CODEWITHSUNDEEP
matlabmatrixneural-network
Mohamed
Mohamed

Reputation: 45

Neural Network Multiple Inputs and one output

i want to create a Neural Network with "three (2D) Matrices" as a inputs , and

the output is a 1 (2D) Matrix , so the three inputs is :

1-2D Matrix Contains ( X ,Y ) Coordinates From a device

2-2D Matrix Contains ( X ,Y ) Coordinates From another different Device

3-2D Matrix Contains the True exact( X , Y ) Coordinates that i already

measured it ( i don't know if that exact include from the inputs or What??)

***Note that each input have his own Error and i want to make the Neural Network

to Minimize that error and choose the best result depends on the true exact (X,Y)***

**Notice that : im Working on object tracking that i extracting (x,y) Coordinate

from the camera and the other device is same the data so for example

i will simulate the Coordinates as Follows:

{ (1,2), (1,3), (1,4), (1,5) , (1,6).......}

and so on

For Sure the Output is one 2D Matrix the best or the True Exact (x,y) ,So im a

beginner and i want to understand how to create this Network with this Different

inputs and choose the best training method to have the Best Results ...?!

thanks in Advance

Upvotes: 0

Views: 2202

Answers (1)

SpinyNormam
SpinyNormam

Reputation: 183

It sounds like what you want is a HxWx2 input where the first channel (depthwise layer) is your 1st input and the 2nd channel is your 2nd input. Your "true exact" coordinates would be the target that your net output is compared to, rather than being an input.

Note that neural nets don't really handle regression (real-valued outputs) very well - you may get better results dividing your coordinate range into buckets and then treating it as a classification problem instead (use softmax loss vs regression mean-squared error loss).

Expanding on the regression vs classification point:

A regression problem is one where you want the net to output a real value such as a coordinate value in range 0-100. A classification problem is one where you want the net to output a set of propabilities that your input belongs to a given class it was trained on (e.g. you train a net on images belonging to classes "cat" "dog" and "rabbit").

It turns out that modern neural nets are much better at classification than regression, because the way they work is basically by subdividing the N-dimensional input spaces into sub-regions corresponding to the outputs they are being trained to make. What they are naturally doing is classifying.

The obvious way to turn a regression problem into a classification problem, which may work better, is to divide your desired output range into sub-ranges (aka buckets) which you treat as classes. e.g. instead of training your net to output a single (or multiple) value in range 0-100, instead train it to output class probabilities representing, for example, each of 10 separate sub-ranges (classes) 0-9, 10-19, 20-20, etc.

Upvotes: 1

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