tune param

mieskolainen · Jul 28, 2024 · c8e3bfb · c8e3bfb
1 parent 37eb044
commit c8e3bfb
Showing 1 changed file with 10 additions and 10 deletions.
diff --git a/configs/zee/models.yml b/configs/zee/models.yml
@@ -233,11 +233,11 @@ lzmlp0: &LZMLP
     #lossfunc: 'binary_Lq_entropy'     
     #q: 0.8                           # Lq exponent (q < 1 -> high density vals emphasized, q > 1 then low emphasized) 
 
-    SWD_beta: 5.0e-2                  # Sliced Wasserstein [reweighting regularization]
+    SWD_beta: 1.0e-3                  # Sliced Wasserstein [reweighting regularization]
     SWD_p: 1                          # p-norm (1,2,..), 1 perhaps more robust
-    SWD_num_slices: 10000             # Number of MC projections (higher the better)
+    SWD_num_slices: 1000              # Number of MC projections (higher the better)
     SWD_mode: 'SWD'                   # 'SWD' (basic)
-    SWD_norm_weights: False           # Normalization enforced
+    SWD_norm_weights: True            # Normalization enforced
 
     lipschitz_beta:    5.0e-5         # lipschitz regularization (use with 'lzmlp')
     #logit_L1_beta: 1.0e-2            # logit norm reg. ~ beta * torch.sum(|logits|)
@@ -322,11 +322,11 @@ fastkan0: &FASTKAN
     #lossfunc: 'binary_Lq_entropy'    # binary_cross_entropy, cross_entropy, focal_entropy, logit_norm_cross_entropy
     #q: 0.8                           # Lq exponent (q < 1 -> high density vals emphasized, q > 1 then low emphasized) 
 
-    SWD_beta: 5.0e-2                  # Sliced Wasserstein [reweighting regularization]
+    SWD_beta: 1.0e-3                  # Sliced Wasserstein [reweighting regularization]
     SWD_p: 1                          # p-norm (1,2,..), 1 perhaps more robust
-    SWD_num_slices: 10000             # Number of MC projections (higher the better)
+    SWD_num_slices: 1000              # Number of MC projections (higher the better)
     SWD_mode: 'SWD'                   # 'SWD' (basic)
-    SWD_norm_weights: False           # Normalization enforced
+    SWD_norm_weights: True            # Normalization enforced
 
     #lipshitz_beta: 1.0e-4            # Lipshitz regularization (use with 'lzmlp')
     #logit_L1_beta: 1.0e-2            # logit norm reg. ~ beta * torch.sum(|logits|)
@@ -415,12 +415,12 @@ dmlp0: &DMLP
     #lossfunc: 'binary_Lq_entropy'
     #q: 0.8                           # Lq exponent (q < 1 -> high density vals emphasized, q > 1 then low emphasized) 
 
-    SWD_beta: 5.0e-2                  # Sliced Wasserstein [reweighting regularization]
+    SWD_beta: 1.0e-3                  # Sliced Wasserstein [reweighting regularization]
     SWD_p: 1                          # p-norm (1,2,..), 1 perhaps more robust
-    SWD_num_slices: 10000             # Number of MC projections (higher the better)
+    SWD_num_slices: 1000              # Number of MC projections (higher the better)
     SWD_mode: 'SWD'                   # 'SWD' (basic)
-    SWD_norm_weights: False           # Normalization enforced
-
+    SWD_norm_weights: True            # Normalization enforced
+    
     #logit_L1_beta: 1.0e-2            # logit norm reg. ~ lambda * torch.sum(|logits|)
     logit_L2_beta: 5.0e-3             # logit norm reg. ~ lambda * torch.sum(logits**2)