<p>You can still use only one model for that kind of problem.</p>

<p>However, your model will have a 5-fold architecture as follows:</p>

<ol>
<li>A first "core" model taking in input your dog photos</li>
<li>A second model taking in input the output of the core model and predicting the age</li>
<li>A third model taking in input the output of the core model and predicting the gender</li>
<li>A fourth model taking in input the output of the core model and predicting the breed</li>
<li>A fifth model taking in input the output of the core model and predicting the coordinates...</li>
</ol>

<p>While theoretically feasible, this may be a bit cumbersome to put in place in order to have good results for all models.</p>

<p>That is more or less what google brain did <a href="https://arxiv.org/abs/1706.05137" rel="nofollow noreferrer">here</a> (except that their models are more diverse).</p>

<p>PS: For this kind of question, you should rather ask them on stats.stackexchange.com or datascience.stackexchange.com</p>
Can the same model classify + predict continuous values?
<p>Suppose I have a dataset of photos of dogs. There is only a single dog per photograph. What does the output vector for a single sample look like, and how do I train the network if I want it to:</p>

<ul>
<li><p>predict the age (in days) of the dog in the picture</p></li>
<li><p>classify the dog's gender</p></li>
<li><p>classify the dog's breed</p></li>
<li><p>predict (x,y) coordinates of dog's nose within the picture (where each coordinate is a value between 0-1, indicating percentage of the distance from top left corner of the input image)</p></li>
</ul>