_runner.FeedingQueueRunner(

            queue=queue, enqueue_ops=enqueue_ops,

            feed_fns=feed_fns)


However I got an error of 



TypeError: Cannot interpret feed_dict key as Tensor: The name 'face_detection/x1' refers to an Operation, not a Tensor. Tensor names must be of the form "<op_name>:<output_index>".


But why are they looking at my feed_dict keys, while my feed_dict values are tensors that they don't want to look at?



Thanks!!!






tensorflow




tensorflow






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Jul 16 '16 at 1:24









nekodesunekodesu

12717




12717








  • 3





    You should add the code which raises the error, but it seems you need to add ":0" at the end of the tensor name, like "face_detection/x1:0"

    – Olivier Moindrot
    Jul 16 '16 at 14:14














  • 3





    You should add the code which raises the error, but it seems you need to add ":0" at the end of the tensor name, like "face_detection/x1:0"

    – Olivier Moindrot
    Jul 16 '16 at 14:14








3




3





You should add the code which raises the error, but it seems you need to add ":0" at the end of the tensor name, like "face_detection/x1:0"

– Olivier Moindrot
Jul 16 '16 at 14:14





You should add the code which raises the error, but it seems you need to add ":0" at the end of the tensor name, like "face_detection/x1:0"

– Olivier Moindrot
Jul 16 '16 at 14:14












1 Answer
1






active

oldest

votes


















0














In tensorflow if you want to restore a graph and use it, before saving the graph you should give your desired variables, placeholders, operations etc a unique name.



For an example see below.





W = tf.Variable(0.1, name='W')

X = tf.placeholder(tf.float32, (None, 2), name='X')

mult = tf.multiply(W,X,name='mult')


Then, once the graph is saved, you could restore and use it as follows. Remember to bundle your tensors with quotation marks. And if you are finding a value of a tensor, add :0 at the end of the tensor name as tensorflow requires it to be in "op_name:output_index" format.



with tf.Session() as sess:

    new_saver = tf.train.import_meta_graph('your_model.meta')

    new_saver.restore(sess, tf.train.latest_checkpoint('./'))

    print(sess.run('mult:0', feed_dict={'X:0': [[1,4],[2,9]]}))





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    1 Answer
    1






    active

    oldest

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    active

    oldest

    votes






    active

    oldest

    votes









    0














    In tensorflow if you want to restore a graph and use it, before saving the graph you should give your desired variables, placeholders, operations etc a unique name.



    For an example see below.





    W = tf.Variable(0.1, name='W')
    
X = tf.placeholder(tf.float32, (None, 2), name='X')
    
mult = tf.multiply(W,X,name='mult')


    Then, once the graph is saved, you could restore and use it as follows. Remember to bundle your tensors with quotation marks. And if you are finding a value of a tensor, add :0 at the end of the tensor name as tensorflow requires it to be in "op_name:output_index" format.



    with tf.Session() as sess:
    
    new_saver = tf.train.import_meta_graph('your_model.meta')
    
    new_saver.restore(sess, tf.train.latest_checkpoint('./'))
    
    print(sess.run('mult:0', feed_dict={'X:0': [[1,4],[2,9]]}))





    share|improve this answer






























      0














      In tensorflow if you want to restore a graph and use it, before saving the graph you should give your desired variables, placeholders, operations etc a unique name.



      For an example see below.





      W = tf.Variable(0.1, name='W')
      
X = tf.placeholder(tf.float32, (None, 2), name='X')
      
mult = tf.multiply(W,X,name='mult')


      Then, once the graph is saved, you could restore and use it as follows. Remember to bundle your tensors with quotation marks. And if you are finding a value of a tensor, add :0 at the end of the tensor name as tensorflow requires it to be in "op_name:output_index" format.



      with tf.Session() as sess:
      
    new_saver = tf.train.import_meta_graph('your_model.meta')
      
    new_saver.restore(sess, tf.train.latest_checkpoint('./'))
      
    print(sess.run('mult:0', feed_dict={'X:0': [[1,4],[2,9]]}))





      share|improve this answer




























        0












        0








        0







        In tensorflow if you want to restore a graph and use it, before saving the graph you should give your desired variables, placeholders, operations etc a unique name.



        For an example see below.





        W = tf.Variable(0.1, name='W')
        
X = tf.placeholder(tf.float32, (None, 2), name='X')
        
mult = tf.multiply(W,X,name='mult')


        Then, once the graph is saved, you could restore and use it as follows. Remember to bundle your tensors with quotation marks. And if you are finding a value of a tensor, add :0 at the end of the tensor name as tensorflow requires it to be in "op_name:output_index" format.



        with tf.Session() as sess:
        
    new_saver = tf.train.import_meta_graph('your_model.meta')
        
    new_saver.restore(sess, tf.train.latest_checkpoint('./'))
        
    print(sess.run('mult:0', feed_dict={'X:0': [[1,4],[2,9]]}))





        share|improve this answer















        In tensorflow if you want to restore a graph and use it, before saving the graph you should give your desired variables, placeholders, operations etc a unique name.



        For an example see below.





        W = tf.Variable(0.1, name='W')
        
X = tf.placeholder(tf.float32, (None, 2), name='X')
        
mult = tf.multiply(W,X,name='mult')


        Then, once the graph is saved, you could restore and use it as follows. Remember to bundle your tensors with quotation marks. And if you are finding a value of a tensor, add :0 at the end of the tensor name as tensorflow requires it to be in "op_name:output_index" format.



        with tf.Session() as sess:
        
    new_saver = tf.train.import_meta_graph('your_model.meta')
        
    new_saver.restore(sess, tf.train.latest_checkpoint('./'))
        
    print(sess.run('mult:0', feed_dict={'X:0': [[1,4],[2,9]]}))






        share|improve this answer














        share|improve this answer



        share|improve this answer








        edited Nov 14 '18 at 10:37

























        answered Oct 30 '18 at 12:18









        avinavin

        284




        284
































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