constellationnet/train.py

import constellation
from constellation import util
import torch

# Number of symbols to learn
order = 4

# Number of training examples in an epoch
epoch_size = 10000

# Number of epochs
num_epochs = 20000

# Number of epochs to skip between every loss report
loss_report_epoch_skip = 500

# File in which the trained model is saved
output_file = 'output/constellation-order-{}.pth'.format(order)

model = constellation.ConstellationNet(
    order=order,
    encoder_layers_sizes=(4,),
    decoder_layers_sizes=(4,),
    channel_model=constellation.GaussianChannel()
)

# Train the model with random data
model.train()
print('Starting training with {} epochs\n'.format(num_epochs))

criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())

running_loss = 0

for epoch in range(num_epochs):
    classes_dataset = util.get_random_messages(epoch_size, order)
    onehot_dataset = util.messages_to_onehot(classes_dataset, order)

    optimizer.zero_grad()
    predictions = model(onehot_dataset)
    loss = criterion(predictions, classes_dataset)
    loss.backward()
    optimizer.step()

    # Report loss
    running_loss += loss.item()

    if epoch % loss_report_epoch_skip == loss_report_epoch_skip - 1:
        print('Epoch {}/{}'.format(epoch + 1, num_epochs))
        print('Loss is {}'.format(running_loss))
        running_loss = 0

print('\nFinished training\n')

# Print some examples of reconstruction
model.eval()
print('Reconstruction examples:')
print('Input vector\t\t\tOutput vector after softmax')

with torch.no_grad():
    onehot_example = util.messages_to_onehot(torch.arange(0, order), order)
    raw_output = model(onehot_example)
    raw_output.required_grad = False
    reconstructed_example = torch.nn.functional.softmax(raw_output, dim=1)

    for index in range(order):
        print('{}\t\t{}'.format(
            onehot_example[index].tolist(),
            '[{}]'.format(', '.join(
                '{:.5f}'.format(x)
                for x in reconstructed_example[index].tolist()
            ))
        ))

print('\nSaving model as {}'.format(output_file))
torch.save(model.state_dict(), output_file)