Body: <p>I want a model that outputs the pixel coordinates of the tip of my forefinger, and whether it's touching something or not. Those would be 3 output neurons: 2 for the X-Y coordinates and 1, with a sigmoid activation, wich predicts the probability whether it's touching or not.</p>
  
  <p>What do I need to change in the squeezenet model in order to do this?</p>
  
  <p>(PS: the trained model needs to be the fastest possible (in latency), that's why I wanted to use SqueezeNet)</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-06T01:29:37.663Z
Comments.Text: Can you clarify why you why need 3 output neurons? Also, why would one have the sigmoid?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-06T07:54:08.700Z
Comments.Text: In general, any architecture can be combined with different "heads". Yes, you could take an existing model and use a different output layer. Instead of predicting a vector with 3 elements, it would make sense to predict 1) 2 elements using a regression loss like MSE 2) another scalar with sigmoid using a binary cross entropy loss. That would be a multi-task learning setup.
Comments.UserDisplayName: Mathias Müller
Tags: <deep-learning><ai-design><activation-functions><regression>
Title: Can SqueezeNet be used for regression?
UserDisplayName: Orly
ThreadText: # Can SqueezeNet be used for regression?
  
  (Asked by: username_0 on 2020-05-05)
  
  I want a model that outputs the pixel coordinates of the tip of my forefinger, and whether it's touching something or not. Those would be 3 output neurons: 2 for the X-Y coordinates and 1, with a sigmoid activation, wich predicts the probability whether it's touching or not.
  
  What do I need to change in the squeezenet model in order to do this?
  
  (PS: the trained model needs to be the fastest possible (in latency), that's why I wanted to use SqueezeNet)
  
  **Tags:** deep-learning, ai-design, activation-functions, regression
  > **Comments:**
  > **username_1**: Can you clarify why you why need 3 output neurons? Also, why would one have the sigmoid?  
  > **username_2**: In general, any architecture can be combined with different "heads". Yes, you could take an existing model and use a different output layer. Instead of predicting a vector with 3 elements, it would make sense to predict 1) 2 elements using a regression loss like MSE 2) another scalar with sigmoid using a binary cross entropy loss. That would be a multi-task learning setup.  
  
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  ## 0 answers
  
  