generated from mathias/template-go-web
chore(loop): 6 more iters — plateau at ~0.34-0.37 (1/6 kept)
Toy encoder near ceiling. 1 kept (val_vol_r2 0.3032→0.3442), 5 reverts. Consistent plateau = time to swap in TS-JEPA backbone (#3/#5). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -5,3 +5,9 @@
|
|||||||
| 1 | 0.3749 | +0.0928 | KEEP | 2s | gpu=0% vram=10054/12227MiB temp=34°C | iter1 |
|
| 1 | 0.3749 | +0.0928 | KEEP | 2s | gpu=0% vram=10054/12227MiB temp=34°C | iter1 |
|
||||||
| 1 | 0.3011 | +0.0776 | KEEP | 2s | gpu=0% vram=10054/12227MiB temp=34°C | iter1 |
|
| 1 | 0.3011 | +0.0776 | KEEP | 2s | gpu=0% vram=10054/12227MiB temp=34°C | iter1 |
|
||||||
| 2 | 0.3032 | +0.0021 | KEEP | 2s | gpu=0% vram=10054/12227MiB temp=35°C | iter2 |
|
| 2 | 0.3032 | +0.0021 | KEEP | 2s | gpu=0% vram=10054/12227MiB temp=35°C | iter2 |
|
||||||
|
| 1 | 0.2759 | -0.0273 | revert | 2s | gpu=0% vram=10054/12227MiB temp=34°C | iter1 |
|
||||||
|
| 2 | 0.3442 | +0.0410 | KEEP | 2s | gpu=0% vram=10054/12227MiB temp=34°C | iter2 |
|
||||||
|
| 3 | 0.3371 | -0.0071 | revert | 2s | gpu=0% vram=10054/12227MiB temp=34°C | iter3 |
|
||||||
|
| 4 | 0.3143 | -0.0299 | revert | 2s | gpu=0% vram=10054/12227MiB temp=34°C | iter4 |
|
||||||
|
| 5 | 0.3355 | -0.0087 | revert | 2s | gpu=0% vram=10054/12227MiB temp=34°C | iter5 |
|
||||||
|
| 6 | 0.2377 | -0.1065 | revert | 2s | gpu=0% vram=10054/12227MiB temp=35°C | iter6 |
|
||||||
|
|||||||
+1
-1
@@ -1,5 +1,5 @@
|
|||||||
{
|
{
|
||||||
"val_vol_r2": 0.30321519081159654,
|
"val_vol_r2": 0.23767155122897032,
|
||||||
"n_test": 275,
|
"n_test": 275,
|
||||||
"knobs": {
|
"knobs": {
|
||||||
"WINDOW": 20,
|
"WINDOW": 20,
|
||||||
|
|||||||
@@ -47,10 +47,10 @@ class Encoder(nn.Module):
|
|||||||
super().__init__()
|
super().__init__()
|
||||||
self.net = nn.Sequential(
|
self.net = nn.Sequential(
|
||||||
nn.Flatten(),
|
nn.Flatten(),
|
||||||
nn.Linear(win * 2, 128),
|
nn.Linear(win * 2, 256),
|
||||||
nn.LayerNorm(128),
|
nn.LayerNorm(256),
|
||||||
nn.GELU(),
|
nn.GELU(),
|
||||||
nn.Linear(128, emb)
|
nn.Linear(256, emb)
|
||||||
)
|
)
|
||||||
|
|
||||||
def forward(self, x):
|
def forward(self, x):
|
||||||
@@ -61,7 +61,7 @@ def main():
|
|||||||
(Xtr, ytr), (Xte, yte) = build()
|
(Xtr, ytr), (Xte, yte) = build()
|
||||||
Xtr_t = torch.tensor(Xtr, device=dev)
|
Xtr_t = torch.tensor(Xtr, device=dev)
|
||||||
enc = Encoder(WINDOW, EMBED_DIM).to(dev)
|
enc = Encoder(WINDOW, EMBED_DIM).to(dev)
|
||||||
dec = nn.Sequential(nn.Linear(EMBED_DIM, 128), nn.GELU(), nn.Linear(128, WINDOW * 2)).to(dev)
|
dec = nn.Sequential(nn.Linear(EMBED_DIM, 256), nn.GELU(), nn.Linear(256, WINDOW * 2)).to(dev)
|
||||||
opt = torch.optim.Adam(list(enc.parameters()) + list(dec.parameters()), lr=LR)
|
opt = torch.optim.Adam(list(enc.parameters()) + list(dec.parameters()), lr=LR)
|
||||||
|
|
||||||
for _ in range(EPOCHS): # SSL: masked reconstruction of the window
|
for _ in range(EPOCHS): # SSL: masked reconstruction of the window
|
||||||
|
|||||||
Reference in New Issue
Block a user