Classification and regression on same deep learning models? Suppose I have a dataset of photos of people's faces. One face per image. What does the output vector for a single sample look like, and how do I train the network if I want it to:


*

*predict the age (in days) of the person in the image

*classify the person's gender

*classify country of origin (say, out of only 7 possible options)

*predict (x,y) coordinates of person'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)
 A: This is a strange question.  "What does the output vector for a single sample look like?",  well, you write the model, so you decide how you chooses to code "the output", which I take to mean $Y$, the response variable that you want to predict. So for your examples:


*

*predict the age (in days) of the person in the image.   Output is one number, the age (what else?). But see Convolutional Neural Network Scale Sensitivity

*classify the person's gender: Most people still think that gender is binary, so 0/1 coding should do. If in Canada, maybe more complicated ... 

*classify country of origin (say, out of only 7 possible options):  This is a categorical response, so you could use a dummy encoding.

*predict (x,y) coordinates of person'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):  Just encode $(x,y)$ in the obvious way, as a pair


If I have misunderstood the question, just explain better. 
