using pretrained convnet figure training and validation accuracy for simple feature extraction figure training and validat ion loss for simple feat ure ext ract ion you reach validation accuracy of about %--much better than you achieved in the previous section with the small model trained from scratch but the plots also indicate that you're overfitting almost from the start--despite using dropout with fairly large rate that' because this technique doesn' use data augmentationwhich is essential for preventing overfitting with small image datasets feature extraction with data augmentation nowlet' review the second technique mentioned for doing feature extractionwhich is much slower and more expensivebut which allows you to use data augmentation during trainingextending the conv_base model and running it end to end on the inputs this technique is so expensive that you should only attempt it if you have access to gpu--it' absolutely intractable on cpu if you can' run your code on gputhen the previous technique is the way to go note licensed to