Python script for visualizing a few deep neural net variant topologies using python graph-tool

network_visualizer.py

#!/usr/bin/env python

from graph_tool.all import *

# Vanilla
vg = Graph()
vil = [vg.add_vertex() for i in xrange(3)]
vd1 = [vg.add_vertex() for i in xrange(10)]
vd2 = [vg.add_vertex() for i in xrange(10)]
vol = [vg.add_vertex() for i in xrange(6)]
for i in vil:
	for j in vd1:
		vg.add_edge(i, j)
for i in vd1:
	for j in vd2:
		vg.add_edge(i, j)
for i in vd2:
	for j in vol:
		vg.add_edge(i, j)
varf = arf_layout(vg)
vfruch = fruchterman_reingold_layout(vg)
graph_draw(vg, output_size = (1000, 1000), output = "vanilla_arf.png", pos = varf)
graph_draw(vg, output_size = (1000, 1000), output = "vanilla_fruch.png", pos = vfruch)

# Separate layers
sg = Graph()
sil = [vg.add_vertex() for i in xrange(3)]

for i in xrange(6):
	start = sg.add_vertex()

	for j in sil:
		sg.add_edge(j, start)
	rep = [sg.add_vertex() for k in xrange(4)]

	for k in rep:
		sg.add_edge(start, k)

	for j in xrange(4):

		if j == 3:
			nrep = [sg.add_vertex() for k in xrange(1)]
		else:
			nrep = [sg.add_vertex() for k in xrange(4)]

		for n in rep:
			for m in nrep:
				sg.add_edge(n, m)
		rep = nrep


sarf = arf_layout(sg)
sfruch = fruchterman_reingold_layout(sg)
graph_draw(sg, output_size = (1000, 1000), output = "separate_arf.png", pos = sarf)
graph_draw(sg, output_size = (1000, 1000), output = "separate_fruch.png", pos = sfruch)