restrict experiment, add loop option + dropout

model.py

@@ -16,6 +16,8 @@ class ColorTransformerModel(L.LightningModule):
         bias: bool = False,
         alpha: float = 0,
         lr: float = 0.01,
+        loop: bool = False,
+        dropout=0.5,
     ):
         super().__init__()
         self.save_hyperparameters()
@@ -28,9 +30,16 @@ class ColorTransformerModel(L.LightningModule):
         w = self.hparams.width
         d = self.hparams.depth
         bias = self.hparams.bias
-        midlayers = []
-        for _ in range(d):
-            midlayers += [nn.Linear(w, w, bias=bias), t()]
+        if self.hparams.loop:
+            midlayers = [nn.Linear(w, w, bias=bias), t()] * d
+        else:
+            midlayers = sum(
+                [
+                    [nn.Linear(w, w, bias=bias), nn.Dropout(self.dropout), t()]
+                    for _ in range(d)
+                ],
+                [],
+            )
 
         self.network = nn.Sequential(
             nn.Linear(3, w, bias=bias),

newsearch.py

@@ -27,10 +27,10 @@ learning_rate_values = [1e-3]
 # learning_rate_values = [5e-4]
 
 # alpha_values = [0, .25, 0.5, 0.75, 1]  # alpha = 0 is unsupervised. alpha = 1 is supervised.
-alpha_values = [0.99]
+alpha_values = [0.9]
 # widths = [2**k for k in range(4, 13)]
 # depths = [1, 2, 4, 8, 16]
-widths, depths = [512], [1, 2, 4]
+widths, depths = [512], [8]
 
 batch_size_values = [256]
 max_epochs_values = [100]
@@ -79,6 +79,7 @@ python newmain.py fit \
 --model.width {w} \
 --model.depth {d} \
 --model.bias true \
+--model.loop true \
 --model.transform relu \
 --trainer.min_epochs 10 \
 --trainer.max_epochs {me} \