79 lines
2.9 KiB
Python
79 lines
2.9 KiB
Python
import torch.nn as nn
|
||
|
||
|
||
class ConstellationNet(nn.Module):
|
||
def __init__(
|
||
self,
|
||
order=2,
|
||
encoder_layers_sizes=(),
|
||
decoder_layers_sizes=()
|
||
):
|
||
"""
|
||
Create an encoder-decoder network to automatically shape a
|
||
constellation of symbols for efficient communication over an optical
|
||
fiber channel.
|
||
|
||
:param order: Order of the constellation, i.e. the number of messages
|
||
that are to be transmitted or equivalently the number of symbols whose
|
||
placements in the constellation have to be learned.
|
||
:param encoder_layers_sizes: Shape of the encoder’s hidden layers. The
|
||
size of this sequence is the number of hidden layers, with each element
|
||
being a number which specifies the number of neurons in its channel.
|
||
:param decoder_layers_sizes: Shape of the decoder’s hidden layers. Uses
|
||
the same convention as `encoder_layers_sizes` above.
|
||
"""
|
||
super().__init__()
|
||
|
||
# Build the encoder network taking a one-hot encoded message as input
|
||
# and outputting an I/Q vector. The network additionally uses hidden
|
||
# layers as specified in `encoder_layers_sizes`
|
||
prev_layer_size = order
|
||
encoder_layers = []
|
||
|
||
for layer_size in encoder_layers_sizes:
|
||
encoder_layers.append(nn.Linear(prev_layer_size, layer_size))
|
||
encoder_layers.append(nn.ReLU())
|
||
prev_layer_size = layer_size
|
||
|
||
encoder_layers += [
|
||
nn.Linear(prev_layer_size, 2),
|
||
nn.ReLU(),
|
||
# TODO: Normalization step
|
||
]
|
||
|
||
self.encoder = nn.Sequential(*encoder_layers)
|
||
|
||
# TODO: Add real channel model
|
||
self.channel = nn.Identity()
|
||
|
||
# Build the decoder network taking the noisy I/Q vector received from
|
||
# the channel as input and outputting a probability vector for each
|
||
# original message. The network additionally uses hidden layers as
|
||
# specified in `decoder_layers_sizes`
|
||
prev_layer_size = 2
|
||
decoder_layers = []
|
||
|
||
for layer_size in decoder_layers_sizes:
|
||
decoder_layers.append(nn.Linear(prev_layer_size, layer_size))
|
||
decoder_layers.append(nn.ReLU())
|
||
prev_layer_size = layer_size
|
||
|
||
# Softmax is not used at the end of the network because the
|
||
# CrossEntropyLoss criterion is used for training, which includes
|
||
# LogSoftmax
|
||
decoder_layers.append(nn.Linear(prev_layer_size, order),)
|
||
|
||
self.decoder = nn.Sequential(*decoder_layers)
|
||
|
||
def forward(self, x):
|
||
"""
|
||
Perform encoding and decoding of an input vector and compute its
|
||
reconstructed vector.
|
||
|
||
:param x: Original one-hot encoded data.
|
||
:return: Reconstructed vector.
|
||
"""
|
||
symbol = self.encoder(x)
|
||
noisy_symbol = self.channel(symbol)
|
||
return self.decoder(noisy_symbol)
|