Now one of the problems that originate with large strayed is that we may not be able to cover the entire
image.
For example let us try to cover this image with a five by five window moving with a straight of three
so the first neuron covers these first 25 pixels.
Now in the next trade we will cover these next 25 pixels.
So we have a trade of three.
After the fourth pixel we called an electron divide pixels and so on till this point where our neuron
is seeing these 25 pixels now in the next trade we do not have quantified pixels.
We will be out of pixel columns which can be covered.
So if I take next trade there are only 20 pixels available and not 25.
This is a problem because we want uniformity that is each neuron should have same 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 14 by 14 image one line of pixel removed from all
the sides.
This 14 by 14 image can be covered with a fiber five window and a of three this particular option is
called valid birding which actually means that we are not using any birding 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 blank pixels for example here we add
one more layer of pixels.
We can then move forward with a straight of three and the last neuron will also have a view of 25 pixels
guarantee 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 Burton height by default padding arguments in our software are set
to valid.
If you think the border of your image stores important information in those scenarios only we will change
this parameter to see it's valid works well.
Here is the definition of two arguments that we have covered.
First this trade 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 a second neuron is looking at this
blue rectangle.
Then we have a straight off to we can specify both horizontal and vertical straight.
Separately by four straight.
Value is set at 1.
The second concept that we discussed is off fighting 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 padding.
If we are adding additional black pixels so that our window can cover the bordered 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 convolution earlier.