prepare animations (supervised and not)

model.py

@@ -36,7 +36,7 @@ class ColorTransformerModel(L.LightningModule):
         else:
             midlayers = sum(
                 [
-                    [nn.Linear(w, w, bias=bias), nn.Dropout(self.dropout), t()]
+                    [nn.Linear(w, w, bias=bias), nn.Dropout(self.hparams.dropout), t()]
                     for _ in range(d)
                 ],
                 [],

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 = [0.1]
-# widths = [2**k for k in range(4, 13)]
-# depths = [1, 2, 4, 8, 16]
-widths, depths = [512], [4]
+alpha_values = [0, 0.1]
+widths = [2**k for k in range(4, 13)]
+depths = [1, 2, 4, 8, 16]
+# widths, depths = [512], [4]
 
 batch_size_values = [256]
 max_epochs_values = [100]
@@ -80,7 +80,7 @@ python newmain.py fit \
 --model.depth {d} \
 --model.bias true \
 --model.loop true \
---model.transform relu \
+--model.transform tanh \
 --trainer.min_epochs 10 \
 --trainer.max_epochs {me} \
 --trainer.log_every_n_steps 3 \
@@ -88,7 +88,7 @@ python newmain.py fit \
 --trainer.limit_val_batches 50 \
 --trainer.callbacks callbacks.SaveImageCallback \
 --trainer.callbacks.init_args.final_dir out \
---trainer.callbacks.init_args.save_interval 0 \
+--trainer.callbacks.init_args.save_interval 1 \
 --optimizer torch.optim.{opt} \
 --optimizer.init_args.lr {lr} \
 --trainer.callbacks+ lightning.pytorch.callbacks.LearningRateFinder