optimizer search

newsearch.py

@@ -27,7 +27,17 @@ widths = [2**k for k in range(4, 15)]
 batch_size_values = [256]
 max_epochs_values = [100]
 seeds = list(range(21, 1992))
-
+optimizers = [
+    "Adagrad",
+    "Adam",
+    "SGD",
+    "AdamW",
+    "LBFGS",
+    "RAdam",
+    "RMSprop",
+    "SparseAdam",
+    "Adadelta",
+]
 # Generate all possible combinations of hyperparameters
 all_params = [
     (alpha, lr, bs, me, s, w)
@@ -37,6 +47,7 @@ all_params = [
     for me in max_epochs_values
     for s in seeds
     for w in widths
+    for opt in optimizers
 ]
 
 
@@ -44,7 +55,7 @@ all_params = [
 search_params = sample(all_params, min(NUM_JOBS, len(all_params)))
 
 for idx, params in enumerate(search_params):
-    a, lr, bs, me, s, w = params
+    a, lr, bs, me, s, w, opt = params
     cmd = f"cd ~/colors && python main.py --alpha {a} --lr {lr} --bs {bs} --max_epochs {me} --seed {s} --width {w}"
     cmd = f"""
 python newmain.py fit \
@@ -62,7 +73,7 @@ python newmain.py fit \
 --trainer.callbacks callbacks.SaveImageCallback \
 --trainer.callbacks.init_args.final_dir out \
 --trainer.callbacks.init_args.save_interval 0 \
---optimizer torch.optim.Adam \
+--optimizer torch.optim.{opt} \
 --optimizer.init_args.lr {lr} \
 --lr_scheduler lightning.pytorch.cli.ReduceLROnPlateau \
 --lr_scheduler.init_args.monitor hp_metric \