optimizer.zero_grad()
loss.backward()
optimizer.step()
m.track_loss(loss)
for validation_batch in validation_loader:
in_features, t_slf = validation_batch
in_features = in_features.to(run.device)
t_slf = t_slf.to(run.device)
preds, mu, logvar = network(in_features)
mse_valid = mse_loss(t_slf, preds)
valid_loss = vae_loss(mse_valid, mu, logvar)
m.track_validation_loss(valid_loss)
m.end_epoch()
for param_group in optimizer.param_groups:
print(param_group['lr'])
print('epoch: {}, loss: {}, v_loss: {}, lr: {}'.format(
epoch, m.item_loss, m.validation_loss, lr))
torch.save(
{
'epoch': epoch,
'model_state_dict': network.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': m.epoch_loss,
'lr': lr
}, MODEL_PATH_VAE)