<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>

CC BY-SA 4.0
2020-05-06T01:29:37.663Z
Can you clarify why you why need 3 output neurons? Also, why would one have the sigmoid?
nbro
CC BY-SA 4.0
2020-05-06T07:54:08.700Z
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.
Mathias Müller
<deep-learning><ai-design><activation-functions><regression>
Can SqueezeNet be used for regression?
Orly
# 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

