added episilon for equality check

major layout changes in the network

pairwise_compare.py

@@ -13,12 +13,12 @@ import pairwise_comp_nn as comp_nn
 DEVICE = torch.accelerator.current_accelerator() if torch.accelerator.is_available() else "cpu"
 
 # Valves
-DIMENSIONS = 1
-TRAIN_STEPS = 10000
+DIMENSIONS = 2
+TRAIN_STEPS = 5000
 TRAIN_BATCHSZ = 8192
-TRAIN_PROGRESS = 100
-BATCH_LOWER = -512.0
-BATCH_UPPER = 512.0
+TRAIN_PROGRESS = 10
+BATCH_LOWER = -100.0
+BATCH_UPPER = 100.0
 DO_VERBOSE_EARLY_TRAIN = False
 MODEL_PATH = "./files/pwcomp.model"
 LOGGING_PATH = "./files/output.log"
@@ -46,17 +46,15 @@ def plt_embeddings(model: comp_nn.PairwiseComparator):
 
     for i in range(embeddings.shape[1]):
         plt.plot(xs.squeeze(), embeddings[:, i], label=f"dim {i}")
-        plt.legend()
-        plt.savefig(EMBED_CHART_PATH)
-        plt.show()
-
+        
+    plt.legend()
+    plt.savefig(EMBED_CHART_PATH)
+    #plt.show()
     csv_data = list(zip(xs.squeeze().tolist(), embeddings.tolist()))
     with open(file=EMBEDDINGS_DATA, mode="w", newline='') as f:
         csv_file = csv.writer(f)
         csv_file.writerows(csv_data)
     
-
-
 def get_torch_info():
     log.info("PyTorch Version: %s", torch.__version__)
     log.info("HIP Version: %s", torch.version.hip)
@@ -78,13 +76,10 @@ def sample_batch(batch_size: int, low=BATCH_LOWER, high=BATCH_UPPER):
     a = (high - low) * torch.rand(batch_size, 1) + low
     b = (high - low) * torch.rand(batch_size, 1) + low
 
-    # train for if a > b
-    y = (a > b).float()
+    epsi = 1e-4
+    y = torch.where(a > b + epsi, 1.0,
+        torch.where(a < b - epsi, 0.0, 0.5))
 
-    # removed but left for my notes; it seems training for equality hurts classifing results that are ~eq
-    # when trained only on "if a > b => y", the model produces more accurate results when classifing if things are equal (~.5 prob).
-    # eq = (a == b).float()
-    # y = gt + 0.5 * eq
     return a, b, y
     
 def training_entry():
@@ -150,7 +145,7 @@ def infer_entry():
     with torch.no_grad():
         probs = torch.sigmoid(model(a, b))
 
-    log.info(f"Output probabilities for {pairs.count} pairs")
+    log.info(f"Output probabilities for {pairs.__len__()} pairs")
     for (x, y), p in zip(pairs, probs):
         log.info(f"P({x} > {y}) = {p.item():.3f}")