Tensor Shape 'Reshape' during the dataflow












1












$begingroup$


I would like to combine an Autoencoder with a LSTM. However, the 'timestep' is a block for the implements and I would like to train them together. Is there a solution to the tensor 'reshape'? I mean, it's not the simple 'Reshape' but for using timestep data.










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    1












    $begingroup$


    I would like to combine an Autoencoder with a LSTM. However, the 'timestep' is a block for the implements and I would like to train them together. Is there a solution to the tensor 'reshape'? I mean, it's not the simple 'Reshape' but for using timestep data.










    share|improve this question









    New contributor




    Jie Wang is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















      1












      1








      1





      $begingroup$


      I would like to combine an Autoencoder with a LSTM. However, the 'timestep' is a block for the implements and I would like to train them together. Is there a solution to the tensor 'reshape'? I mean, it's not the simple 'Reshape' but for using timestep data.










      share|improve this question









      New contributor




      Jie Wang is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I would like to combine an Autoencoder with a LSTM. However, the 'timestep' is a block for the implements and I would like to train them together. Is there a solution to the tensor 'reshape'? I mean, it's not the simple 'Reshape' but for using timestep data.







      lstm autoencoder reshape






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      Jie Wang is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      share|improve this question









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      edited yesterday









      Ethan

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      560222






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      asked yesterday









      Jie WangJie Wang

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      Jie Wang is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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