Answers.Body: <p>An interesting way to check for redundancy not only in the first layer but in any layer is to see the weights of you neuron. Let's consider the input layer: The nodes that are connected with the weights that have high values are the most important for your NN performance and in other words your input to these nodes in what mostly makes sense for your task. </p>
  
  <p>If you are in Keras, you can use the <code>get_weights method()</code>. You may have a look <a href="https://keras.io/layers/about-keras-layers/" rel="nofollow noreferrer">here</a>.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-02-23T09:30:16.750Z
Answers.LastActivityDate: 2018-02-23T09:30:16.750Z
Answers.UserDisplayName: ekoulier
Body: <p>Lately I've been wondering.
  Is there's a way to locate redundant/unnecessary/misleading inputs by analysis of weights in the first layer?</p>
  
Tags: <neural-networks>
Title: Identify unnecessary inputs of NN
UserDisplayName: gnivirht
ThreadText: # Identify unnecessary inputs of NN
  
  (Asked by: username_0 on 2018-02-19)
  
  Lately I've been wondering.
  Is there's a way to locate redundant/unnecessary/misleading inputs by analysis of weights in the first layer?
  
  **Tags:** neural-networks
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2018-02-23. Score: 2
  
  An interesting way to check for redundancy not only in the first layer but in any layer is to see the weights of you neuron. Let's consider the input layer: The nodes that are connected with the weights that have high values are the most important for your NN performance and in other words your input to these nodes in what mostly makes sense for your task.
  
  If you are in Keras, you can use the `get_weights method()`. You may have a look [here](https://keras.io/layers/about-keras-layers/).
  
  
  ---
  
  