Answers.Body: <p>Regularizer's are used as a means to combat over fitting.They essentially create a cost function penalty which tries to prevent quantities from becoming to large.  I have primarily used kernel regularizers. First I try to control over fitting using dropout layers. If that does not do the job or leads to poor training accuracy I try the Kernel regularizer. I usually stop at that point. I think activity regularization would be my next option to prevent outputs from becoming to large. I suspect weight regularization effectively can pretty much achieve the same result.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-07-19T00:50:58.523Z
Answers.LastActivityDate: 2020-07-19T00:50:58.523Z
Answers.UserDisplayName: Gerry P
Body: <p>For Keras on TensorFlow, a layer class constructor comes with these:</p>
  <ul>
  <li><code>kernel_regularizer</code>=...</li>
  <li><code>bias_regularizer</code>=...</li>
  <li><code>activity_regularizer</code>=...</li>
  </ul>
  <p>For example, Dense layer:<br/>
  <a href="https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense#arguments_1" rel="nofollow noreferrer">https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense#arguments_1</a></p>
  <p>The first one, <code>kernel_regularizer</code> is easy to understand, it regularises weights, makes weights smaller to avoid overfitting on training data only.</p>
  <p>Is <code>kernel_regularizer</code> enough? When should I use <code>bias_regularizer</code> and <code>activity_regularizer</code> too?</p>
  
Tags: <neural-networks><keras><regularization><weights>
Title: When would bias regularisation and activation regularisation be necessary?
UserDisplayName: Dan D.
ThreadText: # When would bias regularisation and activation regularisation be necessary?
  
  (Asked by: username_0 on 2020-07-18)
  
  For Keras on TensorFlow, a layer class constructor comes with these:
  
  * `kernel_regularizer`=...
  * `bias_regularizer`=...
  * `activity_regularizer`=...
  
  For example, Dense layer:  
  <https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense#arguments_1>
  
  The first one, `kernel_regularizer` is easy to understand, it regularises weights, makes weights smaller to avoid overfitting on training data only.
  
  Is `kernel_regularizer` enough? When should I use `bias_regularizer` and `activity_regularizer` too?
  
  **Tags:** neural-networks, keras, regularization, weights
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-07-19. Score: 2 | Accepted answer
  
  Regularizer's are used as a means to combat over fitting.They essentially create a cost function penalty which tries to prevent quantities from becoming to large. I have primarily used kernel regularizers. First I try to control over fitting using dropout layers. If that does not do the job or leads to poor training accuracy I try the Kernel regularizer. I usually stop at that point. I think activity regularization would be my next option to prevent outputs from becoming to large. I suspect weight regularization effectively can pretty much achieve the same result.
  
  
  ---
  
  