Using autoencoders to optimize two-dimensional signal constellations for fiber optic communication systems
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from constellation import util
import torch
from matplotlib import pyplot
import matplotlib
from mpl_toolkits.axisartist.axislines import SubplotZero
# Number learned symbols
order = 16
# Color map used for decision regions and points
color_map =
# File in which the trained model is saved
input_file = 'output/constellation-order-{}.pth'.format(order)
# Restore model from file
model = torch.load(input_file)
# Setup plot
fig = pyplot.figure()
ax = SubplotZero(fig, 111)
ax, model.get_constellation(),, model.decoder,
grid_step=0.001, noise_samples=0