Please note that I have also included the .py file for the same notebook in case the notebook doesn't open in github.
The following python code will use the OS library to use Operating System libraries, giving us access to the file system, and the zipfile library allowing you to unzip the data.
The contents of the .zip are extracted to the base directory /tmp/horse-or-human, which in turn each contain horses and humans subdirectories
One thing to pay attention to in this sample: We do not explicitly label the images as horses or humans.
Here, instead of explicitly labelling the images, we have used something called an ImageGenerator, and is coded to read images from subdirectories, and automatically label them from the name of that subdirectory.
Also, we have added convolutional layers, and flatten the final result to feed into the densely connected layers.
Finally we add the densely connected layers.
Note that because we are facing a two-class classification problem, i.e. a binary classification problem, we will end our network with a sigmoid activation, so that the output of our network will be a single scalar between 0 and 1, encoding the probability that the current image is class 1(as opposed to class 0).