‫Next is the concept of stride, the first neuron in the convutional layer was looking at these sets
‫of pixels.
‫Now, the next neuron in convutional layer will have a slightly shifted, receptive field.
‫Out the window.
‫This shift, in the view from one neuron to another is called the stride.
‫So if the next neuron is looking at these sets of pixels, you can see that this window, which is for
‫1 the next neuron, is shifted two pixels to the right.
‫So in this scenario, we say that this window has a stride of two each neuron on the convolutional
‫layer will have a window shifted to pixels to the right.
‫So if we continue this, the third neuron will be looking at a set of 25 pixels starting from
‫here.
‫So this will be the field of view for the 3rd neuron and so on.
‫Similarly, a stride of four means that you are taking a jump of four pixels when you look at the next
‫neuron so the second neuron will straight away.
‫Look at these sets of pixels, which is four pixels shifted towards the right.
‫Now, if you use a small stride, then there will be a lot of overlap between these two deceptive
‫Feeds.
‫So if you look at this stride of two, you can see that this three by five rectangle is common between
‫the receptive field of neuron one and deceptive.
‫Firld of neuron 2
‫So we have fifteen pixels common for neuron one and neuron 2
‫But if you have a stride of 4.
‫In that scenario, only this single line is common for neuron one, a neuron to sow only five pixels
‫are common.
‫So there is small overlap.
‫If this trade is large and there is larger overlap, if straight is small.
‫Also if the stride is large.
‫Since the overlap will be less, therefore fewer neurons will be required in the upper layer.
‫So if we have a stride of 4 then the neurons in the upper layer, we'll be less than if we have a stride
‫of two.
‫So the stride will determine the size of upper layer and the amount of overlap and the deceptive fields.