def test_early_stopping():
game, data = MockGame(), Dataset()
early_stopper = core.EarlyStopperAccuracy(threshold=0.9)
trainer = core.Trainer(game=game,
optimizer=torch.optim.Adam(game.parameters()),
train_data=data,
validation_data=data,
callbacks=[early_stopper])
trainer.train(1)
assert trainer.should_stop
def main(params):
opts = get_params(params)
print(opts)
device = opts.device
n_a, n_v = opts.n_a, opts.n_v
opts.vocab_size = n_v
train_data = AttributeValueData(n_attributes=n_a,
n_values=n_v,
mul=1,
mode='train')
train_loader = DataLoader(train_data,
batch_size=opts.batch_size,
shuffle=True)
test_data = AttributeValueData(n_attributes=n_a,
n_values=n_v,
mul=1,
mode='test')
test_loader = DataLoader(test_data,
batch_size=opts.batch_size,
shuffle=False)
print(f'# Size of train {len(train_data)} test {len(test_data)}')
if opts.language == 'identity':
sender = IdentitySender(n_a, n_v)
elif opts.language == 'rotated':
sender = RotatedSender(n_a, n_v)
else:
assert False
receiver = Receiver(n_hidden=opts.receiver_hidden,
n_dim=n_a * n_v,
inner_layers=opts.receiver_layers)
receiver = core.RnnReceiverDeterministic(
receiver,
opts.vocab_size + 1, # exclude eos = 0
opts.receiver_emb,
opts.receiver_hidden,
cell=opts.receiver_cell,
num_layers=opts.cell_layers)
diff_loss = DiffLoss(n_a, n_v, loss_type=opts.loss_type)
game = core.SenderReceiverRnnReinforce(sender,
receiver,
diff_loss,
receiver_entropy_coeff=0.05,
sender_entropy_coeff=0.0)
optimizer = core.build_optimizer(receiver.parameters())
loss = game.loss
early_stopper = core.EarlyStopperAccuracy(1.0, validation=False)
trainer = core.Trainer(game=game,
optimizer=optimizer,
train_data=train_loader,
validation_data=test_loader,
callbacks=[
core.ConsoleLogger(as_json=True,
print_train_loss=True),
early_stopper
],
grad_norm=1.0)
trainer.train(n_epochs=opts.n_epochs)
core.close()
