we also recommend you go through the section "automated feature engineering with deep learningin to learn more about extracting feature representations from images using convolutional layers cnn based deep learning classifier with pretrained models building classifier from scratch has its own set of pros and cons yeta more refined approach is to leverage pre-trained models over large complex datasets there are many famous cnn architectures like lenetresnetvgg- vgg- and so on these models have deep and complex architectures that have been fine-tuned and trained over diverselarge datasets hencethese models have been proven to have amazing performance on complex object recognition tasks since obtaining large labeled datasets and training highly complex and deep neural networks is timeconsuming task (training complex cnn like vgg- could take few weekseven using gpusin practicewe utilize conceptwhat is formally termed as transfer learning this concept of transfer learning helps us leverage existing models for our tasks the core idea is to leverage the learningwhich the model learned from being trained over large dataset and then transfer this learning by re-using the same model to extract feature representations from new images there are several strategies of performing transfer learningsome of which are mentioned as followspre-trained model as feature extractorthe pre-trained model is used to extract features for our dataset we build fully connected classifier on top of these features in this case we only need to train the fully connected classifierwhich does not take much time fine-tuning pre-trained modelsit is possible to fine-tune an existing pre-trained model by fixing some of the layers and allowing others to learn/update weights apart from the fully connected layers usually it is observed that initial layers capture generic features while the deeper ones become more specific in terms of feature extraction thusdepending upon the requirementswe fix certain layers and fine-tune the rest in this sectionwe see an example where we will utilize pre-trained conv-network as feature extractor and build fully connected layer based classifier on top of it and train the model we will not train the feature extraction layers and hence leverage principles of transfer learning by using the pre-trained conv layers for feature extraction the vgg- model from the visual geometry group of the oxford university is one state-of-the-art convolutional neural network this has been shown to perform extremely well on various benchmarks and competitions vgg is -layer conv-net trained on imagenet dataset imagenet is visual database of hand-annotated images amounting to million spanning across , categories this model has been widely studied and used in tasks such as transfer learning ##note more details on this and other research by the vgg group is available at ox ac uk/~vgg/research/very_deepthis pretrained model is available through the keras applications module as mentionedwe will utilize vgg- to act as feature extractor to help us build classifier on cifar dataset since we would be using vgg- for feature extractionwe do not need the top (or fully connectedlayers of this model keras makes this as simple as setting single flag value to false the following snippet loads the vgg- model architecture consisting of the conv layers and leaves out the fully connected layers