block _conv (conv (nonenonenone block _conv (conv (nonenonenone block _pool (maxpooling (nonenonenone ================================================================total params , , trainable params , , non-trainable params from the preceding outputyou can see that the architecture is huge with lot of layers figure - depicts the same in an easier-to-understand visual depicting all the layers remember that we do not use the fully connected layers depicted in the extreme right of figure - we recommend checking out the paper very deep convolutional networks for large-scale image recognition by karen simonyan and andrew zisserman of the visual geometry groupdepartment of engineering scienceuniversity of oxford the paper is available at figure - visual depiction of the vgg- architecture loading the cifar training and test datasets is the same as discussed in the previous section we perform similar one hot encoding of the labels as well since the vgg model has been loaded without the final fully connected layersthe predictfunction of the model helps us get the extracted features on our dataset the following snippet extracts the features for both training and test datasets in [ ]bottleneck_features_train vgg_model predict(x_trainverbose= bottleneck_features_test vgg_model predict(x_testverbose=