In this lecture, we will understand the concept of a very good.
We have been saying they know that a cell in the Convolutional did get information from a set of big
cells or a set of cells in the previously, for example, that red cell in the Convolutional.
It is getting information from these nine cells and this writer, Daniel.
But what does this mean?
How is it getting the information from all these pixels?
We have 25 pixels here.
And our cell here can have only one value, which should be the representative value for these 25 pixels.
So we need to find a way to convert these 25 values of pixels into one value.
This is done by using a feed filter is a matrix of same dimensions as our window of receptive feed.
So if the window is five Crossfade.
Think that is also a dimension five.
CROSSFIRE.
If it is up three close three feet, there will also be of three across three dimensions.
No, we have a window of five into five pixels containing pixel value and we have a five in two five
matrix containing some values.
We multiply each pixel value with the corresponding filter value.
And add all of these products up.
So the pixel value hit.
Will be multiplied with zero point for the next pixel value will be multiplied with zero point three.
And so on.
And all these products will be added to.
This will give us one number.
And this number will represent information in these 25 pixels.
Now the question comes, how do we decide the values and district that?
The answer to this is very pleasing.
We do not have to decide these values.
I'd network will learn these values also.
So when we are training our model, these values will be self-reliant.
Now to demonstrate how it does work and how they are able to extract certain features out.
I have taken a five into five foot image.
With zero one type pixel values and a three by three feet that.
Look at the speed that.
This figure looks like a cross.
That is the diagonal values are one and the other are Z2.
If we use this figure with a straight up one, we get this output.
The D.A. below shows you how we get this output.
How the picture values are multiplied and their product values are added up to good first value.
Then the next value and then the next and so on.
This final output, which we get after applying, if we get is called a feature map.
A feature map, because each way that highlights some feature of the input image, the images on the
right are demonstrating how particular features are highlighted by Fragos.
For example, if we use a vertical feet, that that is the middle column of this matrix is one one one,
and these side columns are zero zero zero.
This type of thing that transforms the image to this image.
Notice that vertical white lanes are enhanced and the rest of the image is blurred.
Similarly, if we use the horizontal for that.
That is this middle role will be one one, one.
And top and bottom row will consist of Zeitels.
If we use such a horizontal filter.
We got this image.
You can notice that horizontal white lines are highlighted and the rest is blurred.
This is what Peter does.
A fate that is a set of values which transforms the window by doing some of products.
What we get after the playing of that is called a feature map, each feature map has some particular
feature highlighted.
So what we will do is we will use many types of quader.
So that each filter creates different feature maps containing different features.
This means our convolutional live is going to be a bundle of feature maps.
And each region map has some particular highlighted feature.
Important thing to notice here is what happens in the next lit.
So this cell.
In the first feature map of Convolutional led to.
What does this seat.
Is it only this rectangle on the first feature map of previously?
Or this rectangle on all feature maps in the previously.
The answer is that each sale on Convolutional leered two will be getting information of all the featured
maps and the previously.
Because only then can these cells combine the different features to find more high level features.
Let's summarize again for clarity.
We apply a filter on the previous list of data to extract features.
The output after a playing field is called a feature map.
We apply many different types of windows to extract many different types of features.
This gives us a bundle of feature maps.
The first Bundalong feature maps is called Convolutional Leered One.
Congressional leered to Volks on these extracted features.
To extract even higher level features.
Next, we are going to discuss about the input layer.
Input, it also has multiple layers of information.
These layers are called Jenelle's.
We talk about tunnels in the next video.