Update.

[beaver.git] / beaver.py

diff --git a/beaver.py b/beaver.py
index afec61d..de38ff4 100755 (executable)
--- a/beaver.py
+++ b/beaver.py
@@ -172,9 +172,10 @@ def compute_perplexity(model, split="train"):
 def one_shot(gpt, task):
     t = gpt.training
     gpt.eval()
-
+    mode='head'
+    dim_in=args.dim_model * (args.nb_blocks * 2 if mode=='deep' else 1)
     model = nn.Sequential(
-        nn.Linear(args.dim_model, args.dim_model),
+        nn.Linear(dim_in, args.dim_model),
         nn.ReLU(),
         nn.Linear(args.dim_model, args.dim_model),
         nn.ReLU(),
@@ -187,7 +188,7 @@ def one_shot(gpt, task):
 
         acc_train_loss, nb_train_samples = 0, 0
         for input, targets in task.policy_batches(split="train"):
-            output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
+            output_gpt = gpt(mygpt.BracketedSequence(input), mode=mode).x
             output = model(output_gpt)
             targets = targets * (input.unsqueeze(-1) == maze.v_empty)
             output = output * (input.unsqueeze(-1) == maze.v_empty)
@@ -205,7 +206,7 @@ def one_shot(gpt, task):
 
         acc_test_loss, nb_test_samples = 0, 0
         for input, targets in task.policy_batches(split="test"):
-            output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
+            output_gpt = gpt(mygpt.BracketedSequence(input), mode=mode).x
             output = model(output_gpt)
             targets = targets * (input.unsqueeze(-1) == maze.v_empty)
             output = output * (input.unsqueeze(-1) == maze.v_empty)
@@ -224,7 +225,7 @@ def one_shot(gpt, task):
         # -------------------
         input = task.test_input[:32, : task.height * task.width]
         targets = task.test_policies[:32]
-        output_gpt = gpt(mygpt.BracketedSequence(input), with_readout=False).x
+        output_gpt = gpt(mygpt.BracketedSequence(input), mode=mode).x
         output = model(output_gpt)
         # losses = (-output.log_softmax(-1) * targets + targets.xlogy(targets)).sum(-1)
         # losses = losses * (input == maze.v_empty)