So far, we have been talking about single class prediction, meaning that my network can only predict
one category, like I give it a circle.
It tells me this is a circle of this circle.
I give it an image and it tells me this is a specific image that you trained me for or this is not how
about if I have multiple classes?
Let's say I have a network that I wanted to predict.
If this is a circle, is it a triangle or is it a square is a direct angle?
Well, I want it to give me multiple classes, and I wanted to give me a percentage of how close this
prediction to the output.
So what I mean is, let's say here in this case, I wanted to give me that, OK, this prediction is
90 percent a circle and only 10 percent a triangle.
So it's 90 percent sure that this is a circle and 10 percent sure that this is a triangle.
OK, stuff like that.
So first, what we do is, as usual, put forward all the triangle pictures I have.
I'm going to fit for them and of course, I'm going to be training them.
So let's say here I feed forward my triangle, OK?
And I am expecting a zero percent circle and I am expecting 100 percent triangle.
Right.
But let's say after I feed this forward and it calculates its the stuff, it says this is 50 percent,
51 percent circle and 49 percent triangle.
Well this is not really very accurate and that's wrong.
So what we do is we basically propagate and we calculate those weights one more time.
OK, and I keep giving it pictures of triangles until I get some decent training and testing percentage.
OK, so I give training until maybe I get an accuracy of, let's say, 90 percent or 95 percent.
Now I will do the same thing for the circle.
OK, so in every batch I'm going to be giving it pictures of triangles and pictures of circles and I'm
going to be hiding the answer from it in order to learn from it.
OK, so I keep feeding these two and always I'm going to look at the calculated values.
I will let it look at the expected values and I will let it propagate in order to adjust the weights.
So it's very similar to a single class.
But this time I have multiple classes.
I have multiple categories of images that I am battling here and I am expecting some decent prediction
for my multi classes data.
OK, here we are using also percentage are not a binary output.
I mean, even in single class you could use percentage instead of a zero and one that's totally OK.
But for multiple classes it's much better if you use percentages in order to do that.
OK, now when we start building our models, we will see all of these indicators.
But as I said, bear with me until we finalize all the concepts before we go into the hands on practical
exercises.