Hit everyone and welcome back to this class advanced convolutional neural networks
in this lecture we're going to move onto the next exercise which is building.
The first few layers of resonate.
So far we've built the coffee block and the identity block which are two essential pieces of Reznick.
Once we have these it's just a matter of patching them together to make the full rest.
But as a sanity test what we would like to do is just build the first few layers and confirm that we
get the same output as the Karin's version of resonant.
This is a much harder exercise than the previous exercises because before we only needed to make sure
the shape was right.
Now we have to make sure we actually end up with the right values
so the starter file I made for you is T.F. Reznick first layer's started up high again.
I've left a lot of it empty on purpose.
So you are free to make your own design choices.
Just make sure you don't change anything in the main section because that specifies the standard interface
that the model needs to have.
One major no is that resonate will have a different number of layers depending on which version of Cairnes
you're using.
So you'll need to upgrade to at least version 2 for your code to be consistent with mine.
And just to recap what we want to do is build everything up to and including the first Kambula.
So we have starting from the input A Kavalier a warm layer a rail you a max pool and then the first
convoke we go up to there check that output is the same as Cara's and then exit.
So your job is to fill in the class partial Reznick.
One of the hardest parts is going to be copying the weights over from Cara's.
Note that in order to get the parameters of a Harris layer you just call the get Waite's function.
It usually returns the matrix in a biased vector.
If that layer has them otherwise it returns an empty list.
This may be particularly tricky with or because all four parameters have the same shape.
So you need to figure out which one is which.
Good luck.