def main(params):
opts = get_params(params)
print(opts)
label_mapping = torch.LongTensor([x % opts.n_labels for x in range(100)])
print('# label mapping', label_mapping.tolist())
kwargs = {'num_workers': 1, 'pin_memory': True} if opts.cuda else {}
transform = transforms.ToTensor()
train_dataset = datasets.MNIST('./data',
train=True,
download=True,
transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=opts.batch_size,
shuffle=True,
**kwargs)
train_loader = DoubleMnist(train_loader, label_mapping)
test_dataset = datasets.MNIST('./data', train=False, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=16 * 1024,
shuffle=False,
**kwargs)
test_loader = DoubleMnist(test_loader, label_mapping)
sender = Sender(vocab_size=opts.vocab_size)
receiver = Receiver(vocab_size=opts.vocab_size,
n_classes=opts.n_labels,
n_hidden=opts.n_hidden)
sender = core.GumbelSoftmaxWrapper(sender, temperature=opts.temperature)
game = core.SymbolGameGS(sender, receiver, diff_loss_symbol)
optimizer = core.build_optimizer(game.parameters())
intervention = CallbackEvaluator(test_loader,
device=opts.device,
loss=game.loss,
is_gs=True,
var_length=False,
input_intervention=False)
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=[
core.ConsoleLogger(as_json=True),
EarlyStopperAccuracy(opts.early_stopping_thr),
intervention
])
trainer.train(n_epochs=opts.n_epochs)
core.close()
def main(params):
opts = get_params(params)
print(json.dumps(vars(opts)))
kwargs = {'num_workers': 1, 'pin_memory': True} if opts.cuda else {}
transform = transforms.ToTensor()
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('./data', train=True, download=True,
transform=transform),
batch_size=opts.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('./data', train=False, transform=transform),
batch_size=16 * 1024, shuffle=False, **kwargs)
n_classes = 10
binarize = False
test_loader = SplitImages(TakeFirstLoader(test_loader, n=1), rows_receiver=opts.receiver_rows,
rows_sender=opts.sender_rows, binarize=binarize, receiver_bottom=True)
train_loader = SplitImages(train_loader, rows_sender=opts.sender_rows, rows_receiver=opts.receiver_rows,
binarize=binarize, receiver_bottom=True)
sender = Sender(vocab_size=opts.vocab_size)
receiver = Receiver(vocab_size=opts.vocab_size, n_classes=n_classes)
sender = core.GumbelSoftmaxWrapper(sender, temperature=opts.temperature)
game = core.SymbolGameGS(sender, receiver, diff_loss_symbol)
optimizer = core.build_optimizer(game.parameters())
intervention = CallbackEvaluator(test_loader, device=opts.device, loss=game.loss,
is_gs=True,
var_length=False,
input_intervention=True)
trainer = core.Trainer(game=game, optimizer=optimizer, train_data=train_loader,
validation_data=test_loader,
callbacks=[core.ConsoleLogger(as_json=True),
EarlyStopperAccuracy(opts.early_stopping_thr),
intervention])
trainer.train(n_epochs=opts.n_epochs)
core.close()