Now, the input to this layer is going to be some dimension.

It's going be some n by n by number of channels in the previous layer.

Now, I'm going to modify this notation a little bit.

I'm going to us superscript l- 1,

because that's the activation from

the previous layer, l- 1 times nc of l- 1.

And in the example so far, we've been just using images of the same height and width.

That in case the height and width might differ,

l am going to use superscript h and superscript w, to denote the height and

width of the input of the previous layer, all right?

So in layer l, the size of the volume will be nh

by nw by nc with superscript squared bracket l.

It's just in layer l, the input to this layer Is whatever you had for

the previous layer, so that's why you have l- 1 there.

And then this layer of the neural network will itself output the value.

So that will be nh of l by nw of l, by nc of l,

that will be the size of the output.

And so whereas we approve this set that the output volume size or

at least the height and weight is given by this formula,

n + 2p- f over s + 1, and then take the full of that and round it down.

In this new notation what we have is that the outputs value that's in layer l,

is going to be the dimension from the previous layer,

plus the padding we're using in this layer l,

minus the filter size we're using this layer l and so on.

And technically this is true for the height, right?

So the height of the output volume is given by this, and you can compute it

with this formula on the right, and the same is true for the width as well.

So you cross out h and

throw in w as well, then the same formula with either the height or

the width plugged in for computing the height or width of the output value.