Answers.Body: <p>The number of dimensions is a hyperparameter of your model, and you should do a hyperparameter search, like with any other parameters. There's also a tradeoff between dimension and training speed, so it should be small enough to be trainable in a reasonable time.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-12-28T16:14:25.487Z
Answers.LastActivityDate: 2019-12-26T15:31:10.470Z
Answers.LastEditDate: 2019-12-26T15:31:10.470Z
Answers.UserDisplayName: Konstantin Solomatov
Body: <p>How should we choose the dimensions of the encoding layer in auto-encoders?</p>
  
Tags: <neural-networks><autoencoders><hyperparameter-optimization><variational-autoencoder><hyper-parameters>
Title: How should we choose the dimensions of the encoding layer in auto-encoders?
UserDisplayName: Neha soni
ThreadText: # How should we choose the dimensions of the encoding layer in auto-encoders?
  
  (Asked by: username_0 on 2018-12-27)
  
  How should we choose the dimensions of the encoding layer in auto-encoders?
  
  **Tags:** neural-networks, autoencoders, hyperparameter-optimization, variational-autoencoder, hyper-parameters
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2018-12-28. Score: 3
  
  The number of dimensions is a hyperparameter of your model, and you should do a hyperparameter search, like with any other parameters. There's also a tradeoff between dimension and training speed, so it should be small enough to be trainable in a reasonable time.
  
  
  ---
  
  