Hi,

I’ve just started coding my NN after a month or two of reading.

after tweaking some models and spending time on some trials&errors with keras,

I think I’ll to stick to this NN (described below), but to my knowledge, so far, it’s neither a classification, nor a regression.

the number of inputs (samples) : 20,000

number of features per input : 1500

number of outputs : **7 (but all are float values in range [-1,1])**

e.g: a possible output is : (0.2, 0.5, 0.0, -0.6, 0.8, -0.3, 1)

**is this 7 outputs, or 1 output ?**

I’m having a hard time, trying to understand how my loss, and accuracy is gonna be computed/used.

output is not a single float (a scalar value),

not a 2D point (a dot on x,y plane),

not a 3D point (a point on x,y,z space)

but it’s a seven-float thing. (a point in 7D space? )

**can I just use this 7D points as targets, and train on them using usual MSE and MAE and other similar loss method**s ?

I know I can’t use Softmax activation at last layer, and since I can’t define targets as classes, class weights can’t be calculated. So it definitely is not a classification problem.

but, **how do I build a valid model for such output ?**

(based on some readings, is it correct to not to use a activtion function on last layer ?)