diff options
Diffstat (limited to 'src/nn_train.py')
| -rwxr-xr-x | src/nn_train.py | 27 |
1 files changed, 6 insertions, 21 deletions
diff --git a/src/nn_train.py b/src/nn_train.py index cde7a98..8f1a73e 100755 --- a/src/nn_train.py +++ b/src/nn_train.py @@ -201,8 +201,7 @@ class SelfPlayRecords: class Net(Module): def __init__(self, inp_dim: int, hidden: int) -> None: super().__init__() - # learned 7-way cell-state embedding (empty, {black, white} x - # {flat, standing, road}); the slot layout must match nn.c + # learned cell-state embedding, see nn.c self.embedding = Embedding(7, 1) self.hidden = Linear(inp_dim, hidden, bias=True) self.output = Linear(hidden, 1, bias=True) @@ -302,7 +301,7 @@ class Net(Module): _ = f.write(f"/* {p} */\n") _ = f.write(f'#include "weights_{size}.h"\n\n') _ = f.write( - f"const float {prefix}_embedding[7] =\n" + f"const float {prefix}_embedding[{len(E)}] =\n" f"{{\n{_fmt_flat(E, per_line=7)}\n}};\n\n" ) _ = f.write( @@ -548,24 +547,6 @@ def main() -> None: help="number of previous self-play instances to mix in with current dataset during training", ) _ = p.add_argument( - "--eps-start", - type=float, - default=0.15, - help="initial self-play epsilon: in asymmetric self-play one side (chosen at random per game) plays a uniform random move with this probability while the greedy side has its plies recorded", - ) - _ = p.add_argument( - "--eps-min", - type=float, - default=0.01, - help="self-play epsilon floor", - ) - _ = p.add_argument( - "--eps-decay", - type=float, - default=0.9, - help="per-iteration epsilon decay", - ) - _ = p.add_argument( "--td-lambda", type=float, default=0.7, @@ -639,6 +620,10 @@ def main() -> None: (root / "data").mkdir(exist_ok=True) champ = setup(size=args.size, root=root) + + num_params = sum(p.numel() for p in champ.parameters() if p.requires_grad) + print(f"Number of trainable parameters: {num_params:,}") + for iter in range(1, args.iters + 1): champ = run_step( batch=args.batch, |