Hello.
Welcome back.
Let's see what one layer of convolution looks like.
So our input is denoted over here by a superscript 0 because the input layer is often called Layer 0.
Our weight is denoted by top use superscript 1.
So to compute for 0 1 we can form the input with the weight and then we add the buyers.
B this gives us c 1.
So z 1 equals w 1 which is the weight convolution a 0 which is the input plus B which is the bias to
compute the activation a one we simply take c 1 and apply a 1 activation function.
Now let's see how to find the number of parameters of a layer.
Let's say we have 10 failed tests and each filter has a shape of three by three by three.
We find a number of parameters per filter by simply doing three multiplied by three which apply by three.
And this of course this would give us twenty seven.
Also there would be a bias term for each field to be the bias each filter has a bias.
So we have to add one to it.
So twenty seven plus one gives us twenty eight.
And because we are below with 10 filters then we have to multiply what twenty eight by ten and this
gives us two hundred and eighty.
And as you can see from this calculation the only thing taken into account when finding the number of
parameters is the shape and number of filters.
This means no matter how big the input image is the parameters only depend on the filter size.
Let's take a look at the notation we shall use or the notations which will use when talking about convolution
or new network layer is denoted as L such that when we want to talk about the filter size that pattern
described the number of channels etc. we simply write the respective letters and add superscript square
brackets l the previous layer is the input of the current layer therefore we use L minus 1 to denote
input H and W denote height and with respectively.
This is all there is for this lesson and if you have any questions just let me know.
I shall see you later.