TensorFlow Restore from Checkpoint multiple times











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I have a Tensorflow checkpoint from which to restore my model:



logits, endpoints = mobilenet_v2.mobilenet(images)

saver = tf.train.Saver()

saver.restore(sess, checkpoint)


What I need to somehow create multiple instances of this model, which I can feed different inputs and compare the outputs, something like:



logits1, endpoints1 = mobilenet_v2.mobilenet(images1)

logits2, endpoints2 = mobilenet_v2.mobilenet(images2)

saver.restore(sess, checkpoint)


and then I want to use endpoints1 and endpoints2 in my code later on. Of course, this fails as variables try to be initiated twice (I guess).



Is there a way I can achieve this?






python tensorflow neural-network deep-learning







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    up vote
    0
    down vote

    favorite












    I have a Tensorflow checkpoint from which to restore my model:



    logits, endpoints = mobilenet_v2.mobilenet(images)
    
saver = tf.train.Saver()
    
saver.restore(sess, checkpoint)


    What I need to somehow create multiple instances of this model, which I can feed different inputs and compare the outputs, something like:



    logits1, endpoints1 = mobilenet_v2.mobilenet(images1)
    
logits2, endpoints2 = mobilenet_v2.mobilenet(images2)
    
saver.restore(sess, checkpoint)


    and then I want to use endpoints1 and endpoints2 in my code later on. Of course, this fails as variables try to be initiated twice (I guess).



    Is there a way I can achieve this?






    python tensorflow neural-network deep-learning







    share|improve this question
























      up vote
      0
      down vote

      favorite









      up vote
      0
      down vote

      favorite











      I have a Tensorflow checkpoint from which to restore my model:



      logits, endpoints = mobilenet_v2.mobilenet(images)
      
saver = tf.train.Saver()
      
saver.restore(sess, checkpoint)


      What I need to somehow create multiple instances of this model, which I can feed different inputs and compare the outputs, something like:



      logits1, endpoints1 = mobilenet_v2.mobilenet(images1)
      
logits2, endpoints2 = mobilenet_v2.mobilenet(images2)
      
saver.restore(sess, checkpoint)


      and then I want to use endpoints1 and endpoints2 in my code later on. Of course, this fails as variables try to be initiated twice (I guess).



      Is there a way I can achieve this?






      python tensorflow neural-network deep-learning







      share|improve this question













      I have a Tensorflow checkpoint from which to restore my model:



      logits, endpoints = mobilenet_v2.mobilenet(images)
      
saver = tf.train.Saver()
      
saver.restore(sess, checkpoint)


      What I need to somehow create multiple instances of this model, which I can feed different inputs and compare the outputs, something like:



      logits1, endpoints1 = mobilenet_v2.mobilenet(images1)
      
logits2, endpoints2 = mobilenet_v2.mobilenet(images2)
      
saver.restore(sess, checkpoint)


      and then I want to use endpoints1 and endpoints2 in my code later on. Of course, this fails as variables try to be initiated twice (I guess).



      Is there a way I can achieve this?






      python tensorflow neural-network deep-learning




      python tensorflow neural-network deep-learning






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 7 at 9:07









      Vidak

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