def run(net):
# Create dataloaders
trainloader, valloader = prepare_data()
net = net.to(device)
optimizer = torch.optim.SGD(net.parameters(),
lr=hps['lr'],
momentum=0.9,
nesterov=True,
weight_decay=0.0001)
scheduler = ReduceLROnPlateau(optimizer,
mode='max',
factor=0.5,
patience=10,
verbose=True)
criterion = nn.CrossEntropyLoss()
best_acc_v = 0
print("Training", hps['name'], "on", device)
for epoch in range(hps['n_epochs']):
acc_tr, loss_tr = train(net, trainloader, criterion, optimizer)
logger.loss_train.append(loss_tr)
logger.acc_train.append(acc_tr)
acc_v, loss_v = evaluate(net, valloader, criterion)
logger.loss_val.append(loss_v)
logger.acc_val.append(acc_v)
# Update learning rate if plateau
scheduler.step(acc_v)
# Save logs regularly
if (epoch + 1) % 5 == 0:
logger.save(hps)
# Save the best network and print results
if acc_v > best_acc_v:
save(net, hps)
best_acc_v = acc_v
print('Epoch %2d' % (epoch + 1),
'Train Accuracy: %2.2f %%' % acc_tr,
'Val Accuracy: %2.2f %%' % acc_v,
'Network Saved',
sep='\t\t')
else:
print('Epoch %2d' % (epoch + 1),
'Train Accuracy: %2.2f %%' % acc_tr,
'Val Accuracy: %2.2f %%' % acc_v,
sep='\t\t')
args.batch_accumulation),
)
avg_loss, iteration = train_loop(
model,
train_loader,
optimizer,
criterion,
scheduler,
args,
iteration,
)
avg_val_loss, score, val_preds = evaluate(args,
model,
valid_loader,
criterion,
val_shape=len(valid_set))
print("Epoch {}/{}: \t loss={:.4f} \t val_loss={:.4f} \t score={:.6f}".
format(epoch + 1, args.epochs, avg_loss, avg_val_loss, score))
with open(log_file, "a") as csvfile:
writer = csv.writer(csvfile)
writer.writerow([epoch + 1, avg_loss, avg_val_loss, score])
torch.save(
model.state_dict(),
os.path.join(fold_checkpoints,
"model_on_epoch_{}.pth".format(epoch)),
)
val_preds_df = val_fold_df.copy()[["qa_id"] + args.target_columns]