Added expts

itsmemala · Mar 18, 2024 · d029a67 · d029a67
1 parent ec18833
commit d029a67
Show file tree

Hide file tree

Showing 5 changed files with 415 additions and 30 deletions.
diff --git a/CL_Metrics.ipynb b/CL_Metrics.ipynb
diff --git a/Collas24 BehaviourSH-MAS.txt b/Collas24 BehaviourSH-MAS.txt
@@ -36,6 +36,10 @@
 ## ANCL MAS (Avg)
 !python  FABR//run.py --bert_model 'bert-base-uncased' --experiment annomi --approach bert_adapter_ewc_ancl --imp function --baseline ewc_ancl --backbone bert_adapter --note random0 --idrandom 0 --seed 0 --scenario dil --use_cls_wgts True --train_batch_size 32 --num_train_epochs 50 --valid_loss_es 0.002 --lr_patience 5 --learning_rate 0.00003 --lamb 0.1 --alpha_lamb 0.05 --ancl True --save_wd_old_magn True --my_save_path /content/gdrive/MyDrive/Collas24/BehavSH/BehavSH_ANCLMAS.1.1/
 
+## CHSF ANCL MAS ##
+!python  FABR//run.py --bert_model 'bert-base-uncased' --experiment annomi --approach bert_adapter_ewc --imp function --baseline ewc --backbone bert_adapter --note random0 --idrandom 0 --seed 0 --scenario dil --use_cls_wgts True --train_batch_size 32 --num_train_epochs 50 --valid_loss_es 0.002 --lr_patience 5 --learning_rate 0.00003 --custom_lamb 0,0 --break_after_task 1 --my_save_path /content/gdrive/MyDrive/Collas24/BehavSH/BehavSH_MAS_t1gold/
+!python  FABR//run.py --bert_model 'bert-base-uncased' --experiment annomi --approach bert_adapter_ewc_ancl --imp function --baseline ewc_ancl --backbone bert_adapter --note random0 --idrandom 0 --seed 0 --scenario dil --use_cls_wgts True --train_batch_size 32 --num_train_epochs 50 --valid_loss_es 0.002 --lr_patience 5 --learning_rate 0.00003 --custom_lamb 0,2 --custom_alpha_lamb 0,0.01 --ancl True --break_after_task 1 --my_save_path /content/gdrive/MyDrive/Collas24/BehavSH/BehavSH_ANCLMAS_t1.1/
+
 ## Adapt-KT
 (Fisher Avg)
 !python  FABR//run.py --bert_model 'bert-base-uncased' --experiment annomi --approach bert_adapter_ewc_freeze --imp function --backbone bert_adapter --baseline ewc_freeze --note random0 --idrandom 0 --seed 0 --scenario dil --use_cls_wgts True --train_batch_size 32 --num_train_epochs 50 --valid_loss_es 0.002 --lr_patience 5 --learning_rate 0.00003 --lamb 5 --adapt_type kt --save_wd_old_magn True --my_save_path /content/gdrive/MyDrive/Collas24/BehavSH/BehavSH_NoL1LAMAS_AdaptKT.9/

diff --git a/Collas24 IntentSH-MAS.txt b/Collas24 IntentSH-MAS.txt
@@ -53,4 +53,8 @@
 !python  FABR//run.py --bert_model 'bert-base-uncased' --experiment hwu64 --approach bert_adapter_ewc --imp function --backbone bert_adapter --baseline ewc --note random0 --idrandom 0 --seed 0 --scenario cil --use_rbs True --train_batch_size 32 --num_train_epochs 50 --valid_loss_es 0.002 --lr_patience 5 --learning_rate 0.003 --lamb 100 --save_wd_old_magn True --my_save_path /content/gdrive/MyDrive/Collas24/IntentSH_MAS_wlast.3/
 
