By now you would have noticed that every architecture has in general two parts.
The first part of the architecture is a conventional base.
This includes conventional layers and pulling layers.
It could be one conversational and one willingly.
It could be tens of them.
It could be hundreds of them.
However if you look at the architectures most of them in the first part had convolution.
Basically the output of that conventional base goes into a fully connected neural network.
So the job of convolution base is very generic.
It is to find out and highlight certain features from the input images.
For example if you're inputting cats and dogs data the job of conventional base would be to highlight
eyes ears whiskers claws etc. So all of these individual features of the image are highlighted by the
Congressional base.
The job of fully connected neural network is to use these identified features to classify the image
whether it is a dog or whether it is a cat.
So if you have a neural network which is already trained in identifying certain features and then classifying
those images and now you have a new problem in which you are also trying to find the same features maybe
you are trying to have a different classification.
But if the import images have the same features in that case you can use these same convolution base
of pre-teen models.
For example in 2014 the AI unless we asked each challenge had one million images of different animals
and there were 1000 different animals to which these images belong.
The convolutions base of the winning networks what identifying features of different animals and declassified
in the end were only classifying those features into which animal it is.
And what is the breed of that animal.
So now if we are only running a model to classify cats and dogs.
This is the similar kind of input images that that particular talent had.
So a model that is trained on 2014 data that can be used in our problem also.
So this is the concept of crossover learning or feature extraction.
We are going to use some part of our pre train model mostly the conventional base because conversational
base is more genetic.
It is only finding features and we will put a new classifier in front of the Congressional base that
classifier will be trained by our system and that will be trained to classify and identify are images
into the classes that we have.
So this kind additional base will remain the same.
We will have a new classified on top of it to classify our images.
The advantages of doing this is that it saves a lot of time because we do not have to train this part
of the network.
Another good thing is these are proven models.
They are one of the best in finding the features.
So when we take their conversational base we can be assured that the features extracted from the images
would be the best also.
These models are trained on huge data set.
They had input data of millions of images.
So even if you have a small dataset from which featured extraction could have been difficult.
These models are already trained to extract features on large amount of data and the best thing is that
very easy to use.
They are part of the Get Us library.
It only takes a few lines of code to download all the weight of all the neurons in the econ relational
base and those can be used straightaway.
So in that project we will see how using pre-teen models we can achieve a higher level of accuracy even
if we have small amount of data to not model.