def learn(exp_name, dataset, model=None, optimizer=None, loss=None,
rng_seed=1234, num_epochs=10, split=(0.7, 0.2, 0.1), bsz=64):
if model is None:
in_size = dataset[0][0].numel()
if isinstance(dataset[0][1], (int, long, float, complex)):
out_size = 1
else:
out_size = dataset[0][1].numel()
model = get_model(in_size, out_size)
model = Network(model)
if loss is None:
if isinstance(dataset[0][1], (int, long, float, complex)):
reg = True
else:
reg = False
loss = get_loss(regression=reg)
if optimizer is None:
optimizer = get_optimizer(model)
opt_hyperparams = optimizer.param_groups[0]
opt_hyperparams = {k: opt_hyperparams[k] for k in opt_hyperparams if not k == 'params'}
exp = ro.Experiment(exp_name, {
'model': str(model),
'optimizer': str(optimizer),
'opt_hyperparams': opt_hyperparams,
'loss': str(loss),
'rng_seed': rng_seed,
'num_epochs': num_epochs,
'bsz': bsz,
'split': split,
})
th.manual_seed(rng_seed)
np.random.seed(rng_seed)
if args.cuda:
th.cuda.manual_seed(rng_seed)
model.cuda()
print('Splitting dataset in ' + str(split[0]) + ' train, ' + str(split[1]) + ' Validation, ' + str(split[2]) + ' Test')
dataset = split_dataset(dataset, split[0], split[1], split[2])
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
train_loader = th.utils.data.DataLoader(dataset, batch_size=bsz, shuffle=True, **kwargs)
dataset.use_valid()
valid_loader = th.utils.data.DataLoader(dataset, batch_size=bsz, shuffle=True, **kwargs)
dataset.use_test()
test_loader = th.utils.data.DataLoader(dataset, batch_size=bsz, shuffle=True, **kwargs)
train_errors = []
valid_errors = []
# Start training
for epoch in range(num_epochs):
print('\n\n', '-' * 20, ' Epoch ', epoch, ' ', '_' * 20)
dataset.use_train()
train_errors.append(train(train_loader, model, loss, optimizer))
print('Training error: ', train_errors[-1])
dataset.use_valid()
valid_errors.append(test(valid_loader, model, loss))
print('Validation error: ', valid_errors[-1])
# Benchmark on Test
dataset.use_test()
test_error = test(test_loader, model, loss)
print('Final Test Error: ', test_error)
# Save experiment result
exp.add_result(test_error, {
'train_errors': train_errors,
'valid_errors': valid_errors,
})
# Plot Results
if not os.path.exists('./results'):
os.mkdir('./results')
p = Plot('Convergence')
x = np.arange(0, len(train_errors), 1)
p.plot(x, np.array(train_errors), label='Train')
p.plot(x, np.array(valid_errors), label='Validation')
p.set_axis('Epoch', 'Loss')
b = Plot('Final Error')
b.bar(x=[train_errors[-1], valid_errors[-1], test_error],
labels=['Train', 'Validation', 'Test'])
cont = Container(1, 2, title=exp_name)
cont.set_plot(0, 0, p)
cont.set_plot(0, 1, b)
cont.save('./results/' + exp_name + '.pdf')
