Using tf.batch_scatter_add in Keras












2















I'm looking for a way to use tf.scatter_add with Keras batches.
Shape of outputs: (?, 1000) and shapes of indices and updates are (?, 100) each.



Try1: Using Keras tensors 



vals = tf.scatter_add(outputs, indices, updates)


This throws an error:




'Tensor' object has no attribute '_lazy_read'




Try2: Tried using k.variable which should be updatable 



vals = K.variable(outputs)

vals = tf.scatter_add(vals, inputs[1], inputs[2])



ValueError: initial_value must have a shape specified:

Tensor("scatter_add_43/zeros_like:0", shape=(?, 1000), dtype=float32))




Any clues? Scatter_add and batch_scatter_add result in the same errors. Will I need to write a custom layer for this? Seems like even that will run into one of the above errors.






python tensorflow keras







share|improve this question





























    2















    I'm looking for a way to use tf.scatter_add with Keras batches.
    Shape of outputs: (?, 1000) and shapes of indices and updates are (?, 100) each.



    Try1: Using Keras tensors 



    vals = tf.scatter_add(outputs, indices, updates)


    This throws an error:




    'Tensor' object has no attribute '_lazy_read'




    Try2: Tried using k.variable which should be updatable 



    vals = K.variable(outputs)
    
vals = tf.scatter_add(vals, inputs[1], inputs[2])



    ValueError: initial_value must have a shape specified:

    Tensor("scatter_add_43/zeros_like:0", shape=(?, 1000), dtype=float32))




    Any clues? Scatter_add and batch_scatter_add result in the same errors. Will I need to write a custom layer for this? Seems like even that will run into one of the above errors.






    python tensorflow keras







    share|improve this question



























      2












      2








      2








      I'm looking for a way to use tf.scatter_add with Keras batches.
      Shape of outputs: (?, 1000) and shapes of indices and updates are (?, 100) each.



      Try1: Using Keras tensors 



      vals = tf.scatter_add(outputs, indices, updates)


      This throws an error:




      'Tensor' object has no attribute '_lazy_read'




      Try2: Tried using k.variable which should be updatable 



      vals = K.variable(outputs)
      
vals = tf.scatter_add(vals, inputs[1], inputs[2])



      ValueError: initial_value must have a shape specified:

      Tensor("scatter_add_43/zeros_like:0", shape=(?, 1000), dtype=float32))




      Any clues? Scatter_add and batch_scatter_add result in the same errors. Will I need to write a custom layer for this? Seems like even that will run into one of the above errors.






      python tensorflow keras







      share|improve this question
















      I'm looking for a way to use tf.scatter_add with Keras batches.
      Shape of outputs: (?, 1000) and shapes of indices and updates are (?, 100) each.



      Try1: Using Keras tensors 



      vals = tf.scatter_add(outputs, indices, updates)


      This throws an error:




      'Tensor' object has no attribute '_lazy_read'




      Try2: Tried using k.variable which should be updatable 



      vals = K.variable(outputs)
      
vals = tf.scatter_add(vals, inputs[1], inputs[2])



      ValueError: initial_value must have a shape specified:

      Tensor("scatter_add_43/zeros_like:0", shape=(?, 1000), dtype=float32))




      Any clues? Scatter_add and batch_scatter_add result in the same errors. Will I need to write a custom layer for this? Seems like even that will run into one of the above errors.






      python tensorflow keras




      python tensorflow keras






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Nov 26 '18 at 7:23









      barbsan

      2,38321222




      2,38321222










      asked Nov 25 '18 at 18:00









      N. SawantN. Sawant

      111




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