I've taken a quick course in neural networks to better understand them and now I'm trying them out for myself in R. I'm following this documentation of Keras.
The way I understand what is happening:
We are inputting a series of images and transforming these images to numerical matrices based on the arrangement of the pixels and colors in those pixels. We then build a neural network model to learn the pattern of these arrangements, depending on the classification (0 to 9). We then use the model to predict which class an image belongs to. I'll be honest and admit I'm not entirely sure what y_train and x_train is. I simply see it as one training and one validation set so I'm not sure what the difference between x and y is.
My question:
I've followed the steps to the T and the model runs fine and the predictions look like they do in the documentation. Ultimately, the prediction looks like this:
I take this to mean that observation 1 in x_test is predicted to be a category 7.
However, looking at x_test it looks like this: 
There is a 0 in every column and row, also if I scroll further down. This is where I get confused. I'm also not sure how I view the original images to view for myself how well they are predicting them. I would eventually like to draw a number myself in paint or so and then see if the model can predict it, but for that I need to first understand what is going on. I feel I am close but I just need a little nudge!