deep learning for computer vision 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 block _conv (convolution (none block _conv (convolution (none block _conv (convolution (none block _pool (maxpooling (none ===============================================================total params , , trainable params , , non-trainable params the final feature map has shape ( that' the feature on top of which you'll stick densely connected classifier at this pointthere are two ways you could proceedrunning the convolutional base over your datasetrecording its output to numpy array on diskand then using this data as input to standalonedensely connected classifier similar to those you saw in part of this book this solution is fast and cheap to runbecause it only requires running the convolutional base once for every input imageand the convolutional base is by far the most expensive part of the pipeline but for the same reasonthis technique won' allow you to use data augmentation licensed to