all files (logs/models/images) go into ./files

using AdamW optim again, AdamW is the go to for small toys and transformers.
refactored NN classes to thier own module under pairwise_comp_nn.py

pairwise_comp_nn.py

@@ -0,0 +1,34 @@
+import torch
+from torch import nn
+
+# 2) Number "embedding" network: R -> R^d
+class NumberEmbedder(nn.Module):
+    def __init__(self, d=8):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(1, 16),
+            nn.ReLU(),
+            nn.Linear(16, d),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+# 3) Comparator head: takes (ea, eb) -> logit for "a > b"
+class PairwiseComparator(nn.Module):
+    def __init__(self, d=8):
+        super().__init__()
+        self.embed = NumberEmbedder(d)
+        self.head = nn.Sequential(
+            nn.Linear(2 * d + 1, 16),
+            nn.ReLU(),
+            nn.Linear(16, 1),
+        )
+
+    def forward(self, a, b):
+        ea = self.embed(a)
+        eb = self.embed(b)
+        delta_ab = a - b
+        x = torch.cat([ea, eb, delta_ab], dim=-1)
+
+        return self.head(x)  # logits