# How to plot Loss Landscape with more than 2 weights in the network

For a single neuron with 2 weights, I can plot the loss landscape and it looks like this (OR data, sigmoid activation, MAE loss):

But, when the neuron accepts more inputs, which means more than 2 weights required, or when there are more neurons, more layers in the network; how should the 3D loss landscape be plotted?

• I don't think you can, as the weights increase so does the number of dimensions. You could perhaps plot 3 weights using color in the graph as the 4th dimension, but I don't think you really could for more Oct 18, 2019 at 4:17
• @Recessive oh, but i can see some very complex loss landscape this way: pyimagesearch.com/wp-content/uploads/2019/10/…
– Dee
Oct 18, 2019 at 4:30
• the image in the comment right above is from this article: pyimagesearch.com/2019/10/14/…
– Dee
Oct 18, 2019 at 4:31
• Nope, that's actually just a mislabelled graph by medium. The original is about halfway down this page: firsttimeprogrammer.blogspot.com/search/label/…. You'll see this is a graph for f(x,y) = x**2 + y**2 –2*x*y. You can see the (falsely) modified image halfway down this page: medium.com/@RosieCampbell/…. EDIT: The medium post actually specifies this graph is for only 2 weights. It's just the pyimagesearch that loses this in translation. Oct 18, 2019 at 6:44