‫All right.
‫And now we've got this fancy function create model.
‫What's happening here when we call model summary?
‫After we've created an instance of our model, we've got layout type, carer's layer.
‫So we go up here, we've got hub carer's layer that makes sense.
‫We've got output shape multiple and param number 5,432,713.
‫That's a fairly large number when we come here, total parameters and then we have down here trainable
‫params and non trainable params.
‫And it seems that the non trainable params line up with this params number or parameter params is short
‫for parameters.
‫And we have another layer here called dense with param number 120,240.
‫That corresponds to our dense layer here.
‫Hmm.
‫Well.
‫This is one of the amazing things in the benefits of transfer learning.
‫The reason it says non trainable params 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 V two 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.
‫Figo image net.
‫There we go.
‫So it's weights were originally obtained by training on the ILS VRC 2012 CLS data set for image classification
‫image net.
‫Hmm.
‫Okay, let's look up that image net.
‫They mention it?
‫Image net is an image database organized according to the word net.
‫Hierarchy.
‫Okay.
‫Currently we have an average of over 500 images per node.
‫Okay.
‫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 patterns 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, expand it 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 if we come back.
‫This is what our base layer is.
‫Those five and a half million patterns that mobile net v2 has found within 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 120,000 parameters.
‫So this is our 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 patterns 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.
‫You might be wondering what a 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.