Now, let's take a look at Colonel size and depth.
These are some parameters that actually control the size of our convolutional filter and controls the
size of our feature map as well.
So let's get started.
So some parameters that control the cone filter, as I mentioned, are colonel size and depth depth
meaning meaning one for greyscale skill tree fire RGV, which you've seen previously.
There's also a stride and padding, which we'll do in the following chapters.
So let's take a look at this.
Previously, when we were using our filters, we only used a tree by tree filter.
Can there be order sizes?
Can it be a four by four, five by five or seven by seven?
Does it have to be square?
What's going on?
Actually, yes, it has to be square, and it has to be odd.
No filters.
And I'll explain to you why.
So firstly, before I go on to that explanation, what I want to show you is if you use a five by five
filter here on a six by six image here you're going to get a two by two output and hope.
So imagine this here.
Imagine we overlay this five by five matrix on our six by six grid here.
It's going to occupy this range here to this range here.
So you can you can see that point is going to be one available position.
We can move it to the right.
So we shifted one to the right suit now covers this area here and then we shifted down dome and it covers
this area here.
And then we shifted to the right again.
So you can see there's only four positions.
This matrix can be overlaid onto this input image, and that's how you get two by two here.
So here's a little simple formula for calculating the future map size.
So the end is the dimensions of the image.
And remember, the images have to be square in convolutional neural networks.
So it's six by six here and then minus five by five, which is a filter here.
Plus one, which is two two, is the dimension of our feature map output.
So that's an easy, simple way to calculate it.
So I said we couldn't have on even numbered filters.
Why is that?
Well, if you go back to the slide here, you can see when you put the five by five matrix here, there's
a center point in the middle right here.
This a little point, right right here.
However, if it was a four by four matrix, there's no center point that does cause a problem now because
what it does, it basically messes up the symmetry of our or filters, and that can lead to distortions
across layers.
So while it can work and there's no mathematical reason why you can't have an even numbered filter in
theory, in practice, convolutional networks don't perform well with even sized filters to work much
better with odd size filters because of that symmetry around the Centrepoint or anchor point.
So let's move on to debt.
Remember, I referred to depth as being the depth, the number of channels in an image.
Well, that doesn't always refer to that in some nomenclature.
Depth can refer to how many, like how many kernels of filters you're using, which technically it's
wrong because it's actually four dimensions instead of a two dimension.
But nevertheless, some researchers tend to use it often.
Maybe when you're talking about grayscale images than depth would refer to, the two dimension of it
can also refer to the four dimension.
Generally, depth does refer to the two dimension.
It kind of means that there's kind of always indicates the color of the image what it's color or grayscale.
So that concludes this chapter on depth.
And let's see what what we did initially.
Just rehash it kernel size.
So in the next chapter, we're going to take a look at putting.
And then afterwards, we're going to take a look at straight.
So stay tuned for those lessons and we'll see you in the next section.
Thank you.