name = 'mdl_res34localpool_168168_lessaugs_mucm_fixed_adam_onecycle_fld3of5'
logger = CSVLogger(learn, name)
# =========================================================================================
# learn.fit_one_cycle(
# 100,
# max_lr=0.001,
# wd=0.0,
# pct_start=0.0,
# div_factor=100.,
# #final_div=100.,
# callbacks=[logger, SaveModelCallback(learn, monitor='metric_tot', mode='max', name=name), MuCmCallback(learn)]
# )
learn.fit(100,
lr=0.001,
wd=0.0,
callbacks=[
logger,
SaveModelCallback(learn,
monitor='metric_tot',
mode='max',
name=name),
MuCmCallback(learn),
ReduceLROnPlateauCallback(learn,
patience=10,
factor=0.5,
min_lr=.00001)
])
# =========================================================================================
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.,
callbacks=[logger, SaveModelCallback(learn, monitor='metric_tot', mode='max', name=name), MuCmCallback(learn)]
)