deep learning for computer vision because models behave just like layersyou can add model (like conv_baseto sequential model just like you would add layer listing adding densely connect ed classifier on op of he convolut ional base from keras import models from keras import layers model models sequential(model add(conv_basemodel add(layers flatten()model add(layers dense( activation='relu')model add(layers dense( activation='sigmoid')this is what the model looks like nowmodel summary(layer (typeoutput shape param ===============================================================vgg (model(none flatten_ (flatten(none dense_ (dense(none dense_ (dense(none ===============================================================total params , , trainable params , , non-trainable params as you can seethe convolutional base of vgg has , , parameterswhich is very large the classifier you're adding on top has million parameters before you compile and train the modelit' very important to freeze the convolutional base freezing layer or set of layers means preventing their weights from being updated during training if you don' do thisthen the representations that were previously learned by the convolutional base will be modified during training because the dense layers on top are randomly initializedvery large weight updates would be propagated through the networkeffectively destroying the representations previously learned in kerasyou freeze network by setting its trainable attribute to falseprint('this is the number of trainable weights 'before freezing the conv base:'len(model trainable_weights)this is the number of trainable weights before freezing the conv base conv_base trainable false print('this is the number of trainable weights 'after freezing the conv base:'len(model trainable_weights)this is the number of trainable weights after freezing the conv base licensed to