Modify loss

Author: ikostrikov2

main.py

@@ -19,10 +19,10 @@
  help='number of meta optimizer steps (default: 100)')
 parser.add_argument('--truncated_bptt_step', type=int, default=20, metavar='N',
  help='step at which it truncates bptt (default: 20)')
-parser.add_argument('--updates_per_epoch', type=int, default=10, metavar='N',
+parser.add_argument('--updates_per_epoch', type=int, default=100, metavar='N',
  help='updates per epoch (default: 100)')
-parser.add_argument('--max_epoch', type=int, default=100, metavar='N',
- help='number of epoch (default: 100)')
+parser.add_argument('--max_epoch', type=int, default=10000, metavar='N',
+ help='number of epoch (default: 10000)')
 parser.add_argument('--hidden_size', type=int, default=10, metavar='N',
  help='hidden size of the meta optimizer (default: 10)')
 parser.add_argument('--no-cuda', action='store_true', default=False,
@@ -85,6 +85,9 @@ def main():
  keep_states=k > 0, model=model, use_cuda=args.cuda)
 
  loss_sum = 0
+ prev_loss = torch.zeros(1)
+ if args.cuda:
+ prev_loss = prev_loss.cuda()
  for j in range(args.truncated_bptt_step):
  x, y = next(train_iter)
  if args.cuda:
@@ -104,7 +107,10 @@ def main():
  # Compute a loss for a step the meta optimizer
  f_x = meta_model(x)
  loss = F.nll_loss(f_x, y)
- loss_sum += loss
+
+ loss_sum += (loss - Variable(prev_loss))
+
+ prev_loss = loss.data
 
  # Update the parameters of the meta optimizer
  meta_optimizer.zero_grad()
@@ -117,7 +123,7 @@ def main():
  # value
  decrease_in_loss += loss.data[0] / initial_loss.data[0]
 
- print("Epoch: {}, average final/initial loss ratio: {}".format(epoch,
+ print("Epoch: {}, final loss {}, average final/initial loss ratio: {}".format(epoch, loss.data[0],
  decrease_in_loss / args.updates_per_epoch))
 
 if __name__ == "__main__":