def train(raw, flags):
data_processor = DataProcessor(flags.forecast_length, flags.batch_size,
flags.window)
train_loader, val_loader = data_processor.get_train_test_data(
raw, flags.validation_ratio)
model = DeepAR(cov_dim=data_processor.num_features,
hidden_dim=flags.num_units,
num_layers=flags.num_layers,
num_class=len(raw['type'].unique()),
embedding_dim=flags.embedding_size,
batch_first=True,
dropout=flags.dropout)
opt = torch.optim.Adam(model.parameters(), lr=flags.learning_rate)
teacher_ratio = flags.teacher_ratio
loss_history = []
loss_fn = gaussian_likelihood_loss
model, opt, start_epoch = load_checkpoint(flags.checkpoint_path, model,
opt)
if start_epoch >= flags.num_epochs:
print('start_epoch is larger than num_epochs!')
epoch = start_epoch
# TODO: add early stop
for epoch in range(start_epoch, flags.num_epochs):
for step, data in enumerate(train_loader):
avg_loss, _ = _forward(data, model, loss_fn, flags.window,
flags.forecast_length, teacher_ratio)
loss_history.append(avg_loss)
opt.zero_grad()
avg_loss.backward()
opt.step()
teacher_ratio *= flags.teacher_ratio_decay
validation_loss = evaluate(val_loader, model, loss_fn, flags.window,
flags.forecast_length)
print('Epoch: %d' % epoch)
print("Training Loss:%.3f" % avg_loss)
print("Validation Loss:%.3f" % validation_loss)
print('Teacher_ratio: %.3f' % teacher_ratio)
print()
print('Model training completed and save at %s' % flags.checkpoint_path)
state = {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': opt.state_dict()
}
if not os.path.exists(flags.checkpoint_path):
os.mkdir(flags.checkpoint_path)
torch.save(state, flags.checkpoint_path + '/model.pt')
data_processor.save(flags.checkpoint_path)
return model, loss_history
def train(raw, flags):
data_processor = DataProcessor(flags.forecast_length, flags.batch_size,
flags.window)
train_loader, val_loader = data_processor.get_train_test_data(
raw, if_scale=True, val_ratio=flags.validation_ratio)
model = LSTM(data_processor.num_features,
flags.num_units,
output_dim=flags.output_dim,
num_layers=flags.num_layers,
batch_first=True,
dropout=flags.dropout)
if flags.loss == 'mse':
loss_fn = torch.nn.MSELoss()
else:
loss_fn = SMAPE()
opt = torch.optim.Adam(model.parameters(), lr=flags.learning_rate)
teacher_ratio = flags.teacher_ratio
loss_history = []
model, opt, start_epoch = load_checkpoint(flags.checkpoint_path, model,
opt)
if start_epoch >= flags.num_epochs:
print('start_epoch is larger than num_epochs!')
epoch = start_epoch
# TODO: add early stop
for epoch in range(start_epoch, flags.num_epochs):
for step, data in enumerate(train_loader):
avg_loss, _, acc = _forward(data, model, loss_fn, flags.window,
flags.forecast_length, True,
teacher_ratio)
loss_history.append(avg_loss)
opt.zero_grad()
avg_loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)
opt.step()
teacher_ratio *= flags.teacher_ratio_decay
val_loss, val_acc = evaluate(val_loader, model, loss_fn, flags.window,
flags.forecast_length)
print('Epoch: %d' % epoch)
print("Training Loss:%.3f" % avg_loss)
print('Training Avg Accuracy:%.3f' % acc)
print("Validation Loss:%.3f" % val_loss)
print("Validation Accuracy:%.3f" % val_acc)
print('Teacher_ratio: %.3f' % teacher_ratio)
print('Gradients:%.3f' % torch.mean((torch.stack(
[torch.mean(torch.abs(p.grad)) for p in model.parameters()], 0))))
print()
print('Model training completed and save at %s' % flags.checkpoint_path)
state = {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': opt.state_dict()
}
if not os.path.exists(flags.checkpoint_path):
os.mkdir(flags.checkpoint_path)
torch.save(state, flags.checkpoint_path + '/model.pt')
data_processor.save(flags.checkpoint_path)
return model, loss_history