# =========================================================================================
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),