 ## ANCL MAS (Fisher Avg) ##
-!python  FABR//run.py --bert_model 'bert-base-uncased' --experiment hwu64 --approach bert_adapter_ewc_ancl --imp function --backbone bert_adapter --baseline ewc_ancl --note random0 --idrandom 0 --seed 0 --scenario cil --use_rbs True --train_batch_size 32 --num_train_epochs 50 --valid_loss_es 0.002 --lr_patience 5 --learning_rate 0.003 --lamb 100 --alpha_lamb 50 --ancl True --save_wd_old_magn True --my_save_path /content/gdrive/MyDrive/Collas24/IntentSH_ANCLMAS_wlast.3.1/
+!python  FABR//run.py --bert_model 'bert-base-uncased' --experiment hwu64 --approach bert_adapter_ewc_ancl --imp function --backbone bert_adapter --baseline ewc_ancl --note random0 --idrandom 0 --seed 0 --scenario cil --use_rbs True --train_batch_size 32 --num_train_epochs 50 --valid_loss_es 0.002 --lr_patience 5 --learning_rate 0.003 --lamb 100 --alpha_lamb 50 --ancl True --save_wd_old_magn True --my_save_path /content/gdrive/MyDrive/Collas24/IntentSH_ANCLMAS_wlast.3.1/
+
+### CHSF ANCL MAS ##
+!python  FABR//run.py --bert_model 'bert-base-uncased' --experiment hwu64 --approach bert_adapter_ewc --imp function --backbone bert_adapter --baseline ewc --note random0 --idrandom 0 --seed 0 --scenario cil --use_rbs True --train_batch_size 32 --num_train_epochs 50 --valid_loss_es 0.002 --lr_patience 5 --learning_rate 0.003 --custom_lamb 0,0 --break_after_task 1 --my_save_path /content/gdrive/MyDrive/Collas24/IntentSH_MAS_wlast_t1gold/
+!python  FABR//run.py --bert_model 'bert-base-uncased' --experiment hwu64 --approach bert_adapter_ewc_ancl --imp function --backbone bert_adapter --baseline ewc_ancl --note random0 --idrandom 0 --seed 0 --scenario cil --use_rbs True --train_batch_size 32 --num_train_epochs 50 --valid_loss_es 0.002 --lr_patience 5 --learning_rate 0.003 --custom_lamb 0,0.5 --custom_alpha_lamb 0,0.01 --ancl True --break_after_task 1 --my_save_path /content/gdrive/MyDrive/Collas24/IntentSH_ANCLMAS_wlast_t1.1.1/
diff --git a/approaches/bert_adapter_ewc_ancl.py b/approaches/bert_adapter_ewc_ancl.py
@@ -211,6 +211,10 @@ def train(self,t,train,valid,args,num_train_steps,save_path,train_data,valid_dat
                 for n in self.fisher.keys():
                     self.lamb[n] = (1/(self.args.learning_rate*self.fisher[n]))/self.args.lamb_div
                     self.lamb[n] = torch.clip(self.lamb[n],min=torch.finfo(self.lamb[n].dtype).min,max=torch.finfo(self.lamb[n].dtype).max)
+            elif phase=='mcl' and self.args.custom_lamb is not None:
+                # Set EWC lambda for subsequent task
+                self.lamb = self.args.custom_lamb[t+1] if t+1<=self.args.break_after_task else 0
+                self.alpha_lamb = self.args.custom_alpha_lamb[t+1] if t+1<=self.args.break_after_task else 0
 
             if phase=='fo':
                 fo_model=utils.get_model(self.model)

diff --git a/approaches/bert_adapter_ewc_freeze.py b/approaches/bert_adapter_ewc_freeze.py
@@ -288,7 +288,6 @@ def train(self,t,train,valid,args,num_train_steps,save_path,train_data,valid_dat
             elif phase=='mcl' and self.args.custom_lamb is not None:
                 # Set EWC lambda for subsequent task
                 self.lamb = self.args.custom_lamb[t+1] if t+1<=self.args.break_after_task else 0
-                self.alpha_lamb = self.args.custom_alpha_lamb[t+1] if t+1<=self.args.break_after_task else 0
 
             if phase=='fo':
                 fo_model=utils.get_model(self.model)