Update.

[culture.git] / quizz_machine.py

diff --git a/quizz_machine.py b/quizz_machine.py
index 239dc68..bf36d0b 100755 (executable)
--- a/quizz_machine.py
+++ b/quizz_machine.py
@@ -311,7 +311,6 @@ class QuizzMachine:
             self.test_c_quizzes.append(new_c_quizzes)
 
     def comput_correctness(self, c_quizzes, models_for_validation):
             self.test_c_quizzes.append(new_c_quizzes)
 
     def comput_correctness(self, c_quizzes, models_for_validation):

-        ###############################################################

         # Create the reverse quizzes
 
         token_forward, token_backward = self.problem.direction_tokens()
         # Create the reverse quizzes
 
         token_forward, token_backward = self.problem.direction_tokens()


@@ -328,11 +327,9 @@ class QuizzMachine:
         ar_mask = self.make_ar_mask(c_quizzes)
         seq_logproba = torch.empty(ar_mask.size(0), device=self.device)
 
         ar_mask = self.make_ar_mask(c_quizzes)
         seq_logproba = torch.empty(ar_mask.size(0), device=self.device)
 

-        ###############################################################
-        # Check how many of the other models can solve them in both
-        # directions
+        # Check how many of models can solve the quizzes in both directions

 
 

-        nb_correct = []
+        nb_correct = 0

 
         for model in models_for_validation:
             result = c_quizzes.clone()
 
         for model in models_for_validation:
             result = c_quizzes.clone()


@@ -369,14 +366,13 @@ class QuizzMachine:
                 (reverse_c_quizzes == reverse_result).long().min(dim=-1).values
             )
 
                 (reverse_c_quizzes == reverse_result).long().min(dim=-1).values
             )
 

-            nb_correct.append((correct * reverse_correct)[None, :])
+            nb_correct += correct * reverse_correct

 
 

-        return torch.cat(nb_correct, dim=0).sum(dim=0)
+        return nb_correct

 
 

-    def generate_quizzes(self, nb, model_for_generation, min_ave_seq_logproba):
-        ###############################################################
-        # Generate quizzes with model
+    ###############################################################

 
 

+    def generate_quizzes(self, nb, model_for_generation, min_ave_seq_logproba):

         c_quizzes = torch.empty(
             nb, self.train_w_quizzes.size(1), device=self.device, dtype=torch.int64
         )
         c_quizzes = torch.empty(
             nb, self.train_w_quizzes.size(1), device=self.device, dtype=torch.int64
         )