weird setup, but sometimes possible to learn labels

model.py

@@ -56,7 +56,7 @@ class ColorTransformerModel(L.LightningModule):
         #     torch.norm(outputs - labels), torch.norm(1 + outputs - labels)
         # ).mean()
         distance = torch.norm(outputs - labels).mean()
-        loss = distance
+        loss = p_loss
 
         self.log("train_loss", distance)
         self.log("hp_metric", loss)

newsearch.py

@@ -6,7 +6,7 @@ import numpy as np  # noqa: F401
 from lightning_sdk import Machine, Studio  # noqa: F401
 
 # consistency of randomly sampled experiments.
-seed(202419920921)
+seed(19920921)
 
 NUM_JOBS = 100
 
@@ -28,8 +28,8 @@ alpha_values = [0]
 widths = [2**k for k in range(4, 13)]
 depths = [1, 2, 4, 8, 16]
 # learning_rate_values = [5e-4]
-batch_size_values = [16, 64, 256]
-max_epochs_values = [100]
+batch_size_values = [256]
+max_epochs_values = [10]
 seeds = list(range(21, 1992))
 optimizers = [
     "Adagrad",
@@ -71,10 +71,11 @@ python newmain.py fit \
 --model.width {w} \
 --model.depth {d} \
 --model.bias true \
+--model.transform tanh \
 --trainer.min_epochs 10 \
 --trainer.max_epochs {me} \
 --trainer.log_every_n_steps 3 \
---trainer.check_val_every_n_epoch 10 \
+--trainer.check_val_every_n_epoch 1 \
 --trainer.limit_val_batches 50 \
 --trainer.callbacks callbacks.SaveImageCallback \
 --trainer.callbacks.init_args.final_dir out \