No, there is a step now, there is a step that is called compile that we need to do, and this step
we are going to specify what is the back propagation parameters.
Now, this is a little bit advanced and we did not really go into its details.
So we are going to see how we can do it.
But just remember that it is related to back propagation, which we have talked about really briefly
before.
But I wanted to give you an idea about it so that you don't write code that you don't understand at
all.
So let's see how we can parameterize Auerback Propagator.
So we're going to type classifier.
Remember, this is not a layer.
This is for the back propagation.
So we're going to say classifier dot compile and we need to choose an optimizer.
Now, the optimizer is going to help us learn parameters with certain criteria.
Now I optimizer is an algorithm that we use in order to change the weights, the low grade, in order
to reduce the losses and increase, let's say, the accuracy.
OK, so you can think about it like this.
So it is the main backer propagator here.
So there is multiple back propagation algorithm.
And here we are going to call one that's called Adam.
OK, next we have Bellus.
So there is also multiple loss functions that propagate back.
Remember, the laws need to be calculated in order to be propagated and the weights will change according
to this loss.
And this function should have a certain form now, a function we are going to use.
It's called categorical cross entropy.
So we're going to type it.
OK.
This is our loss.
Next is the metric we're going to type metrics and here we are saying that we want to try to increase
accuracy.
So our main target here is actually accuracy.
Remember, we want an accurate model that gives us accurate predictions.
Now, there is other metrics like losses, let's say, where you can optimize for less loss.
But for now, without complicating things further, we are going to stick to accuracy.
We are going to stick to optimizer called Adam, which is the propagator and a lost function that is
called categorical growth.
And that's a.