  Studying neural network loss landscapes provides insights into the nature of
the underlying optimization problems. Unfortunately, loss landscapes are
notoriously difficult to visualize in a human-comprehensible fashion. One
common way to address this problem is to plot linear slices of the landscape,
for example from the initial state of the network to the final state after
optimization. On the basis of this analysis, prior work has drawn broader
conclusions about the difficulty of the optimization problem. In this paper, we
put inferences of this kind to the test, systematically evaluating how linear
interpolation and final performance vary when altering the data, choice of
initialization, and other optimizer and architecture design choices. Further,
we use linear interpolation to study the role played by individual layers and
substructures of the network. We find that certain layers are more sensitive to
the choice of initialization, but that the shape of the linear path is not
indicative of the changes in test accuracy of the model. Our results cast doubt
on the broader intuition that the presence or absence of barriers when
interpolating necessarily relates to the success of optimization.

Machine Learning;Artificial Intelligence;Machine Learning