model = nn.Sequential(
nn.Linear(args.dim_model, args.dim_model),
nn.ReLU(),
- nn.Linear(args.dim_model, 4)
+ nn.Linear(args.dim_model, args.dim_model),
+ nn.ReLU(),
+ nn.Linear(args.dim_model, 4),
).to(device)
for n_epoch in range(args.nb_epochs):
for input, targets in task.policy_batches(split="train"):
output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
output = model(output_gpt)
+ targets = targets * (input.unsqueeze(-1) == maze.v_empty)
+ output = output * (input.unsqueeze(-1) == maze.v_empty)
loss = (
- -(output.log_softmax(-1) * targets).sum(-1).mean()
- + targets.xlogy(targets).sum(-1).mean()
+ -(output.log_softmax(-1) * targets).sum()
+ / (input == maze.v_empty).sum()
+ + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
)
acc_train_loss += loss.item() * input.size(0)
nb_train_samples += input.size(0)
for input, targets in task.policy_batches(split="test"):
output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
output = model(output_gpt)
+ targets = targets * (input.unsqueeze(-1) == maze.v_empty)
+ output = output * (input.unsqueeze(-1) == maze.v_empty)
loss = (
- -(output.log_softmax(-1) * targets).sum(-1).mean()
- + targets.xlogy(targets).sum(-1).mean()
+ -(output.log_softmax(-1) * targets).sum()
+ / (input == maze.v_empty).sum()
+ + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
)
acc_test_loss += loss.item() * input.size(0)
nb_test_samples += input.size(0)
f"diff_ce {n_epoch} train {acc_train_loss/nb_train_samples} test {acc_test_loss/nb_test_samples}"
)
+ # -------------------
+ input = task.test_input[:32, : task.height * task.width]
+ targets = task.test_policies[:32]
+ output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
+ output = model(output_gpt)
+ losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1)
+ losses = losses * (input == maze.v_empty)
+ losses = losses / losses.max()
+ losses = losses.reshape(-1, args.maze_height, args.maze_width)
+ input = input.reshape(-1, args.maze_height, args.maze_width)
+ maze.save_image(
+ os.path.join(args.result_dir, f"oneshot_{n_epoch:04d}.png"),
+ mazes=input,
+ score_paths=losses,
+ )
+ # -------------------
+
gpt.train(t)
_, predicted_paths = self.seq2map(result)
maze.save_image(
os.path.join(args.result_dir, f"result_{n_epoch:04d}.png"),
- mazes,
- paths,
- predicted_paths,
- maze.path_correctness(mazes, predicted_paths),
+ mazes=mazes,
+ target_paths=paths,
+ predicted_paths=predicted_paths,
+ path_correct=maze.path_correctness(mazes, predicted_paths),
)
model.train(t)