In this lecture we will understand the concept of a fully good we have been saying the law that a cell
in the convolutions earlier gets information from a set of pixels or a set of cells in the previous
previously for example that red cell in the convolution earlier is getting information from these nine
cells.
And this red rectangle 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 feeder filter is a matrix of same dimensions as our window of deceptive feet.
So if the window is five cross five figure that is also a dimension 5.5 if it is of three cross three
feet there will also be of three cross three dimensions.
Now we have a window of five into five pixels containing pixel value and we have a five in 2 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 here 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 up.
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 dispirited.
The answer to this is very pleasing.
We do not have to decide these values our network will learn these values also so when we are training
our model these values will be self learned
not to demonstrate how it does work and how they are able to extract certain features out I have taken
a five into five input image with zero one type pixel values and a three by three filtered
look at this way that this data looks like a cross that is the diagonal values are one and the other
are zero if we use this filter with a straight up one we get this output
the data below shows you how we get this output how the vector values are multiplied and their product
values are added up to good the first value then the next value and then the next and so on.
This final output which we get after applying the filter is called a feature map a feature map because
each way to highlight some feature of the input image the images on the right are demonstrating how
particular features are highlighted by filters.
For example if we use a vertical feet that is the middle column of this matrix is 1 1 1 and these side
columns are 0 0 0 this type of leader transforms the image to this image.
Notice that vertical white lines are enhanced and the rest of the image is blurred similarly if we use
the horizontal for the that is this middle row will be 1 1 1 and top and bottom row will consist of
zeros if we use such horizontal filter.
We got this image.
You can notice that horizontal white lines are highlighted and rest is blurred.
This is what operator does a filter is a set of values which transforms the window by doing some of
products what we get after applying 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 filter so that each filter creates different feature
maps containing different features.
This means our convolution earlier is going to be a bundle of feature maps and each feature 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 convolution layer to what does the see.
Is it only this rectangle on the first feature map of previously or this rectangle on all feature maps
in the previously nonsense is that each cell on conditional Layer 2 will be getting information of all
the feature maps in the previously because only then can these cells combine the different features
to find more high level features
and summaries again for clarity we apply a filter on the previous layer of data to extract features
the output after applying filter is called a feature map we apply many different types of reactors to
extract many different type of features.
This gives us a bundle of feature maps the first bundle of feature maps is called constitutional Layer
congressional Layer 2 works on these extracted features to extract even higher level of features
next we are going to discuss about the input layer input layer also has multiple layers of information
these layers are called channels we talk about channels in the next video.