deep learning for computer vision stated earlier that it' necessary to freeze the convolution base of vgg in order to be able to train randomly initialized classifier on top for the same reasonit' only possible to fine-tune the top layers of the convolutional base once the classifier on top has already been trained if the classifier isn' already trainedthen the error signal propagating through the network during training will be too largeand the representations previously learned by the layers being fine-tuned will be destroyed thus the steps for fine-tuning network are as follow add your custom network on top of an already-trained base network freeze the base network train the part you added unfreeze some layers in the base network jointly train both these layers and the part you added you already completed the first three steps when doing feature extraction let' proceed with step you'll unfreeze your conv_base and then freeze individual layers inside it as reminderthis is what your convolutional base looks likeconv_base summary(layer (typeoutput shape param ===============================================================input_ (inputlayer(none block _conv (convolution (none block _conv (convolution (none block _pool (maxpooling (none block _conv (convolution (none block _conv (convolution (none block _pool (maxpooling (none block _conv (convolution (none block _conv (convolution (none block _conv (convolution (none block _pool (maxpooling (none block _conv (convolution (none block _conv (convolution (none block _conv (convolution (none block _pool (maxpooling (none licensed to