deep learning for computer vision let' save the model--you'll use it in section listing saving he model model save('cats_and_dogs_small_ 'and let' plot the results againsee figures and thanks to data augmentation and dropoutyou're no longer overfittingthe training curves are closely tracking the validation curves you now reach an accuracy of % relative improvement over the non-regularized model figure training and validat ion accuracy with dat augmentat ion figure training and validat ion loss with dat augmentat ion by using regularization techniques even furtherand by tuning the network' parameters (such as the number of filters per convolution layeror the number of layers in the network)you may be able to get an even better accuracylikely up to or but it would prove difficult to go any higher just by training your own convnet from scratchbecause you have so little data to work with as next step to improve your accuracy on this problemyou'll have to use pretrained modelwhich is the focus of the next two sections licensed to