X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=b5ac916f1939d0fda01f70931dabb2cb7d0abe60;hb=3c6931f8ddc8160550e026d9e9610ef71260ce10;hp=6e13ff878bae27c818d337a53a413f3ce1a35180;hpb=aa09a883611d6e323b4e8e678b867097fc13afaf;p=mygptrnn.git

diff --git a/mygpt.py b/mygpt.py
index 6e13ff8..b5ac916 100755
--- a/mygpt.py
+++ b/mygpt.py
@@ -655,24 +655,15 @@ class Caterpillar(nn.Module):
         self.rec_V[:, :, t0:t1] = next_V.flatten(2, 3)
         self.rec_K[:, :, t0:t1] = next_K.flatten(2, 3)
 
-        if self.training and self.proba_flashback:
+        if self.training and self.proba_flashback > 0.0:
+            # insert_flash_back(self.rec_V,V,self.rec_K,K,t0,t1,CL,proba=self.proba_flashback / CL,)
+
             # This piece of code makes the assumption that there is
             # nothing informative before t0, otherwise we'd have to
             # implement a cache for V and K too. This should not be
             # too much of a problem since this is used only during
             # train, where full sequence are available
 
-            # insert_flash_back(
-            # self.rec_V,
-            # V,
-            # self.rec_K,
-            # K,
-            # t0,
-            # t1,
-            # CL,
-            # proba=self.proba_flashback / CL,
-            # )
-
             n = torch.arange(N, device=X.device)[:, None, None, None]
             t = torch.arange(t0, t1, device=X.device)[None, None, :, None]
             dv = torch.arange(DV, device=X.device)[None, None, None, :]