print("actual class:{}format(np nonzero(y_test[img_idx])[ ][ ])test_image =np expand_dims(x_test[img_idx]axis= bf vgg_model predict(test_image,verbose= pred_label clf_model predict_classes(bf,batch_size= ,verbose= print("predicted class:{}format(pred_label[ ])if show_probaprint("predicted probabilities"print(clf_model predict_proba(bf)the following is the output of the predict_labelfunction when tested against couple of images from the test dataset as depicted in figure - we correctly predicted the images belong to class label (dogand (truck)figure - predicted labels from pre-trained cnn based classifier this section demonstrated the power and advantages of transfer learning instead of spending time reinventing the wheelwith few lines of codewe were able to leverage state of the art neural network for our classification task the concept of transfer learning is what forms the basis of neural style transferwhich we will discuss in the next section artistic style transfer with cnns paintings (or for that matter any form of artrequire special skill which few have mastered paintings present complex interplay of content and style photographs on the other hand are combination of perspectives and light when the two are combinedthe results are spectacular and surprising one such example is shown in figure -