Now, one of the problems that originate with large strayed is that we may not be able to cover the
entire image.
For example, you're just trying to cover this image with a five by five window moving with a straight
of three.
So the first neuron covers these first twenty five pixels.
Now, in the next trade, we will cover these next twenty five pixels.
So we have this trade of three after the fourth pixel.
We got another twenty five pixels and so on.
At this point where are neuron is seeing these 25 pixels.
Now, in the next raid, we do not have quantified pixels.
We will be out of pixel columns which can be covered.
So if I take next raid, there are only 20 pixels available and not 25.
This is a problem because we want uniformity.
That is each neuron should have seem receptive field of 25 pixels.
In this situation, we have two options.
First option is to ignore these extra pixels at the border.
So since we cannot cover the last two pixel columns, we leave out one pixel column from left and one
pixel column on the right.
So instead of a 16 by 16 image, we will consider only a 14 by 14 image, one line of pixel remove from
all the eight.
This 14 by 14 image can be covered with a fiber five window.
And a street of three.
This particular option.
It's called Valid Birdying.
Which actually means that we are not using any padding.
And that we only use valid window locations and ignore the extra pixels at the border.
Second option is adding extra rows and columns of dummy pixels or black pixels.
For example, here we add one more layer of pixels.
We can then move forward with a straight of three.
And the last neutron will also have a view peeled off twenty five pixels grindy from the original image
and five offer artificially generated blank pixels.
This type of padding is called same padding, which means bad in such a way so as to have an output
which can be covered by a window of same Bertan height.
By default, batting arguments in our software are said to be valid if you think the border of your
image stores important information in those scenarios only we will change this parameter to saying it's
valid works well.
Here is the definition of two arguments that we have covered.
First, it straight straight denotes how many steps we take in each step of convolution.
So in the first step, that is our first neuron is looking at this red rectangle.
And the second neuron is looking at this blue rectangle.
Then we have strayed off to.
We can specify both horizontal and vertical strait separately.
By default, straight value is set at one.
The second concept that we discussed is of buiding.
Padding is the process of adding zeros to the input image to maintain the dimension of output as an
input.
If we decide to ignore the border values, which could not be covered due to our large trade, in that
case, we are not using any padding for which the argument is valid burning.
If we are adding additional black pixels so that our window can cover the border pixels, also in that
scenario, we are using same padding.
So these are the two arguments that we will need to specify when we train our convolutional leered.