X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=sidebyside;f=beaver.py;h=afec61d4a506161e0da2e449d2dfa3445e386110;hb=71a5d04a1decec9d71be93cb816a15a8c0de83a2;hp=c3b7e09c1714199729a737a70725b3944ab787b7;hpb=a0e547917131af0b353e3bf31a062c9b35c8dd18;p=beaver.git

diff --git a/beaver.py b/beaver.py
index c3b7e09..afec61d 100755
--- a/beaver.py
+++ b/beaver.py
@@ -172,6 +172,7 @@ def compute_perplexity(model, split="train"):
 def one_shot(gpt, task):
     t = gpt.training
     gpt.eval()
+
     model = nn.Sequential(
         nn.Linear(args.dim_model, args.dim_model),
         nn.ReLU(),
@@ -190,10 +191,10 @@ def one_shot(gpt, task):
             output = model(output_gpt)
             targets = targets * (input.unsqueeze(-1) == maze.v_empty)
             output = output * (input.unsqueeze(-1) == maze.v_empty)
+            # loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
             loss = (
                 -(output.log_softmax(-1) * targets).sum()
                 / (input == maze.v_empty).sum()
-                + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
             )
             acc_train_loss += loss.item() * input.size(0)
             nb_train_samples += input.size(0)
@@ -208,10 +209,10 @@ def one_shot(gpt, task):
             output = model(output_gpt)
             targets = targets * (input.unsqueeze(-1) == maze.v_empty)
             output = output * (input.unsqueeze(-1) == maze.v_empty)
+            # loss = (output.softmax(-1) - targets).abs().max(-1).values.mean()
             loss = (
                 -(output.log_softmax(-1) * targets).sum()
                 / (input == maze.v_empty).sum()
-                + targets.xlogy(targets).sum() / (input == maze.v_empty).sum()
             )
             acc_test_loss += loss.item() * input.size(0)
             nb_test_samples += input.size(0)
@@ -225,9 +226,14 @@ def one_shot(gpt, task):
         targets = task.test_policies[:32]
         output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
         output = model(output_gpt)
-        losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1)
-        losses = losses * (input == maze.v_empty)
-        losses = losses / losses.max()
+        # losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1)
+        # losses = losses * (input == maze.v_empty)
+        # losses = losses / losses.max()
+        # losses = (output.softmax(-1) - targets).abs().max(-1).values
+        # losses = (losses >= 0.05).float()
+        losses = (
+            (F.one_hot(output.argmax(-1), num_classes=4) * targets).sum(-1) == 0
+        ).float()
         losses = losses.reshape(-1, args.maze_height, args.maze_width)
         input = input.reshape(-1, args.maze_height, args.maze_width)
         maze.save_image(