data_bunch = DataBunch(train_dl=training_loader, valid_dl=validation_loader)
# =========================================================================================
classifier = mdl_ResDenHybrid()
class SGD_m5(SGD):
def __init__(self, *args, **kwargs):
super().__init__(momentum=0.5, *args, **kwargs)
learn = Learner(
data_bunch,
classifier,
loss_func=Loss_combine_weighted_v2(),
opt_func=SGD_m5,
metrics=[Metric_grapheme(), Metric_vowel(), Metric_consonant(), Metric_tot()]
)
name = 'mdl_ResDenHybrid_sgd_lessaugs_mucm_fixed_raw_onecycle_fld1of5'
logger = CSVLogger(learn, name)
# =========================================================================================
learn.fit_one_cycle(
160,
max_lr=0.05,
wd=0.0,
pct_start=0.0,
div_factor=50.,
final_div=100.,
# ### model
classifier = Simple50GeM()
# In[8]:
logging_name = 'MG_model_script_ModelMg_SeperateHead_BestAugs_3ChSize128Pad3_OneCycle'
learn = Learner(data_bunch,
classifier,
loss_func=Loss_combine_weighted(),
opt_func=Over9000,
metrics=[
Metric_grapheme(),
Metric_vowel(),
Metric_consonant(),
Metric_tot()
])
logger = CSVLogger(learn, logging_name)
learn.clip_grad = 1.0
# learn.split([classifier.cls])
learn.unfreeze()
# In[9]:
learn.fit_one_cycle(160,
max_lr=1e-2,
wd=0.,
pct_start=0.0,