The output gives us the amount of images in each split as well as the number of classifications. If this were set in “binary” the classifications would read 2 instead of 2
Now that the splits have occurred, we need to build the actual feature extraction model to fit the data into. After some research and testing, MobileNet_v2 gave the best results. Now we can fit the model and train it.
I added the adam variable in order to be able to change the learning rate of the algorithm if I needed to. I think this is a good practice if only to understand how the process and functions correlate to the outputs.
The history variable will be the data frame that we use to analyze and visualize the model. Within this variable the epochs define the amount of “rounds” the model will train through.
Patience refers to the amount of epochs that must pass without increased accuracy for the model to stop proceeding to the next epoch.