Amusing thing I found in the Introduction to Flux

Modified from Lecture 1’s file 1300.Intro-to-Flux.jl.ipynb:

using CSV, DataFrames

apples = DataFrame(CSV.File(datapath("data/apples.dat"), delim='\t', normalizenames=true))
bananas = DataFrame(CSV.File(datapath("data/bananas.dat"), delim='\t', normalizenames=true));

x_apples  = [ [,] for row in eachrow(apples)]
x_bananas = [ [,] for row in eachrow(bananas)];

xs = [x_apples; x_bananas] # vertical stacking of the features
ys = [fill(0, size(x_apples)); fill(1, size(x_bananas))]; # labels

using Flux, Plots
model = Dense(2, 1, σ)
lossfun(feature, label) = Flux.mse(model(feature), label)
plot(lossfun.(xs, ys)) # Array of losses for each feature-label pair.

This results in the amusing plot that seems to have a very regular spiking interspersed in two rolling fields. Any idea why this is true?