‫By now, you you'd have noticed that every architecture has in general two parts.
‫The first part of the architecture is a convolutional base.
‫This includes convolutional layers and pooling layers.
‫It could be one convolutional 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 convolutional.
‫Basically, the output of that convolutional base goes into a fully connected neural network.
‫So the job of convolutional base is very generic.
‫It is to find out and highlight certain features from the input images.
‫For example, if you are inputting cats and dogs data, the job of convolutional base would be to highlight
‫eyes, ears.
‫This goes Glaus, etc..
‫So all of these individual features of the image are highlighted by the conditional 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 convolutional
‫base of retrain 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 convolutional base of the winning networks were identifying features of different animals and declassifies.
‫In the end, were only classifying those features and do which animal it is.
‫And what is the breed of that animal?
‫So now if we're only learning 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 crosswell learning or feature extraction.
‫We are going to use some part of our pre train model, mostly the convolutional base, because convolutional
‫base is more genetic.
‫It is only finding features and we will put a new classifier in front of the convolutional 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 convolutional 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 convolutional base, we can be assured that the features extracted from the images
‫would be the best.
‫Also, these models are trained on huge does it.
‫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 they're very easy to use.
‫They are part of the get us library.
‫It only takes a few lines of good to download all the weight of all the neurons in the convolutional
‫base.
‫And those can be used straightaway.
‫So in that project, we will see how using pre train models, we can achieve higher level of accuracy
‫even if we have a small amount of data to train model.