# ========================================================================================= classifier = mdl_res34_localpool() classifier.load_state_dict( torch.load( 'mdl_res34localpool_168168_lessaugs_mucm_fixed_adam_onecycle_fld3of5_backup.pth' )) learn = Learner(data_bunch, classifier, loss_func=Loss_combine_weighted_v2(), opt_func=Adam, metrics=[ Metric_grapheme(), Metric_vowel(), Metric_consonant(), Metric_tot() ]) # learn.clip_grad = 1.0 name = 'mdl_res34localpool_168168_lessaugs_mucm_fixed_adam_onecycle_fld3of5' logger = CSVLogger(learn, name) # ========================================================================================= # learn.fit_one_cycle( # 100, # max_lr=0.001,
# In[7]: classifier = Net() # In[8]: learn = Learner( data_bunch, classifier, loss_func=Loss_combine_weighted_v2(), opt_func=Over9000, metrics=[Metric_grapheme(), Metric_vowel(), Metric_consonant(), Metric_tot()] ) name = 'new_baseline_hengs_LessAugs_211_Mu10_Wd0_fit160epochs' logger = CSVLogger(learn, name) # learn.unfreeze() # In[ ]: # learn.fit_one_cycle( # 64, # max_lr=.01,
classifier = Simple50GeM_ArcFace_Single(n_classes=168) # In[8]: logging_name = 'Simple50GeM_AllMish_MoreAugs_Single_ArcFace_1of7' learn = Learner( data_bunch, classifier, #loss_func=Loss_single(), loss_func=AdvancedLoss_Single(), opt_func=Over9000, metrics=[Metric_grapheme()] ) logger = CSVLogger(learn, logging_name) learn.clip_grad = 1.0 # learn.split([classifier.cls]) learn.unfreeze() # In[9]: learn.fit_one_cycle( 64, # max_lr=slice(0.2e-2, 1e-2),