All right.
And now we've got this fancy function create model what's happening here when we call model dot summary
after we've created an instance of our model.
We've got layout type Kerry's layout.
So we go up here we've got Okay hub carriers layer that makes sense.
We've got output shape multiple and program number five million four hundred thirty two thousand seven
hundred thirteen.
That's a fairly large number.
When we come here total parameters that we have down here trainable prams and non trainable prams and
it seems that the non trainable prams line up with this parameter number or parameter parameters sure
for parameters.
And we have another layer here called dense with param number one hundred twenty thousand turn of 40
that corresponds to our dense layer here.
Mm hmm.
Well this is one of the amazing things in the benefits of transfer learning.
The reason it says non trainable programs or parameters is because we're using mobile Net V2.
And now if we come up here these are the patterns that mobile Net V2 has learned throughout these layers
here or by repeatedly going through if we come back up to the top of this article by repeatedly going
through our images or the images that mobile Net V2 was originally trained on better.
That's the correct terminology there.
So it's gone through all of these images.
And if you're wondering what images did mobile Net V2 go through.
Well it trained on if we read through here we go image net.
There we go.
So it's whites were originally obtained by training on the I L SVR RC 2012 CLSA data set for image classification.
Image net.
Mm hmm.
Okay let's look up that image net they mentioned it image net is an image database organized according
to the word knit hierarchy.
Okay.
Currently we have an average of over 500 images per node.
OK.
So essentially image net is a large database of 14 over 14 million images.
And so that these patterns these parameters are what mobile Net has learned by training on image net.
And so this is the beautiful thing about transfer learning is that all these patents that mobile Net
has learned through these layers here and by the way.
This is why they call it deep learning because essentially each one of these layers is a layer in our
neural network.
So if you imagine this expanded out each one of them is a layer and hence the reason they call it deep
is because the diagrams are usually they have an input layer and then it just keeps going down and down
and down and down and down.
And so inherently what each of these layers actually are is a small model within themselves.
So these are just multiple pattern finding models layered on top of each other.
Hence the term deep learning.
So for come back this is what our base layer is those five and a half million patterns that mobile Net
V2 has found within the image net and now we get to utilize those patterns that it's found in our own
problem.
So rather than try and find them from scratch on our own which could take hours and lots of compute
power that we don't really have we get to utilize them and train our own.
One hundred and twenty thousand parameters so this is out our dense layer here.
That's what we're going to be training so we're going to be passing out images to mobile Net.
It's going to be using its baseline patents to figure out what's in our images.
And then finally we're going to stick this layer on top and go hey we've got our own data data that
isn't an image net but let's use what you figured out in image net for classifying which dog breed is
in a certain photo.
Now that we have a model we probably need to make some callbacks and you might be wondering what are
callbacks.
Well let's save that for the next video where we create some callbacks and then after that we'll create
a function that's going to train our model.
That sounds like a good idea to me.