Hello.
Welcome back.
Let's analyze a complete convolution or your network and this arrangement over here.
We take an input image of size fetch a 9 by fetch a 9 by 3 and we confirm it with 10 tests of size through
by 3 using a pattern of 0 and astride of 1.
As we have written over here this gives us an output of thirty seven by thirty seven by ten.
We convert this output with twenty four tests of size five by five applying astride of two and pattern
of zero.
This will give us seventeen by seventeen by twenty outputs as we have over here.
We take this new output and control it with 40 50 tests of size 5 by 5.
Applying astride of two and a pattern of zero this will give us 7 by 7 by 40 as we see over here.
Finally we take this three dimensional output and unroll it into a single vector.
This gives us one thousand nine hundred and sixty long column vector seven multiplied by seven multiplied
by 40.
Of course one thousand nine hundred and sixty.
This is a short summary of convolution or neural network in convolution or neural networks.
We have three types of letters.
We have the convolution on there.
We have the constitutional layer that Portlandia and d formerly connected layer before we examine an
example depicting these three types of layers.
Let's first talk about the polling data in the next lesson.